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 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
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[Any] ... | 89 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ ) -> float:
if not nums:
raise ValueError('List is empty' )
return sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 89 | 1 |
import collections
import os
import re
from pathlib import Path
UpperCamelCase__ = "src/transformers"
# Matches is_xxx_available()
UpperCamelCase__ = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
UpperCamelCase__ = re.compile(r"^_import_stru... | 548 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextConf... | 548 | 1 |
from timeit import timeit
def a ( snake_case__: int ):
'''simple docstring'''
if number < 0:
raise ValueError('''the value of input must not be negative''' )
lowercase_ = 0
while number:
number &= number - 1
result += 1
return res... | 97 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Dict = 1
__lowerCamelCase : str = 2
while i * i <= n:
__lowerCamelCase : int = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
... | 669 | 0 |
def _a ( UpperCamelCase_ : int = 1_000 ) -> int:
"""simple docstring"""
lowerCAmelCase__ = 3
lowerCAmelCase__ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
... | 115 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class lowercase__ ( nn.Module ):
a_ =42
a_ =42
a_ =0.0
a_ =1
a_ =1
a_ ... | 115 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : Tuple = {
"""huggingface/time-series-transformer-tourism-monthly""": (
... | 98 |
'''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 accel... | 150 | 0 |
'''simple docstring'''
import argparse
import datetime
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : str ) -> str:
UpperCAmelCase_ : List[str] = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
... | 644 |
'''simple docstring'''
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : list[int] ) -> list[list[int]]:
UpperCAmelCase_ : int = []
if len(SCREAMING_SNAKE_CASE__ ) == 1:
return [nums.copy()]
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ... | 644 | 1 |
from __future__ import annotations
class lowerCamelCase_ :
def __init__( self , lowerCamelCase_=None ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = data
_UpperCamelCase = None
def __repr__( self ) -> int:
... | 147 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {"""vocab_file""": """vocab.txt"""}
... | 147 | 1 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCamelCase : List[str] = logging.getLogger(__name_... | 684 |
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_flax, require_tf, require_torch
from... | 684 | 1 |
'''simple docstring'''
def A ():
_lowerCAmelCase = []
_lowerCAmelCase = 1
while len(__lowerCamelCase ) < 1e6:
constant.append(str(__lowerCamelCase ) )
i += 1
_lowerCAmelCase = """""".join(__lowerCamelCase )
retur... | 5 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_comm... | 37 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
__UpperCAmelCase = sorted(numsa + numsa )
__UpperCAmelCase , __UpperCAmelCase = divmod(len(_lowerCAmelCase ) , 2 )... | 617 |
'''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: Union[s... | 617 | 1 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ (a ):
'''simple docstring'''
_UpperCamelCase = (UnCLIPScheduler,)
def UpperCamelCase_ ( self ,**_... | 50 |
'''simple docstring'''
import operator
def A__ ( __lowerCAmelCase : list , __lowerCAmelCase : bool = False , __lowerCAmelCase : list | None = None ):
lowerCamelCase__ = operator.lt if reverse else operator.gt
lowerCamelCase__ = solution o... | 50 | 1 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : float , ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tang... | 505 |
"""simple docstring"""
a = 256
# Modulus to hash a string
a = 1_000_003
def _snake_case ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
_A = len(_snake_case )
... | 505 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Any , *_lowercase :Dict , **_lowercase :List... | 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ... | 655 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [0] * len(_UpperCamelCase )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [1] * len(_UpperCamelCase )
for values in graph.values():
for i in v... | 673 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 673 | 1 |
'''simple docstring'''
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils i... | 72 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__lowercase ):
UpperCAmelCase__ = ['''note_seq''']
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ):
"""simple docstri... | 626 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from ut... | 703 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A__ ( __lowerCamelCase = "laptop" ):
SCREAMING_SNAKE_CASE_ = F'''https://www.amazon.in/laptop/s?k={product}'''
SCREAMING_SNAKE_CASE_ = {
'''User-Agent''': '''Mozilla/5.0 (X11;... | 597 | 0 |
def lowerCamelCase_ ( UpperCamelCase_ , UpperCamelCase_ = " " ):
_a : List[str] = []
_a : Union[str, Any] = 0
for index, char in enumerate(UpperCamelCase_ ):
if char == separator:
split_words.append(string[last_index:ind... | 471 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : List[str] = logging.get_logger(__name__)
__UpperCAmelCase : Dict = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}
class l... | 471 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {
"configuration_gpt_neox_japanese": ["GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXJapaneseConfig"],
"t... | 704 |
from __future__ import annotations
def lowercase_ ( _A : str , _A : list[str] | None = None , _A : dict[str, float] | None = None , _A : bool = False , ):
"""simple docstring"""
lowerCamelCase__ : Tuple = ... | 5 | 0 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ = 100 ):
A_ : Any = 0
A_ : Optional[int] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(... | 180 |
"""simple docstring"""
from __future__ import annotations
def __UpperCamelCase ( snake_case__ , snake_case__ ):
if len(snake_case__ ) == 0:
return False
A_ : Union[str, Any] = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a_li... | 180 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
__lowerCAmelCase ... | 703 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
... | 129 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import loggi... | 506 |
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _A ( A ,A ,A ,A ) -> List[Any]:
lowercase : Optional[int] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не... | 372 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : Tuple ) -> Union[str, Any]:
'''simple docstring'''
_A = len(__A )
_A = len(matrix[0] )
_A = min(__A , __A )
for row... | 701 |
"""simple docstring"""
def _snake_case ( _snake_case : list , _snake_case : list ) -> float:
'''simple docstring'''
_validate_point(_snake_case )
_validate_point(_snake_case )
if len(_snake_case ) != len(_sna... | 505 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_snake_case : Tuple = ... | 22 |
'''simple docstring'''
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import... | 71 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = list(range(len(lowerCAmelCase__ ) ) )
A__ = [v / w for v, w in zip(lowerCA... | 554 |
"""simple docstring"""
import torch
from transformers import AutoModel
class snake_case_ ( torch.nn.Module ):
"""simple docstring"""
def __init__( self , __a="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 554 | 1 |
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 __magic_name__ (snake_case_ ):
'''simple docstring'''
def... | 33 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __magic_name__ (snake_case_ ):
'''simple docstring'''
__lowercase : List[str] = ['image_processor', 'tokenizer']
__lowercase :... | 33 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeIm... | 705 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _UpperCAmelCase ( a : List[Any] ):
snake_case__ = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ = FileLock(str(tmpdir / """foo.lock""" ) )
snake_case__ = 0.... | 99 | 0 |
from collections.abc import Sequence
def snake_case ( snake_case__ :Sequence[float] , snake_case__ :float) -> float:
return sum(c * (x**i) for i, c in enumerate(snake_case__))
def snake_case ( snake_case__ :Sequence[float] , snake_case__ ... | 401 | from __future__ import annotations
def snake_case ( snake_case__ :list[int]) -> int:
if not nums:
return 0
_A = nums[0]
_A = 0
for num in nums[1:]:
_A , _A = (
max_excluding + num,
... | 401 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_f... | 237 | """simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase : List[str] =logging.get_log... | 237 | 1 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
a__ : Dict = HfApi()
a__ : List[str] = {}
# fmt: off
a__ : Dict = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_30... | 51 |
import math
import random
def UpperCamelCase ( snake_case__ : float , snake_case__ : bool = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def UpperCamelCase ... | 40 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( __lowercase ):
@staticmethod
@abstractmethod
def snake_case_ ( _lowerCAmelCase ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
... | 702 |
from __future__ import annotations
from math import pow, sqrt
def lowerCAmelCase ( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float ) -> dict[str, float]:
"""simple docstring"""
if (resistance, reactance,... | 146 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate... | 284 |
'''simple docstring'''
def snake_case ( snake_case : list , snake_case : list , snake_case : int , snake_case : int , snake_case : int ) -> int:
"""simple docstring"""
if index == number_of_items:
return 0
lowerCAmelCase = ... | 284 | 1 |
from ...configuration_utils import PretrainedConfig
_SCREAMING_SNAKE_CASE : Optional[int] = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq''': (
'''htt... | 720 |
import math
class UpperCAmelCase__ :
"""simple docstring"""
def lowercase_ ( self : int , __lowerCamelCase : list[list[float]] , __lowerCamelCase : list[int] ) -> int:
SCREAMING_SNAKE_CASE__ = 0.0
SCREAMING_SNAKE_CASE__ = 0.0
f... | 472 | 0 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> str:
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
UpperCamelCase = str(bin(UpperCamelCase_ ) )[2:] # remove the leading "0b"
UpperCamelCase ... | 537 | import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
MaxNewT... | 537 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
... | 99 |
from math import factorial
a__ = {str(digit): factorial(digit) for digit in range(1_0)}
def _UpperCAmelCase ( a : int ):
if not isinstance(a , a ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter ... | 99 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> str:
"""simple docstring"""
snake_case_ : str = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCamelCase_... | 60 |
from __future__ import annotations
def __lowercase( UpperCAmelCase__ ):
"""simple docstring"""
lowerCamelCase = 2
lowerCamelCase = []
while i * i <= n:
if n % i:
i += 1
else:
... | 623 | 0 |
'''simple docstring'''
def a_ ( __UpperCAmelCase ) -> list:
"""simple docstring"""
snake_case: List[Any] =len(__UpperCAmelCase )
for _ in range(__UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , ... | 347 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transfor... | 347 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 328 |
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_o... | 328 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
UpperCamelCase : List[str] = logging.get_logger(__name__)
class lowerCamelCase__ ( UpperCAmelCase_ ):
def __init__( self : Tuple , *_lowercase : ... | 709 |
"""simple docstring"""
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docstring"""
while b:
A , A = b, a % b
return a
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> int:
"""simple docst... | 91 | 0 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can als... | 190 |
def UpperCamelCase__( UpperCamelCase__ : str = "The quick brown fox jumps over the lazy dog" , )->bool:
A__ = set()
# Replace all the whitespace in our sentence
A__ = input_str.replace(''' ''' , '''''' )
for alpha in input_str:
... | 190 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase : Optional[int] = {
"configuration_efficientnet": [
"EFFICIENTNET_PRETRAINED_CO... | 715 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models impo... | 100 | 0 |
from __future__ import annotations
def __magic_name__ ( lowerCAmelCase_):
'''simple docstring'''
lowerCamelCase_ : int = str(lowerCAmelCase_)
return n == n[::-1]
def __magic_name__ ( lowerCAmelCase_ = 100_0000):
'''simple d... | 250 |
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
__magic_name__ = 1.054_571_817E-34 # unit of ℏ : J * s
__magic_name__ = 3E8 # unit of c : m * s^-1
def __magic_name... | 250 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Dict = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_AR... | 704 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class UpperCAmelCas... | 472 | 0 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - ... | 12 |
from __future__ import annotations
class snake_case__ :
def __init__( self , UpperCamelCase_ ) -> None:
"""simple docstring"""
a_ : Dict = order
# a_{0} ... a_{k}
a_ : Union[str, Any] = [1.0] + [0.0] * order... | 419 | 0 |
lowerCAmelCase = 256
# Modulus to hash a string
lowerCAmelCase = 1_000_003
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : List[str] = len(lowercase_ )
__UpperC... | 675 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def __SCREAMING_SNAKE_CASE ( lowercase_ = "mumbai" ) -> Generator[tuple[str, ... | 675 | 1 |
"""simple docstring"""
import cva
import numpy as np
class a__ :
def __init__( self :Optional[int] , _lowerCamelCase :Dict , _lowerCamelCase :int ):
'''simple docstring'''
if k in (0.04, 0.06):
UpperCamelCase_ : Optional[Any] =k
... | 357 |
from ...processing_utils import ProcessorMixin
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = '''SpeechT5FeatureExtractor'''
__UpperCAmelCase = '''SpeechT5Tokenizer'''
def __init__( self , lowercase... | 313 | 0 |
import math
import tensorflow as tf
from packaging import version
def lowercase_ (A : Optional[int] ):
snake_case__ : Optional[Any] = tf.convert_to_tensor(A )
snake_case__ : Any = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ... | 705 |
def lowercase_ (A : list[int] ):
snake_case__ : Tuple = []
if len(A ) == 1:
return [nums.copy()]
for _ in range(len(A ) ):
snake_case__ : str = nums.pop(0 )
snake_case__ : Optional[int] ... | 243 | 0 |
'''simple docstring'''
import copy
import re
class a__:
a_ : Any = '''hp'''
a_ : Optional[Any] = {}
a_ : str = None
@classmethod
def _lowercase ( cls , _UpperCAmelCase , _UpperCAmelCase ) -> str:
snake_... | 538 |
'''simple docstring'''
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONF... | 538 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__lowerCAmelCase = logging.get_logger(__name__)
def snake_case_ ( snake_case , snake_case ... | 704 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__lowerCAmelCase = '''<<<<<<< This should probably be modified because it mentions: '''
__lowerCAmelCase = ... | 335 | 0 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 109 |
"""simple docstring"""
import numpy as np
def A_ ( snake_case_ : Tuple ,snake_case_ : Any ,snake_case_ : str ,snake_case_ : Optional[int] ,snake_case_ : List[str] ):
'''simple docstring'''
UpperCamelCase : int = int(np... | 499 | 0 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
lowerCAmelCase_ = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 im... | 122 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_ )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase ... | 122 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
SCREAMING_SNAKE_CASE_ = {'''vocab_file''': '''vocab.txt''', '''tokenizer_f... | 426 |
"""simple docstring"""
from __future__ import annotations
from scipy.special import comb # type: ignore
class a :
"""simple docstring"""
def __init__( self , snake_case_ ) -> Union[str, Any]:
_UpperCAmelCase = list_of_points
# D... | 426 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',
'''CLIPSegVisionC... | 703 | """simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.js... | 635 | 0 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
__lowerCamelCase : Any = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import wo... | 404 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( lowercase_):
"""simple docstring"""
lowerCAmelCase_ = (UnCLIPScheduler,)
def UpperCamelCase__ ( self : ... | 404 | 1 |
def snake_case ( UpperCAmelCase : int ):
A = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def snake_case ( UpperCAmelCase : int = 1_00 ):
A = 1
A = 2
for i in range(2, max_n + 1 ... | 110 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase_ = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def snake_case ( UpperCAmelCase : str = "mumbai" ):
A = BeautifulS... | 110 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavavec... | 678 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 678 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = 42
SCREAMING_SNAKE_CASE__ = 42
def __A ( lowerCamelCase_ ):
"""simple docstring... | 713 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : ... | 79 | 0 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
a__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase ( SCREA... | 477 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
... | 477 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCAmelCase_ : List[Any] = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""a... | 701 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __A ( UpperCamelCase__ ):
UpperCamelCase = """Speech2TextFeatureExtractor"""
UpperCamelCase = """Speech2TextTokenizer"""
def __i... | 367 | 0 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging... | 146 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCamelCase__ ( __A :Optional[int] ):
"""simple docstring"""
return DownloadCommand(args.model ,args.cache_dir ,args.force ,args.trust_remote_code )
class __... | 268 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig... | 117 |
"""simple docstring"""
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, Lis... | 117 | 1 |
import math
def UpperCAmelCase ( UpperCAmelCase )-> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(UpperCAmelCase )
def UpperCAmelCase ( UpperCAme... | 393 |
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("socket.socket" )
@patch("builtins.open" )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
"""simple docstring"""
... | 163 | 0 |
'''simple docstring'''
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 FlaxSchedulerM... | 710 |
'''simple docstring'''
UpperCamelCase : Tuple = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]
def A__ ( __lowerCAmelCase : int ):
lowerCamelCase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits... | 9 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=A__ ):
a__ : Optional[Any] = ["""flax"""]
def __init__( self , *SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
"""simple do... | 134 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get... | 493 | 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,
WavaVecaConfig,
WavaVecaFeatur... | 710 | '''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
lowercase__ : Tuple = pytest.mark.integration
@pytest.mark.parametrize('... | 43 | 0 |
import re
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''' , str_ )]
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ ... | 203 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMix... | 203 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : Tuple = {
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-... | 721 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_availa... | 111 | 0 |
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 is_t... | 230 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowerCAmelCase = logging.get_logger(__name__)
def _lowerCamelCase( lowercase__ , lowercase__ ) -> ... | 230 | 1 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def snake_case_ ():
UpperCAmelCase = 9
UpperCAmelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
... | 358 |
'''simple docstring'''
def snake_case_ (_a : list[list[int]] , _a : int , _a : int , _a : list[int] ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 358 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consider... | 253 |
def lowerCAmelCase_ ( __UpperCAmelCase: float ) -> float:
return 10 - x * x
def lowerCAmelCase_ ( __UpperCAmelCase: float , __UpperCAmelCase: float ) -> float:
# Bolzano theory in order to find if there is a root between a and b
... | 253 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class UpperCamelCase :
"""simple docstring"""
pass
| 707 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def snake_case ( UpperCAmelCase : List[Any] ):
A = [
'encoder.version',
'decoder.version',
'model.encoder.version',
... | 110 | 0 |
def _UpperCamelCase ( snake_case__ ) -> Optional[Any]:
__UpperCAmelCase : Dict = []
if len(snake_case__ ) == 1:
return [nums.copy()]
for _ in range(len(snake_case__ ) ):
__UpperCAmelCase : List[Any] = nums.pop(0 )
... | 382 |
from __future__ import annotations
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : list[str] | None = None ):
"""simple docstring"""
snake_case__ : Optional[int] =word_bank or []
# create a table
snake_case__ : ... | 381 | 0 |
import math
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int ):
assert isinstance(__UpperCamelCase , __UpperCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Ne... | 711 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 601 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"""vocab... | 384 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _UpperCAmelCase ( _lowerCamelCase : NDArray[floataa] , _lowerCamelCase : NDArray[floataa] , _lowerCamelCase : list[int] , _lowerCame... | 384 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ : Optional[int] = logging.get_logger(__name__)
sn... | 711 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _UpperCAmelCase : Optional[int], _UpperCAmelCase : Tuple, _UpperCAmelCase : Any):
UpperCamelCase = 0
if start < end:... | 350 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_UpperCamelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_Uppe... | 146 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_UpperCamelCase = 2048
_UpperCamelCase = 4096
_UpperCamelCase = 42
_UpperCamelCase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
_UpperCamelCase = {'''null''': 0, '''short'''... | 146 | 1 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configur... | 714 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBi... | 211 | 0 |
"""simple docstring"""
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,
res... | 19 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCAmelCase ( ) -> str:
"""simple docstring"""
A = HfArgumentParser(UpperCamelCase__ )
A = parser.parse_args_into_dataclasses()[0]
A = Te... | 641 | 0 |
from torch import nn
class _lowerCAmelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCamelCase : Optional[Any] , UpperCamelCase : Dict ):
'''simple docstring'''
super().__init__... | 703 |
from random import randint, random
def lowerCamelCase_ ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: bool = False , lowerCAmelCase: bool = False , lowerCAmelCase: int = 5 , )-> list:
_snake_case : Dict ... | 669 | 0 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : int = logging.get_logger(__name__)
def _SCREAMING_SNAKE_CASE (_Upper... | 4 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner ... | 61 | 0 |
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
SMALL_MODEL_IDENTIFIER,
... | 714 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase )
class A( UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = field(... | 651 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( _A : ... | 491 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCa... | 491 | 1 |
'''simple docstring'''
from collections import deque
def snake_case_ (UpperCamelCase : List[str] ):
'''simple docstring'''
_a = len(UpperCamelCase )
_a = deque()
_a = [False for _ in range(UpperCamelCase )]
... | 377 |
'''simple docstring'''
_snake_case : Any = tuple[float, float, float]
_snake_case : Optional[int] = tuple[float, float, float]
def snake_case_ (UpperCamelCase : Pointad , UpperCamelCase : Pointad ):
'''simple docstring'''
... | 377 | 1 |
import numpy as np
import qiskit
def __SCREAMING_SNAKE_CASE ( lowercase_ = 8 , lowercase_ = None ) -> Any:
'''simple docstring'''
__UpperCAmelCase : int = np.random.default_rng(seed=lowerCAmelCase__ )
# Roughly 25% of the qubits ... | 462 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slo... | 428 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common ... | 266 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _a ( _lowercase : Optional[Any] , _lowercase : List[Any] ):
... | 266 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
def __init__( self : str ):
'''simple docstring'''
# test for the above condition
self.test()
... | 92 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 600851475143 ) -> int:
try:
lowercase : Any =int(__magic_name__ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' ... | 92 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A =logging.get_logger(__name__)
__A ={
'shi-labs/nat-mini-in1k-224': 'https://huggingf... | 113 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A ={
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_available():... | 113 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowercase ( _SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def UpperCamelCase ( lowerCamelCase__ : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
raise Not... | 203 |
'''simple docstring'''
def A ():
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def A (__lowerCamelCase :List[Any] ):
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
_lowerCAmelCase = 0
while ... | 5 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowercas... | 479 | from __future__ import annotations
from collections.abc import MutableSequence
class __lowercase :
def __init__( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : MutableSequence[float] ) -> None:
'... | 479 | 1 |
'''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 5 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 493 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case( ... | 237 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCamelCase : Optional[Any] ={
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-fine... | 237 | 1 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
snake_case = (DDPMScheduler,)
def _lowercase ( self , **Up... | 508 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase__ = HfApi()
lowercase__ = {}
# fmt: off
lowercase__ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
... | 508 | 1 |
"""simple docstring"""
UpperCamelCase_ : int = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0... | 482 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.u... | 482 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if not is_tokenizers_available()... | 30 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcesso... | 375 | 0 |
def lowerCAmelCase_ ( lowercase: int ) -> int:
'''simple docstring'''
_UpperCamelCase: str = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def lowerCAmelCase_ ( lowercase: int ) -> int:
'''simple docstring'''
_UpperCamelCa... | 264 | def lowerCAmelCase_ ( lowercase: int = 10**9 ) -> int:
'''simple docstring'''
_UpperCamelCase: List[Any] = 1
_UpperCamelCase: Dict = 2
_UpperCamelCase: Tuple = 0
_UpperCamelCase: int = 0
_UpperCamelCase: Dict = 0
while perimeter <=... | 264 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : int = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfi... | 49 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import ... | 685 | 0 |
_lowercase : Any ='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def A__ ( ) -> None:
A : Dict =input('Enter message: ' )
A : Union[str, Any] =input('Enter key [alphanumeric]: ' )
A : Any =input('Encrypt/Decrypt [e... | 661 | 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
... | 661 | 1 |
import numpy as np
def lowercase__ ( A_: np.ndarray ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def lowercase__ ( A_: np.ndarray ) -> np.ndarray:
"""simple docstring"""
return ... | 68 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCas... | 67 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ch... | 718 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'''configuration_blenderbot''': [
'''BLENDERBOT_PRETRAINED_CONFIG... | 264 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE : Any = get_tests_dir("fixtures/test_sentencepiece_wit... | 635 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"configuration_bridgetower": [
"BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BridgeTowerConfig... | 635 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 704 |
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = sorted(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAK... | 565 | 0 |
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