code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
from math import ceil, floor, sqrt
def _a ( UpperCAmelCase = 2000000 ) -> int:
"""simple docstring"""
lowerCamelCase__ : Tuple = [0]
lowerCamelCase__ : Any = 42
for idx in range(1 , ceil(... | 142 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 0 |
def _lowerCAmelCase (_lowerCAmelCase):
UpperCamelCase_ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _lowerCAmelCase (_lowerCAmelCase = 1_00):
UpperCamelCase_ = 1
UpperCamelCase_ = ... | 128 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 0 |
"""simple docstring"""
__A : Any = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def lowercase ( _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'''simple docstring'''
if not isinstance(__lowerCamelCase , __... | 260 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
return base * power(__lowerCamelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
lowercase : str = ... | 20 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ )-> int:
'''simple docstring'''
if not nums:
return 0
_UpperCAmelCase : Optional[Any] = nums[0]
_UpperCAmelCase : Dict = 0
... | 215 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
def UpperCamelCase ( snake_case__ : Tuple = 10 , snake_case__ : str = 22 ) -> int:
UpperCamelCase : Optional[int] = range(1 , __lowerCamelCase )
UpperCamelCase : Optional[Any] = range(1 , __lowerCamelCase )
return sum(
1 for... | 119 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import ... | 137 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
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 PaddingStrategy, logging
__snake_c... | 176 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 0 |
"""simple docstring"""
def __A ( a_ :Tuple) -> Union[str, Any]:
__a : Optional[Any] = len(__lowerCamelCase)
for i in range(length - 1):
__a : int = i
for k in range(i + 1 , __lowerCamelCase):
if col... | 160 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a ... | 228 | 0 |
'''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_image_inputs
if is_torch_avail... | 97 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPriorConfig",
"JukeboxVQVAEConfig"... | 210 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 0 |
class __SCREAMING_SNAKE_CASE :
def __init__( self : Any , A : str , A : Any , A : Union[str, Any] ) ->Optional[int]:
lowerCamelCase__ : List[str] = None
lowerCamelCase__ : Tuple = None
... | 142 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 0 |
from __future__ import annotations
from math import pi
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if inductance < 0:
rai... | 128 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : str = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/confi... | 260 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> float:
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" )
return (bulk_modulus / density... | 20 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 0 |
'''simple docstring'''
import datasets
A_ : Optional[Any] = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 215 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 119 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pi... | 228 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Dict = {
"fa... | 137 |
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
from accelerate impor... | 228 | 0 |
from __future__ import annotations
__snake_case = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__snake_case = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def _lowercase ( UpperCamelCase_ ) -> list[float]:
'''simple docstring'''
SCREAM... | 176 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builde... | 228 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class __lowercase :
'''simple docstring'''
def __init__( self ):
__a : List[Any] = {}
... | 160 |
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,
)
... | 228 | 0 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__snake_case = pytest.mark.integration
@pytest.mark.parametrize('''path... | 97 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 0 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
requi... | 210 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder ):
def __lowerCamelCase ( s... | 142 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase : Dict =logging.get_logger(__name__)
UpperCAmelCase : str ={
"shi... | 128 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : List[Any] = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_... | 260 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 0 |
from math import pi, sqrt
def _snake_case( SCREAMING_SNAKE_CASE__ ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0... | 20 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : List[str] = {
"google/pix2struct-textcaps-base": (
"https://huggingf... | 215 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, sl... | 119 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ : Dict = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2former-swin-... | 137 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
import inspect
import unittest
class lowercase__ ( unittest.TestCase ):
def A_ ( self : Tuple ):
try:
import diffusers # noqa: F401
except ImportError:
assert False
def A_ ( self : List[str] ):
impor... | 176 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 0 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_to... | 160 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a ... | 228 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-b... | 97 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 0 |
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
__lowercase = gray_code_sequence_string(__lowerCamelCase )
#
# conver... | 210 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 0 |
from __future__ import annotations
import pandas as pd
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> list[int]:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = [0] * no_of_processes
lowerCamelCase__ : Option... | 142 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 0 |
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 _lowercase (a_ ... | 128 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
... | 260 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_commo... | 20 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 0 |
'''simple docstring'''
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : str = logging.get_logger(__name__)
class lowercase ( _lowerCamelCase )... | 215 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __A ( ) -> Any:
a = ArgumentParser(
description=(
"""PyTorch TPU distributed training ... | 228 | 0 |
def UpperCamelCase ( snake_case__ : Union[str, Any] = 100 ) -> int:
UpperCamelCase : Tuple = 0
UpperCamelCase : Tuple = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ ==... | 119 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pi... | 228 | 0 |
from math import sqrt
def lowerCamelCase__ (_UpperCAmelCase = 100_0000):
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 ... | 137 |
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
from accelerate impor... | 228 | 0 |
def _lowercase ( UpperCamelCase_ = 10 ) -> str:
'''simple docstring'''
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or n < 0:
raise ValueError('Invalid input' )
SCREAMING_SNAKE_CASE__ = 10**n
SCREAMING_SNAKE_CASE__ = 28433 * (pow(... | 176 |
__UpperCamelCase : Optional[int] = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builde... | 228 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
A = {
"tiny.en": "https://openaipublic.a... | 160 |
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,
)
... | 228 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 97 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
... | 228 | 0 |
from __future__ import annotations
def UpperCAmelCase ( lowercase , lowercase , lowercase , ):
"""simple docstring"""
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('''You cannot supply more or less than 2 values''' )
... | 210 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelin... | 142 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class __lowerCAmelCase ( pl.LightningModule ):
def __init__( self :Union[str, Any] , __magic_name__ :Optional[int]... | 228 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ():
UpperCamelCase_ = ArgumentParser(
description=(
"PyTorch TPU distributed training ... | 128 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils ... | 260 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase ) -> float:
a = sorted(numsa + numsa )
a , a = divmod(len(__lowerCamelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
ret... | 228 | 0 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowercase : Optional[int] = get_logger(__name__)
lowercase : Tuple = R"\n Args:\n input_ids (`jnp.ndarray` of s... | 20 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixi... | 228 | 0 |
'''simple docstring'''
def snake_case_ ( lowerCAmelCase_ )-> str: # noqa: E741
'''simple docstring'''
_UpperCAmelCase : str = len(__lowerCamelCase )
_UpperCAmelCase : int = 0
_UpperCAmelCase : Any = [0] * n
_Upper... | 215 |
def __A ( __lowerCamelCase , __lowerCamelCase ) -> str:
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(__lowerCamelCase , __lowerCamelCase ) or not number >= 1:
... | 228 | 0 |
import os
from distutils.util import strtobool
def UpperCamelCase ( snake_case__ : str , snake_case__ : List[str] ) -> Optional[Any]:
for e in env_keys:
UpperCamelCase : List[str] = int(os.environ.get(__lowerCamelCase , -1 ) )
if val >= 0:
... | 119 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_... | 228 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
a_ : str = False
class _snake_case ( unittest.TestCase ):
... | 137 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"Jukebo... | 228 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowercase__ ( _UpperCAmelCase ):
def __init__( self : Dict , Upper... | 176 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMix... | 228 | 0 |
"""simple docstring"""
from __future__ import annotations
def __A ( a_ :Optional[int] , a_ :Tuple) -> bool:
if len(__lowerCamelCase) == 0:
return False
__a : Optional[Any] = len(__lowerCamelCase) // 2
if a_list[midpoint] == it... | 160 |
from __future__ import annotations
from collections.abc import Iterator
class __lowerCAmelCase :
def __init__( self :Optional[Any] , __magic_name__ :int ):
'''simple docstring'''
a = value
a ... | 228 | 0 |
'''simple docstring'''
import argparse
import json
import subprocess
def a ( __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Optional[int] = (
f'''curl -H "Accept: application/vnd.github+json"... | 97 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 228 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a : Tuple = logging.get_logger(__name__)
__a : Union[str, Any] = {
"nielsr/canine-s": 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
__a ... | 210 |
def __A ( __lowerCamelCase ) -> int:
a = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __A ( __lowerCamelCase = 100 ) -> int:
a = 1
a = 2
for i in range(2 , max_n + 1 ... | 228 | 0 |
from __future__ import annotations
import math
def _a ( UpperCAmelCase ) -> list[int]:
"""simple docstring"""
if num <= 0:
lowerCamelCase__ : Optional[int] = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(__lowe... | 142 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Dict =logging.get_logger(__name__)
UpperCAmelCase : List[Any] ={
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-10... | 128 |
import math
from datetime import datetime, timedelta
def __A ( __lowerCamelCase ) -> datetime:
a = year % 19
a = year % 4
a = year % 7
a = math.floor(year / 100 )
a = math.floor((13 + 8 * leap_... | 228 | 0 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def lowercase ( _SCREAMING_SNAKE_CASE : Any ):
'''simple docstring'''
_UpperCAmelCase = min(__lowerCamelCase ) # min() finds the minimum value
_UpperCAmelCa... | 260 |
from __future__ import annotations
def __A ( __lowerCamelCase , __lowerCamelCase = None ) -> list[list[str]]:
a = word_bank or []
# create a table
a = len(__lowerCamelCase ) + 1
a = []
for _ in range(__lowerCamelCa... | 228 | 0 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowercase : str = 500000
lowercase : Tuple = os.path.split(__file__)
lowercase : Tuple = os.path.join(RESULTS_BASEPATH, """... | 20 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
__UpperCamelCase : List[str... | 228 | 0 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# ful... | 229 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 1 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 1 |
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from de... | 229 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 1 |
'''simple docstring'''
import qiskit
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
__lowerCAmelCase = qiskit.Aer.get_backend("""aer_simulator... | 229 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 1 |
'''simple docstring'''
import os
def UpperCamelCase_ ( ) -> Optional[int]:
'''simple docstring'''
with open(os.path.dirname(snake_case_ ) + """/grid.txt""" ) as f:
__lowerCAmelCase = [] # noqa: E741
for _ in range(20 ):
l... | 229 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 1 |
'''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_image_inputs
if is_t... | 229 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.conf... | 229 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints... | 229 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 1 |
'''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
from ... | 229 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 1 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_star... | 229 | '''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : str = logging.get_logger(__name__)
_A : str = [
['''a... | 229 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_A : Optional[Any] = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
... | 229 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( ... | 229 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_A : str = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
... | 229 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : str = logging.get_logger(__name__)
_A : List[Any] = {
'''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.j... | 229 | '''simple docstring'''
import argparse
import gc
import json
import os
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
... | 229 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_A : ... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
fo... | 229 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import chec... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import AlbertConfig, 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... | 229 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNA... | 229 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class _lowercase :
'''simple docstring'''
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ) -> Union[str, Any]:
__lowerCAm... | 229 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A : int = True
except (ImportError, ModuleNotFoundError):
_A : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quie... | 229 | 1 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def UpperCamelCase_ ( *snake_case_ : Dict ) -> Any:
'''simple docstring'''
if not isinstance(snake_case... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int , snake_case_ : int ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
__lowerCAmelCase =... | 229 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_A : int = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:... | 229 | '''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
def a ( self : int ) -> Optional[Any]:
return ... | 229 | 1 |
'''simple docstring'''
class _lowercase :
'''simple docstring'''
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : str = "" , SCREAMING_SNAKE_CASE__ : bool = False ) -> None:
# Mapping from the first character of the prefix of the node
__... | 229 | '''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax ... | 229 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : str = {'''configuration_reformer''': ['''REFORMER_PRETRAINE... | 229 | '''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
@abstractmethod
def a ( SCREAMING_SNAKE_CASE__ : ArgumentParser ) -> Tup... | 229 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def UpperCamelCase_ ( snake_case_ : float , snake_case_ : float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError("... | 229 | '''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class _lowercase :
'''simple docstring'''
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE... | 229 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( snake_case_ : list[int] ) -> int:
'''simple docstring'''
__lowerCAmelCase = len(snake_case_ ) // 2
# choose the middle 3 elements
__lowerCA... | 229 | '''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase_ ( sn... | 229 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, s... | 229 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class _lowercase ... | 229 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Union[str, Any] ... | 229 | '''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAtt... | 229 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def UpperCamelCase_ ( snake_case_ : int = 1_00_00_00 ) -> int:
'''simple docstring'''
__lowerCAmelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if ou... | 229 | '''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging i... | 229 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : int = logging.get_logger(__name__)
_A : List[str] = {
'''facebook/s2t-small-librispeech-asr''': (
'''https://huggingface.co/facebook/s2t-small-li... | 229 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCamelCase_ ( snake_case_ : Any ) -> Optional[Any]:
'''simple docstring'''
__lowerCAmel... | 229 | 1 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTes... | 229 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def UpperCamelCase_ ( ) -> Tuple:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os impor... | 229 | '''simple docstring'''
import numpy as np
import qiskit
def UpperCamelCase_ ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
'''simple docstring'''
__lowerCAmelCase = np.random.default_rng(see... | 229 | 1 |
'''simple docstring'''
def UpperCamelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
if not isinstance(snake_case_ , snake_case_ ):
__lowerCAmelCase = f"""Input value of [number={number}] must be an integer"""
raise ... | 229 | '''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
_A : str = logging.get_logger(__name__)
_A : str = [
['''a... | 229 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_A : Dict = logging.get_logger(__name__)
# TODO: upload to AWS
_A : Tuple = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yj... | 229 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowercase ( ... | 229 | 1 |
'''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-2.0
... | 229 | '''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _lowercase ( datasets.BuilderConfig ):
'''simple docstring... | 229 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( snake_case_ : Optional[int] , snake_case_ : List[str] , snake_case_ : str , snake_case_ : Optional[Any] ) -> Any: # noqa: E741
... | 229 | '''simple docstring'''
import argparse
import gc
import json
import os
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
... | 229 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Any = ["""torch""", """torchsde"""]
def __init__( self : Optional[Any]... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : Union[str, Any]=2_81_23 ) -> str:
'''simple docstring'''
__lowerCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
fo... | 229 | 1 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class _lowercase :
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> Tuple:
__lowerCAmelCase = {}... | 229 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.uti... | 229 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : Tuple = logging.get... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str
_SCREAMING_SNAKE_CASE : int
def UpperCamelCase_... | 229 | '''simple docstring'''
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class _lowercase :
'''simple docstring'''
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNA... | 229 | 1 |
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