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 |
|---|---|---|---|---|
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 PIL import Image
from ..image_u... | 232 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 152 | 0 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
A_ : List[Any] = get_tes... | 365 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowercase :
"""simple docstring"""
UpperCAmelCase = 42
UpperCAmelCase = 42
class ... | 349 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
'''process... | 340 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
'''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''Conv... | 340 | 1 |
import argparse
import os
import re
lowerCamelCase__ = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowerCamelCase__ = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCamelCase__ = re.compile(R'''^\s*"([^"]+)":''')
# Pat... | 63 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaFo... | 63 | 1 |
'''simple docstring'''
import math
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase = 0 , lowerCAmelCase = 0 ):
"""simple docstring"""
_lowerCAmelCase = end or len(lowerCAmelCase )
for i in range(lowerCAmelCase ... | 70 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers... | 108 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if len(lo... | 351 |
'''simple docstring'''
from __future__ import annotations
import math
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
if no... | 299 | 0 |
'''simple docstring'''
from statistics import mean
import numpy as np
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : str = 0
# Number of proce... | 37 |
'''simple docstring'''
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_ident... | 37 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowerCamelCase = logging.getLogger(__name__)
class _a :
def __init__( self : str )-> Any:
... | 354 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'''YituTech/conv-bert-base''': '''https://h... | 211 | 0 |
from string import ascii_uppercase
_lowercase : List[str] ={char: i for i, char in enumerate(ascii_uppercase)}
_lowercase : Dict =dict(enumerate(ascii_uppercase))
def lowerCAmelCase_ ( _lowercase : str , _lowercase : str) -> str:
""... | 170 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ... | 170 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ (__a : str = "AAPL" ):
"""simple docstring"""
_a : Tuple = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
_a : List[Any] = BeautifulSoup(reque... | 5 |
'''simple docstring'''
__lowerCAmelCase = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""... | 5 | 1 |
"""simple docstring"""
import numpy as np
def lowercase (snake_case__ : List[str] , snake_case__ : int , snake_case__ : List[Any] , snake_case__ : int , snake_case__ : Union[str, Any] ) -> Dict:
... | 155 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_chec... | 155 | 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,
)
__UpperCAmelCase = {
"""configuration_albert""": [... | 358 |
"""simple docstring"""
from __future__ import annotations
__UpperCAmelCase = 1.6021e-19 # units = C
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float , ) -> tuple[str, float]:
'''simple docstring'''
... | 1 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 206 |
'''simple docstring'''
import re
def a ( lowerCamelCase__ ):
'''simple docstring'''
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def a ( lowerCamelCase__ ):
'''simple docstring'''
A_ : Optional[int] = split_input(str_... | 206 | 1 |
"""simple docstring"""
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Optional[Any], lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Tuple... | 272 |
"""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, ran... | 272 | 1 |
"""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():
... | 144 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import ded... | 64 | 0 |
import math
import random
def a__ ( UpperCAmelCase : float , UpperCAmelCase : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_lowerCamelCase : Optional[Any] = 0.0_2
... | 99 |
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 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
W... | 284 |
from timeit import timeit
def a_ ( lowerCAmelCase_ : int ):
if number < 0:
raise ValueError('the value of input must not be negative' )
__lowerCAmelCase = 0
while number:
number &= number - 1
result += 1
return result
def a_ ( lowerCAmelCase_ : int ):... | 284 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 185 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTes... | 185 | 1 |
'''simple docstring'''
# Copyright 2021 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
#... | 104 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A ( ):
"""simple docstring"""
with offline(OfflineSimulationMode.CONNECT... | 104 | 1 |
import doctest
from collections import deque
import numpy as np
class lowercase :
'''simple docstring'''
def __init__(self ) -> None:
"""simple docstring"""
UpperCAmelCase__ = [2, 1, 2, -1]
UpperCAmelCase__ = [1, 2, 3, 4]
... | 366 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_UpperCamelCase = logging.get_logger(__name__)
class lowercase ( _UpperCamelCase ):
'''simple docstring'''
def __init__(self , *__a , **__a ) -> None:
... | 335 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_A = TypeVar('''T''')
_A = TypeVar('''U''')
class A ( Generic[T, U] ):
def __init__( self, UpperCamelCase__, UpperCamelCase__ ):
"""simple docstring"""
... | 278 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A ( unittest.TestCase ):
@property
def ... | 278 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
a = logging.get_logger(__name__)
a... | 271 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_availa... | 271 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[Any] ) ... | 38 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[Any]:
# vision encoder
if "img_enco... | 129 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> List[Any]:
if "cls_token" in name:
... | 129 | 1 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ ) -> str:
lowerCamelCase = len(snake_case__ )
lowerCamelCase = len(snake_case__ )
lowerCamelCase = (
first_str_length if first_str_length > second_str_length else seco... | 291 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCamelCase = False
if low == high:
return swa... | 291 | 1 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : List[Any] ):
lowerCAmelCase__ : List[Any] = {}
... | 74 |
"""simple docstring"""
from manim import *
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
def __lowerCAmelCase ( self : List[Any] ):
lowerCAmelCase__ : List[str] = Rectangle(height=0.5 ,width=0.5 )
lowerCAmelCase__ : ... | 74 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCAmelCase__ = get_logger(__name__)
class a__ ( enum.Enum ):
"""simple docstring"""
__lowerCamelCase = 'all_checks'
__lowerCamelC... | 68 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | 15 | 0 |
'''simple docstring'''
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... | 334 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE( __lowercase = 4 ) -> list[list[int]]:
A: Tuple = abs(__lowercase ) or 4
return [[1 + x + y * row_size for x in range(__lowercase )] for y in range(__lowercase )]
de... | 334 | 1 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __A( nn.Module ):
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE_ = 16 , SCREAMING_SNAKE_CASE_ = 88 , SCREAMING_SNAKE_CASE_ = No... | 244 |
lowerCamelCase_ = frozenset(
[
'''prompt''',
'''height''',
'''width''',
'''guidance_scale''',
'''negative_prompt''',
'''prompt_embeds''',
'''negative_prompt_embeds''',
'''cross_attention_kwargs''',
]
)
lowerCamelCase_ = frozen... | 244 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Union[str, Any] = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de... | 57 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : str = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():
raise Option... | 57 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
def __snake_case ( _lowerCAmelCase : Union[str, ... | 300 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_... | 300 | 1 |
"""simple docstring"""
def A_ ( snake_case_ : List[Any] ,snake_case_ : Any ,snake_case_ : Optional[Any] ,snake_case_ : Dict=None ):
'''simple docstring'''
UpperCamelCase : str = (path or []) + [u]
for v in graph[u]:
... | 352 |
"""simple docstring"""
from typing import Any
class lowerCamelCase :
def __init__( self , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = data
UpperCamelCase : Optional[Any] = None
def __repr__( self ):... | 27 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''Canine... | 188 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase = logging.get_logger(__name__)
... | 188 | 1 |
import os
import pytest
from attr import dataclass
UpperCamelCase__ : int = """us-east-1""" # defaults region
@dataclass
class lowerCamelCase_ :
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 'arn:aws:iam::558105141721:role/sagemaker_executi... | 350 |
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> int:
"""simple docstring"""
a = ''''''
for i in table:
res += inp[i - 1]
return res
def SCREAMING_SNAKE_CASE__ ( snake_case_ ) -> int:
"""simple docstring"""
... | 330 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a , _a):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0")
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0")
if principal <= 0:
raise ValueError("prin... | 76 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
a_ = logging.getLogger(__name__)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any... | 76 | 1 |
def __lowerCamelCase ( lowerCamelCase__ = 1_000 ):
"""simple docstring"""
lowercase__ : int = 1, 1
lowercase__ : List[Any] = []
for i in range(1 , n + 1 ):
lowercase__ : Dict = prev_numerator + 2 * pr... | 366 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = '''▁'''
lowerCAm... | 121 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_snake_case = False
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docst... | 283 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, 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 ... | 142 | 0 |
def lowerCAmelCase_ ( __a ) -> Optional[int]:
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
lowerCamelCase__: int =len(__a )
lowerCamelCase__: str =max(__a )
lowerCamelCase__: Optional[Any] ... | 273 |
def lowerCAmelCase_ ( __a , __a ) -> Tuple:
"""simple docstring"""
assert x is not None
assert y is not None
lowerCamelCase__: Any =len(__a )
lowerCamelCase__: int =len(__a )
# declaring the array for storing the dp values
lowerCamelCase__: L... | 273 | 1 |
"""simple docstring"""
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, Pipeli... | 16 |
# flake8: noqa
# Lint as: python3
lowerCamelCase : Optional[Any] = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .log... | 233 | 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 __lowerCamelCase ( __snake_case , unittest.TestCase ):
lowerCamelCase_ : Any ... | 34 |
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 UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]... | 26 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.model... | 277 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
... | 123 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
__lowerCAmelCase : List[Any] ="examples/"
__lowerCAmelCase : Dict ={
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
... | 123 | 1 |
from __future__ import annotations
import numpy as np
def __lowerCAmelCase ( a__ ) -> tuple[np.ndarray, np.ndarray]:
__a , __a = np.shape(a__ )
if rows != columns:
__a = (
'''\'table\' has to be of square shaped array but got a '''
... | 6 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentence... | 208 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_UpperCamelCase ... | 114 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _lowerCAmelCase ( ) -> int:
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_ren... | 114 | 1 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def a__ ( lowerCAmelCase__... | 181 |
'''simple docstring'''
# Copyright 2023 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-... | 181 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A ( *_UpperCAmelCase : Any , _UpperCAmelCase : Optional[Union[Dict, Any]] = None , _UpperCAmelCase : Optional[int]=True , ... | 290 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 290 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase : Union[str, Any] = ... | 42 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __UpperCAmelCase ( _lowerCamelCase ):
def lowerCamelCase ( self , lowerCAmelCase_ ):
""... | 42 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 357 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
def UpperCamelCase__ ( A__ ) -> Optional[int]:
if not head:
return True
# split the list to two parts
snake_case__ : List[str] = head.next, head
while fast and fast.next:
snake_case__ : Any = fast.next.next
snake_ca... | 143 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"""face... | 242 | 0 |
'''simple docstring'''
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import... | 361 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"roberta-base": "https://h... | 332 | 0 |
import unittest
from typing import Dict, List, Optional, Union
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... | 245 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 342 | 0 |
'''simple docstring'''
from __future__ import annotations
import queue
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Union[str, Any]:
A_ : Tuple = data
A_ : List[str] = None
... | 360 |
'''simple docstring'''
import math
def UpperCAmelCase ( a_ ) -> list:
"""simple docstring"""
A_ : List[Any] = [True] * n
A_ : List[Any] = False
A_ : Union[str, Any] = False
A_ : List[Any] ... | 164 | 0 |
'''simple docstring'''
import torch
def snake_case_ ( ):
"""simple docstring"""
if torch.cuda.is_available():
lowercase_ : int = torch.cuda.device_count()
else:
lowercase_ : Optional[int] = ... | 93 |
'''simple docstring'''
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __lowerCAmelCase (__lowerCAmel... | 234 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTra... | 370 | '''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : int | float | str , snake_case : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
a : ... | 345 | 0 |
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
UpperCAmelCase_ = logging.get_logger(__name__... | 201 |
from math import factorial
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k <... | 201 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
... | 365 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testi... | 215 | 0 |
import os
import string
import sys
_UpperCAmelCase : Tuple = 1 << 8
_UpperCAmelCase : str = {
"""tab""": ord("\t"),
"""newline""": ord("\r"),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""... | 222 |
"""simple docstring"""
import operator as op
lowerCAmelCase : Dict = """scaler.pt"""
lowerCAmelCase : Tuple = """pytorch_model"""
lowerCAmelCase : Union[str, Any] = """random_states"""
lowerCAmelCase : Union[str, Any] ... | 291 | 0 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.... | 133 |
"""simple docstring"""
from typing import Any
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = data
lowerCAmelCas... | 133 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch... | 310 |
def _A ( _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) for n in range(_lowercase )]
i... | 310 | 1 |
_lowercase: Tuple = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import ArrayaD, A... | 361 |
# using dfs for finding eulerian path traversal
def a( A : int , A : Optional[Any] , A : Any , A : Optional[int]=None ) -> List[str]:
"""simple docstring"""
a = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 71 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class A__ ( __snake_case , unittest.TestCase ):
_UpperCAmelCase :Optional[Any] = DownBlockaD #... | 52 |
def A_ ( _lowerCAmelCase ) -> str:
UpperCamelCase : List[Any] = ""
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 A_ ( _lowerCAmelCase ) -> dict[str, str]:
UpperCamelCase : Optional[An... | 52 | 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 : int =importlib.util.find_spec("s3fs") is not None
if _has_safs:
... | 123 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils... | 123 | 1 |
"""simple docstring"""
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
... | 60 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __magic_n... | 300 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a :Union[str, Any] = logging.get_... | 351 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__a :int = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__a :Any = [file for file in filepaths if file != file.lower... | 329 | 0 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : List[Any] = logging.get_logger(__name__)
# TODO Update this
snake_case : Union[str, Any] = {
'''facebook/... | 94 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerC... | 302 | 0 |
"""simple docstring"""
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo... | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class A( nn.Module ):
'''simple docstring'''
UpperCamelCase = 42
UpperCamelCase = jnp.floataa
def a__ ( self : int ) -> str:
"""simple docstring"""
... | 204 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_S... | 204 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 353 |
import datasets
from .evaluate import evaluate
lowercase_ = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n"
l... | 282 | 0 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion impo... | 326 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.m... | 326 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have ... | 305 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _A (__a , __a , __a ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = ('... | 91 | def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_lowercase )
SCREAMING_SNAKE_CASE : Any = len(_lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = [[False for _ in range(m + 1 ... | 182 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A_ : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def lo... | 316 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
while second != 0:
lowerCamelCase__ : Tuple = first & second
first ^= second
lowerCamelCase__ : int = c << 1
return first
if __name__ == "__main__":
i... | 316 | 1 |
'''simple docstring'''
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,
... | 163 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__A =get_tests_dir('fixtures/s... | 163 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_lowerCamelCase : Any = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
t... | 206 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_forma... | 206 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
# ===== initialization =====
_A : List[str] = Mock()
_A ... | 26 |
'''simple docstring'''
import math
from collections.abc import Callable
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
snake_case_ = xa
snake_case_ = xa
while True:
if x_n == x... | 56 | 0 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils i... | 350 | import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import... | 105 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int , snake_case_ : int ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def lowerCAmelCase_ ( ) -> None:
'''simple docstring'''
as... | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER... | 96 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Dict = r"\n ... | 350 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)... | 122 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_t... | 78 |
"""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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils ... | 78 | 1 |
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Optional[int] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class lowercase_ :
def __init__( self , ... | 284 | import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case ... | 284 | 1 |
import pprint
import requests
__UpperCamelCase : Union[str, Any] = """https://zenquotes.io/api"""
def a_ ( ) -> Tuple:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def a_ ( ) -> List[str]:
... | 307 |
'''simple docstring'''
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Dict ):
'''simple docstring'''
_a : Dict = {}
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
print(self.vert... | 271 | 0 |
"""simple docstring"""
def lowercase (SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int=None ) -> Tuple:
SCREAMING_SNAKE_CASE = (path or []) + [u]
... | 353 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lower... | 38 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_case_ = '... | 90 |
import baseaa
def lowerCamelCase_ ( UpperCamelCase__ : str ) -> bytes:
"""simple docstring"""
return baseaa.aaaencode(string.encode('utf-8' ) )
def lowerCamelCase_ ( UpperCamelCase__ : bytes ... | 90 | 1 |
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 HfArgumentParser, TrainingArguments
fr... | 353 | import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import ar... | 20 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Time... | 72 |
"""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 install... | 148 | 0 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRobertaT... | 50 | from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 50 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case ( unittest.Tes... | 98 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : int = logging.get_logger(__name__)
lowerCAmelCase__ : str = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# ... | 98 | 1 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowercase__ : Union[str, Any] = get_logger(__name__)
class _UpperCAmelCase ( enum.Enum):
_lowerCAmelCase : Dict = """... | 363 |
"""simple docstring"""
from math import pow
def __lowercase ( _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, solut... | 155 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, i... | 312 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
fr... | 312 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_sp... | 360 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvaila... | 320 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 10_00 ) -> str:
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 185 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def UpperCamelCase( __UpperCamelCase : List[str] ):
... | 103 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, s... | 352 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 0 |
'''simple docstring'''
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path im... | 237 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
res... | 202 | 0 |
'''simple docstring'''
import pytest
a_ = '__dummy_dataset1__'
a_ = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + ... | 222 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlip... | 222 | 1 |
from __future__ import annotations
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase ):
# Checks if the entire collection has been sorted
if len(lowerCamelCase ) <= 1 or n <= 1:
return
insert_next(lowerCamelCase, n - 1 )
rec_insertion_sort(lowerCamelCase, n - 1 )
... | 236 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExt... | 236 | 1 |
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 UpperCamelCase__( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Option... | 371 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
a__: int = {
'gwf-44... | 39 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
... | 196 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case , snake_ca... | 196 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def _a ( ):
__lowerCAmelCase ... | 350 |
import math
def _a ( SCREAMING_SNAKE_CASE_ : int ):
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 numbers, all multiples of 3 are not primes
r... | 102 | 0 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
... | 64 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models ... | 163 | 0 |
"""simple docstring"""
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, DDIMSche... | 371 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {
"""snap-research/efficientformer-l1-300""": (
... | 298 | 0 |
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