code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vis... | 346 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Any = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFI... | 87 | 0 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class a ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self ) -> None:
_a ... | 424 |
'''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_ : Any = {
"YituT... | 424 | 1 |
def UpperCamelCase_ ( __a , __a , __a , __a ) -> List[Any]:
if height >= 1:
move_tower(height - 1 , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
move_disk(__UpperCamelCase , __UpperCamelCase )
move_to... | 37 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
snake_case : List[str] = [8, 5, 9, 7]
snake_case : int = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
snake_case : Optional[Any] = [
[3, 2, 1, 4... | 566 | 0 |
from math import sqrt
def lowerCamelCase_ ( lowerCAmelCase__ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negati... | 224 |
from __future__ import annotations
def lowerCamelCase_ ( lowerCAmelCase__ : int = 4 ) -> list[list[int]]:
'''simple docstring'''
A = abs(lowerCAmelCase__ ) or 4
return [[1 + x + y * row_size for x in range(lowerCAmelCase__ )] for y in ran... | 224 | 1 |
'''simple docstring'''
from manim import *
class snake_case__ ( snake_case_):
def A ( self : Any ) -> Any:
UpperCAmelCase_ : List[str] = Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_ : Dict ... | 541 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSche... | 178 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_snake_case ,_snake_case ) -> bool:
'''simple docstring'''
if len(_snake_case ) == 0:
return False
__UpperCamelCase = len(_snake_case ) // 2
if a_list[midpoint] == item:
return True
if it... | 228 |
"""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
... | 228 | 1 |
'''simple docstring'''
from collections import defaultdict
def snake_case ( a_ : Optional[int] , a_ : List[Any] ) -> bool:
"""simple docstring"""
UpperCamelCase_ : Any = first_str.lower().strip()
UpperCamelCase_ : List[Any]... | 208 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 9 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
Musicg... | 707 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docs... | 98 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__A : Optional[int] = TypeVar('''T''')
class _UpperCAmelCase ( Generic[T] ):
def __init__( self ... | 231 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowercase ( __snake_case ... | 231 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def _a ( lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : typing.Counter[int] = Counter()
for base in range(1 , max_perimeter + 1 ):
... | 717 |
'''simple docstring'''
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
Up... | 47 | 0 |
from math import pi, sqrt, tan
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ,... | 89 |
from __future__ import annotations
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )-> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' ... | 393 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
SCREAMING_SNAKE_CASE_: List[str] =logging.getLogger(__name__)
class __A ( UpperCamelCase__ ):
... | 415 | '''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_: Dict ={
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Au... | 415 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/reso... | 0 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing impor... | 536 | 0 |
'''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 (
Au... | 136 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def _a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
snake_case : Dict =[0] * no_of_processes
snake_case : Dict =[0] * no_of_processes
# Initialize r... | 136 | 1 |
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... | 377 |
'''simple docstring'''
import json
import sys
def a__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : int ) -> Tuple:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8" ) as f:
UpperCAmelCase_ : ... | 71 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dim... | 702 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Dict = logging.get_logger(__name__)
__lowerCamelCase :... | 448 | 0 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.co... | 596 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNe... | 596 | 1 |
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, req... | 720 |
import qiskit
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : int ):
"""simple docstring"""
__lowerCamelCase : Dict = qiskit.Aer.get_backend("""aer_simulator""" )
__lowerCamelCase : ... | 458 | 0 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from to... | 165 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a__ : Union[str, Any] = logging.get_logger(__name__)
class lowerc... | 165 | 1 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Any = logging.get_logger(__name__)
UpperCAmelCase : Union[str, Any] = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/mai... | 47 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
UpperCAmelCase : Any = TypeVar('T')
UpperCAmelCase : str = TypeVar('U')
class lowerCamelCase (Generic[T, U] ):
def __init__( self ... | 47 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
a = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def UpperCAmelCase_ ( ):
lowercase_ = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
lowercase_ = os.path.join(_UpperCAmelCase , """w... | 412 |
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase ... | 4 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCamelCase : Optional[int] ="\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_UpperCamelCase :... | 707 |
'''simple docstring'''
import functools
def lowerCamelCase_ ( A_ , A_ ):
__lowerCamelCase = len(A_ )
__lowerCamelCase = len(A_ )
@functools.cache
def min_distance(A_ , A_ ) -> int:
# if first word index is overflow - delete all fro... | 575 | 0 |
'''simple docstring'''
def lowercase__( __UpperCamelCase: int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
raise TypeError('Input value must be an \'int\' type' )
SCREAMING_SNAKE_CASE : i... | 28 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedu... | 440 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/ma... | 695 |
'''simple docstring'''
def UpperCamelCase( UpperCAmelCase_ , UpperCAmelCase_ ):
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase_ ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 695 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( a_ : int ):
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
Upp... | 539 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_g... | 539 | 1 |
"""simple docstring"""
from __future__ import annotations
def __a ( A = 4 ):
'''simple docstring'''
lowercase__ = abs(lowercase_ ) or 4
return [[1 + x + y * row_size for x in range(lowercase_ )] for y in range(lowercase_ )]
def __a ( ... | 707 | """simple docstring"""
from typing import Any
import numpy as np
def __a ( A ):
'''simple docstring'''
return np.array_equal(A , matrix.conjugate().T )
def __a ( A , A ):
'''simple docstring'''
lowercase__ = v.co... | 668 | 0 |
"""simple docstring"""
from collections import namedtuple
UpperCAmelCase__ = namedtuple("""from_to""", """from_ to""")
UpperCAmelCase__ = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_0_0_0),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.0_0454, 264.172),
"... | 277 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.js... | 277 | 1 |
import os
import sys
_lowerCAmelCase : Optional[int] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassi... | 364 |
import torch
from transformers import AutoModel
class _UpperCamelCase ( torch.nn.Module ):
def __init__( self :str , lowerCamelCase :Tuple="sayef/fsner-bert-base-uncased" ) -> int:
super(lowerCamelCase , self ).__init__()
UpperCAmelCase__ = ... | 364 | 1 |
"""simple docstring"""
class __snake_case :
def __init__( self: List[Any] ):
__lowerCamelCase = {}
def __a ( self: Optional[Any] ):
print(self.vertex )
for i in self.vertex:
print(A_ ... | 281 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_... | 158 | 0 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class lowerCAmelCase ( unittest.TestCase ):
def lowercase ( self ):
debug_launcher(test_script.main )
def ... | 646 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCamelCase ( _A : np.ndarray , _A : float ) -> np.ndarray:
"""simple docstring"""
lowerCAmelCase : Union[str, Any] = math.sqrt(_A )
lowerCAmelCase : ... | 646 | 1 |
'''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 lowercase_ :
"""simple docstring"""
@prop... | 41 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_snake_case : Tuple = False
_snake_case : Optional[int] = True
_snake_case : Any = False
... | 441 | 0 |
'''simple docstring'''
from __future__ import annotations
class _A :
'''simple docstring'''
def __init__( self : int , lowerCamelCase : int )-> None:
snake_case__ : int = order
# a_{0} ... a_{k}
snake_... | 719 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqa... | 172 | 0 |
from math import ceil
def __UpperCamelCase ( A , A ):
UpperCamelCase__ = list(range(0 , A ) )
UpperCamelCase__ = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
UpperCamelCase__ ... | 415 | from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 415 | 1 |
'''simple docstring'''
def snake_case_ ( a__ : int ,a__ : int ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case_ ( ):
"""simple docstring"""
assert or_gate(0 ,0 ) == ... | 163 |
'''simple docstring'''
from statistics import mean, stdev
def snake_case_ ( a__ : list ,a__ : int = 3 ):
"""simple docstring"""
__lowercase = min(a__ )
__lowercase = max(a__ )
# normalize data
return [round((x - x_... | 163 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _UpperCAmelCase ( UpperCamelCase: Union[tf.Tensor, np.ndarray] ):
"""simple docstring"""
if isinstance(UpperCamelCase ... | 611 |
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_pr... | 611 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def snake_case ( UpperCAmelCase : Tuple ):
def wrapper(*UpperCAmelCase : List[str], **UpperCAmelCase : str ):
... | 110 |
from math import factorial
class UpperCamelCase :
"""simple docstring"""
def __init__( self : Any ,_SCREAMING_SNAKE_CASE : List[Any] ,_SCREAMING_SNAKE_CASE : List[str] ) -> List[str]:
'''simple docstring'''
A = real
if isinstance(... | 110 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_t... | 44 |
from __future__ import annotations
import math
import random
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: Dict ):
lowercase__ : list[Any] = []
lowercase__ : int = 0
lower... | 266 | 0 |
'''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... | 711 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-textcaps-base/resolv... | 175 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a__ ( _UpperCamelCase : List[Any] ):
if "cls_token" in name:
__lowerCamelCase = name.replace('''cls_token''' ,'''vit... | 175 | 1 |
'''simple docstring'''
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.se... | 687 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Optional[int] = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_... | 687 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : List[str] = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data... | 107 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logg... | 680 | 0 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->bool:
"""simple docstring"""
__lowercase : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowercase : set[int] = set()
... | 704 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase ) ->int:
"""simple docstring"""
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(_lowerCamelCase, _lowerCamelCase ):
raise TypeError("Input value must be a 'int' t... | 281 | 0 |
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,
)
_lowerCamelCase = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paw... | 6 |
'''simple docstring'''
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 i... | 229 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
lowerCamelCase ={
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bic... | 462 |
import math
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperCamelCase__ : List[str] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(UpperCamelCase__ )
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ = 1 / 1_2_3_4_5 ):... | 462 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _UpperCAmelCase ( lowerCAmelCase__):
_low... | 123 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
... | 123 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
snake_case_ : List[str] = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MA... | 709 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_... | 191 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tok... | 37 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 223 | 0 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
lowerCAmelCase__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .... | 681 |
"""simple docstring"""
import math
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return math.sqrt(SCREAMING_SNAKE_CASE ) * math.sqrt(SCREAMING_SNAKE_CASE ) == num
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''... | 681 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""google/umt5-small""": """https://huggingface.co/go... | 558 | 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 = pytest.mark.integration
@pytest.mark.parametrize('path' ,['... | 558 | 1 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( lowerCamelCase_ : int = 4 ):
SCREAMING_SNAKE_CASE__ = abs(lowerCamelCase_ ) or 4
return [[1 + x + y * row_size for x in range(lowerCamelCase_ )] for y in range(lowerCamelCase_ )... | 719 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accele... | 112 | 0 |
"""simple docstring"""
import pprint
import requests
lowerCamelCase_ = '''https://zenquotes.io/api'''
def snake_case ( ):
return requests.get(API_ENDPOINT_URL + "/today" ).json()
def snake_case ( ):
return requests.get(API_ENDPOINT_URL + "/random" ).json()
if __name__ ==... | 95 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models ... | 351 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__lowercase = {"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}
try:
if no... | 700 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__lowercase = logging.get_logger(__name__)
__lowercase = {... | 563 | 0 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_n... | 602 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils impor... | 602 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowerCAmelCase = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) ... | 675 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCAmelCase = """sshleifer/bart-tiny-random"""
lowerCAme... | 675 | 1 |
'''simple docstring'''
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowercase__ : List[str] = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.se... | 98 |
'''simple docstring'''
def a__ ( lowercase : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
_UpperCamelCase = 0
_UpperCamelCase = len(lowercase ) - 1
_UpperCamelCase = 0
while index >=... | 98 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}
... | 707 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require... | 162 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
lowerCamelCase : Tuple = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for T... | 460 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class __lowerCAmelCase :
'''simple docstring'''
def __init__(self : str , UpperCamelCase : list[str] ):
'''simple docstring'''
lowercas... | 460 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 705 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INP... | 52 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
f... | 105 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase__ (__snake_case ):
"""simple docstring"""
__UpperCamelCase : Any = (CMStochasticIterativeScheduler,)
__UpperCa... | 648 | 0 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Optional[Any] = (DDPMScheduler,)
def snake_case_ ( self... | 284 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):... | 284 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=lowercase_ ):
lowercase = ['keras_nlp']
def __init__( self ,*__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
'''simple docstring'''
... | 425 | """simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase__( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : L... | 425 | 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... | 291 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 291 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : str ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowercase : List[Any] =sorted(string.lower() ... | 92 |
def lowercase ( _a ) -> int:
if not isinstance(_a ,_a ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
UpperCAmelCase_: List[Any] = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through ea... | 137 | 0 |
'''simple docstring'''
def lowercase_ ( _lowercase = 1_000_000 ) -> int:
'''simple docstring'''
lowerCamelCase_ : str = set(range(3 , _lowercase , 2 ) )
primes.add(2 )
for p in range(3 , _lowercase , 2 ):
if p not... | 702 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def lowercase_ ( _lowercase ) -> list[list[float]]:
'''simple docstring'''
lowerCamelCase_ : Tuple = Decimal
# Check if the provided matrix has 2 rows and ... | 357 | 0 |
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
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
_SCREAM... | 550 |
class A__ :
"""simple docstring"""
def __init__( self , __snake_case ):
snake_case = n
snake_case = [None] * self.n
snake_case = 0 # index of the first element
snake_case ... | 550 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = '... | 712 |
'''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
lowerCAmelCase__ = logging.getLogger()
@unittest.s... | 172 | 0 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
... | 33 |
'''simple docstring'''
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class Up... | 508 | 0 |
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase__ = '''src/transfor... | 721 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGenerat... | 51 | 0 |
"""simple docstring"""
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 535 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 222 | 0 |
'''simple docstring'''
__lowerCamelCase : Tuple = """Input must be a string of 8 numbers plus letter"""
__lowerCamelCase : Any = """TRWAGMYFPDXBNJZSQVHLCKE"""
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
if not isinstance(__UpperCAme... | 418 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require... | 418 | 1 |
"""simple docstring"""
import requests
def _UpperCAmelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
lowerCAmelCase__ = {"""Content-Type""": """application/json"""}
lowerCAmelCase__ = requests.post(lowerCAmelCase__ , json={"""text""": message_bod... | 644 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowercase ( lowerCAmelCase__ ):
def wrapper(*lowerCAmelCase__ ,**lowerCAmelCase__ ):
lowerCamelCase_ = timeit... | 29 | 0 |
from __future__ import annotations
from math import pow, sqrt
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueEr... | 675 |
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclust... | 675 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
lowercase : Tuple = logging.getLogger(__name__)
class a__ :
def __init__( self ... | 423 | def UpperCAmelCase_ ( _UpperCAmelCase ):
lowerCamelCase_: Any = current_set.copy()
for row_index, row in enumerate(_UpperCAmelCase ):
lowerCamelCase_: Optional[Any] = row[0]
for column_index, column in enumerate(_UpperCAmelCase ):
... | 423 | 1 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( _A: Optional[in... | 718 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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
... | 571 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():... | 628 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 1 |
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
A : str = logging.get_logger(__name__)
class lowerCame... | 356 | 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_ver... | 356 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
f... | 14 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class... | 14 | 1 |
from __future__ import annotations
from typing import TypedDict
class a__ ( lowerCAmelCase_ ):
lowerCamelCase__: str
lowerCamelCase__: int
def _a ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("""The parame... | 478 |
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_UNKNOWN_IDENT... | 478 | 1 |
'''simple docstring'''
import math
import qiskit
def UpperCamelCase__ ( lowerCAmelCase = 1 , lowerCAmelCase = 1 , lowerCAmelCase = 1 ):
"""simple docstring"""
if (
isinstance(__a , __a )
or isinstance(__a... | 207 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
UpperCamelCase_ : Dict = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
... | 115 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTok... | 718 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
B... | 474 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 201 |
'''simple docstring'''
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mod... | 120 | 0 |
import math
import tensorflow as tf
from packaging import version
def __a ( A__ : Any ):
SCREAMING_SNAKE_CASE = tf.convert_to_tensor(A__ )
SCREAMING_SNAKE_CASE = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
... | 698 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__A : Optional[int] = logging.get_... | 698 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False)... | 64 | def A__ ( snake_case_ : float , snake_case_ : float ):
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import do... | 64 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( _A = 1000 ):
a : str = 2**power
a : Optional[Any] = str(_A )
a : List[Any] = list(_A )
a : Optional[Any] = 0
for i in list_num:
sum_of_num += int(_A )
return sum_of_n... | 195 |
'''simple docstring'''
class a__:
def __init__( self : Dict , __snake_case : Optional[int] , __snake_case : Any , __snake_case : Tuple ):
a : List[str] = name
a : Dict = value
a : List[str] ... | 195 | 1 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__na... | 624 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfi... | 624 | 1 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_param... | 703 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __snake_ca... | 158 | 0 |
'''simple docstring'''
def snake_case ( a_ : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(a_ , a_ ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase_ : Dict = [0, 1... | 208 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
... | 208 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class lowerCAmelCase_ ( _lowercase ):
'''simple docstring'''
_lowerCamelCase: Dict = '''MCTCTFeatureExtractor'''
_lowerCamelCase: Op... | 22 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Dict ) -> int:
A = {}
def _SCREAMING_SNAKE_... | 22 | 1 |
import numpy as np
a_ = [
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
class lowercase__ ... | 339 |
from __future__ import annotations
def _a ( UpperCamelCase_ : list[int] , UpperCamelCase_ : int ) -> list[int]:
"""simple docstring"""
lowerCAmelCase__ = 0
lowerCAmelCase__ = len(UpperCamelCase_ ) - 1
while i < j:
if ... | 339 | 1 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase ):
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list cannot be empty" )
__snake_case : int ... | 701 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDCo... | 203 | 0 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = '''▁'''
__snake_c... | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __snake_case :List[Any] , __snake_case :Union[str, Any]=1000 ) -> int:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__... | 693 | 0 |
from __future__ import annotations
__magic_name__ = list[list[int]]
# assigning initial values to the grid
__magic_name__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
... | 700 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase ):
if len(__lowerCAmelCase ) == 0:
return False
snake_case__ = len(__lowerCAmelCase ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 530 | 0 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
__UpperCamelCase : List[str] = 100
__UpperCamelCase : int = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__UpperCamelCase : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if... | 248 | import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = OmegaConf.load(_lowercase )
SCREAMING_SNAKE_... | 248 | 1 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
A_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classe... | 123 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class ... | 123 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 27 |
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_xlnet imp... | 587 | 0 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase_ : str , UpperCamelCase_ : Dict = None , UpperCamelCase_ : str = None ):
if start is None:
_lowerCAmelCase : Dict = 0
if end is None:
_lowerCAmelCase : Op... | 703 |
def _UpperCAmelCase (UpperCamelCase_ : str ):
'''simple docstring'''
_lowerCAmelCase : List[str] = [int(UpperCamelCase_ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(UpperCamelCase_ ) == 4 and all(0 <= int(UpperCamelCase_ )... | 196 | 0 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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 require... | 33 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbos... | 500 | 0 |
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 BackboneTesterMixin
f... | 139 |
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 (
BitC... | 139 | 1 |
from __future__ import annotations
class __lowerCamelCase :
def __init__( self: Optional[int],A_: str,A_: str ):
'''simple docstring'''
__UpperCamelCase, __UpperCamelCase = text, pattern
__UpperCamelCase, __Up... | 1 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 0 |
from collections.abc import Callable
def _lowerCamelCase( lowerCAmelCase__ : Callable[[float], float] , lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : float = a
SCREAMING_SNAKE_CA... | 97 |
import math
def _lowerCamelCase( lowerCAmelCase__ : float , lowerCAmelCase__ : float ):
'''simple docstring'''
return math.pow(lowerCAmelCase__ , 2 ) - a
def _lowerCamelCase( lowerCAmelCase__ : float ):
'''simple docstring'''... | 97 | 1 |
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