code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''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.utils.iterators import ThreadedI... | 47 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase : List[str] = logging.get_logger(__name__)
lowerCamelCase : List[Any] = ... | 47 | 1 |
from __future__ import annotations
UpperCamelCase : str = tuple[int, int, int]
UpperCamelCase : Optional[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCamelCase : Optional[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# ... | 366 |
"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import ... | 263 | 0 |
def lowerCAmelCase__(__snake_case ) -> str:
'''simple docstring'''
if not numbers:
return 0
if not isinstance(lowercase__ ,(list, tuple) ) or not all(
isinstance(lowercase__ ,lowercase__ ) for number in numbers ):
raise ValueError('''numbers m... | 209 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A = {
"configuration_blip": [
"BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BlipConfi... | 164 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : str )-> str:
_lowerCamelCase = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def SCREAMING_SNAKE_CASE... | 80 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int )-> list[int]:
if num <= 0:
raise ValueError('Input must be a positive integer' )
_lowerCamelCase = [True] * (num + 1)
_lowerCamelCase = 2
while p * p <= num:
... | 80 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
lowerCAmelCase__ : Optional[int] ='''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
... | 257 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ : Optional[Any] ={
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2C... | 257 | 1 |
'''simple docstring'''
# Imports
import numpy as np
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , _lowerCAmelCase : int=None , _lowerCAmelCase : Optional[Any]=None , _lowerCAmelCase : Optional... | 48 |
'''simple docstring'''
import warnings
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 com... | 48 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise Valu... | 50 |
from itertools import count
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 50 ) -> int:
lowerCamelCase__ : Optional[Any] = [1] * min_block_length
for n in count(_UpperCAmelCase ):
fill_count_functions.append(1 )
for block_length in range(_UpperCAmelC... | 50 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _lowerCAmelCase ( UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple = True , UpperCAmelCase : Optional[Any] = math.inf , UpperCAmelCase : Optio... | 364 |
"""simple docstring"""
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 onnxruntim... | 157 | 0 |
from collections.abc import Callable
import numpy as np
def UpperCamelCase( __UpperCamelCase : Callable ,__UpperCamelCase : float ,__UpperCamelCase : float ,__UpperCamelCase : float ,__UpperCamelCase : float ):
lowerCAmelCase_ : Optional[int] ... | 103 |
def a__ ( snake_case = 1_000_000 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1
__SCREAMING_SNAKE_CASE : Optional[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = {1: 1}
for inputa in range(2 , snake_case ):
__SCREAMING_SNAKE... | 303 | 0 |
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 = {
"""facebook/levit... | 201 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase ( yaml.SafeLoader ):
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Any ):
'''simple docstring'''
lowercase__ ... | 201 | 1 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> list[int]:
'''simple docstring'''
if length <= 0 or not isinstance(__lowercase , __lowercase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1... | 79 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case : List[Any] = logging.getLogger(__name__)
snake_case : O... | 240 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class A__ ( _snake_case ):
lowercase = "Speech2TextFeatureExtractor"
lowercase = "Speech2TextTokenizer"
def __init__( self , UpperCamel... | 101 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
'''configuration_blenderbot''': [
... | 101 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCamelCase ( lowerCAmelCase__ = "isbn/0140328726" ):
'''simple docstring'''
lowercase = olid.strip().strip('''/''' ) # Remove leading/trailing whitespace & slashes
... | 101 |
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
... | 101 | 1 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> ... | 351 |
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 import ... | 285 | 0 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->list:
if len(_SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
a__: List[str] = False
if low == high:
... | 290 | """simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase__ ... | 290 | 1 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = ""
lowercase_ = ""
lowercase_ = []
def A__ ( self , U... | 297 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 297 | 1 |
from __future__ import annotations
import pandas as pd
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : list[int] , __magic_name__ : list[int] , __magic_name__ : int ) -> list[int]:
"""simple docstring"""
UpperCamelCase :List[str] = [0] * no_of_proce... | 38 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str = "" ):
__UpperCamelCase =url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__UpperCamelCase =BeautifulSoup(request... | 62 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A ( lowercase , lowercase , lowercase = 1 / sqrt(2 ) ) -> IIRFilter:
'''simple docstring'''
UpperCamelCase = tau * frequency / samplerate
UpperCamelCase = sin(lowercase )
U... | 110 |
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 lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
@register_to_config
def __init__( sel... | 110 | 1 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
lowerCamelCase = datasets.utils.logging.get_logger(__name__)
@dataclass
class _Up... | 166 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.d... | 166 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( lowercase__ , lowercase__ = None , lowercase__ = None , lowercase__ = False , ):
_lowerCamelCase : List[Any] = cipher_alphabet or [chr(lowercas... | 12 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
_lowerCamelCase : List[str] = len(lowercase__ )
_lowerCame... | 12 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowercase__( unittest.TestCase ):
"""simple docstring"""
def _lowercase ( self : List[str] ) -> ... | 30 |
"""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 __lowerCAmelCase ( un... | 45 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCAmelCase__ = re.compile(r'''\b(a|an|the)\b''', re.UNICODE)
lowerCAmelCase__ = None
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Tuple ... | 121 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and ... | 121 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : list, _lowerCAmelCase : list, _lowerCAmelCase : int ):
"""simple docstring"""
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError('''The length of profit and weight must be sa... | 320 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
@require_torch
def _UpperCA... | 320 | 1 |
"""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 OptionalDependencyNotA... | 310 |
"""simple docstring"""
import torch
from transformers import AutoModel
class __SCREAMING_SNAKE_CASE ( torch.nn.Module ):
'''simple docstring'''
def __init__( self : Dict , __a : Tuple="sayef/fsner-bert-base-uncased" ) -> Dict:
super(__a , ... | 310 | 1 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, 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, ids_tensor... | 183 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmel... | 296 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_P... | 353 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ (__lowercase ):
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViTImageProcessor'''
lowerCamel... | 22 | 0 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _snake_case ( nn.Module ):
def __init__( self : Tuple , UpperCAmelCase : int = 16 , UpperCAmelCase :... | 135 | """simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 135 | 1 |
'''simple docstring'''
class _snake_case ( lowercase_ ):
pass
class _snake_case ( lowercase_ ):
pass
class _snake_case :
def __init__( self ) -> Tuple:
'''simple docstring'''
snake_case_ = [
[],
... | 364 |
'''simple docstring'''
def UpperCamelCase_( snake_case : list[int] , snake_case : int ):
'''simple docstring'''
snake_case_ = len(snake_case )
snake_case_ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
... | 92 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 21 |
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_dense_index,
)
import transformers
from transformers im... | 21 | 1 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softm... | 363 |
import random
def _snake_case( SCREAMING_SNAKE_CASE__ : list , SCREAMING_SNAKE_CASE__ : str ) -> tuple:
'''simple docstring'''
A__ , A__ , A__ = [], [], []
for element in data:
if element < pivot:... | 282 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : list[list[int | float]] ):
'''simple docstring'''
_lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = len(matrix[0] )
_lowerCAmelCase = min(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_... | 158 |
'''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 import... | 158 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
UpperCAmelCase : Union[str, Any] = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin... | 331 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
UpperCAmelCase : List[str] = datasets.utils.logging.get_logger(__name__)
@... | 331 | 1 |
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,
)
A__ : Dict = {
'''configuration_albert''': ['''ALBERT_PRETR... | 207 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from... | 240 | 0 |
"""simple docstring"""
from __future__ import annotations
A__ : Any = tuple[int, int, int]
A__ : Any = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
A__ : Optional[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
# -------------------------- default sel... | 209 |
"""simple docstring"""
def _snake_case ( lowerCamelCase__ : Optional[Any] ) -> Optional[int]:
if not head:
return True
# split the list to two parts
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] =head.next, head
w... | 209 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __UpperCAmelCase ( self , _UpperCamelCase ) -> int:
wit... | 29 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx... | 369 | """simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils impor... | 54 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = ... | 81 | """simple docstring"""
import numpy as np
import qiskit
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 8 ,_lowerCamelCase : int | None = None ) -> str:
_lowerCAmelCase : int = np.random.default_rng(seed=_lowerCamelCase )
# Roughly 25% ... | 44 | 0 |
import os
import sys
__lowercase = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClas... | 367 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 0 |
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) or number < 0:
raise ValueError('Input must be a non-negative integer' )
snake_case_ = 0
whil... | 285 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase ):
'''simple docstring'''
__A : Any = ["flax"]
def __init__( self , *__A , **__A ):
"""simple docstring"""
... | 283 | 0 |
"""simple docstring"""
from typing import Any
class __snake_case :
def __init__( self , lowercase) -> Tuple:
'''simple docstring'''
a__: str = data
a__: Tuple = None
class __snake_case :
... | 365 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE = 1000000 ) ->int:
a__: Any = limit + 1
a__: List[str] = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_S... | 203 | 0 |
def A__ ( SCREAMING_SNAKE_CASE__) -> float:
return 10 - x * x
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(SCREAMING_SNAKE_CASE__) * equation(SCREAMING_SN... | 111 |
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 jax.numpy as jnp
... | 111 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
fr... | 355 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Union[str, ... | 84 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : List[Any] = {
"""configuration_elec... | 202 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_A : int = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that develop... | 202 | 1 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
SCREAMING_SNAKE_CASE : ... | 252 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional ... | 252 | 1 |
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_available
from transformers.models.fsmt.co... | 30 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Dict ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , ... | 315 | 0 |
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''simple docstring'''
while b:
UpperCAmelCase_ , UpperCAmelCase_ = b, a % b
return a
def A ( __UpperCAmelCase , __UpperCAmelCase ) -> int:
'''... | 368 |
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
UpperCamelCase_ = logging.get_logger(__name__)
Upp... | 344 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hugging... | 140 | 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 P... | 140 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'shi-labs/nat-min... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__lowerCAmelCase = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_e... | 270 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_UpperCAmelCase = logg... | 140 | from typing import Any
import numpy as np
def UpperCamelCase ( __lowercase : np.ndarray ):
'''simple docstring'''
return np.array_equal(__lowercase ,matrix.conjugate().T )
def UpperCamelCase ( __lowercase : np.ndarray ,__lowercase ... | 140 | 1 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import Backbo... | 19 |
import datasets
from .evaluate import evaluate
a__ : Dict = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.062... | 19 | 1 |
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
import transformers
from transformers import (
... | 270 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCAmelCase__ ( __lowercase ):
@staticmethod
@abstractmethod
def __A ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> str:
raise NotImplementedError()
@abstractmethod
def __A... | 270 | 1 |
from math import sqrt
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
UpperCamelCase__ = 0
for i in range(1, int(sqrt(UpperCamelCase__ ) + 1 ) ):
if n % i == 0 and i != sqrt(UpperCamelCase__ ):
... | 35 | 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,
)
lowercase = {
"""configuration_xlm_roberta""... | 35 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_UpperCAmelCase : Tuple =False
class snake_case__( unittest.TestCase ):
'''simple docstr... | 262 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Any = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIV... | 95 | 0 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowerCamelCase__ ... | 63 | import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase__ ( a__ , a__ , a__ ) ->int:
'''simple docstring'''
_UpperCame... | 63 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCAmelCase : List[Any] =logging.get_logger(__name__)
class a_ ( _lowerCAmelCase ):
def __init__( self : Opti... | 223 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase : Tuple ={
'''facebook/vit-mae-base''': '''https://huggingface.co/faceb... | 223 | 1 |
import pprint
import requests
UpperCAmelCase_ = """https://zenquotes.io/api"""
def lowerCamelCase__ ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def lowerCamelCase__ ( ) -> list:
... | 295 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase_ = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui W... | 295 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
A : Dict = False
class _lowercase ( unittest.... | 184 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase_ ( _A : Optional[int] , _A : bool = True , _A : float = math.inf , _A : float = -math.inf , _A : float = math.inf , _A : float = ... | 184 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.... | 370 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps,... | 138 | 0 |
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
@r... | 205 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTe... | 35 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
__lowercase ... | 52 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 52 | 1 |
from __future__ import annotations
from collections.abc import Callable
UpperCamelCase__ = list[list[float | int]]
def _a ( SCREAMING_SNAKE_CASE_ : Matrix , SCREAMING_SNAKE_CASE_ : Matrix ):
__lowerCAmelCase = len(SCREAMING_SNAKE_CASE_ ... | 92 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
UpperCamelCase__ = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""A... | 92 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 175 |
"""simple docstring"""
def snake_case_ ( A_ : float ):
'''simple docstring'''
if edge <= 0 or not isinstance(A_, A_ ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def ... | 175 | 1 |
from math import pi, sqrt, tan
def _UpperCamelCase ( snake_case__ ) -> float:
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def _UpperCamelCase ( snake_c... | 157 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 39 | 0 |
lowercase : int = """
# 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
"""
lowercase : Tuple ... | 362 |
def A_ ( A__ ) -> int:
if not isinstance(A__ , A__ ):
raise TypeError('only integers accepted as input' )
else:
a__ : List[Any] = str(abs(A__ ) )
a__ : Optional[int] = [list(A__ ) for char in range(le... | 225 | 0 |
"""simple docstring"""
# 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
... | 263 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import l... | 263 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowercase = 1000 ) -> int:
return sum(2 * a * ((a - 1) // 2) for a in range(3 ,n + 1 ) )
if __name__ == "__main__":
print(solution())
| 360 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoRea... | 176 | 0 |
"""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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize... | 290 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str = " " ):
'''simple docstring'''
_UpperCAmelCase = []
_UpperCAmelCase = 0
for index, char in enumerate(_SCREAMING_SNAKE_CASE )... | 260 | 0 |
def A__ ( SCREAMING_SNAKE_CASE__) -> int:
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) or number < 0:
raise ValueError("""Input must be a non-negative integer""")
__snake_case: Optional[Any] = 0
while number:
# This way we arrive at next set... | 293 |
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase : int = "docs/source/en/_toctree.yml"
def A__ ( SCREAMING_SNAKE_CASE__) -> Dict:
__snake_case: Union[str, Any] = defaultdict(SCREAMING_SNAKE_CASE__)
for doc in model_d... | 293 | 1 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int = 1_000 ):
'''simple docstring'''
lowercase__ : Optional[int] = 2**power
lowercase__ : Optional[Any] = 0
while n:
lowercase__ , lowercase__ : Union[str, Any] = r + n % ... | 214 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host... | 214 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a__ ( UpperCAmelCase : dict ) -> tuple:
return (dat... | 352 |
from ..utils import DummyObject, requires_backends
class __UpperCAmelCase ( metaclass=lowerCamelCase__ ):
UpperCamelCase = ["""onnx"""]
def __init__( self : int, *__A : Optional[Any], **__A : Dict ):
requires_backends(self, ['''on... | 99 | 0 |
"""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: int = logging.get_logger(__name__)
A: int = {
"vocab_file": "vocab.json",
"merges_file": "merges.t... | 109 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def _snake_case ( UpperCamelCase : Callable , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
UpperCAmelCase : Any... | 109 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstrings.py
lowe... | 35 | from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
... | 35 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionMode... | 135 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __lowercase ( _UpperCamelCase = 8 ) ->str:
"""simple docstring"""
lowercase : List[str] = ascii_letters + digits + punctuation
... | 337 | 0 |
'''simple docstring'''
def a__ ( lowercase : int = 1000 ) -> int:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = 1, 1
_UpperCamelCase = []
for i in range(1, n + 1 ):
_UpperCamelCase = prev_numerator + 2 * prev_denominator
... | 355 |
'''simple docstring'''
import random
class __lowerCAmelCase :
"""simple docstring"""
@staticmethod
def snake_case__ ( lowerCAmelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
_UpperCamelCase ... | 287 | 0 |
from __future__ import annotations
def lowercase_ ( _lowerCamelCase : list):
if not nums:
raise ValueError("List is empty")
return sum(_lowerCamelCase) / len(_lowerCamelCase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 87 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoPr... | 354 |
"""simple docstring"""
from __future__ import annotations
import math
class __lowerCamelCase :
'''simple docstring'''
def __init__( self : Dict , a_ : int ):
lowerCAmelCase_ : Union[str, Any] = size
# approximate the ove... | 161 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNC... | 65 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCamelCase : Tuple = logging.getLogger(__name__... | 146 | 0 |
def UpperCamelCase ( _A : List[str] , _A : List[str] , _A : List[str] , _A : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:... | 198 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 198 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_s... | 31 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n... | 31 | 1 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int) -> List[str]:
'''simple docstring'''
def is_in_circle(_lowerCamelCase : float , _lowerCamelCa... | 366 |
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PI... | 151 | 0 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase = _modexpt(UpperCamelCase__ , exponent // 2 , UpperCamelCase__ )... | 273 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 1 |
import unittest
import numpy as np
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 ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 177 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfi... | 177 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase : Dict = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Lxmer... | 99 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWit... | 33 | 0 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__a = logging.getLogger(__name__)
class __a( _a ):
"""simple docstring"""
def __init__( self ,_SCRE... | 235 |
from math import ceil
def lowerCamelCase__ ( _lowercase = 1001 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
UpperCAmelCase_ : List[Any] = 2 * i +... | 235 | 1 |
import torch
def lowercase_( ):
'''simple docstring'''
if torch.cuda.is_available():
lowerCamelCase : List[Any] = torch.cuda.device_count()
else:
lowerCamelCase : Any = 0
print(f"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__main_... | 283 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def _SCREAMING_SNAKE_CASE ( _lowercase : int ) ->str:
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
rais... | 105 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase__ =10
def __UpperCamelCase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ... | 90 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
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 (
C... | 90 | 1 |
'''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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transfo... | 139 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _snake_case ( _a , unittest.TestCase ):
_A : st... | 139 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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
#
... | 182 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : str = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное об... | 182 | 1 |
'''simple docstring'''
def __lowerCamelCase ( lowerCAmelCase_ = 50 ) -> List[Any]:
_a : Dict = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length ... | 89 |
"""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 import GenerationTesterMixin
... | 320 | 0 |
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
_lowerCAmelCase : Tuple = ... | 353 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_lowerCAmelCase : Optional[Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
im... | 70 | 0 |
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__a = logging.get_logger(__name__)
__a = 'T5Config'
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
... | 30 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 1 |
import math
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 SchedulerMixin, SchedulerOutput
class UpperCAmelCase__ ( A_ , A_ ):
"""simple docstring"""
Up... | 117 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_A = logging.get_logger(__name__)
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , *A_ , **A_ ) -> None:
warnings.warn... | 117 | 1 |
"""simple docstring"""
from __future__ import annotations
import requests
__A : Optional[Any] = set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post... | 33 |
def __snake_case ( __UpperCamelCase : bytes ):
"""simple docstring"""
return "".join([hex(__UpperCamelCase )[2:].zfill(2 ).upper() for byte in list(__UpperCamelCase )] )
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
... | 312 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_av... | 371 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output... | 273 | 0 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 4 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__snake_case ="""\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding ... | 4 | 1 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
fr... | 254 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax i... | 254 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, Stable... | 124 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCamelCase : Any = logging.get_... | 124 | 1 |
"""simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Any = credit_card_number
a__: Tuple ... | 363 | """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 import Con... | 203 | 0 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 49 |
"""simple docstring"""
import math
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 SchedulerMixin, SchedulerOutput
class lowercase ( __UpperCAmelCase , __... | 167 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSo... | 368 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
SCREAMING_SNAKE_CASE__ : Tuple = collections.namedtuple("_Datasets... | 339 | 0 |
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