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 |
|---|---|---|---|---|
import math
def a__ ( A__, A__ = 0, A__ = 0 ):
SCREAMING_SNAKE_CASE_ : Any = end or len(lowerCAmelCase_ )
for i in range(lowerCAmelCase_, lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = i
SCREAMING_SNAKE_C... | 101 | import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_: int ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
de... | 666 | 0 |
'''simple docstring'''
def __UpperCamelCase ( _A : int ) -> int:
"""simple docstring"""
if not isinstance(_A , _A ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does... | 646 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Any = logging.get_logger(__name__)
_lowerCAmelCase : List[str] = {
'xlm-r... | 646 | 1 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_SCREAMING_SNAKE_CASE = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
... | 401 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_avail... | 315 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class snake_case_ :
'''simple docstring'''
lowerCamelCase = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
lowerCamelCase ... | 253 |
def __a ( __UpperCAmelCase : str ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def __a ( __UpperCAmelCase : str ) -> bool:
"""simple docstring"""
lowerCamelCase_ : ... | 253 | 1 |
import numpy as np
def __lowercase ( snake_case ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __lowercase ( snake_case ):
"""simple docstring"""
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
imp... | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowercase : List[str] =5_0000
_lowercase : str =5000
_lowercase , _lowercase : List[str] =os.path.split(__file__)
_lowercase : Union[str, A... | 364 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction... | 63 |
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, requi... | 63 | 1 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from... | 59 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : Dict ={
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 54 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_A : Optional[Any] = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTN... | 330 | '''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging imp... | 330 | 1 |
'''simple docstring'''
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : str ):
"""simple docstring"""
assert x is not None
assert y is not None
_lowerCamelCase : Optional[int] = len(UpperCamelCase__ )
_lowerCamelCase : int ... | 44 |
'''simple docstring'''
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_f... | 442 | 0 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
fro... | 706 |
import doctest
from collections import deque
import numpy as np
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : Optional[Any] ) -> None:
SCREAMING_SNAKE_CASE__ = [2, 1, 2, -1]
SCREAMING_SNAKE_CASE__ = [1, 2, 3, 4]
def ... | 472 | 0 |
UpperCAmelCase_ = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def A__ ( SCREAMING_SNAKE_CASE_ : float ) -> str:
... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggingface.... | 32 | 1 |
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
return np.dot(SCREAMING_SNAKE_CASE , SCREAMING_SN... | 710 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensio... | 20 | 0 |
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils import stable_softm... | 101 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
lowerCAmelCase__ = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
lowerCAmelCase__ = '''
Args:
... | 645 | 0 |
"""simple docstring"""
from __future__ import annotations
def A__ ( __lowerCamelCase ): # This function is recursive
"""simple docstring"""
_lowerCAmelCase = len(__lowerCamelCase )
# If the array contains only one element, we return it (it's the stop condition of
... | 309 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils imp... | 309 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
... | 510 |
"""simple docstring"""
def snake_case ( _a: int , _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
lowerCamelCase__ = n - k
# Calc... | 510 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase : Tuple = logging.get_logger(__name__)
UpperCamelCase : Tuple = {
"""roberta-base""": "... | 151 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase_ ( __a , __a , __a , __a , __a , __a , __a , __a , __a , ) -> float | int:
for nxt, d in graph[v]:
if nxt in visited_forward:
... | 151 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedul... | 8 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transf... | 44 | 0 |
'''simple docstring'''
class UpperCamelCase__ : # Public class to implement a graph
"""simple docstring"""
def __init__( self : Tuple , __A : int , __A : int , __A : list[list[bool]] ):
"""simple docstring"""
_lowercase = row
... | 602 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Any = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 602 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
__SCREAMING_... | 236 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE :Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE :List[str] = {
'''google/realm-cc-news-pretrained-embedder''': (
'''https://huggingface... | 236 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE : List[str] = {
"configuration_rag": ["RagConfig"],
"retrieval_rag": ["RagRetriever"],
"tokenizati... | 714 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE ... | 238 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # no... | 152 | '''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorS... | 152 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/... | 76 |
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCAmelCase_ ( _A ):
'''simple docstring'''
def _lowercase ( self : List[Any] , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : Union[str, A... | 76 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational im... | 121 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCamelCase : Any = 'Usage of script: script_name <size_of_canvas:int>'
_lowerCamelCase : Dict = [0] * 100 + [1] * 10
random.shuffle(choice... | 121 | 1 |
"""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.configuration_bark impor... | 498 |
"""simple docstring"""
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
A_ = logging.get_logger(__name__)
A_ = {'''vocab_file'... | 498 | 1 |
'''simple docstring'''
import argparse
import os
import re
import packaging.version
lowercase : int = 'examples/'
lowercase : int = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.... | 649 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
"""... | 649 | 1 |
"""simple docstring"""
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("""hub/hopper-medium-v2/unet/hor32""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/unet/hor128""", exist_ok=True)
os.makedirs("""hub/hopper-medium-v2/value_function""", e... | 562 |
"""simple docstring"""
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = ... | 562 | 1 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models... | 580 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import 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, rand... | 580 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class lowerCAmelCase__ ( __snake_... | 716 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
clas... | 234 | 0 |
'''simple docstring'''
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"google/umt5-small": "https://huggingface.co/google/umt5-small/re... | 270 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_ = "."
# Internal TensorFlow ops... | 270 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
r... | 712 |
from __future__ import annotations
import requests
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(__lowerCamelCase ).json()
def lowerCa... | 381 | 0 |
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 lowercase_ ( _UpperCamelCase ... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
from math import factorial, pi
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ = 30):
'''simple docstring'''
if not isinstance(lowerCAmelCase_ , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstanc... | 73 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.... | 73 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabl... | 21 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
... | 21 | 1 |
from __future__ import annotations
class UpperCAmelCase__ :
def __init__( self : Any , a_ : int , a_ : Tuple ):
'''simple docstring'''
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = text, pattern
__... | 720 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCAmelCase__ ( __UpperCamelCase ):
'''simple docstring'''
@require_torch
def sn... | 241 | 0 |
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : Any = len(_lowercase )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCAmelCase_ : int = 0
print(_lowerca... | 30 |
'''simple docstring'''
from __future__ import annotations
import queue
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
SCREAMING_SNAKE_CA... | 28 | 0 |
"""simple docstring"""
from math import factorial
def lowerCamelCase_ ( _lowerCamelCase = 100 ):
return sum(map(_lowerCamelCase , str(factorial(_lowerCamelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 696 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a_ ( metaclass=snake_case_ ):
'''simple docstring'''
lowerCamelCase__ : str = ['speech']
def __init__(self, *lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring... | 696 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 |
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ):
raise TypeError("only integers accepted as input" )
else:
SCREAMING_SNAKE_CASE : Optional[int] = str(abs(lowercase ) )
SCREAMING_SNAKE... | 62 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase = "cpu" , _lowerCamelCase = None ) ->Dict:
_UpperCAmelCase =torch.load(a__ , map_location=a__ )
for k, v in tqdm(state_dict.items() ):
if not isinstan... | 714 |
import functools
from typing import Any
def lowerCamelCase__ ( _lowerCamelCase , _lowerCamelCase ) ->bool:
# Validation
if not isinstance(_lowerCamelCase , _lowerCamelCase ) or len(_lowerCamelCase ) == 0:
raise ValueError("the string should be not empty string" )
if not isinstance(_... | 592 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
'''simple docstring'''
_A = None
def snake_case__( self: Optional[Any] ):
lowercase__ : ... | 266 |
def SCREAMING_SNAKE_CASE__ ( _lowercase : int , _lowercase : int ) -> Optional[int]:
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_lowercase , int(b / 2 ) ) * actual_power(_lowercase , int(b / 2 ... | 266 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 167 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
fro... | 167 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 299 |
"""simple docstring"""
from __future__ import annotations
def lowercase_ ( __UpperCAmelCase ) -> list[int]:
lowerCAmelCase__ : int = 2
lowerCAmelCase__ : int = []
while i * i <= n:
if n % i:
i += 1
else:
... | 299 | 1 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
"t5-small": "https://hugg... | 367 |
from math import pow, sqrt
def lowerCAmelCase_ ( *lowerCamelCase ):
__magic_name__ : Tuple =len(lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
ro... | 367 | 1 |
"""simple docstring"""
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --hos... | 142 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
_lowerCAmelCase : int = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
... | 246 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, sl... | 400 |
from collections import defaultdict
class lowercase__ :
def __init__( self : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
SCREAMING_SNAKE_CASE__ = total # total no of tasks (N)
# DP table will have a dimension of (2... | 400 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_SCREAMING_SNAKE_CASE = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_SCREAMING_SNAKE_CASE = typing.Union[np.floataa, int, float] # noqa: UP007
def lowercase( Upp... | 537 |
"""simple docstring"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class Upp... | 289 | 0 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils imp... | 218 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def snake_case_ (__A : int ) -> str:
__lowerCAmelCase : str = int(__A )
... | 218 | 1 |
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_available
from ...test_configur... | 84 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 0 |
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_head,
ftp_ge... | 247 |
from ..utils import DummyObject, requires_backends
class A (metaclass=SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCamelCase : Any = ['''keras_nlp''']
def __init__( self : Any , *__lowerCAmelCase : Any ... | 247 | 1 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerat... | 374 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
im... | 374 | 1 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ :... | 715 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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... | 256 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] ={
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_available():
... | 650 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...... | 625 | 0 |
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers import (
AutoConfig,
... | 62 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, O... | 62 | 1 |
'''simple docstring'''
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
@req... | 51 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class A__:
def __init__( self : str ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = {}
... | 482 | 0 |
def lowerCAmelCase__ ( a__ ) ->Tuple:
'''simple docstring'''
_UpperCamelCase = 0
_UpperCamelCase = len(a__ )
for i in range(n - 1 ):
for j in range(i + 1 , a__ ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def ... | 82 | import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load... | 82 | 1 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_UpperCamelCase : List[Any] =models.Sequential()
... | 206 | """simple docstring"""
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from... | 656 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( )-> Dict:
A__ = ArgumentParser(
description=(
'''PyTorch TPU distributed training launch help... | 713 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase__ ( UpperCamelCase_ : dict )-> tuple:
... | 526 | 0 |
def __snake_case ( _lowerCAmelCase : int ) -> str:
if number > 0:
raise ValueError("input must be a negative integer" )
A_ : List[Any] = len(bin(_lowerCAmelCase )[3:] )
A_ : Optional[int] = bin(abs(_lowerCAmelCase ... | 454 |
from timeit import timeit
_lowerCAmelCase : Tuple = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a plan a canal panama"
}
# En... | 454 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_... | 10 | import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCo... | 10 | 1 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __a ... | 335 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __a :
__lowercase : float
__lowercase : TreeNode | None = None
__lowercase : TreeNode | None = None
def snake_case_ ( snake_case )... | 335 | 1 |
SCREAMING_SNAKE_CASE__ : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def _a ( lowercase__ : bytes ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Optio... | 85 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowercase__ : int = logging.get_logger(__name__)
lowercase__ : Any = ... | 98 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
fr... | 320 | '''simple docstring'''
def lowerCamelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(UpperCAmelCase__ ) - ngram_size + 1 )]
if __name__ == "__main_... | 320 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipel... | 93 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Union[str, Any] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MCTCTFeatureEx... | 336 | 0 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 278 |
def A ( UpperCAmelCase ):
if n == 1 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
return 0
elif n == 2:
return 1
else:
_snake_case : List[Any] = [0, 1]
for i in range(2 , n + 1 ):
... | 278 | 1 |
def lowerCamelCase__ ( _A , _A ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
snake_case_ = str(bin(_A ) )[2:] # remove the leading "0b"
snake_case_ = str(bi... | 376 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids... | 376 | 1 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class snake_case ( _UpperCamelCase):
def __lt__( self : List[Any] , a__ : Any ) ... | 621 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base impor... | 621 | 1 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _UpperCamelC... | 42 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : Tuple = OmegaConf.loa... | 374 | 0 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(UpperCAmelCase__) , ... | 710 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_opt... | 308 | 0 |
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_availabl... | 67 | '''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, PyTorchBenchma... | 251 | 0 |
'''simple docstring'''
from typing import Any
def UpperCamelCase_ ( A__ : list ):
'''simple docstring'''
if not input_list:
return []
lowerCAmelCase_ : int = [input_list.count(A__ ) for value in input_list]
lowerCA... | 398 |
'''simple docstring'''
import numpy as np
def UpperCamelCase_ ( A__ : np.array ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def UpperCamelCase_ ( A__ : np.array ):
'''simple do... | 398 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedCon... | 56 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
loggin... | 343 | 0 |
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()
def __lowerCame... | 712 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 81 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( a_ : int | str ) -> bool:
__SCREAMING_SNAKE_CASE :Optional[int] = str(a_ )
return n == n[::-1]
def __lowerCamelCase ( a_ : int = 1_00_00_00 ) -> ... | 498 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowerCamelCase_ = (3, 9, -1_1, 0, 7, 5, 1, -1)
lowerCamelCase_ = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class _SCREAMIN... | 498 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase : str = FileLock(str(tmpdir / "foo.lock" ) )
lowerCamelCase : Optional[i... | 231 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowercase_( SCREAMING_SNAKE_CASE_ = "isbn/0140328726" ):
'''simple docstring'''
lowerCamelCase : List[Any] = olid.strip().strip("/" ) # Remove leading... | 231 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Tuple = {
'huggingface/time-series-trans... | 601 |
'''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
... | 601 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case :Any = logging.get_logger(__name__)
__snake_case :Dict = '''▁'''
__snake_case :Optio... | 60 |
__snake_case :str = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# ... | 60 | 1 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__A =logging.get_logger(__name__)
def lowerCamelCase_ ( lowerCamelCase__ , lo... | 463 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__A ='''\
@inproceedings{snover-etal-2006-study,
title = "A Study of Translation Edit Rate with Targeted Human Annotation",
author = "Snover, Matthew and
Dorr, Bonnie and
Schwartz, Rich and
... | 463 | 1 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__snake_case = logging.get_logger(__name__)
__snake_case = OrderedDict(
[... | 719 | '''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _a ( __a ):
"""simple docstring"""
def __init__( self : ... | 603 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : Union[str, Any] = {
'''asapp/sew-d-tiny-1... | 8 |
from __future__ import annotations
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if start is None:
A_ = 0
if end is None:
A_ = le... | 203 | 0 |
'''simple docstring'''
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch... | 700 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( A_ : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] =olid.strip().strip("/" ) # Remove leading/trailing whitespa... | 582 | 0 |
def lowerCamelCase( a__):
_SCREAMING_SNAKE_CASE =len(a__)
for i in range(length - 1):
_SCREAMING_SNAKE_CASE =i
for k in range(i + 1 ,a__):
if collection[k] < collection[least]:
_SCREAMING_SNAKE_CASE =k
if least != i:
_... | 691 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowerCamelCase( a__):
def wrapper(*a__ ,**a__):
_SCREAMING_SNAKE_CASE =timeit.default_timer()
_SCREAMING_SNAKE_CASE =fun... | 691 | 1 |
"""simple docstring"""
def A__ ( _UpperCAmelCase : Dict , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str ) -> Dict:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i... | 717 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.uti... | 150 | 0 |
"""simple docstring"""
from __future__ import annotations
def a ( __UpperCAmelCase : list[float] , __UpperCAmelCase : Any ) -> Optional[Any]:
print(f'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(__UpperCAmel... | 96 |
'''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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_ut... | 672 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__magic_name__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_Upp... | 679 | from timeit import timeit
def UpperCAmelCase__( __UpperCAmelCase : int ):
if number < 0:
raise ValueError('the value of input must not be negative' )
__snake_case : Dict = 0
while number:
number &= number - 1
result += 1
return result
def ... | 679 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
'processing_... | 511 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...... | 111 | 0 |
import operator
def _snake_case ( __snake_case , __snake_case = False , __snake_case = None ) -> list:
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = operator.lt if reverse else operator.gt
UpperCAmelCase_ : int = so... | 455 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class snake_case_ (TensorFormatte... | 455 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowercase__ ( A_: str ) -> Optional[int]:
"""simple docstr... | 68 |
'''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_tenso... | 292 | 0 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase__ ( lowercase__ ):
... | 319 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
__lowerCAmelCase = logging.... | 319 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case : Optional[int] ... | 131 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_r... | 99 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
__UpperCamelCase : List[str] = """1"""
__UpperCamelCase : Any = """0"""
__UpperCamelCase : Dict = """1"""
__UpperCamelCase : int = ort.Session... | 53 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ... | 53 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : int = 1 , lowerCamelCase__ : int = 1000 ):
'''simple docstring'''
A: Dict = 1
A: str = 0
for divide_by_number in range(lowerCamelCase__ , digit + 1 ):
... | 135 |
'''simple docstring'''
import numpy as np
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : List[Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Dict , lowerCamelCase__ : Optional[int] ):
'''simple docstring'... | 135 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "https... | 228 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def lowercase (_snake_case ,_snake_case ,_snake_case=1024 ,_snake_case=1024 ,_snake_case=False ,**_snake_case ) ->... | 228 | 1 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common... | 41 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCAmelCase ) )
def A__( __lowerCAmelCase , __lowerCAmelCase , ... | 304 | 0 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_u... | 29 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__sna... | 29 | 1 |
import datasets
a : List[Any] = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger\n ... | 556 |
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 556 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ... | 53 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_... | 53 | 1 |
from __future__ import annotations
import math
def __magic_name__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_):
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0")
if len(l... | 250 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
__magic_name__ =... | 250 | 1 |
from maths.prime_factors import prime_factors
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> int:
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__lowercase = F"""Input value of [number={number}] must be an integer"""
raise T... | 688 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 688 | 1 |
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_availa... | 70 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int , lowerCAmelCase : int ):
"""simple docstring"""
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
"""simple docstring"""
assert and_g... | 561 | 0 |
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
_enforce_args(snake_case__ , snake_case__ )
if n == 0:
return 0
__lowerCAmelCase = float('-inf' )
for i in range(1 , n + 1 ):
... | 717 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsMo... | 102 | 0 |
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