code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from... | 31 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data imp... | 187 | 0 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 1 |
# flake8: noqa
# Lint as: python3
_a = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_prog... | 39 |
'''simple docstring'''
from __future__ import annotations
def __a ( UpperCAmelCase , UpperCAmelCase ) ->Tuple:
"""simple docstring"""
if len(UpperCAmelCase ) <= 1 or n <= 1:
return
insert_next(UpperCAmelCase , n - 1 )
rec_insertion_sort(UpperCAmelCase , n - ... | 258 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_se... | 355 |
def UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
A , A : Optional[Any] = len(_lowerCamelCase ), len(grid[0] )
if (
min(_lowerCamelCase , _lowerCamelCase ) < 0
or row == row_lengt... | 256 | 0 |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
imp... | 50 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__lowerCAmelCase : Optiona... | 9 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}
try:
if not is_torch_available():
raise Opti... | 365 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def A ( __UpperCAmelCase , __UpperCAmelCase=() , __UpperCAmelCase=None , __UpperCAmelCase="no" , __... | 344 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A: Tuple = {
"configuration_git": ["GIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "GitConfig", "GitVisionConfig"],
"processing_git": ["GitProcessor"],
}
try:
... | 109 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def A_ ( A__ ) -> float:
return np.dot(A__ , A__ )
class A__ :
"""simple docstring"""
def __init__( self , *,
lowercase = np.inf , lowercas... | 99 | 0 |
_UpperCAmelCase : Dict = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> float:
return 10 - x * x
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_UpperCAmelCase ) * equation(_Up... | 45 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
A_ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
A_ = (((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
A_ = [2, 4, 1, 5]
A_ = ... | 139 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[int] = {
"""configuration_llama""": [""... | 202 | 0 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 196 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> list:
UpperCamelCase__ : Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
UpperCamelCase__ : Tuple = True
for i in range(0 , le... | 196 | 1 |
from math import factorial
__snake_case = {str(digit): factorial(digit) for digit in range(1_0)}
def _A ( _lowercase ) -> int:
"""simple docstring"""
if not isinstance(_lowercase , _lowercase ):
raise TypeError('Parameter num... | 310 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 336 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
# Se... | 355 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_lowerCAmelCase = object()
# For specifying empty leaf dict `{}`
_lowerCAmelCase = object(... | 98 | 0 |
import qiskit
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: int , SCREAMING_SNAKE_CASE_: int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
... | 68 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
lowerCAmelCase__ = logging.get_logger(__name__)
class a__ ... | 68 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE_ )
class _UpperCAmelCase ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__A... | 353 | import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 63 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A ={
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIV... | 34 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A... | 34 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase... | 351 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase ( _UpperCamelCase : list[float] , _UpperCamelCase : list[float] ) -> float:
'''simple docstring'''
__UpperCAmelCase : Tuple = sorted(numsa + numsa )
__Up... | 320 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class __snake_case ( unittest.TestCase ):
_a = JukeboxTokenizer
_a = {
'''artist''': '''Zac Brown Band''',
'''genres''': '''Country... | 103 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
UpperCAmelCase = logging.get_logger(__na... | 256 | 0 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _a ( lowerCAmelCase):
"""simple docstring"""
UpperCamelCase__ = (IPNDMScheduler,)
UpperCamelCase__ = (("num_inference_steps", 50),)
def low... | 353 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__A : Tuple = logging.getLogger()
@unittest.skip("... | 326 | 0 |
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.utils import floats... | 228 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow... | 344 | 0 |
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,
ConditionalDetrForSegmentation,
... | 90 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase__ ( __lowercase ,unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = Ph... | 90 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available(... | 45 |
"""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,
)
lowercase_ = {"con... | 45 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ... | 356 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''post_extrac... | 299 | 0 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCAmelCase ... | 196 |
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 impo... | 196 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import B... | 159 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def _UpperCAmelCase (UpperCamelCase_ : Sequence[float] , UpperCamelCase_ : int , UpperCamelCase_ : int ):
'''simple docstring'''
... | 159 | 1 |
'''simple docstring'''
from __future__ import annotations
UpperCAmelCase : Tuple = [-1_0, -5, 0, 5, 5.1, 1_1, 1_3, 2_1, 3, 4, -2_1, -1_0, -5, -1, 0]
UpperCAmelCase : List[Any] = [-5, 0, 5, 5.1, 1_1, 1_3, 2_1, -1, 4, -1, -1_0, -5, -1, 0, -1]
def a__ ( a__ ):
... | 267 | """simple docstring"""
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
f... | 98 | 0 |
'''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 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = ["""image_processor""", """tokenizer"""]
lower... | 48 | 1 |
'''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,
)
UpperCamelCase__: List[Any] = pytest.mark.inte... | 23 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE... | 63 | 0 |
"""simple docstring"""
import math
def lowerCamelCase_ (UpperCamelCase__ : Optional[int] ):
return math.sqrt(UpperCamelCase__ ) * math.sqrt(UpperCamelCase__ ) == num
def lowerCamelCase_ (UpperCamelCase__ : Optional[Any] ):
_UpperCAmelCase : Option... | 371 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase_ ():
_UpperCAmelCase : int = HfArgumentParser(UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = parser.parse_args... | 68 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetT... | 282 |
import logging
import os
from .state import PartialState
class A__ ( logging.LoggerAdapter ):
@staticmethod
def a__ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = PartialState()
return not m... | 325 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCAmelCase ( __A ):
"""simple docstring"""
lowerCamelCase = ['''image_processor''', '''tokenizer''']
lowerCamelCase = '''... | 355 |
'''simple docstring'''
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
return math.sqrt(a_ ) * math.sqrt(a_ ) == num
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
... | 164 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
... | 91 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.s... | 326 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, F... | 367 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from tran... | 189 | 0 |
import os
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowerCamelCase = len(grid[0] )
__lowerCamelCase = len(UpperCamelCase__ )
__lowerCamelCase ... | 90 |
import collections
import inspect
import unittest
from transformers import FocalNetConfig
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 impo... | 90 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : int = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/ed... | 218 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_p... | 218 | 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 ModelTesterMi... | 265 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stab... | 265 | 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/lic... | 58 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCr... | 58 | 1 |
from math import ceil
def _lowerCAmelCase ( lowerCAmelCase_ :int = 1_001 )->int:
'''simple docstring'''
snake_case_ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
snake_case_ = 2 * i + 1
snake_case_ ... | 159 |
def _lowerCAmelCase ( lowerCAmelCase_ :Union[str, Any] , lowerCAmelCase_ :Tuple , lowerCAmelCase_ :Any )->List[Any]:
'''simple docstring'''
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowerCAmelCase_ , ... | 159 | 1 |
"""simple docstring"""
class __a :
"""simple docstring"""
def __init__( self : Optional[Any] , lowercase_ : List[Any] ):
# we need a list not a string, so do something to change the type
UpperCamelCase__ : List[str] =arr.split(''','''... | 157 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __a ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ['image_processor', 'tokenizer']
SCREAMING_SNAKE_CASE_ ... | 157 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
'configuration_efficientformer': [
'EFFICIENTFOR... | 48 |
import random
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> tuple:
lowerCamelCase , lowerCamelCase , lowerCamelCase : Any = [], [], []
for element in data:
if element < pivot:
less.append(_SCREAMING_S... | 48 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slo... | 362 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional im... | 185 | 0 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
... | 108 |
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.test... | 68 | 0 |
import enum
import shutil
import sys
UpperCAmelCase__ , UpperCAmelCase__ = shutil.get_terminal_size()
UpperCAmelCase__ = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class lowercase_ ( enum.Enum ):
'''simple docstr... | 367 |
def _a ( a :list ) -> list:
if len(a ) <= 1:
return lst
a = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a , a = lst[i], lst[i - 1]
i -= 1
if i == 0:
a = 1
return lst
if __name__ == "__main_... | 26 | 0 |
import tensorflow as tf
from ...tf_utils import shape_list
class __A ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1 , ... | 209 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A = logging.get_logger(__name__)
__A = {
"facebook/convnextv2-tiny-1k-224": "https://... | 164 | 0 |
from __future__ import annotations
__A = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __A ( _lowercase ):
'''simple docstring'''
_A = []
_A = len(_lower... | 350 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class SCREAMING_SNAKE_CASE ... | 75 | 0 |
"""simple docstring"""
from __future__ import annotations
class UpperCAmelCase :
"""simple docstring"""
def __init__( self , _UpperCAmelCase ):
lowercase__: Optional[Any] = order
# a_{0} ... a_{k}
lowercase__: Optional[int] = [1.0] + ... | 177 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __a ( A__ , A__ )... | 189 | 0 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ( __UpperCamelCase ):
__sn... | 28 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelC... | 28 | 1 |
from numpy import exp, pi, sqrt
def UpperCamelCase_( _snake_case : int , _snake_case : float = 0.0 , _snake_case : float = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
... | 218 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __magic_name__ ( lowerCAmelCase_ ):
S... | 218 | 1 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggi... | 352 |
from __future__ import annotations
from typing import TypedDict
class _snake_case ( lowercase__):
UpperCamelCase__ : str
UpperCamelCase__ : int
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
... | 224 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
lowercase_ = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def lowerCamelCase ( __lowerCamelCase : Tuple , __lowerCamelCase : int ) ->List[An... | 58 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase_ = logging.getLogger(__name__)
if __name__ == "__m... | 58 | 1 |
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
_lowercase: Any = logging.get_logger(__name__)
_l... | 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = ... | 71 | 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_torch_avai... | 157 | import numpy as np
import datasets
_snake_case = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mah... | 157 | 1 |
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCamelCase : Optional[Any] ... | 306 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : str ):
__lowercase : Tuple ... | 306 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCAmelCase_ = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-large-v1''': '''https... | 8 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_c... | 185 | 0 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from u... | 238 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , unitte... | 238 | 1 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
cl... | 76 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impor... | 26 | 0 |
"""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.configuration_pegasus impor... | 149 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ... | 149 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowercase__ :
def __init__( self , __UpperCAmelCase=2 , __UpperCAmelCase=3 , __UpperCAmelCase=64 , __UpperCAmelCase=None )-> Tup... | 340 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
Ada... | 75 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fr... | 281 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 281 | 1 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
... | 28 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 50 ) -> int:
"""simple docstring"""
UpperCamelCase = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for b... | 28 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ..... | 282 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare... | 282 | 1 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class ... | 78 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : int = 100 ) -> int:
"""simple docstring"""
lowerCAmelCase_ : Any = (n * (n + 1) // 2) ** 2
lowerCAmelCase_ : Optional[int] = n * (n + 1) * (2 * n + 1) // 6
r... | 224 | 0 |
'''simple docstring'''
_lowercase : Dict = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
... | 21 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
... | 21 | 1 |
'''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict ):
'''simple docstring'''
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > numbe... | 346 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 71 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : List[str] =logging.get_logger(__name_... | 352 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a_ ( _lowerCAmelCase ):
@staticmethod
@abstractmethod
def lowercase__ ( lowercase : ArgumentParser ):
"""simple docs... | 147 | 0 |
import heapq
import sys
import numpy as np
UpperCamelCase = tuple[int, int]
class __UpperCAmelCase :
def __init__( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = []
_SCREAMING_... | 306 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalG... | 306 | 1 |
from datetime import datetime
import requests
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
lowercase__ = requests.get(base_url + url ).json()[0]['''urls''... | 359 |
from abc import ABC, abstractmethod
from typing import List, Optional
class _a ( UpperCamelCase__ ):
def __init__( self: Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
self.test()
def ... | 93 | 0 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
fr... | 74 | """simple docstring"""
import operator
def _lowerCamelCase( a , a = False , a = None ):
__a = operator.lt if reverse else operator.gt
__a = solution or []
if not arr:
return solution
__a = [arr.pop(0 )]
for i, i... | 261 | 0 |
import math
def UpperCAmelCase__ ( ):
lowercase :Optional[Any] = input("Enter message: " )
lowercase :List[str] = int(input(F"Enter key [2-{len(lowerCamelCase ) - 1}]: " ) )
lowercase :int = input("Encryption/Decryption [e/d]: " )
if mode.lower().starts... | 360 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Con... | 158 | 0 |
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 ModelTesterMixin, UNet... | 149 |
from torch import nn
def lowerCAmelCase_ ( A_):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"Unsupported activation funct... | 149 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.util... | 201 |
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__ ... | 201 | 1 |
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXT... | 281 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridCo... | 281 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCamelCase__ = ['''small''', '''medium''', '''large''']
lowerCamelCase__ = '''lm_head.decoder.weight'''
lowerCamelCase__ = '''lm_head.weight'''
def lowerCAmelCase__ ( a__ , a__ ) -... | 63 | # Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class _UpperCAmelCase ( Tenso... | 63 | 1 |
from collections.abc import Sequence
def a_ ( __lowercase : Sequence[float] , __lowercase : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(__lowercase ) )
def a_ ( __lowercase : Sequence[float] , __lowercase : float ) -> float:
... | 282 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_lowerCamelCase ... | 282 | 1 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> list[int]:
lowercase__ = [0 for i in range(len(_SCREAMING_SNAKE_CASE ) )]
# initialize interval's left pointer and right pointer
lowercase__ , lowercase__ = 0, 0
for i in range(1 , len(_SCR... | 269 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 269 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_... | 21 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 21 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@requ... | 203 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def __a ( _SCREAMING_SNAKE_CASE ) ->bool:
a__: Any = credit_card_number
a__: Tuple ... | 203 | 1 |
"""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.utils import floa... | 109 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transform... | 147 | 0 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class SCREAMING_SNAKE_CASE__ ( snake_case_):
def __init__( self , *A_ , **A_ )-> Union[str, Any]:
'''simple docstring'''
... | 251 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : int = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.jso... | 251 | 1 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str ) -> List[str]:
if n_term == "":
return []
__a = []
for temp in range(int(__SCREAMING_SNAKE_CASE ) ):
series.append(f'''1/{temp + 1}''' if series else '''1''' )
return ... | 45 |
'''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():
_lowercase : Union[str, Any] = yaml.safe_load(
"\\nname: \"\"\nallow_emp... | 93 | 0 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _UpperCamelCase ( ) -> List[str]:
"""simple docstring"""
lowerCAmelCase__ = {
'repo_name': ['test_repo1', 'test_... | 122 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_co... | 122 | 1 |
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelera... | 73 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def __a(SCREAMING_SNAKE_CASE_ : float ):
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error" )
return quad(SCREAMING_SNAKE_CASE_ , 0 , SCREA... | 158 | 0 |
"""simple docstring"""
def __UpperCamelCase ( _A : int = 1000 ) ->int:
"""simple docstring"""
lowerCamelCase_ =2**power
lowerCamelCase_ =str(_A )
lowerCamelCase_ =list(_A )
lowerCamelCase_ =0
for i ... | 371 |
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 _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
def __init__( self , _SCRE... | 49 | 0 |
"""simple docstring"""
import argparse
__UpperCamelCase = '''docs/source/_static/js/custom.js'''
def UpperCAmelCase ( UpperCAmelCase ) -> int:
with open(UpperCAmelCase , encoding='utf-8' , newline='\n' ) as f:
snake_case_ = f.... | 69 | '''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_co... | 272 | 0 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__lowerCamelCase = datasets.utils.logging.get_logger(__name__)
@datacl... | 154 | """simple docstring"""
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class UpperCamelCase__( unittest.TestCase ):
def snake_case__ ( self ) -> Optional[int]:
... | 154 | 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-... | 63 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__ ( self : str ):
_a = [
"... | 63 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_a : List[str] = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a ... | 358 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def _lowerCAmelCase ( lowercase ) -> List[str]:
__lowerCAmelCase = np.max(lowercase , axis=-1 , keepdims=lowercase )
__lowerCAmelCase = np.exp(outputs - maxes )
... | 46 | 0 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from url... | 207 |
"""simple docstring"""
import math
lowerCamelCase__ : List[Any] = 10
lowerCamelCase__ : Optional[int] = 7
lowerCamelCase__ : Dict = BALLS_PER_COLOUR * NUM_COLOURS
def UpperCamelCase ( _lowerCAmelCase : int = 20 ) -> str:
_UpperCAm... | 246 | 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,
r... | 224 |
import fire
from utils import calculate_rouge, save_json
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ ):
lowercase__ = [x.strip() for x in open(SCREAMING_SNAKE_CASE_ ).readlines()]
lowercas... | 224 | 1 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __lowerCAmelCase ( lowercase : List[str] , lowercase : int , lowercase : List[An... | 203 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__snake_case = {
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 E... | 203 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class _snake_case :
def __init__( self , _a ):
__magic_name__ : Any = str(id_ )
__magic_name__ : Any = None
__magic_name__ : List[str] = None
__magic... | 359 |
import numpy
class _snake_case :
def __init__( self , _a , _a ):
__magic_name__ : Optional[Any] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
#... | 41 | 0 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _a ( ... | 251 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 251 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2Visi... | 358 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWi... | 322 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 122 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_... | 122 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a =logging.get_logger("""transformers.models.speecht5""")
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ... | 113 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a ={
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
... | 113 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = "x" , __lowerCAmelCase = 10**-10 , __lowerCAmelCase = 1 , )-> List[str]:
"""simple docstring"""
_UpperC... | 39 |
import logging
from transformers.configuration_utils import PretrainedConfig
__snake_case :Any = logging.getLogger(__name__)
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[Any] = '''masked_bert'''
def __init__( self : str ... | 49 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-... | 357 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils i... | 4 | 0 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
__... | 154 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__A : int = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
if not is_tor... | 154 | 1 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
if num <= 0:
__magic_name__ : List[str] = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(UpperCAmelCase_ ... | 352 |
def lowerCAmelCase_ ( _snake_case : int ) -> int:
'''simple docstring'''
assert isinstance(_snake_case , _snake_case ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
__magic_name__ : Dict = F'''T... | 41 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
fro... | 52 |
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__) # pylint: disable=invalid-name
... | 46 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional ... | 363 |
import os
from datetime import datetime as dt
from github import Github
_lowerCamelCase : List[Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def _UpperCAmelCase ():
''... | 159 | 0 |
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