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 |
|---|---|---|---|---|
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
UpperCame... | 66 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowerCAmelCase_ ( lowercase_ : Callable , lowercase_ : float , lowercase_ : float , lowercase_ : float , lowercase_ : float ):
'''simple docstring'''
__SCREA... | 674 | 0 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( UpperCamelCa... | 359 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
... | 359 | 1 |
def A_ ( lowercase_ ) -> Optional[int]:
_snake_case : int = 0
for ch in input_str:
_snake_case : Tuple = ord(lowercase__ )
_snake_case : Any = pow(2 , lowercase__ )
# If we already turned on bit ... | 326 |
'''simple docstring'''
import operator as op
def snake_case_ ( lowercase__ ):
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Any = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
Upp... | 199 | 0 |
from __future__ import annotations
import math
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase ):
A : Dict = u
for i in range(1 , SCREAMING_SNAKE_CASE_ ):
A : Optional[Any] = temp * (u - i)
return temp
def Up... | 701 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import Gra... | 17 | 0 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip i... | 275 |
'''simple docstring'''
from collections.abc import Callable
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : Callable | None = None ) -> None:
# Stores actual heap items.
... | 275 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( __UpperCamelC... | 368 |
'''simple docstring'''
from __future__ import annotations
from PIL import Image
# Define glider example
__magic_name__ : str = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 368 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Union[str, Any] ) -> List[str]:
__lowerCAmelCase = ... | 53 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomR... | 466 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thu... | 597 |
import socket
def A__ ( ):
SCREAMING_SNAKE_CASE_ = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE_ = socket.gethostname()
SCREAMING_SNAKE_CASE_ = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
with open('''Received_file''', ... | 597 | 1 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_... | 525 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_mo... | 525 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowercase :
'''simple docstring'''
... | 708 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFA... | 391 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils... | 35 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_commo... | 354 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _snake_case ( _snake_case : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
_A = olid.strip().strip... | 505 |
"""simple docstring"""
# using dfs for finding eulerian path traversal
def _snake_case ( _snake_case : str , _snake_case : List[Any] , _snake_case : List[Any] , _snake_case : Optional[Any]=None ) -> int:
... | 505 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Union[str, Any] = {
'''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}
try:
if not is_torch_available():
raise Opti... | 108 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet ... | 108 | 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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
... | 701 | """simple docstring"""
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table... | 121 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
A_ = TypeVar("""_T""")
class __lowerCamelCase ( Generic[_T] ):
def __init__( self , UpperCAmelCase = None ):
lowerCamelCase_ = list(iterable or [] )
lowe... | 29 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 0 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class A_ ( logging.LoggerAdapter ):
"""simple docstring"""
@staticmethod
def lowercase_ ( __UpperCAmelCase ) -> int:
a : int = PartialState()
return not main... | 509 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .t... | 509 | 1 |
"""simple docstring"""
from math import factorial
def _snake_case ( snake_case__ : int , snake_case__ : int , snake_case__ : float ):
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the fun... | 91 |
'''simple docstring'''
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
UpperCamelCase =TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", "|",... | 208 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}
try:
if not is_torch_available():
raise O... | 519 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( lowercase__ , lowercase__ ):
"""simple docstring"""
@register_to... | 519 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _UpperCAmelCase( unittest.TestCase , lowerCamelCase ):
def UpperCAmelCase ( self) -> List[Any]:
'''simp... | 19 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main... | 19 | 1 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_... | 706 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_ut... | 440 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( UpperCamelCase__ , unittest.TestCase ):
_lowercase : str = ... | 43 |
def __a ( __UpperCAmelCase : int = 100 ) -> int:
"""simple docstring"""
lowerCamelCase_ : Any = set()
lowerCamelCase_ : int = 0
lowerCamelCase_ : Tuple = n + 1 # maximum limit
for a in rang... | 488 | 0 |
"""simple docstring"""
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 = 'sr... | 702 |
"""simple docstring"""
snake_case = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_ ):
SCREAMING_SNAKE_CASE = 0
while number:
# Increased Speed Slightly by checking every 5 digits toge... | 406 | 0 |
import logging
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,
... | 70 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( __lowercase ):
lowercase__: Any = ['''image_processor''', '''tokenizer''']
lowercase__: Any = ''... | 26 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=lowercase__ ):
lowerCamelCase : Any = ['''note_seq''']
def __init__( self : Union[str, Any] , *UpperCAmelCase__ : Any , **UpperCAmelCase__ : int ) -> ... | 705 |
'''simple docstring'''
from __future__ import annotations
def a_ ( lowerCamelCase : list , lowerCamelCase : int ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase ... | 513 | 0 |
'''simple docstring'''
import sys
lowercase =(
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'668966489504452445231617318... | 446 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : str = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =set()
# Replace all the whitespace in our sentence
_UpperCAmelCase : Dict ... | 446 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ) -> tuple[str, float]:
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
... | 207 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InformerConfig""",
],
}
t... | 207 | 1 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class _SCREAMING_SNAKE_CASE :
def __init__(self , UpperCAmelCase):
'''simple docstring'''
__UpperCAmelCase =data
__UpperCAmelCase =[0x67_452_301, 0xEF_CDA_B89, 0x98_BAD... | 132 |
from importlib import import_module
from .logging import get_logger
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_logger(__name__)
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=None ):
UpperC... | 79 | 0 |
def _lowercase ( a_ : Tuple ) -> int:
__magic_name__ = []
__magic_name__ = set({'(', '[', '{'} )
__magic_name__ = set({')', ']', '}'} )
__magic_name__ = {'{': '}', '[': ']', '(': ')'}
for i in range(len(a_ ) ):
... | 715 |
import requests
from bsa import BeautifulSoup
def _lowercase ( a_ : str = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
__magic_name__ = BeautifulSoup(requests.get(a_ ).text ,'html.parser' )
__magic_name__ ... | 184 | 0 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {"vocab_file"... | 366 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
'''... | 366 | 1 |
import baseaa
def lowercase_ ( __UpperCAmelCase ) -> bytes:
return baseaa.aaaencode(string.encode("""utf-8""" ) )
def lowercase_ ( __UpperCAmelCase ) -> str:
return baseaa.aaadecode(__UpperCAmelCase ).decode("""utf-8""" )
if __name__ == "__main__":
import... | 719 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_u... | 507 | 0 |
from torch import nn
def UpperCamelCase( __UpperCamelCase : Optional[Any] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f"""Unsupported activation function: {act... | 171 |
import random
from typing import Any
def UpperCamelCase( __UpperCamelCase : list ):
for _ in range(len(__UpperCamelCase ) ):
lowerCAmelCase_ : Union[str, Any] = random.randint(0 ,len(__UpperCamelCase ) - 1 )
lowerCAmelCase_ : List[Any] = ... | 171 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_lowerCamelCase = importlib.util.find_spec("""s3fs""") is not None
if _has_sa... | 323 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_ava... | 323 | 1 |
"""simple docstring"""
# Copyright 2022 The HuggingFace 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
... | 465 |
"""simple docstring"""
from __future__ import annotations
from statistics import mean
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = [0] * no_of_processes
__lowerCAmelCase = [0] * no_of_processes
# Initialize remaining_t... | 465 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowercase = {
'configuration_layoutlmv3': [
'LAYOUTLMV3_PRET... | 701 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 242 | 0 |
from math import pow
def a__ ( A__, A__, A__, A__, A__, ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
SCREAMING_SN... | 101 |
from statistics import mean, stdev
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = 3 ) -> list:
lowerCamelCase : Optional[int] = min(_SCREAMING_SNAKE_CASE )
lowerCamelCase : Union[str, Any] = max(_SCREAMING_SNAKE_CASE )
... | 311 | 0 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __snake_case ( lowerCAmelCase__ ):
__lowerCAmelCase : List[Any] = CustomTokenizer
pass
| 620 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
UpperCamelCase__ : int = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teen... | 620 | 1 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A ( UpperCamelCase_ ):
UpperCamelCase__ : List[str] =(PNDMScheduler,)
UpperCamelCase__ : Dict =(('num_inference_steps', 50),)
def lowerCamelCase ... | 464 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = '▁'
lowerCamelCase = {'vocab_file': 'pr... | 464 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
... | 175 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _SCR... | 175 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _A( snake_case__ ):
... | 239 |
import numpy as np
def _SCREAMING_SNAKE_CASE ( a , a , a , a , a ) -> Optional[Any]:
__A : List[Any] = int(np.ceil((x_end - xa) / h ) )
__A : Tuple = np.zeros((n + 1,) )
__A : Tuple = ya
__A ... | 239 | 1 |
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,... | 702 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI... | 688 | 0 |
from __future__ import annotations
def _lowerCAmelCase ( UpperCamelCase__: int , UpperCamelCase__: str , UpperCamelCase__: List[str] , ) -> Union[str, Any]:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueErr... | 641 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConf... | 484 | 0 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCAmelCase__ = numpy.array([0, 0])
lowerCAmelCase__ = numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__ = numpy.array([1, 0]... | 712 |
'''simple docstring'''
def _A ( A__ = 1000000 ):
"""simple docstring"""
__lowercase = set(range(3 , A__ , 2 ) )
primes.add(2 )
for p in range(3 , A__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , ... | 624 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNe... | 675 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( l... | 675 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase_ : Any = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class _UpperCamelCase ( sn... | 702 |
lowerCamelCase_ : Optional[Any] = """
# 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
"""
lowerCamelCase_ ... | 670 | 0 |
"""simple docstring"""
import math
def lowerCamelCase__ ( __snake_case ) -> int:
"""simple docstring"""
_UpperCamelCase = 0
_UpperCamelCase = 0
while num > 0:
_UpperCamelCase = num % 8
... | 19 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
... | 455 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, D... | 639 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 639 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 183 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 682 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_availabl... | 703 | '''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowercase = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse... | 605 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _UpperCamelCase ( __UpperCamelCase ) -> None:
create_state_space_tree(__UpperCamelCase ,[] ,0 )
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ) -> ... | 42 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dat... | 340 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase : Optional[Any] = {"""configuration... | 341 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def __UpperCamelCase ( snake_case , snake_case , snake_case , snake_case , snake_case , snake_case ) -> np.ndarray:
'''simple docstring'''
if (ksize ... | 341 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : Optional[int] , ... | 24 |
def lowerCAmelCase_ (lowercase__ : float , lowercase__ : int ) -> float:
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowercase__ ) , lowercase__ )
return number - int(lowercase__ )
if __name_... | 668 | 0 |
def a__ ( A_ = 1000 ):
'''simple docstring'''
return sum(e for e in range(3, A_ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 76 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__lowerCAmelCase : str = logging... | 76 | 1 |
import os
import sys
UpperCAmelCase = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
Aut... | 84 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowerCamelCase : Tuple = '''\
@misc{chen2021evaluating,
title={Evaluating... | 367 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from tra... | 710 |
"""simple docstring"""
import numpy as np
def _snake_case ( lowercase__ : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def _snake_case ( lowercase__ : np.ndarray ) -> np.ndarray... | 256 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ (_a , unittest.TestCase ):
lowercase_ : int = Phobe... | 615 |
from __future__ import annotations
def a_ ( __lowerCAmelCase ):
if not nums:
return 0
lowerCAmelCase__ = nums[0]
lowerCAmelCase__ = 0
for num in nums[1:]:
lowerCAmelCase__ , lowerCAmelCase__ = (
max_excluding + num,
... | 615 | 1 |
'''simple docstring'''
def UpperCAmelCase_ (__a : str ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 319 |
'''simple docstring'''
def UpperCAmelCase_ (__a : int ):
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_a : Optional[Any] = 1
_a : str = 1
while repunit:
_a : Union[str, Any] = (1_0 * repunit... | 319 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Union[str, Any] = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
... | 108 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__a: Union[str, Any] = logging.get_logger(__name__)
__... | 108 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from di... | 157 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
l... | 157 | 1 |
# Copyright 2022 The HuggingFace 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 by app... | 60 | import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,... | 197 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCamelCase = logging.get_logger(__name__)
def __lowe... | 515 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_p... | 515 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_... | 69 |
import operator as op
__UpperCamelCase : Optional[Any] = "scaler.pt"
__UpperCamelCase : Optional[Any] = "pytorch_model"
__UpperCamelCase : str = "random_states"
__UpperCamelCase : Optional[int] = "optimizer"
__UpperCamelCase : Optional[int] ... | 468 | 0 |
from __future__ import annotations
from fractions import Fraction
def __lowerCAmelCase ( A , A ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def __lowerCAmelCase ( A ):
UpperCAmelCase_ = []
Upper... | 268 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( lowercase ):
SCREAMING_SNAKE_CASE__ = (KDPMaDiscreteScheduler,)
SCREAMING_SNAKE_CASE__ = 10
def... | 268 | 1 |
'''simple docstring'''
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def __UpperCAmelCase ( _UpperCAmelCase : str ) -> Optional[int]:
... | 69 |
'''simple docstring'''
def __UpperCAmelCase ( _UpperCAmelCase : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 69 | 1 |
'''simple docstring'''
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , ... | 459 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
lowercase = min(lowerCAmelCase_ ) # min() finds the minimum value
lowercase = max(lowerCAmelCase_ ) # max() finds... | 459 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def a_ ( __... | 598 | """simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase ( a__ : dict , a__ : str , a__ : set , a__ : set , a__ : dict , a__ : dict , a__ : ... | 420 | 0 |
def UpperCamelCase ( _A : list[int] , _A : list[int] , _A : int )-> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_A ) )
def ... | 232 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def UpperCamelCase ( _A : list , _A : list , _A : list , _A : list , _A : lis... | 232 | 1 |
def A_ ( _UpperCAmelCase = 1_00 ):
SCREAMING_SNAKE_CASE_: Optional[Any] = 0
SCREAMING_SNAKE_CASE_: str = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_... | 671 |
from math import asin, atan, cos, radians, sin, sqrt, tan
lowerCAmelCase : Union[str, Any] = 637_8137.0
lowerCAmelCase : int = 635_6752.31_4245
lowerCAmelCase : Union[str, Any] = 6378137
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _Up... | 671 | 1 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metri... | 346 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase :List[str] = logging.get_logger(__name__)
lowerCamelCase :List[str] = {}
class UpperCAmelCase ( __snake_case ):
a: str = "llama"
a: List[str] ... | 346 | 1 |
# Lint as: python3
import itertools
import os
import re
lowercase_ : List[Any] = re.compile(r'''([A-Z]+)([A-Z][a-z])''')
lowercase_ : Tuple = re.compile(r'''([a-z\d])([A-Z])''')
lowercase_ : Dict = re.compile(r'''(?<!_)_(?!_)''')
lowercas... | 304 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
if exponent == 1:
return base
if exponent % 2 == 0:
_snake_case : str = _modexpt(__lowerCAmelCase , exponent // 2 , __lowerCAmelCase ) % modulo_value
... | 304 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__A : List[str] = pd.read_csv('sample_data.csv', header=None)
__A : List[Any] = df.... | 709 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
... | 698 | 0 |
import os
from collections.abc import Iterator
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE = "." ) -> Iterator[str]:
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(_SCREAMING_SNAKE_CASE ):
_A = [d for d in d... | 27 |
from collections.abc import Callable
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
_A = a
_A = b
if function(_SCREAMING_S... | 27 | 1 |
import math
from collections.abc import Iterator
from itertools import takewhile
def A_ ( A__ ) -> str:
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 nu... | 714 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torc... | 392 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from... | 280 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _a ):
'''simple do... | 113 | 0 |
'''simple docstring'''
def UpperCAmelCase_ (__a : str , __a : int ):
"""simple docstring"""
_a : list[list[str]] = [[] for _ in range(__a )]
_a : Optional[Any] = key - 1
if key <= 0:
raise ValueError('Heig... | 701 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_visi... | 319 | 0 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTes... | 650 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : Tuple )-> Dict:
_lowerCamelCase = [1]
for i in range(2 , snake_case ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * ... | 650 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common im... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
"configuration_owlvit... | 51 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transfor... | 75 |
from pathlib import Path
import fire
def a__ ( snake_case , snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = Path(snake_case )
__SCREAMING_SNAKE_CASE : Dict = Path(snake_case )
dest_dir.mkdir(exist_ok... | 74 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__snake_case = logging.get_logger(__name__)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
def __init__( self , *__UpperCAmel... | 285 |
"""simple docstring"""
from typing import Any
def A_ ( _lowerCAmelCase : list ):
"""simple docstring"""
if not input_list:
return []
_a = [input_list.count(_lowerCAmelCase ) for value in input_list]
_a = max(_lowerCAmelCase ... | 285 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 105 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 556 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedu... | 704 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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/LI... | 348 | 0 |
'''simple docstring'''
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'):
_snake_case : Tuple = {
'linear': PIL.Image.Resampling.BILINEAR,
... | 22 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase_ = """src/transformers"""
# Matches is_xxx_available()
lowercase_ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowercase_ ... | 235 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_commo... | 706 |
'''simple docstring'''
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimen... | 145 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : int = 5000_0000 ) -> int:
"""simple docstring"""
__lowerCamelCase = set()
__lowerCamelCase = int((limit - 24) ** (1 / 2) )
__lowerCamelCase = set(range(3 , prime_squar... | 469 |
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bool:
"""simple docstring"""
__lowerCamelCase = 0
for ch in input_str:
__lowerCamelCase = ord(UpperCamelCase__ )
__lowerCamelCase = pow(2 , UpperCa... | 469 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Net... | 556 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ = False )-> bool:
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3,... | 556 | 1 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
... | 660 | '''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__snake_case : Union[str, Any] = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''... | 660 | 1 |
from __future__ import annotations
lowercase : Any = 1.6021e-19 # units = C
def A_ ( A__ , A__ , A__ , ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )... | 392 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( A__ ) -> float:
return np.dot(A__ , A__ )
class A__ :
"""simple docstring"""
def __init__( self , *,
lowercase = np.inf... | 392 | 1 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
_lowerCamelCase = ['''image_processor''', '''token... | 617 |
'''simple docstring'''
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import... | 286 | 0 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> float:
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError('days_between_payments must be > 0... | 476 | 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,
... | 476 | 1 |
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowercase : str = logging.get_logger(__name__)
class lowerCamelCase__ ( __lowercase):
'''simple docstring... | 557 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> int:
'''simple docstring'''
__UpperCamelCase : Tuple = 1
for i in range(1 , num + 1):
fact *= i
return fact
def _SCREAMING_SNAKE_CASE... | 557 | 1 |
"""simple docstring"""
import math
A: List[str] = 1_0
A: List[Any] = 7
A: Dict = BALLS_PER_COLOUR * NUM_COLOURS
def _snake_case ( UpperCamelCase : Any = 20 ):
UpperCAmelCase : Optional[Any] = math.comb(__A , __A )
UpperCAmelCase : Any = mat... | 715 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , unittest.TestCase ):
_... | 359 | 0 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_av... | 400 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart i... | 400 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowerCAmelCase_ : Optional[int] = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_... | 521 |
'''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 ( lowercase : str , lowercase : i... | 521 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers im... | 236 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test impo... | 236 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["datase... | 206 |
_SCREAMING_SNAKE_CASE : dict[tuple[int, int, int], int] = {}
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
... | 206 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
UpperCamelCase = """encoder-decoder"""
UpperCamelCase =... | 21 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableT... | 317 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmR... | 703 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_lowerCAmelCase = {
"""c... | 306 | 0 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : list[list[str]] , snake_case__ : int , ):
A = len(snake_case__ )
# If row is equal ... | 91 |
"""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
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''goog... | 91 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE ={
"""configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
"""tok... | 717 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .s... | 614 | 0 |
"""simple docstring"""
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase : int = 1
_lowerCamelCase : List[Any] = 1
while repunit:
_lowerCamelCase : List[str] = ... | 434 | """simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import ... | 434 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def lowerCamelCase__ ( __lowerCAmelCase : Any ):
"""simple docstring"""
return input_array.reshape((input_array.size, 1) ... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_extraction_wav2ve... | 279 | 0 |
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