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 dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
SCREAMING_SNAKE_CASE__ = Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE__ = Mapping[str... | 150 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
__a : List[Any] = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , 'html.parser' ... | 27 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
... | 124 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case... | 124 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar('T')
class lowerCamelCase__( Generic[T]):
def __init__( self: List[str] , UpperCamelCase_: T ):
__lowerCamelCase = dat... | 12 |
import numpy as np
__snake_case = [
["""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"""],
]
... | 259 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowerCAmelCase ( UpperCAmelCase__ : int, UpperCAmelCase__ : int = 2, UpperCAmelCase__ : int = 1, UpperCAmelCase__ : int = 3, ) ->int | None:
# A value less than 2 can c... | 296 |
"""simple docstring"""
# 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/LICENS... | 296 | 1 |
'''simple docstring'''
import numpy as np
class UpperCAmelCase :
def __init__( self :int , lowercase_ :List[Any]=None , lowercase_ :Any=None , lowercase_ :Optional[Any]=None , lowercase_ :Optional[int]=None , lowercase_ ... | 237 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str = "cpu" , lowerCAmelCase__ : Union[str, None] = None ) -> None:
... | 224 | 0 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = [False] * len(_UpperCamelCase )
__lowerCAmelCase = []
queue.append(_UpperCamelCase )
__... | 259 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple d... | 259 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if part... | 70 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeniz... | 70 | 1 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import ... | 3 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main... | 3 | 1 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
__snake_case : List[Any] = 100
__snake_case : Dict = set(range(3, NUM_PRIMES, 2))
primes.add(2)
__snake_case : int
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prim... | 134 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoForm... | 134 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE = {
'''configuration_trajectory_transformer''': [
'''TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'''... | 362 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowercase :
'''simple... | 217 | 0 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils im... | 178 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_acc... | 178 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : Dict = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microso... | 70 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : List[Any] = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Fun... | 70 | 1 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int ) ->int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowerCamelCase ( __lowerCamelCase : int ) ->bool:
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE ... | 58 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttent... | 58 | 1 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParse... | 56 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
while b:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = b, a % b
return a
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase )... | 56 | 1 |
class A :
def __init__(self : str , __UpperCAmelCase : List[str] , __UpperCAmelCase : Any , __UpperCAmelCase : Tuple ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase__ = None
UpperCA... | 65 | """simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__A = False
class UpperCAmelCase (unittest.TestCase )... | 177 | 0 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_... | 85 | """simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_hea... | 85 | 1 |
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 import Dataset
from tran... | 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __a ( UpperCAmelCase ):
_a : ... | 329 | 1 |
import math
import sys
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if number != int(a__ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of input... | 370 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 87 | 0 |
"""simple docstring"""
from math import factorial, radians
def a__ ( snake_case__ , snake_case__ = 18 , snake_case__ = 10 ) -> float:
lowerCamelCase = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
lowerCamel... | 291 |
"""simple docstring"""
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():
fro... | 291 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The co... | 371 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={
'configuration_table_transformer': [
'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TableTransformerConfig',
'Tab... | 283 | 0 |
import os
import numpy
import onnx
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_lowercase : Optional[Any] = a.name
_lowercase : Dict = b.name
_lowercase : List[str] = ''
_lowercase : ... | 21 |
"""simple docstring"""
# 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.... | 224 | 0 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 300 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_snake_case = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentati... | 300 | 1 |
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
lowercase : int = logging.get_logger(__name__)
lowercase : Dict ... | 232 | from torch import nn
class _lowercase ( nn.Module ):
def __init__( self : Any , snake_case : Dict , snake_case : Union[str, Any] ) -> Dict:
"""simple docstring"""
super().__init__()
UpperCamelCase_ : List[Any] = class_size
Upper... | 175 | 0 |
def UpperCAmelCase ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
SCREAMING_SNAKE_CASE :List[str] = generate_large_matrix()
SCREAMING_SNAKE_CASE ... | 124 |
def UpperCAmelCase ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
SCREAMING_SNAKE_CASE :List[str] = generate_large_matrix()
SCREAMING_SNAKE_CASE ... | 124 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a__ : Any = logging.get_logger(__name__)
a__ : Optional[int] ... | 161 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_model... | 161 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
UpperCamelCase_ = get_tests_dir() +... | 59 |
from sklearn.metrics import matthews_corrcoef
import datasets
UpperCamelCase_ = '''
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and fa... | 59 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '''▁'''
Up... | 195 |
import math
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> bool:
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are prime... | 325 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_i... | 368 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowerCamelCase_:
'''simple docstring'''
def __init__( self ):
_lowerCamelCase = ''''''
_lowerCamelCase = ''''''
_lowerCam... | 73 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
fro... | 217 |
"""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__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_... | 217 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common ... | 363 | 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 a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=a )
class __A( a ):
snake_case_ = field(default='''language-modeling''' , metadata={'''include_in_asdict_even_if_is_default... | 6 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__A = logging.get_logger(__name__)
__A ... | 164 | 0 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> bool:
lowercase__ = 0
for ch in input_str:
lowercase__ = ord(_SCREAMING_SNAKE_CASE )
lowercase__ = pow(2 , _SCREAMING_SNAKE_CASE )
# If we already turned on bit for current chara... | 269 |
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowercase_ = {
# 1536-bit
5: {
"""prime""": int(
... | 269 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from t... | 35 |
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 import ... | 310 | 0 |
import json
from typing import 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_bart import BartTokenizer
... | 15 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a__ = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'''text-classification''',
'... | 15 | 1 |
def lowerCAmelCase_ (lowerCAmelCase__: Any , lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
if density <= 0:
raise ValueError("""Impossible fluid density""" )
if bulk_modulus <= 0:
raise ValueError("""Impossible bulk modulus""" ... | 147 |
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_token... | 101 | 0 |
import operator as op
UpperCamelCase_ = '''scaler.pt'''
UpperCamelCase_ = '''pytorch_model'''
UpperCamelCase_ = '''random_states'''
UpperCamelCase_ = '''optimizer'''
UpperCamelCase_ = '''scheduler'''
UpperCamelCase_ = '''pytorch_model.bin'''
UpperCamelCase_ ... | 59 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a : np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 59 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_snake_case = get_logger(__name__)
_snake_case = r"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequenc... | 26 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from ... | 320 | 0 |
"""simple docstring"""
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... | 356 |
"""simple docstring"""
import math
import random
def a__ ( snake_case__ , snake_case__ = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase : Dict = 0.0_2
def a__ ( ... | 168 | 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
f... | 82 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availabl... | 82 | 1 |
import qiskit
def __UpperCamelCase ( _A : int , _A : int ) ->qiskit.result.counts.Counts:
"""simple docstring"""
lowerCamelCase_ =qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
... | 366 |
__A : List[Any] = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
__A : int = [
... | 49 | 0 |
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
__A = logging.get_logger(__name__)
... | 90 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = """MCTCTFeatureExtractor"""
lowerCame... | 23 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataL... | 371 |
import warnings
from functools import wraps
from typing import Callable
def __a ( lowerCAmelCase_ : Callable ) -> Callable:
'''simple docstring'''
@wraps(lowerCAmelCase_ )
def _inner_fn(*lowerCAmelCase_ : List[Any] ,**lowerCAmelCase_ : Tuple ... | 277 | 0 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowercase : Union[str, Any] = re.compile(r"""\b(a|an|the)\b""", re.UNICODE)
lowercase : Union[str, Any] = None
def A_ ( ) -> Dict:
a__ : Dict... | 99 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""roberta-b... | 52 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 206 | def __lowerCamelCase (UpperCAmelCase__ : str , UpperCAmelCase__ : str = " " ):
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
for index, char in enumerate(UpperCAmelCase__ ):
if char == separator:
split_... | 206 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, i... | 328 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->... | 293 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokeni... | 355 |
'''simple docstring'''
import numpy as np
def UpperCAmelCase_ ( __lowerCamelCase : str ,__lowerCamelCase : Optional[Any] ,__lowerCamelCase : int ,__lowerCamelCase : List[str] ,__lowerCamelCase : List[Any] ):
lowercase_ :Dict = int(np.ceil((x_end - xa) ... | 147 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_c... | 27 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str = "https://www.worldometers.info/coronavirus" ):
__a : List[Any] = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , 'html.parser' ... | 27 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
def _lowerCAmelCase ( self , __UpperCAmelCase ) ... | 369 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common... | 341 | 0 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A__ : Dict = logging.get_logger(__name__)
A__ : List[str] = {'vocab_file': 'vocab.json', 'merges_file': 'merges... | 207 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
def __init__( self ... | 207 | 1 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _snake_case ( _UpperCAmelCase ):
lowerCAmelCase :Tuple = ['image_processor', 'tokenizer']
lowerCAmelCase :Union[str, Any] ... | 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A ={
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
'tokenizati... | 283 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Any = {
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''to... | 112 |
import math
def snake_case_ ( snake_case , snake_case ) -> float:
return math.pow(snake_case , 2 ) - a
def snake_case_ ( snake_case ) -> float:
return 2 * x
def snake_case_ ( snake_case ) -> float:
... | 196 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : List[str] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"M... | 51 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
__UpperCamelCase : Optional[int] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm"... | 51 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : int = {
"configuration_roformer": ["ROFORMER_PRETRAINED_CONFI... | 270 |
import os
def __magic_name__ ( ) -> str:
__lowerCamelCase = os.path.join(os.path.dirname(__lowerCAmelCase ) , '''num.txt''' )
with open(__lowerCAmelCase ) as file_hand:
return str(sum(int(__lowerCAmelCase ) for line in file_hand ) )[:10]
if __n... | 270 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a : Optional[Any] = logging.get_logger(__name__)
a : Optional[int] = {
"""mic... | 351 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : int = {
'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
'tokenization_xlm': ['XLMToken... | 72 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/... | 46 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase )... | 311 | 0 |
"""simple docstring"""
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import ... | 57 |
"""simple docstring"""
from __future__ import annotations
class __UpperCamelCase :
def __init__(self : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : str):
A , A = text, pattern
A , A = len(__SCREAMING_SNAKE_C... | 57 | 1 |
"""simple docstring"""
def _snake_case ( ):
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(lowercase__ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
pr... | 96 |
import unittest
from transformers import BigBirdConfig, 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
from transformers.models.big_bir... | 12 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ (UpperCamelCase__ : list[int] , UpperCamelCase__ : int , UpperCamelCase__ : int , UpperCamelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and a... | 367 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self ) -> Tuple:
_UpperCAmelCase : str = {}
def __lo... | 68 | 0 |
"""simple docstring"""
from __future__ import annotations
import requests
def _snake_case ( snake_case__ : str ):
A = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(snake_case__ ).json()
def _snake_case ( snake_... | 74 |
def _SCREAMING_SNAKE_CASE ( lowercase : int = 10_00 ):
'''simple docstring'''
lowerCamelCase_ = 2**power
lowerCamelCase_ = 0
while n:
lowerCamelCase_ , lowerCamelCase_ = r + n % 10, n // 10
return ... | 204 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_available():
... | 189 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCo... | 189 | 1 |
'''simple docstring'''
from __future__ import annotations
def lowercase_ ( _lowercase ) -> int:
'''simple docstring'''
if not nums:
return 0
lowerCamelCase_ : Optional[int] = nums[0]
lowerCamelCase_ : Optional[int] = 0
for num in nums[... | 318 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
Aut... | 318 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
f... | 367 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
... | 235 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = s.rsp... | 127 |
class A__ :
"""simple docstring"""
def __init__( self , __snake_case , __snake_case , __snake_case ):
snake_case = name
snake_case = value
snake_case = weight
def __repr__( self ):
retur... | 127 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase: Optional[int] = {
'configuration_autoformer': [
'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP'... | 96 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase: Any = argparse.ArgumentParser()
parser.add_argument('--dump_... | 96 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list cannot be empty" )
UpperCAmelCase_ : Any = sum(
cash_flow / ((1... | 268 |
"""simple docstring"""
from __future__ import annotations
import time
lowerCamelCase_ = list[tuple[int, int]]
lowerCamelCase_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0... | 268 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available()... | 364 |
from jiwer import compute_measures
import datasets
SCREAMING_SNAKE_CASE_:str = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation meas... | 115 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : Optional[int] = {
"""configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""]... | 291 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 291 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase ( A ):
'''simple docstring'''
def __init__( self : Dict , ... | 48 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase =SwinConfig(image_size=192 )
... | 48 | 1 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCAmelCase_ ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname... | 26 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase : Optional[Any] = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftF... | 252 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __lowercase ( a__ ) -> Tuple:
def wrapper(*a__ , **a__ ):
__SCREAMING_SNAKE_CASE = timeit.... | 351 |
lowerCAmelCase__ : Optional[int] =9.80_665
def __lowercase ( a__ , a__ , a__ = g ) -> float:
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume < 0:
raise ValueError('Impossi... | 118 | 0 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __A (__UpperCAmelC... | 347 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase : int = {
"facebook/encode... | 136 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
__lowerCAmelCase = {
# 1536-bit
5: {
'''p... | 107 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( lowerCAmelCase_ ) -> str:
return "".join(sorted(lowerCAmelCase_ ) )
def __lowerCamelCase ( lowerCAmelCase_ ) -> list[str]:
... | 107 | 1 |
'''simple docstring'''
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def UpperCAmelCase_ ( __lowerCamelCase : List[str] ):
if (
(cp >= 0x4E00 and cp <= 0x9FFF)
or (cp >= 0x3400 a... | 223 |
"""simple docstring"""
from sklearn.metrics import matthews_corrcoef
import datasets
_a = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It t... | 194 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : str ) ->list:
'''simple docstring'''
__A : List[Any] = [0] * len(snake_case_ )
for i in range(1 ,len(snake_case_ ) ):
# use last results for better performance - d... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : Optional[Any] = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
raise Option... | 184 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import ... | 185 | 0 |
'''simple docstring'''
from __future__ import annotations
class _lowerCAmelCase :
'''simple docstring'''
def __init__(self , UpperCAmelCase ) -> None:
_snake_case = order
# a_{0} ... a_{k}
_snake_case = [1.0] + [0.0] ... | 270 |
'''simple docstring'''
from random import randint, random
def __SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = 5 , ):
_snake_case... | 270 | 1 |
"""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''', lev... | 74 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def... | 74 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
class A (SCREAMING_SNAKE_CAS... | 276 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
... | 276 | 1 |
"""simple docstring"""
snake_case_ = tuple[float, float, float]
snake_case_ = tuple[float, float, float]
def _lowerCAmelCase ( lowercase_ , lowercase_ ):
UpperCAmelCase = end_pointa[0] - end_pointa[0]
UpperCAmelCase = end_p... | 78 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCAmelCase :
'''simple docstring'''
def __init__(self , a_ ):
'''simple docstring'''
__snake_case : List[str] = list_of_points
# Degree determ... | 102 | 0 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transforme... | 35 | import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowercase = {
"""facebook/maskformer-swin-base-ade""": (
... | 35 | 1 |
'''simple docstring'''
def snake_case_ (_a : Tuple ):
for i in range(len(snake_case_ ) - 1 , 0 , -1 ):
UpperCAmelCase = False
for j in range(snake_case_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 34 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransfor... | 133 | 0 |
import inspect
import unittest
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase ( self ) -> str:
try:
import diffusers # noqa: F401
except ImportError:
assert False
def _lowerCAmelCase (... | 353 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers... | 36 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def SCREAMING_SNAKE_CASE ( ... | 44 | """simple docstring"""
import unittest
from transformers import DebertaVaConfig, 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 ModelTes... | 44 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ : Dict = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_availa... | 37 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __UpperCamelCase ( _UpperCAmelCase ):
return "".join(sorted(_UpperCAmelCase ) )
def __UpperCamelCase ( _UpperCAmelCase ):
return word_by_signature[signatu... | 37 | 1 |
def lowerCAmelCase__ ( lowerCamelCase_ : List[Any] = 4000000):
'''simple docstring'''
lowerCAmelCase__ : Dict = []
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.appe... | 129 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase ():
from torch.utils.cpp_extension import load
__lowerCAmelCase = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__lowerCAmelCase = [
... | 301 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : Optional[Any] = logging.get_logger(__name__)
_a : Tuple = {
'post_extract_proj'... | 126 | """simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : list ) -> float:
if not nums:
raise ValueError("""List is empty""" )
return sum(_lowerCamelCase ) / len(_lowerCamelCase )
if __name__ == "__main__":
impo... | 126 | 1 |
def __lowerCamelCase ( UpperCAmelCase_ : list ):
"""simple docstring"""
if len(UpperCAmelCase_ ) < 2:
return collection
def circle_sort_util(UpperCAmelCase_ : list , UpperCAmelCase_ : int , UpperCAmelCase_ : int ) -> bool:
... | 94 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
return "".join(sorted(UpperCAmelCase_ ) )
def __lowerCamelCase ( UpperCAmelCase_ : str... | 94 | 1 |
"""simple docstring"""
import inspect
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_config_docstrings.py
UpperCAmelCase : Tuple ... | 368 |
from __future__ import annotations
import math
def _A ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if num <= 0:
a__ : List[str] =f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(SCREAMING_SNAKE_CASE )
... | 148 | 0 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
... | 323 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 323 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 371 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbo... | 15 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingf... | 201 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 7 | 0 |
from __future__ import annotations
UpperCAmelCase__ : Tuple = list[list[int]]
# assigning initial values to the grid
UpperCAmelCase__ : Matrix = [
[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],
... | 358 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase__ : List[Any] = {
'vo... | 301 | 0 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available(... | 107 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fla... | 60 | 0 |
"""simple docstring"""
def _lowercase ( __snake_case ,__snake_case ) -> float:
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / dens... | 350 |
"""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 impo... | 58 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class snake_case :
'''simple docstring'''
def __init__( self : Tuple, _lowerCamelCase : int ):
'''simple docstring'''
__A = size
# approximate the overall size of segment tree with giv... | 266 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def lowerCAmelCase ( __UpperCamelCase = 1_0_0_0_0_0_0 , __UpperCamelCase = 1_0 ):
"""simple docstring"""
__A = defaultdict(__UpperCamelCase )
for outer_width in range(3 , ... | 266 | 1 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
im... | 350 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
"configuration_efficientformer": [
"EFFICIENTFO... | 11 | 0 |
'''simple docstring'''
from __future__ import annotations
lowerCamelCase = list[list[int]]
# assigning initial values to the grid
lowerCamelCase = [
[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, ... | 166 |
'''simple docstring'''
from typing import List
import numpy as np
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ={key: len(_lowerCAmelCase ) for key, value in gen_kwargs.items() if isinstance(_lowerCAmelCase , _lowerCAmelCase )}
... | 166 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
i... | 357 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _a ( SCREAMING_SNAKE_CASE ):... | 246 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 190 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _lowerCAmelCase ( __snake_case : str , __snake_case : complex , __snake_case : str = "x" , __snake_case : float = 10**-10 , __snake_ca... | 190 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
lo... | 369 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class ... | 121 | 0 |
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