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 |
|---|---|---|---|---|
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ = 1000 ):
snake_case_, snake_case_ = 1, 1
snake_case_ = []
for i in range(1 , n + 1 ):
snake_case_ = prev_numerator + 2 * prev_denominator
snake_case_ = prev_numerator + prev_denominator
... | 8 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or num... | 47 | 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,
XLMRobert... | 127 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from tra... | 127 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
A : Optional[Any] = object()
# For specifying empty leaf dict `{}`
A : Any = object()
def low... | 184 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Acc... | 334 | 0 |
def UpperCAmelCase_ ( __UpperCAmelCase : List[str] , __UpperCAmelCase : Any ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''') | 361 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from ... | 210 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch_available():
... | 10 |
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import r... | 195 | 0 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 77 |
"""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 .... | 77 | 1 |
'''simple docstring'''
from functools import reduce
__a = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6689664... | 35 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prep... | 300 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : Optional[int] ):
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 368 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : str ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =0
for ch in input_str:
_SCREAMING_SNAKE_CASE =ord(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =pow(2 ... | 114 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Trajectory... | 127 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
_SCREAMING_SNAKE_CASE : List[str] = logging.getLogger(__name__)
if is_t... | 127 | 1 |
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase_ : list[int] ) -> Dict:
__lowerCAmelCase = len(__A )
__lowerCAmelCase = [0] * len_array
if len_array > 0:
__lowe... | 361 |
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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
... | 207 | 0 |
'''simple docstring'''
def __lowerCamelCase ( A__ ) -> list:
"""simple docstring"""
UpperCamelCase = len(A__ )
for i in range(1 , A__ ):
UpperCamelCase = collection[i]
UpperCamelCase = 0
UpperCamelCase = i - 1
... | 28 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : str = logging.get_logger(__name__)
__a : Optional[int] = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
),
... | 210 | 0 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
lowerCAmelCase__ = False
class _lowerCam... | 244 |
"""simple docstring"""
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... | 244 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class UpperCAmelCase_ ( logging.LoggerAdapter):
@staticmethod
def _UpperCAmelCase ( a ) -> Dict:
lowercase__ : Any = PartialState()
return not main_p... | 77 | """simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingSt... | 77 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_s... | 267 |
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
UpperCAm... | 267 | 1 |
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_small""": [
"""BLEND... | 59 |
from functools import lru_cache
@lru_cache
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
i... | 114 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttent... | 363 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def UpperCamelCase ( _A : Tuple )-> Dict:
... | 198 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positi... | 94 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
lowercase__ = ('''dense.weight''', '''attention.self.query''', ''... | 207 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowercase ( unittest.TestCase ):
... | 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 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 244 |
from collections.abc import Generator
def __magic_name__ ( ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = 0, 1
while True:
UpperCamelCase__ , UpperCamelCase__ = b, a + b
yield b
def __magic_name__ ( __a ... | 244 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)... | 20 | import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DPR_CONTEXT_ENCODER_... | 20 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
# TODO: upload to AWS
UpperCAmelCase : Optional[Any] = {
'yjernite/retribert-base-uncased': (
'https:/... | 267 |
'''simple docstring'''
from itertools import count
def a__ ( a__ = 50 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [1] * min_block_length
for n in count(a__ ):
fill_count_functions.append(1 )
for block_length in range(a__ ... | 267 | 1 |
__UpperCAmelCase = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
__UpperCAmelCase = [{""... | 139 |
def snake_case_ () -> List[Any]:
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def snake_case_ (__A : Dict ) -> Tuple:
__lowerCAmelCase : Optional[int] = 1
__lowerCAmelCase : Optional[int] = ... | 139 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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_utils import load_image
if is_... | 339 | '''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 UpperCAmelCase ( a__ , unittest.TestCase ):
'''simple docstring''... | 198 | 0 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
A_ :Tuple = logging.get_logger(__name__)
... | 362 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
... | 245 | 0 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__a = "\\n@inproceedings{snover-etal-2006-study,\n title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",\n author = \"Snover, Matthew and\n Dorr... | 35 |
'''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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import ... | 35 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_SCREAMING_SNAKE_CA... | 351 |
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__ : Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase__ ... | 162 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
"""camemb... | 20 |
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():
import torch
i... | 20 | 1 |
"""simple docstring"""
from math import pow
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case, __snake_case, ) -> tuple[int, int]:
"""simple docstring"""
if current_sum == needed_sum:
# If the sum o... | 100 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
"""facebook/xlm-rober... | 100 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main... | 139 |
'''simple docstring'''
def A_ ( snake_case ):
if not all(x.isalpha() for x in string ):
raise ValueError("String must only contain alphabetic characters." )
SCREAMING_SNAKE_CASE:Optional[int] = sorted(string.lower() )
return len(snake_case ) ... | 139 | 1 |
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = multiprocessing.Manager()
__SCR... | 370 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _lowerCAmelCase ( *UpperCamelCase_ , UpperCamelCase_ = None , UpperCamelCase_=True , UpperCamelCase_=2 ):
from .. import __version__
__SCREAMING... | 255 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from ... | 50 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : str = {
"""facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}
... | 245 | 0 |
def lowerCamelCase__ ( A : list[int] ):
'''simple docstring'''
UpperCAmelCase = []
if len(A ) == 1:
return [nums.copy()]
for _ in range(len(A ) ):
UpperCAmelCase = nums.pop(0 )
UpperCAm... | 366 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : List[str] = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 91 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCAmelCase (_UpperCamelCase ):
return len(set(_UpperCamelCase ) ) == len(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers... | 162 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[float]) -> bool:
'''simple docstring'''
if len(_lowerCamelCase) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space")
... | 359 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InformerConfig'... | 151 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( UpperCamelCase_="ro" , UpperCamelCase_="en" , UpperCamelCase_="wmt16" , UpperCamelCase_=None ):
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise I... | 100 |
"""simple docstring"""
__magic_name__ = "Tobias Carryer"
from time import time
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=int(time())): # noqa: B008... | 100 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self , __lowerCamelCase ) -> None:
_SCREAMING_SNAKE_CASE : Optional[int] = value
_SCR... | 325 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, 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_tens... | 325 | 1 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
... | 98 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCamelCase: List[str] = {}
try:
if not is_s... | 255 | 0 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_snake_case : Dict = 0
_snake_case : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, ... | 207 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import ... | 207 | 1 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_commo... | 116 |
"""simple docstring"""
import random
from typing import Any
def _A (__a ) -> list[Any]:
"""simple docstring"""
for _ in range(len(__a ) ):
SCREAMING_SNAKE_CASE_ : Optional[int] = random.randint(0 , len(__a ) - 1 )
... | 91 | 0 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a=2 , a=3 , a=64 , a=None):
lowercase__ : List[str] = ... | 216 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ (__snake_case ):
_... | 216 | 1 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_lowercase : int ="src/transformers"
_l... | 170 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
if len(UpperCAmelCase_ ) < k or k < 0:
raise ValueError('Invalid Input' )
UpperCAmelCase : Tuple = sum(array[:k] )
for i in range(len(UpperCAmelCas... | 151 | 0 |
def snake_case (__lowercase , __lowercase ) -> Any:
'''simple docstring'''
return int(input_a == input_a == 0 )
def snake_case () -> Any:
'''simple docstring'''
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
... | 353 | from __future__ import annotations
import requests
__SCREAMING_SNAKE_CASE : Tuple = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categori... | 284 | 0 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __SCREAMING_SNAKE_C... | 325 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {name: getattr(transformers, name + """Fast""") for n... | 325 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
def __init__( self : List[str] ):
# test for the above condition
self.test()
def snake_case ( self : ... | 121 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class snake_case__:
"""simple docstring"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE : Collection[float] | None = None ):
... | 121 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_ava... | 207 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class _UpperCAm... | 207 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logg... | 351 |
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_xform... | 282 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
lowercase__ =logging.get_logger(__name__)
lowercase__ =OrderedDict(
[
# Base model mappin... | 216 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
lowercase__ ={
'debug': logging.DEBUG,
... | 216 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Opti... | 369 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( __magic_name__ ):
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (('''num_inference_steps''', 50),)
d... | 347 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
__UpperCAmelCase = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
__UpperCAmelCase = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
__UpperCAmelCa... | 84 |
def a_ ( lowerCAmelCase_ : int ):
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 284 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
A : List[str] = logging.get_logger(__name__)
A :... | 146 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : List[Any] = ... | 146 | 1 |
# 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-2.0
#
# Unless required by applicab... | 121 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_im... | 121 | 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 O... | 58 |
"""simple docstring"""
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_video_i... | 58 | 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,
TestCasePl... | 151 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
@property
def A ( sel... | 282 | 0 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common impo... | 261 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 261 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
fr... | 305 |
"""simple docstring"""
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __... | 347 | 0 |
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_video_inputs
if is_torch_available():
import torch... | 351 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: Optional[Any] = '''MCTCTFeatureExtractor'''
UpperCAmelCase__: Optional[int] = '''... | 141 | 0 |
from collections import deque
from math import floor
from random import random
from time import time
class __magic_name__ :
def __init__( self : Optional[int] ) -> str:
'''simple docstring'''
UpperCamelCase__ : str = {}
def UpperCA... | 146 |
from collections.abc import Callable
def _a ( SCREAMING_SNAKE_CASE : Callable[[float], float] , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
UpperCamelCase__ : float = a
UpperCamelCase__ : float = b... | 146 | 1 |
'''simple docstring'''
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
lowercase : Dict = TypeVar("T")
class __UpperCAmelCase ( Generic[T] ):
def __init__( self , lowerCAmelCase_ = True ):
"""simple doc... | 365 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(__A , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'''{solution... | 160 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCamelCase ( __lowerCamelCase : str , __lowerCamelCase : str ) ->bool:
_SCREAMING_SNAKE_CASE = get_failure_array(__lowerCamelCase )
# 2) Step through text searching for pattern
_SCREA... | 58 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttent... | 58 | 1 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 351 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class lowercase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
... | 241 | 0 |
"""simple docstring"""
def _lowerCamelCase( a , a = " " ):
__a = []
__a = 0
for index, char in enumerate(a ):
if char == separator:
split_words.append(string[last_index:index] )
__a = in... | 261 | """simple docstring"""
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 261 | 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_utils import load_i... | 355 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Tuple = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable... | 49 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps... | 42 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : Union[str, Any]=2_81_23 ):
'''simple docstring'''
__lowercase =[1] * (limit + 1)
for i in range(2, int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1, l... | 141 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
_lowerCAmelCase : Optional[Any] = ... | 351 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
r... | 308 | 0 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
... | 34 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_... | 160 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( snake_case : int , snake_case : List[str] )-> Any:
_lowerCamelCase = len(snake_case )
print('The following activities are selected:' )
# The first activity is always selected
_lowerCamelCase ... | 371 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Any ={
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerCon... | 80 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->Any:
'''simple docstring'''
def decorator(_lowercase : List[str] ):
a : Optio... | 105 |
"""simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class __lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCamelCase ( self : Optional... | 241 | 0 |
"""simple docstring"""
from __future__ import annotations
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase__ : str , UpperCAmelCase__ : str ) -> Optional[int]:
__SCREAMING_SNAKE_CASE ... | 195 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Tuple = logging.get_logger(__name__)
a__ : List[Any] = {
'''snap-research/efficientformer-l1-300''': (
'''https:... | 195 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Tuple = logging.get_logger(__name__)
class _a ( __UpperCAmelCase):
"""simple docstring"""
def __init__( self :... | 260 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __snake_case ( ):
__a , __a = 9, 14 # noqa: F841
__a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, ... | 49 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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_utils import... | 371 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_SCREAMING_SNAKE_CASE : Any = False
class _snake_case ... | 92 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
... | 308 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_lowerCamelCase ) , ... | 308 | 1 |
def _lowerCAmelCase ( A__: Tuple ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = []
UpperCAmelCase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''... | 152 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversat... | 152 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str ) -> str:
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 63 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import R... | 80 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class UpperCamelCase_ (... | 4 |
'''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
snake_case__ = l... | 4 | 1 |
from ..utils import DummyObject, requires_backends
class A_ ( metaclass=__lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = ["""flax"""]
def __init__( self , *snake_case , **snake_case ):
requires_backends(self , ... | 195 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vi... | 195 | 1 |
"""simple docstring"""
from maths.prime_factors import prime_factors
def __lowerCAmelCase (_UpperCamelCase ):
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowerCAmelCase : Any = F"Input value of [number={number}] must be an integer"
raise TypeError(_UpperCamelCa... | 359 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu,... | 182 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : list ) -> List[str]:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE_ )]
snake_case : str = []
def generate(lowercase : int , ... | 203 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase... | 92 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/... | 361 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"conf... | 149 | 0 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,... | 152 |
'''simple docstring'''
import socket
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE__ : str =socket.gethostname()
... | 152 | 1 |
import os
import sys
import unittest
A : int = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, r... | 358 | 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 AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 0 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class UpperCAmelCase_ ( unittest.TestCase... | 4 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import loggin... | 4 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class SCREAMING_SNAKE_CASE__ ( UpperCamelCas... | 363 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase : Union[str, Any] = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeS... | 286 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
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 import ConfigTester
from ...test_mo... | 5 | import itertools
import string
from collections.abc import Generator, Iterable
def A ( _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Union[str, Any] = iter(_lowercase )
while True:
SCREAMING_SNAKE_CASE : Optional[Any] = tup... | 182 | 0 |
def __snake_case ( _lowerCAmelCase : bytes ) -> str:
return "".join([hex(_lowerCAmelCase )[2:].zfill(2 ).upper() for byte in list(_lowerCAmelCase )] )
def __snake_case ( _lowerCAmelCase : str ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3... | 70 |
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __magic_name__ ( nn.Module ):
"""simple docstring"""
def __init__( self :int , snake_case :int = 16 , snake_case :int = 88... | 70 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCamelCase_ (UpperCamelCase__ ):
'''simple docstring'''
def _A ( self : List[Any] , A : float... | 31 |
import os
from datetime import datetime as dt
from github import Github
A__: int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',
]
def... | 149 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ : Tuple = logging.get_l... | 350 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_... | 170 | 0 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def __lowerCAmelCase ( UpperCamelCase__ = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def __lowerCAmelCase ( UpperCamel... | 67 |
def UpperCamelCase ( __magic_name__ : str ) -> list:
"""simple docstring"""
if n_term == "":
return []
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1/{temp + 1}''' if series else """1""" )
return series
... | 305 | 0 |
"""simple docstring"""
import pytest
A_ : Dict ="""__dummy_dataset1__"""
A_ : Tuple ="""
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.j... | 368 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A_ : Union[str, Any] =logging.get_logger(__name__)
class __a ( lowerCAmelCase__ ):
def __init__( self , *a__ , **a__ ):
... | 80 | 0 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
... | 249 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase_ : Tuple = logging.get_logger(__name__)
def UpperCAmelCase__ ( _UpperCAmelCase ... | 286 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
_A = version.parse(versio... | 205 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowercase ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ ... | 205 | 1 |
'''simple docstring'''
from __future__ import annotations
from random import random
class UpperCAmelCase :
def __init__( self : Any , __snake_case : int | None = None ) -> str:
_lowerCAmelCase = value
... | 70 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTok... | 70 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 68 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _UpperCAmelCase :
'''simple docstring'''
a__ ... | 68 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class snake_case__ ( A__):
def __init__( self : Union[str, Any] , *_... | 304 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logg... | 170 | 0 |
"""simple docstring"""
def _A ( UpperCamelCase_ : Optional[Any], UpperCamelCase_ : int) -> Optional[int]:
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _A ( UpperCamelCase_ : List[str], UpperCamelCase_ : int=0)... | 144 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 144 | 1 |
"""simple docstring"""
from math import factorial
lowercase__ = {str(digit): factorial(digit) for digit in range(10)}
def __a ( _SCREAMING_SNAKE_CASE ) ->int:
if not isinstance(__A , __A ):
raise TypeError('Parameter number must be int' )
if number < 0:
raise ValueE... | 290 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Any = logging.get_logger(__name__)
a__ : str = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/res... | 80 | 0 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise ValueError("I... | 67 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 67 | 1 |
def a ( A__ : int , A__ : int ) -> Union[str, Any]:
"""simple docstring"""
_lowercase =''
for i in table:
res += inp[i - 1]
return res
def a ( A__ : str ) -> Tuple:
"""simpl... | 205 |
def a ( A__ : int = 1000000 ) -> int:
"""simple docstring"""
_lowercase =1
_lowercase =1
_lowercase ={1: 1}
for inputa in range(2 , A__ ):
_lowercase =0
_lowercase =... | 205 | 1 |
_lowerCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
_lowerCamelCase : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def SCREAMING_SNAKE... | 231 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowerCamelCase : Optional[Any] = {
"""configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE... | 231 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import To... | 68 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"""abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}
class a_... | 68 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase_ = logging.getLogger()
@unittest.skip("Temporarily... | 247 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCAmelCase_ = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def lowerCAmelCase_ ( ... | 247 | 1 |
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