code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConfig",
... | 10 |
import logging
from transformers.configuration_utils import PretrainedConfig
__A = logging.getLogger(__name__)
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "masked_bert"
def __init__(self : Dict , UpperCAmelCase_ ... | 10 | 1 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int = 60_08_51_47_51_43 ):
"""simple docstring"""
try:
SCREAMING_SNAKE_CASE : int = int(__UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('Paramete... | 246 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
SCREA... | 246 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> int:
'''simple docstring'''
while a != 0:
__snake_case , __snake_case : Optional[Any] = b % a, a
return b
def __UpperCAmelCase... | 172 | """simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class UpperCamelCase ( lowercase ):
... | 172 | 1 |
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 21 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : int = {}
SCREAMING_SNAKE_CASE : Any = token... | 76 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __lowerCAmelCase ( lowercase : Dict ) -> Any:
"""simple docstring"""
if not is_accelerate_available():
... | 371 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VAR... | 112 | 0 |
def lowerCamelCase__ ( __lowerCAmelCase : Optional[int] ):
"""simple docstring"""
lowerCAmelCase_ = []
lowerCAmelCase_ = []
lowerCAmelCase_ = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
"+": 1,
"-": 1,
} # Priority of each operator
l... | 231 |
import math
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
lowerCAmelCase_ = 0
lowerCAmelCase_ = 0
while num > 0:
lowerCAmelCase_ = num % 8
lowerCAmelCase_ = octal + (remainder * math.floor(math.pow(10 , __lowerCAme... | 231 | 1 |
'''simple docstring'''
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a : Tuple = 4
a : Tuple = 3
class UpperCamelCase_ (... | 338 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 338 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_c... | 246 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def UpperCamelCase ( _lowerCAmelCase : Dict, _lowerCAmelCase : int=(), _lo... | 246 | 1 |
'''simple docstring'''
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_at... | 48 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any]):
'''simple docstring'''
__lowercase =[]
... | 48 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrFor... | 305 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Any = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co... | 305 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mode... | 43 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 1 |
"""simple docstring"""
from math import factorial
__UpperCAmelCase = {str(d): factorial(d) for d in range(10)}
def _snake_case ( lowercase__ : int ) -> int:
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(lowercase__ ) )
... | 84 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list ):
if any(not isinstance(_lowerCamelCase , _lowerCamelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowerCamelCase )... | 112 | 0 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 371 |
"""simple docstring"""
from math import sqrt
def _snake_case ( _snake_case : int ):
assert isinstance(_snake_case , _snake_case ) and (
number >= 0
), "'number' must been an int and positive"
lowerCAmelCase : Dict = True
# 0 and 1 are none... | 314 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
lowercase__ : List[Any] = 4
lowercase__ : Dict = 3
class lowercase_ ( UpperCamelCase... | 338 | import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowercase__ : Dict = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(... | 338 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
UpperCAmelCase = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal=... | 187 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'huggingface/time-series-transformer-tourism-monthly': (
... | 187 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data ... | 48 |
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__ ... | 48 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_... | 369 |
_snake_case : List[str] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
_snake_case : List[Any] = ... | 207 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowercase = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Con... | 43 | import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__lowerCAmelCase = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeo... | 107 |
'''simple docstring'''
from math import sqrt
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
_a : Dict = 0
for i in range(1 , int(sqrt(lowerCAmelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCAmelCase_ ):
total += i + n // i
elif i == sqr... | 107 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def UpperCAmelCase_ ( __lowercase : List[str] , __lowercase : List[Any] , __lowercase : Tuple ) -> Any:
'''simple docstring'''
_Uppe... | 22 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def UpperCAmelCase_ ( ):
'''simple docstring'''
raise RuntimeError('''CUDA out of memory.''' )
class ... | 314 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
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 imp... | 276 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Dict = logging.get_logger(__name__)
A : Dict = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models at https://h... | 276 | 1 |
from __future__ import annotations
lowercase__ : str = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def lowerCamelCase__ ( _A , _A , _A , _A , _A , ):
'''simple docstring'''
snake_case_... | 187 |
def lowerCamelCase__ ( ):
'''simple docstring'''
snake_case_ = []
snake_case_ = 1
while len(_A ) < 1E6:
constant.append(str(_A ) )
i += 1
snake_case_ = "".join(_A )
return (
int(constant[0] ... | 187 | 1 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( lowercase_ , ... | 368 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRober... | 230 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
fr... | 58 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softmax
... | 207 | 0 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowercase__ : List[Any] = [
... | 155 |
"""simple docstring"""
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 _UpperCAmelCase ( lowerCAmelCase__):
def __init__( self : ... | 155 | 1 |
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,
require_t... | 107 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common ... | 107 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import c... | 109 |
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
snake_case : Dict = get_logger(__name__)
snake_case : str = r'''
Args:
input_ids (`jnp.ndarray` of shape `(ba... | 109 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : int ) -> int:
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
_a : int =F"Input value of [number={number}] must be an integer"
... | 276 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F40... | 276 | 1 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : Union[str, Any] = "T5C... | 134 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_snake_case : Union[str, Any] = ["small", "medium", "large"]
_snake_case : List[Any] = "lm_head.decoder.weight"
_snake_case : Optional[Any] = "lm_head.weight"
def lowerCAmelCase_ ... | 134 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_snake_case = lo... | 294 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _lowerCAmelCase ( __lowe... | 230 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torc... | 363 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def a__ ( ):
raise RuntimeError('CUDA out of memory.' )
class __lowercase (nn.... | 162 | 0 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
a ... | 155 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a = logging.get_logger(__name__)
a = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json... | 155 | 1 |
from __future__ import annotations
def A ( a_ ) -> bool:
__UpperCamelCase : Tuple =len(a_ )
# We need to create solution object to save path.
__UpperCamelCase : Optional[int] =[[0 for _ in range(a_ )] for _ in range(a_ )]
__UpperCamelCase ... | 365 |
from math import pow, sqrt
def A ( *a_ ) -> bool:
__UpperCamelCase : Union[str, Any] =len(a_ ) > 0 and all(value > 0.0 for value in values )
return result
def A ( a_ ,a_ ) -> float | ValueError:
return (
... | 245 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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... | 109 |
"""simple docstring"""
from math import pi, sqrt, tan
def _snake_case ( UpperCamelCase : float ):
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def _snake_case ( UpperCamelCase : ... | 109 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
SCREAMING_SNAKE_CASE__:Union[str, Any] = list[tuple[int, int]]
SCREAMING_SNAKE_CASE__:str = [
[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, 1, 0, 0, ... | 364 | """simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
SCREAMING_SNAKE_CASE__:List[str] = 3
def _lowerCamelCase( a ):
print("Generating primitive root of p" )
while True:
__a = ... | 268 | 0 |
'''simple docstring'''
import os
from math import logaa
def __lowerCamelCase ( __snake_case : str = "base_exp.txt" ) -> int:
"""simple docstring"""
A__ : float =0
A__ : Optional[int] =0
for i, line in enumerate(open(os.pa... | 134 |
'''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,
)
__snake_case : Optional[Any] = {'con... | 134 | 1 |
"""simple docstring"""
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__A : Any = logging.get_logger... | 57 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcess... | 57 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase__ : list[str] ) -> Any:
__SCREAMING_SNAKE_CASE = []
self.adlist.append(
... | 54 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
A_ = len(UpperCAmelCase__ )
# We need to create solution object to save path.
A_ = [[0 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase_... | 162 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
i... | 351 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
SCREAMING_SNAKE_CASE : str = 1
SCREAMING_SNAKE_CASE : Optional[int] = 1
whi... | 246 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : str = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json"""... | 55 |
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 ... | 245 | 0 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor, rand... | 257 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
__UpperCAmelCase = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Ev... | 257 | 1 |
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ... | 306 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class UpperCamelCase_ :
def __init__( self : str ) -> Dict:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : int = ""... | 268 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import lo... | 371 |
import math
def _a ( a :int = 100 ) -> int:
a = sum(i * i for i in range(1 , n + 1 ) )
a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f... | 26 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Union[str, Any] = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",... | 57 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 | 1 |
'''simple docstring'''
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
UpperCamelCase__ = datasets.logging.get_logger(__name__)
UpperCamelCase__ = '''\
@InProceedings{mo... | 371 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCamelCase__ = logging.get_l... | 299 | 0 |
def __A ( __lowerCAmelCase = 100 )-> int:
"""simple docstring"""
_UpperCAmelCase = (n * (n + 1) // 2) ** 2
_UpperCAmelCase = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{... | 39 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObject... | 246 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : Dict = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:... | 370 | '''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules i... | 18 | 0 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Dict = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *_A , **_A ):
... | 257 |
from heapq import heappop, heappush
import numpy as np
def __lowercase ( a__ , a__ , a__ , a__ , ) -> tuple[float | int, list[tuple[int, int]]]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = grid.shape
__SCREAMING_SNAKE_CASE ... | 257 | 1 |
"""simple docstring"""
from collections.abc import Generator
def __lowercase ( ) -> Generator[int, None, None]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 1
while True:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = b, ... | 359 |
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ : Optional[int] = ['''speech''']
def __init__( self , *_A , **_A ):
'''sim... | 118 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemak... | 65 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowercase ( UpperCamelCase__ ):
_a = (DPMSol... | 26 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 48 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from trans... | 48 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...ut... | 205 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxMode... | 299 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_u... | 90 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowercase__ =50000
lowercase__ =5000
lowercase__ , lowercase__ =os.path.split(__file__)
lowercase__ =os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENA... | 90 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _A ( A__ ):
"""simple docstring"""
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__":
import doct... | 104 | import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : Tuple ... | 18 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from... | 182 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline ... | 182 | 1 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.t... | 124 | import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ,... | 118 | 0 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ = 1_00_00_00 ):
lowerCAmelCase__ : Dict = limit + 1
lowerCAmelCase__ : Optional[int] = [0] * limit
for first_term in range(1 , lowerCAmelCase__ ):
for n in range(lowerCAmelCase__ , ... | 360 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
... | 74 | 0 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
SCREAMING_SNAKE_CASE__ : str = '\nimport os\n'
SCREAMING_SNAKE_CASE__ : str = '\ndef foo():\n import os\n return False\n'
SCREAMING_SNAKE_CASE__ : Any = '\ndef foo... | 48 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE__ : Optional[... | 48 | 1 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Pat... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConf... | 226 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version... | 90 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
__A = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ASTConfig... | 90 | 1 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 50 ):
"""simple docstring"""
__UpperCAmelCase : Any = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
... | 367 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as tran... | 16 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 182 | import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_U... | 182 | 1 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.util... | 359 | '''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... | 21 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'vocab_file': 'vocab.txt',
'merges_file': 'bpe.codes',
}
_... | 30 |
"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_su... | 74 | 0 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( __lowerCamelCase ):
for param in module.parameters():
__snake_case : Union[str, Any] = False
def lowerCAmelCase_ ( ):
__snake_case : ... | 134 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applic... | 134 | 1 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __lowercase ( _a , _a=False ):
snake_case_ : Union[str, Any] = OmegaConf.load(_UpperCAmelCase )
if display:
print(yaml.dump(Omega... | 264 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_ava... | 226 | 0 |
"""simple docstring"""
import string
def _SCREAMING_SNAKE_CASE ( lowercase_ ) -> None:
for key in range(len(string.ascii_uppercase ) ):
A__ = ""
for symbol in message:
if symbol in string.ascii_uppercase:
A__ = string.ascii_uppercase.find(lowercase_ ... | 230 |
"""simple docstring"""
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRober... | 230 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import BackboneTe... | 27 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeli... | 16 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : Any = get_tests_dir("fix... | 51 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _a ( SCREAMING_SNAKE_CASE : Optiona... | 51 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[str] = logging.get_logger(__name__)
lowercase__ : int = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-res... | 187 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"SenseTime/deformable-detr": "https://huggi... | 21 | 0 |
'''simple docstring'''
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 ... | 366 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configurati... | 98 | 0 |
'''simple docstring'''
def __lowerCamelCase ( __snake_case : str, __snake_case : str ) -> int:
"""simple docstring"""
if len(__snake_case ) != len(__snake_case ):
raise ValueError("""String lengths must match!""" )
... | 134 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowerCamelCase ( unit... | 134 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase: List[Any] = {
'configuration_bigbird_pegasus': [
'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 53 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, ... | 53 | 1 |
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 OptionalDependencyNotAvailab... | 230 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _lowerCAmelCase ( __lowe... | 230 | 1 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowercase_ ( __SCREAMING_SNAKE_CASE ):
A__ : Optional[int] = ["""image_processor""", """tokenizer"""]
A__ : int = """AutoImageProcessor"""
A__ : List[Any] ... | 261 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def lowerCamelCase__ ( a__ : Dict , a__ : Dict=None ) -> Union[str, Any]:
UpperCamelCase_ = None
if token is n... | 261 | 1 |
def A (__A : int = 1000000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = set(range(3 , __A , 2 ) )
primes.add(2 )
for p in range(3 , __A , 2 ):
if p not in primes:
... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''llama'''
UpperCAmelCase__ : ... | 51 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
I... | 364 |
from functools import lru_cache
def UpperCamelCase ( __magic_name__ : int ) -> set:
"""simple docstring"""
lowercase__ = 2
lowercase__ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.... | 146 | 0 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _UpperCamelCase ( __A ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ = os.path.join(arg... | 80 | """simple docstring"""
def a_ ( lowerCamelCase , lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase__ = str(bin(lowerCamelCase ) )[2:] # remove the leading "0b"
UpperCAm... | 98 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuratio... | 352 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DD... | 275 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a__ : Optional[int] ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 53 |
'''simple docstring'''
import random
def lowercase__ ( __lowercase : list , __lowercase : Optional[Any] ) -> tuple:
"""simple docstring"""
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = [], [], []
for element in ... | 53 | 1 |
from __future__ import annotations
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list[list[int]] ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first co... | 363 | import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca imp... | 20 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 261 | """simple docstring"""
import copy
import re
class snake_case__ :
_snake_case : Dict = """hp"""
_snake_case : List[str] = {}
_snake_case : int = None
@classmethod
def a__ ( cls , lowerCamelCase , lowerCamelCase ):
__a = prefix
... | 261 | 1 |
"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _lowerCamelCase ( ... | 259 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : Tuple = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-96... | 259 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : List[str] = {"""vo... | 55 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import c... | 146 | 0 |
"""simple docstring"""
from math import sqrt
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even... | 324 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.confi... | 324 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_a = {
'''text_branch''': '''text_model''',
'''audio_branch''': '''audio_model.audio_encoder''',
'''attn''': '''attention.self''',
... | 39 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowercase ( lowercase__ ):
__lowerCAmelCase : str = []
__lowerCAmelCase : List[Any] = []
__lowe... | 275 | 0 |
'''simple docstring'''
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 __UpperCamelCase :
@property
def a__ ... | 355 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@... | 8 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca... | 339 |
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_... | 20 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( lowerCamelCase : str ):
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def lowerCAmelCase__ ( lowerCamelCase : str ):
_A : List[str] = credit_card_number
... | 356 |
'''simple docstring'''
def lowerCAmelCase__ ( lowerCamelCase : int ,lowerCamelCase : list[int] ,lowerCamelCase : int ):
def count_of_possible_combinations(lowerCamelCase : int ) -> int:
if target < 0:
return 0
... | 227 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__snake_case = TypeVar("""KT""")
__snake_case = TypeVar("""VT""")
class UpperCAmelCase_ ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self ... | 259 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_commo... | 259 | 1 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in f... | 354 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, i... | 309 | 0 |
'''simple docstring'''
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from... | 324 |
'''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_a... | 324 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, req... | 364 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_A = logging.get_logger(__name__)
_A = {
'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-... | 205 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowerCamelCase : int = TypeVar("T")
class __UpperCAmelCase ( Generic[T] ):
UpperCamelCase = 42 # Cache store of keys
UpperCamelCase = ... | 336 |
from sklearn.metrics import mean_squared_error
import datasets
lowerCAmelCase_ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prette... | 8 | 0 |
class A_ :
'''simple docstring'''
def __init__( self: Optional[int] ):
__lowerCamelCase : Dict = {}
def _snake_case ( self: Union[str, Any] ):
print(self.vertex )
for i in self.vertex:
print(a , ' -> ' , '... | 194 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import... | 194 | 1 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenizatio... | 144 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a... | 227 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def _a ( _snake_case = 200_0000 ):
"""simple docstring"""
UpperCAmelCase = [0]
UpperCAmelCase = 42
for idx in range(1 , ceil(sqrt(target * 2 ... | 234 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case , _snake_case = None , _snake_case = None ):
"""simple docstring"""
if start is None:
UpperCAmelCase = 0
if end is None:
UpperCAmelCase =... | 234 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Dict = {
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
try:
... | 165 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__UpperCAmelCase =TypeVar("T")
__UpperCAmelCase =TypeVar("U")
class a__ ( Generic[T, U] ):
def __init__( self : Dict , a : ... | 237 | '''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
@register_to_config
def __init__( self : Any , *,
... | 237 | 1 |
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