code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedu... | 674 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils... | 674 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
"""simple docstring"""
def lowercase_ ( self ):
__snake_case ... | 679 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
f... | 679 | 1 |
'''simple docstring'''
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
class UpperCAmelCase_ :
'''simple doc... | 5 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 5 | 1 |
import os
from datetime import datetime as dt
from github import Github
a_ : Optional[Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _SCREAMING_SNAKE_CASE ( )... | 678 |
def _SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return " ".join(
''''''.join(word[::-1] ) if len(snake_case_ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('Hey wollef sroirraw')) | 678 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
a = False
a = True
a = False
if __name__ == "__main__":
a = arg... | 350 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/dpr-ctx_encoder-single-nq-base""": (
"""https://huggingface.co/facebook/dpr-ctx_encoder-singl... | 630 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
... | 414 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__a : Optional[int] = lo... | 414 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class __snake_case ( lowerCAmelCase__ ):
def __init__( self ,*a_ ,**a_ ):
"""simple docstring""... | 193 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase_ = """src/diffusers""... | 74 | 0 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_av... | 509 |
"""simple docstring"""
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ ) -> float:
if discount_rate < 0:
raise ValueError('Discount rate cannot be negative' )
if not cash_flows:
raise ValueError('Cash flows list cannot be empty' )
a : List[str... | 509 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM imp... | 276 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from trans... | 276 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Any = """▁"""
lowerc... | 716 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
lowercase : Tuple = {"""vocab_file""": """vocab.json"""}
lowercase : int = {
"""vocab_file""":... | 105 | 0 |
import warnings
from .generation import TFGenerationMixin
class __magic_name__ ( __UpperCAmelCase):
'''simple docstring'''
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be r... | 234 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import lo... | 234 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __UpperCamelCase ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[str] ):
__a : Optional[int] = 0
if st... | 326 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class UpperCamelCase__ ( __lowercase ):
_SCREAMING_SNAKE_CASE : str = field(default="language-modeling" ... | 326 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A_ = logging.get_logger(__... | 143 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ = {
"configuration_instructblip": [
"INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InstructBlipConfig",
"InstructBli... | 143 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
),
}... | 717 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ... | 371 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 100 , ):
snake_case_ = x_start
snake_... | 39 |
import cmath
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = math.radians(SCREAMING_SNAKE_CASE__ )
snake_case_ = math.radians(SCREAMING_SNAKE_C... | 39 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase ={
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Con... | 543 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
UpperCamelCase ="path-to-your-trained-model"
UpperCamelCase =StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
UpperCamelCase ="A photo of sks dog in a bucket"
UpperCamel... | 543 | 1 |
'''simple docstring'''
snake_case_ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowercase ():
SCREAMING_SNAKE_CASE : Optional[Any] = input('''Enter message: ''' )
SCREAMING_SNAKE_CASE : Any = input('''Enter key [alphanumeric]: ''' )
SCREAMING_SNAKE_CAS... | 507 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __low... | 469 | 0 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
f... | 72 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple ={
'''google/pix2struct-textcaps-b... | 72 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Any:
lowe... | 311 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (lowerCAmelCase__ ):
'''simple docstring'''
lowerCamelCase_ : Optional[Any] = (KDPM... | 311 | 1 |
def snake_case (UpperCamelCase : str , UpperCamelCase : int ):
'''simple docstring'''
lowerCamelCase__ = [[] for _ in range(UpperCamelCase )]
lowerCamelCase__ = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" ... | 235 |
def snake_case (UpperCamelCase : dict ):
'''simple docstring'''
lowerCamelCase__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowerCamelCase__ = set()
return any(
node not in visited and depth_first_search(UpperCam... | 235 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImag... | 83 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
... | 46 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPip... | 92 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
a_ = logging.get_logger(__name__)
def UpperCamelCase_ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring""... | 92 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : int = []
__UpperCAmelCase : Optional[int] = []
__UpperCAmelCase : Union[str, Any] = ... | 77 |
'''simple docstring'''
from __future__ import annotations
a_ : int = list[list[int]]
# assigning initial values to the grid
a_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8... | 676 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class ... | 715 |
"""simple docstring"""
_UpperCamelCase = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_... | 74 | 0 |
'''simple docstring'''
def a ( UpperCamelCase_ : Optional[Any] ) -> Optional[Any]:
snake_case__ =len(UpperCamelCase_ )
for i in range(length - 1 ):
snake_case__ =i
for k in range(i + 1 , UpperCamelCase_ ):
if collection[k] < collection[least]:
... | 538 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin... | 538 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}
try:
if not is_... | 140 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowercase ( a , a , a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :int = ("dense.weight", "attention.self.query", "attention.self.key", "... | 140 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 73 | from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
... | 576 | 0 |
'''simple docstring'''
def _a ( _lowercase : int ):
'''simple docstring'''
__UpperCAmelCase : str = generate_pascal_triangle(_lowercase )
for row_idx in range(_lowercase ):
# Print left spaces
for ... | 266 |
'''simple docstring'''
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
... | 266 | 1 |
"""simple docstring"""
def __snake_case ( __A ) -> Tuple:
lowercase : List[str] = [0] * len(__A )
lowercase : str = []
lowercase : Optional[int] = []
lowercase : List[str] = 0
for values in graph.value... | 607 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : List[str] =['image_processor', 'tokenizer']
__lowerCamelCase : Tuple ='ViTImageProcessor'
... | 547 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import ... | 547 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase ):
stooge(__lowerCamelCase , 0 , len(__lowerCamelCase ) - 1 )
return arr
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
if i >= h:
re... | 81 |
def snake_case_ ( _SCREAMING_SNAKE_CASE ):
__lowercase = []
__lowercase = set({"(", "[", "{"} )
__lowercase = set({")", "]", "}"} )
__lowercase = {"{": "}", "[": "]", "(": ")"}
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
if s[i] in open_brackets... | 402 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", ... | 488 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""nvidia/... | 488 | 1 |
from functools import reduce
_UpperCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896... | 243 |
from functools import reduce
_UpperCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896... | 243 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ = {
'''configuration_poolformer''': [
'''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''PoolFormerConfig''',
'''PoolFormerOnnxConfi... | 713 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __UpperCAmelCase ( __a : Tuple ,__a : Dict ,__a : ... | 578 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Opti... | 394 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_s... | 517 | 0 |
'''simple docstring'''
_lowerCamelCase : Optional[Any] = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L... | 704 | '''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _lowerCAmelCase ( ) -> Dict:
'''simple docstring'''
_UpperCamelCase :int =9
_UpperCamelCase :Optional[int] =[
[0, 1, 4],
[0, 7, 8],
... | 512 | 0 |
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =len(lowercase__ )
print("The following activities are selected:" )
# The first activity is always selected
UpperCAmelCase_ =0
print(lowercase_... | 54 |
from __future__ import annotations
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCAmelCase_) , (UpperCAmelCase_)) =extended_euclid(lowercase__ , a % b )
UpperCAmelC... | 54 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 598 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProc... | 598 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_... | 79 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
__magic_name__ : Dict = list[list[float | int]]
def A__ ( A_ , A_ ) -> Matrix:
_lowercase = len(A_ )
_lowercase = [[0 for _ in range(size + 1 )] for _ in range(... | 497 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__A : str = logging.get_logger(__name__)
class __snake_case ( _SCREAMING_SNAKE_CASE):
"""simple docstr... | 398 |
'''simple docstring'''
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __snake_case ( unittest.TestCase):
"""simple docstring... | 398 | 1 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_A = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def lowercase (_snake_case ) -> ... | 505 | from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPM... | 423 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM impo... | 701 | import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
__lowerCAmelCase : List[Any] = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resamp... | 164 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( __UpperCamelCase : Optional[int] , __... | 292 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase_ ( __UpperCamelCase : dict , __UpperCamelCase : str ) -> set[str]:
"""simple docstring"""
_A , _A = set(__UpperCamelCase ), [start]
while stack:
... | 292 | 1 |
'''simple docstring'''
class _a :
"""simple docstring"""
def __init__( self : Optional[int] ):
'''simple docstring'''
lowercase_ = """"""
lowercase_ = """"""
lowercase_ = []
def lowerCamelCase__ ( self : List[str] , lo... | 717 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__snake_case = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""config... | 603 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from util... | 91 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = '''▁'... | 91 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils impor... | 517 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = "docs/source/en/_toctree.yml"
def __lowerCamelCase ( __lowerCAmelCase : Tuple ) -> Optional[int]:
snake_case = defaultdict(__l... | 517 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _UpperCAmelCase ( ... | 611 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import... | 611 | 1 |
"""simple docstring"""
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_... | 556 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 556 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A__ : List[str] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
... | 13 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def a__ ( ) -> Tuple:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as... | 98 | 0 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_snake_case = """src/transformers"""
_snake_case ... | 611 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 | 1 |
def _SCREAMING_SNAKE_CASE ( lowercase : str ):
'''simple docstring'''
lowerCamelCase_ = 0
# if input_string is "aba" than new_input_string become "a|b|a"
lowerCamelCase_ = ''''''
lowerCamelCase_ = ''''''
... | 70 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : str = {
'''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/r... | 231 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from t... | 711 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_=2 , snake_case_=3 , snake_case_=6_4 , snake_case_=Non... | 527 | 0 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
lowerCamelCase = """examples/"""
lowerCamelCase = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"""init""... | 474 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torc... | 474 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowerCamelCase_ : list[int] , lowerCamelCase_ : int ):
__lowercase = 0
__lowercase = len(UpperCAmelCase__ ) - 1
while i < j:
if nums[i]... | 710 |
'''simple docstring'''
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstac... | 56 | 0 |
"""simple docstring"""
import math
def __A ( a_ : List[Any] )-> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = [True] * n
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_CASE : List[Any] = False
SCREAMING_SNAKE_C... | 698 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_ima... | 474 | 0 |
from __future__ import annotations
def lowercase ( a , a , a ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance cannot... | 715 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class _UpperCAmelCase ( lowercase ):
def _snake_case ( self : List[Any] , UpperCAmelCase : str):
with open(UpperCAmelCase , encoding="utf-8") as in... | 140 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class a ( unittest.... | 63 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avai... | 63 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def A_ ( snake_case : Any=None , ... | 451 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configur... | 451 | 1 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewToke... | 31 |
UpperCAmelCase_ = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
UpperCAmelCase_ = ["""a""", """b""", """c""", """d""", """e"""]
def __magic_name__ ( lowercase , lowercase , lowercase ) -> Union[str... | 458 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( _lowercase : Any ) -> Tuple:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 717 | '''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = 10
def UpperCamelCase__ ( _lowercase : list[int] ) -> list[int]:
__UpperCAmelCase: Union[str, Any] = 1
__UpperCAmelCase: Optional[Any] = max(_lowercase )
while placement <= ma... | 466 | 0 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configur... | 297 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( snake_case_ : float , snake_case_ : float , snake_case_ : float ):
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise Valu... | 297 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_bi... | 283 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 283 | 1 |
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase):
UpperCamelCase_ , UpperCamelCase_ = len(__lowercase), len(grid[0])
if (
min(__lowercase , __lowercase) < 0
or row == row_length
or col == col_length
or... | 23 |
snake_case__ : int = '''Input must be a string of 8 numbers plus letter'''
snake_case__ : Optional[int] = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def lowercase ( _lowerCAmelCase ):
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
UpperCAmelCase__ ... | 392 | 0 |
'''simple docstring'''
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.util... | 506 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : list ) -> float:
_a : Union[str, Any] =0
while len(_UpperCAmelCase ) > 1:
_a : Any =0
# Consider two files with minimum c... | 506 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 686 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,):
A__ , A__ = grid.shape
A__ = ... | 260 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
A__: int = TypeVar('''_T''')
class A__ ( Generic[_T] ):
def __init__( self :Optional[int] , SCREAMING_SNAKE_CASE :List[str] = None ) -> ... | 710 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 506 | 0 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase ( lowerCamelCase_ :Any , lowerCa... | 334 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase ( lowerCamelCase_ :int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even... | 334 | 1 |
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
... | 707 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> float:
"""simple docstring"""
if edge <= 0 or not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) *... | 219 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 425 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
_UpperCamelCase : Any = tuple[int, int]
class snake_case__ :
def __init__( self : List[str] , _A : set[int] , _A : Mapping[EdgeT, int] ) -> Non... | 541 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
__A : Optional[int] = list[list[float | int]]
def lowerCamelCase_ ( lowercase__ , lowercase__):
lowerCamelCase__ = len(__UpperCAmelCase)
lowerCamelCase__ = ... | 713 |
'''simple docstring'''
def lowerCamelCase_ ( lowercase__ , lowercase__ , lowercase__):
return round(float(moles / volume) * nfactor)
def lowerCamelCase_ ( lowercase__ , lowercase__ , lowercase__):
return round(float((moles * 0.0_821 * temperature) / (v... | 187 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case )
class UpperCAmelCase_ ( snake_case ):
# `task` is not a ... | 76 |
"""simple docstring"""
a_ = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def __UpperCAmelCase ( __UpperCamelCase ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
... | 76 | 1 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils imp... | 702 | import math
def A__ ( lowercase: int ) -> list:
A : Optional[Any] =[True] * n
A : Tuple =False
A : List[Any] =False
A : Dict =True
for i in range(3, int(n**0.5 + 1 ), 2 ):
... | 661 | 0 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase ) -> bool:
"""simple docstring"""
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
__UpperCAmelCase : List[Any] = 4
__UpperCAme... | 77 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
... | 76 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from d... | 714 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rofo... | 490 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
lowerCamelCase_ : int = SwinConfig(image_size=1_92 )
if "b... | 364 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowercase : List[str] =5_0000
_lowercase : str =5000
_lowercase , _lowercase : List[str] =os.path.split(__file__)
_lowercase : Union[str, A... | 364 | 1 |
'''simple docstring'''
def snake_case ( a_ : str ) -> str:
"""simple docstring"""
UpperCamelCase_ : int = 0
# if input_string is "aba" than new_input_string become "a|b|a"
UpperCamelCase_ : Any = ''''''
Uppe... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if... | 543 | 0 |
from collections import defaultdict
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> bool:
_lowercase : Any = first_str.lower().strip()
_lowercase : List[str] = second_str.lower().strip()
# Remove whitespace
_lowercase : str... | 89 |
'''simple docstring'''
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common imp... | 627 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import... | 595 | """simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils imp... | 595 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 525 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowerC... | 525 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCAmelCase ( a : int ):
if num <= 0:
snake_case__ = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(a )
snake_case__ = [True] * (... | 701 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
exc... | 99 | 0 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase_ = "."
... | 28 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
UpperCamelCase_ = logg... | 28 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
a_ = r'''
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model output... | 717 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggi... | 48 | 0 |
"""simple docstring"""
import sys
def lowerCamelCase_ ( _lowerCamelCase : Dict ):
lowerCamelCase_ = len(_lowerCamelCase )
lowerCamelCase_ = [[0 for x in range(_lowerCamelCase )] for x in range(_lowerCamelCase )]
lowerCamelCase_ = ... | 142 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__lowercase : Dict = """src/transformers"""
# Matches is_xxx_available()
__lowercase : Tuple = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-lin... | 142 | 1 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ ):
assert isinstance(__snake_case , __snake_case ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
UpperCAmelCase : Any = F"""The input value of [n={number}] has to b... | 712 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerca... | 695 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 92 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' ... | 92 | 1 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chann... | 314 |
from collections import deque
from math import floor
from random import random
from time import time
class a__ :
"""simple docstring"""
def __init__( self :Dict ):
lowercase = {}
def __UpperCAmelCase ( self :Dict , lower... | 314 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput... | 280 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torc... | 280 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Tuple = logging.get_logger(__name__)
__lowerCAmelCase : Tuple = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"... | 284 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):... | 284 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _snake_case ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
SCREA... | 73 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TO... | 1 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class SCREAMING_SNAKE_C... | 719 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 0 |
"""simple docstring"""
from __future__ import annotations
__SCREAMING_SNAKE_CASE =list[list[int]]
# assigning initial values to the grid
__SCREAMING_SNAKE_CASE =[
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0,... | 425 | """simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowercase__( __SCREAMING_SNAKE_CASE : An... | 425 | 1 |
"""simple docstring"""
from collections import namedtuple
lowerCAmelCase_ = namedtuple('from_to', 'from_ to')
lowerCAmelCase_ = {
'cubicmeter': from_to(1, 1),
'litre': from_to(0.0_0_1, 1_000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0_4_5_4,... | 122 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinC... | 122 | 1 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication im... | 522 | '''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase ( metaclass=lowerCamelCase ):
'''simple docstring'''
lowerCAmelCase__ = ['''onnx''']
def __init__( self : List[Any] , *UpperCAmelCase__ : Union[... | 390 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a: Optional[Any] = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""co... | 428 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a: Tuple = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],... | 428 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __a ( A__ : list[list[float]] ):
SCREAMING_SNAKE_CASE = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works fo... | 16 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__( self ,... | 421 | 0 |
import string
from math import logaa
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
snake_case_ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
snake_case_ = docum... | 139 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCAmelCase :
'''simple docstring'''
lowerCAmelCase_ = 42
lowerCAmelCase_ = None
lowerCAmelCase_ = None
... | 139 | 1 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ) -> Tuple:
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCas... | 109 | """simple docstring"""
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
SCREAMING_SNAKE_CASE__:str = HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE__:List[str] = parser.parse... | 528 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={"vocab_file": "vo... | 462 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
lowerCamelCase =False
lowerCamelCase =True
lowerCamelCase =False
if __name__ == "__main__":
lowerCamelCase =argparse.ArgumentParser()
... | 462 | 1 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_... | 52 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Optional[int] = """ClapFeatureExtractor"""
__SCREAMING_SNAKE_CASE :List[Any] = ("""Robe... | 432 | 0 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigT... | 142 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as Prop... | 142 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.