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 |
|---|---|---|---|---|
import operator as op
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = []
__magic_name__ = lambda A_, A_ : int(x / y ) # noqa: E731 integer division operation
__magic_name__ = {
'''^''': op.pow,
... | 88 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[int] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[Any] = 0
while number > 0:
lowercase_... | 198 | 0 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_u... | 353 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : Dict =(EulerDiscreteScheduler,)
low... | 139 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_C... | 290 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
a__ : Tuple = {'UserAgent': UserAgent().random}
def _UpperCamelCase ( __A ) -> dict:
'''simple docstr... | 80 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcess... | 359 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _A ( lowercase ):
"""simple docstring"""
a ={}
... | 215 | 0 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range... | 243 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''Undefined for non-integers''' )
elif pre... | 8 | 0 |
"""simple docstring"""
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
a = ge... | 271 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
a = '''docs/source/en/_toctree.yml'''
def _snake_case ( _snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
_A = defaultdict(_snake_c... | 271 | 1 |
def _UpperCamelCase ( snake_case__ ) -> int:
__UpperCAmelCase : Dict = len(UpperCamelCase_ )
__UpperCAmelCase : Tuple = len(matrix[0] )
__UpperCAmelCase : List[Any] = min(UpperCamelCase_, UpperCamelCase_ )
for r... | 157 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _a ( UpperCamelCase_ : int = 3 ) -> qiskit.result.counts.Counts:
"""simple docstring"""
if isinstance(UpperCamelCase_ , Up... | 340 | 0 |
"""simple docstring"""
import math
def UpperCAmelCase__ (lowerCAmelCase_ = 100 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = sum(i * i for i in range(1 , n + 1 ) )
__SCREAMING_SNAKE_CASE = int(math.pow(sum(range(1 ,... | 365 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Tuple = logging.get_logger(__name__)
a__ : List[Any] = {
'''snap-research/efficientformer-l1-300''': (
'''https:... | 195 | 0 |
import operator as op
_A = 'scaler.pt'
_A = 'pytorch_model'
_A = 'random_states'
_A = 'optimizer'
_A = 'scheduler'
_A = 'pytorch_model.bin'
_A = 'pytorch_model.bin.index.json'
_A = 'model.safetensors'
_A ... | 62 |
from __future__ import annotations
def a ( snake_case__: list[int] , snake_case__: int , snake_case__: int , snake_case__: int ):
'''simple docstring'''
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and... | 30 | 0 |
'''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 tensorflow as tf
from transformers... | 13 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from trans... | 13 | 1 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__a = logging.get_logger(__name__)
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
... | 30 |
def a ( snake_case__: int = 100 ):
'''simple docstring'''
lowercase_ = (n * (n + 1) // 2) ** 2
lowercase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"{solution() = }")
| 30 | 1 |
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : int = 'SpeechT5FeatureExtractor'
A : Union[str, Any] = 'SpeechT5Tokenizer'
def __init__( self ,... | 36 |
import datasets
from .evaluate import evaluate
lowercase : Dict = '''\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint ... | 36 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
A... | 326 |
def lowercase_ ( _A : int , _A : int ):
"""simple docstring"""
while a != 0:
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = b % a, a
return b
def lowercase_ ( _A : int , _A : int ):
... | 184 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav... | 362 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_spee... | 230 | 0 |
import csv
import tweepy
# Twitter API credentials
A_ :int = ''''''
A_ :str = ''''''
A_ :Dict = ''''''
A_ :Optional[int] = ''''''
def A ( a_ ) -> None:
# authorize twitter, initia... | 71 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.ber... | 71 | 1 |
from __future__ import annotations
from typing import Any
class A_ ( SCREAMING_SNAKE_CASE__ ):
pass
class A_ :
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Any):
__lowerCamelCase : int = data
__lowerCam... | 359 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
a =3
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
print('Generating primitive root of p' )
while True:
__lowerCamelCase : Tuple = rando... | 113 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : Union[str, Any] ) -> Union[str, Any]: # noqa: E741
"""simple docstring"""
__UpperCamelCase = len(__lowercase )
__UpperCamelCase = 0
__UpperCamelCase = [0] * n
__Upper... | 53 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
... | 340 |
"""simple docstring"""
from collections import defaultdict
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : str = True
for v in tree[start]:
if v not in visited:
... | 340 | 1 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( __SCREAMING_SNAKE_CASE : int | str ):
"""simple docstring"""
lowercase_ : Optional[int] = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
de... | 93 |
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.utils import logging
logging.s... | 123 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {''... | 361 |
"""simple docstring"""
def __A ( a_ :float) -> float:
if edge <= 0 or not isinstance(a_ , a_):
raise ValueError('''Length must be a positive.''')
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def __A ( a_ :float) ... | 188 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase_ (__a : Tuple , __a : List[str] , __a : Union[str, An... | 271 |
'''simple docstring'''
def UpperCAmelCase_ (__a : list , __a : list , __a : int ):
"""simple docstring"""
_a : Optional[Any] = len(__a )
_a : int = [[0] * n for i in range(__a )]
for i in range(__a ):
_a : Tuple = y_points[i]
for i ... | 271 | 1 |
'''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()
d... | 142 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Union[str, Any] = {'''configuration_plbart''': ['... | 142 | 1 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 115 |
"""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 ):
"""sim... | 115 | 1 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 371 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
lowerCamelCase__ = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control... | 310 | 0 |
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 tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 13 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
"""simple docstring"""... | 13 | 1 |
import itertools
import math
def snake_case( __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negat... | 361 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.... | 116 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = list_of_points
# Degree determines the f... | 36 |
import unittest
from transformers import LiltConfig, 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_modeling_common import ModelTesterMixin, id... | 36 | 1 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase : int = logging.getLogger()
def SCREAMING_SNAKE_CASE__ ( lowercas... | 356 |
def SCREAMING_SNAKE_CASE__ ( ) -> Dict:
snake_case : Optional[int] = []
snake_case : Tuple = 1
while len(lowercase ) < 1E6:
constant.append(str(lowercase ) )
i += 1
snake_case : int = """""".join(lowercase )
... | 176 | 0 |
from math import pi, sqrt, tan
def lowerCAmelCase__ ( lowerCamelCase_ : float):
'''simple docstring'''
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''')
return 6 * side_length**2
def lowerCAmelCase__ ( lowerCamelCase_ ... | 129 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__sn... | 129 | 1 |
from torch import nn
def _A ( SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(... | 352 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
UpperCAmelCase : Union[str, Any] = 1.054571817E-34 # unit of ℏ : J * s
UpperCAmelCase : Union[str, Any] = 3E8 # uni... | 148 | 0 |
def __a ( SCREAMING_SNAKE_CASE ) -> set:
'''simple docstring'''
__UpperCAmelCase = set()
# edges = list of graph's edges
__UpperCAmelCase = get_edges(SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edg... | 333 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ ( _a ):
'''simple docstring'''
a__ = (IPNDMScheduler,)
a__ = (("num_inference_steps", 50),)
def lower... | 333 | 1 |
snake_case : List[str] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __lowerCamelCase ( UpperCAmelCase_ : bytes ):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
a :List[str]... | 281 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
a :List[Any] = 0
a :List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_int... | 281 | 1 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_... | 68 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTest... | 68 | 1 |
from collections.abc import Iterable
from typing import Any
class UpperCamelCase_ :
def __init__( self , lowerCAmelCase_ = None ) -> str:
_snake_case = value
_snake_case = None # Added in order to delete a node easier
... | 295 |
from collections.abc import Sequence
def lowerCamelCase__ ( UpperCamelCase__ : Sequence[float] , UpperCamelCase__ : bool = False ) -> float:
'''simple docstring'''
if not arr:
return 0
_snake_case = 0 if allow_empty_subar... | 295 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _lowerCamelCase( ):
print("Making key files..." )
make_key_files("rsa" , 1_0_2_4 )
print... | 261 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
SCREAMING_SNAKE_CASE__:List[Any] = log... | 261 | 1 |
"""simple docstring"""
def UpperCAmelCase ( a_ = 5000_0000 ) -> List[str]:
'''simple docstring'''
lowerCamelCase : List[str] = set()
lowerCamelCase : int = int((limit - 24) ** (1 / 2) )
lowerCamelCase : Tuple = ... | 361 |
"""simple docstring"""
from __future__ import annotations
_A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def UpperCAmelCase ( a_, a_, a_, a_, a_, ):
'''simple docstring'''
lowerCamelCase : Dict ... | 205 | 0 |
# flake8: noqa
# Lint as: python3
_SCREAMING_SNAKE_CASE : Tuple = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable... | 314 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
a__: List[Any] = logging.getLogger()... | 193 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class a__ ( UpperCamelCase__ , unittest.TestCase... | 371 |
import math
def SCREAMING_SNAKE_CASE ( __UpperCamelCase) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 180 | 0 |
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 A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self ... | 99 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : List[Any] ) -> str:
__lowerCamelCase : Tuple = 0
__lowerCamelCase : Optional[int] = len(UpperCAmelCase_ )
for i in range(n - 1 ):
for j in range(i + 1 , UpperCA... | 185 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = input("""Enter image url: """).strip()
print(F'''Downloading image from {url} ...''')
_SCREAMING_SNAKE_CASE = BeautifulSoup(requests.get(url).content,... | 165 | from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtracti... | 165 | 1 |
'''simple docstring'''
import operator
def a_ ( __snake_case : list , __snake_case : bool = False , __snake_case : list | None = None ) -> list:
"""simple docstring"""
lowerCamelCase_ =operator.lt if reverse else o... | 75 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__A : Any = logging.get_logger(__name__)
__A : ... | 154 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diff... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transfo... | 144 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetC... | 285 | 0 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __UpperCamelCase ( nn.Module ):
def __init__(self : Any , __SCREAMING_SNAKE_CASE : int = 1_6 , __SCREAMING_SNAK... | 57 |
"""simple docstring"""
from __future__ import annotations
class __UpperCamelCase :
def __init__(self : Tuple , __SCREAMING_SNAKE_CASE : int = 0):
A = key
def SCREAMING_SNAKE_CASE__ (self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_... | 57 | 1 |
def a_ ( __lowercase : List[Any] , __lowercase : Union[str, Any] ) -> Union[str, Any]:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=... | 282 | """simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_... | 221 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowercase =logging.get_logger(__name__)
lowercase ={
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
# Se... | 242 |
'''simple docstring'''
from typing import Any
def lowerCamelCase__ ( __lowerCamelCase : list , __lowerCamelCase : list , __lowerCamelCase : dict , __lowerCamelCase : dict , __lowerCamelCase : dict , ):
'''simple docstring'''
_validation(
... | 242 | 1 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __UpperCAmelCase :
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor... | 306 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __UpperCAmelCase :
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor # [batch_size x 3]
__snake_case : torch.Tensor... | 306 | 1 |
from PIL import Image
def snake_case ( snake_case__ :Image , snake_case__ :int) -> Image:
_A = (259 * (level + 255)) / (255 * (259 - level))
def contrast(snake_case__ :int) -> int:
return int(128 + factor * (c - 128))
... | 81 | import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
lowercase : Optional[Any] = namedtuple('covid_data', 'cases deaths recovered')
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str = "https://www.worldometers.info/coronavirus/") -... | 232 |
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,
)
fro... | 240 | 0 |
"""simple docstring"""
from collections import defaultdict
class lowerCamelCase__ :
def __init__( self ,A ,A ):
UpperCAmelCase = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all values are set to ... | 361 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCamelCase = {"""UserAgent""": UserAgent().random}
def _a ( _snake_case ):
"""simple docstring"""
U... | 234 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_A = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if not is_torch_availabl... | 278 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {'''vocab_fil... | 326 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Optional[int] = {
'microsoft/unispeech-large-1500h-cv': (
'https:/... | 370 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets impor... | 146 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def SCREAMING_SNAKE_CASE( __lowercase = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(__lowercase ):
A: Tuple = [d for d in dir_names if d != '''scripts'... | 319 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def SCREAMING_SNAKE_CASE( __lowercase , __lowercase , __lowercase=None , **__lowercase ) -> Any:
A: Any = [x.strip() for x in open(__lowercase ... | 319 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class _SCREAMING_SNAKE_CASE ( tf.keras.layers.Layer ):
def __init__( self : Tuple , __lowerCamelCase : int , __lowerCamelCase : List[Any] , __lowerCamelCase : Any , __lowerCamelCase : Union[... | 369 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if ... | 62 | 0 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCAmelCase = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.lin... | 256 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MA... | 249 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_A : str = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 365 |
from __future__ import annotations
def lowercase_ ( A__ , A__ , A__ ) -> int | float:
"""simple docstring"""
if len(A__ ) == 0:
raise ValueError("find_max() arg is an empty sequence" )
if (
left >= len(A__ )
or left < -len(A__ ... | 137 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 263 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '''https://huggingface.co/... | 340 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformer... | 248 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionMod... | 248 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils ... | 268 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils impo... | 349 | 0 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from tra... | 315 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return "".join(sorted(SCREAMING_SNAKE_CASE_ ) )
def a ( SCREAMIN... | 315 | 1 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def a__ ( snake_case__ ) -> List[Any]:
lowerCamelCase = test_file.split(... | 291 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( snake_case__ = 50_00 ) -> int:
lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , sna... | 291 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :Dict = {
'''configuration_table_transformer''': [
'''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TableTransfo... | 135 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase :Dict = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvail... | 135 | 1 |
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
from trans... | 62 | """simple docstring"""
import math
class a :
def lowerCAmelCase_ ( self : Tuple , __lowerCAmelCase : list[list[float]] , __lowerCAmelCase : list[int] ):
_UpperCAmelCase = 0.0
_UpperCAmelCase = 0.0
for i in range(len(__lowerCAm... | 289 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co/huggingface/inf... | 307 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
lowerCamelCase__ = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7"""):
... | 307 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 304 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImagePr... | 304 | 1 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_( snake_case : list[float] ):
'''simple docstring'''
if len(snake_case ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if ... | 92 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Optional[int] = "Alexander Joslin"
import operator as op
from .stack import Stack
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
snake_case_ = {"*": op.mul, "/": op.truediv, "... | 92 | 1 |
import sys
lowercase_ = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617318564030987111... | 205 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import Bnb... | 146 | 0 |
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_flax_available():
... | 146 |
A : Tuple = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def UpperCamelCase ( ) -> None:
"""simple docstring"""
lowercase__ = input("""Enter message: """ )
lowercase__ = input("""Enter key [alphanumeric]: """ )
lowercase__ = input("... | 146 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onn... | 47 |
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'''
_UpperCAmelCase = credit_ca... | 339 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, tor... | 100 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common i... | 100 | 1 |
'''simple docstring'''
def _UpperCamelCase ( __A ) -> str:
'''simple docstring'''
UpperCamelCase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
... | 80 |
'''simple docstring'''
from ....utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
class lowercase_ ( a__ ):
def __init__( self , a , a=None , a=20_48 ):
UpperCamelCase__ = config.__dict__
UpperC... | 80 | 1 |
"""simple docstring"""
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
Chan... | 351 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except O... | 58 | 0 |
from math import factorial
def lowerCAmelCase__( lowercase : int , lowercase : int ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueErr... | 326 |
import math
def lowerCAmelCase__( lowercase : list , lowercase : int = 0 , lowercase : int = 0 ) -> list:
__snake_case : Any = end or len(lowercase )
for i in range(lowercase , lowercase ):
__snake_case : List[str] = i
... | 326 | 1 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'kakaobrain/align-base': 'https... | 303 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise Option... | 303 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class SCREAMING_SNAKE_CASE__ ( nn.Modu... | 282 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Tuple , lowercase : int , lowercase : int , lowercase : float = 0 ):
'''simple docstring'''
_snake_case , _snake_case = row... | 282 | 1 |
'''simple docstring'''
from math import pow
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase , ) -> tuple[int, int]:
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
... | 46 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : List[str] = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_... | 46 | 1 |
"""simple docstring"""
UpperCamelCase : List[str] = "Input must be a string of 8 numbers plus letter"
UpperCamelCase : Optional[int] = "TRWAGMYFPDXBNJZSQVHLCKE"
def A ( snake_case :str ) -> bool:
if not isinstance(snake_case , snake_case ):
__UpperCamelCase =... | 316 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
UpperCamelCase : str = lo... | 316 | 1 |
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
if height >= 1:
move_tower(height - 1 , __magic_name__ , __magic_name__ , __magic_name__ )
move_disk(__magic_name__ , __magic_name__ )
move_tower(height - 1 , __magic_name__ , __magic_name__ , __ma... | 201 |
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = 0
lowercase__ = len(__magic_name__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowercase__ ... | 201 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import Polyno... | 235 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/r... | 235 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_... | 367 |
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,... | 164 | 0 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
fr... | 107 |
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 TFModelTesterMixi... | 107 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCamelCase( lowercase_ ) -> List[Any]:
'''simple docstring'''
snake_case_ = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case_ = FileLock(str(tmpdi... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 0 |
"""simple docstring"""
def lowerCamelCase ( _UpperCamelCase : int ) -> str:
'''simple docstring'''
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
... | 115 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a_ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
'''scale_grad_by_... | 340 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A : List[str] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDepen... | 276 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A : Optional[Any] = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
A : List[s... | 276 | 1 |
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,
)... | 296 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__( nn.Module ):
def __init__( self : Any , __snake_case : int = 16 , __snake_case : int = 88 , __snake_... | 297 | 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_imag... | 355 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowercase : Union[str, Any] = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
... | 21 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: Optional[Any] = logging.get_logger(__name__)
A__: Optional[Any... | 276 |
'''simple docstring'''
class A__ :
def __init__( self :List[str] ) -> List[Any]:
'''simple docstring'''
_a : Tuple =0
_a : Any =0
_a : int ={}
... | 276 | 1 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
def lowercase ( self : Union[str, Any] , lowerCAmelCase_ : Any=None , lowerCAmelCase_ : int=None , lowerCAmelCase_ : Tuple=None , **lower... | 207 |
from math import factorial
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : Dict , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : str ) -> Union[str, Any]:
__lowerCAmelCase = real
if isinstance(lowerCAmelCase_ , lowerCA... | 207 | 1 |
"""simple docstring"""
import os
import pytest
from attr import dataclass
_UpperCamelCase : Any = "us-east-1" # defaults region
@dataclass
class UpperCAmelCase_ :
lowerCamelCase__ : str
lowerCamelCase__ : List[Any] = "arn:aws:iam::558105141721:role/sagemaker_executi... | 77 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 0 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : str = 0
if start < end:
__snake_case : Optiona... | 356 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedul... | 134 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int = 1_00 ):
'''simple docstring'''
lowerCAmelCase = 0
lowerCAmelCase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
... | 46 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
while b:
lowerCAmelCase , lowerCAmelCase = b, a % b
return a
def UpperCAmel... | 46 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> Tuple:
if len(snake_case_ ) != 2 or len(a[0] ) != 2 or len(snake_case_ ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
snake_ca... | 355 | """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():
from .token... | 312 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependenc... | 201 |
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> bool:
return str(__UpperCAmelCase ) == str(__UpperCAmelCase )[::-1]
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> int:
return int(__UpperCAmelCase ) + int(str(__UpperCAmelCase )[::-... | 201 | 1 |
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
lowerCamelCase : Optional[A... | 196 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = None ) -> None:
if start is None:
UpperCamelCase__ : Union[str, Any] = 0
if end is None:
U... | 196 | 1 |
'''simple docstring'''
from typing import Dict, Iterable, 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,
... | 83 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImagePr... | 301 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCAmelCase = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__UpperCAmelCase ... | 145 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__UpperCAmelCase = 'Usage of script: script_name <size_of_canvas:int>'
__UpperCAmelCase = [0] * 100 + [1] * 10
random.shuffle(choice)
... | 145 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
class _UpperCAmelCase :
def __init__( self : Any , A : int ) -> None:
lowercase_ : List[str] = value
... | 33 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/confi... | 34 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
... | 144 |
"""simple docstring"""
_a = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
... | 144 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acce... | 37 |
from math import isqrt, loga
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case , sna... | 82 | 0 |
def UpperCamelCase ( _a = 5_0 ) -> int:
'''simple docstring'''
lowercase_ :int = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for til... | 252 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional ... | 252 | 1 |
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