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 |
|---|---|---|---|---|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class __a( _a ):
"""simple docstring"""
lowerCAmelCase ... | 30 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. 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... | 350 | 0 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, log... | 169 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __snake_case ( a , a ):
... | 169 | 1 |
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 BnbQua... | 216 | from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : complex , lowerCAmelCase : str = "x" , lowerCAmelCase : float = 1_0**-1_0 , lowerCAmelCase : int = 1 , ):
"""simple do... | 216 | 1 |
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,... | 495 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase__ : Union[str, Any] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation... | 495 | 1 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def lowerCamelCase (_SCREAMING_SNAKE_CASE : str ):
if not sentence:
return ""
__a : Optional[int] = dict(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
return lower_to_upp... | 476 |
'''simple docstring'''
from math import ceil
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 1_001 ):
__a : Union[str, Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__a : Optional[Any] = 2 * i + 1
__a : Dict ... | 476 | 1 |
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[str] =None
SCREAMING_SNAKE_CASE_ : Any =None
... | 153 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class lowerCAmelCase_ ( ... | 153 | 1 |
'''simple docstring'''
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
A_ : ... | 38 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case :... | 505 | 0 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
UpperCamelCase =[
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell p... | 543 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCamelCase =logging.get_logger(__name__) # pylint: disable=invalid-name
def ... | 543 | 1 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = '''https://openaipu... | 151 |
"""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, torch_device
f... | 255 | 0 |
import functools
from typing import Any
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or len(lowerCamelCase_ ) == 0:
raise ValueError("the string s... | 713 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
def __init__( self, __a, __a):
'''simple docstring'''
if len(__a) != degree + 1:
raise ValueError(
... | 658 | 0 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM... | 544 | from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
A = input('Enter image url: ').strip()
print(F'''Downloading image from {url} ...''')
A = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image URL is in the cont... | 544 | 1 |
"""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_flax, require... | 282 |
"""simple docstring"""
import string
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = ""
for i in sequence:
__lowerCAmelCase = ord(_UpperCamelCase )
if 65 <= extract <= 90:
output += chr(155 - extract )
... | 282 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeTowerC... | 47 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers im... | 556 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
lowerCAmelCase__ = "Usage of script: script_name <size_of_canvas:int>"
lowerCAmelCase__ = [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def __lowerCamelCase ( ... | 701 | import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowerCAmelCase__ = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowerCAmelCase__ = requests.get(url, header... | 594 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Donut models at https://hugging... | 132 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( ... | 132 | 1 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
A_ : List[str] =logging.get_logger(__name__)
def snake_case_ ( __snake_case : int) -> int:
lowerCAme... | 707 | '''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus imp... | 606 | 0 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _SCREAMING_SNAKE_CASE (A , A , A = "x" , A = 10**-10 , A = 1 , ) -> complex:
"""simple docstring"""
lowercase__ = symbols(A )... | 460 |
'''simple docstring'''
import unittest
import numpy as np
def _SCREAMING_SNAKE_CASE (A , A , A , A = None , ) -> np.ndarray:
"""simple docstring"""
lowercase__ = np.shape(A )
lowercase__ = np.shape(A )
lowercase__ ... | 460 | 1 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : ... | 712 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class __a :
def __init__( self : Optional[Any] , __magic_name__ : int | None = None ) -> Tuple:
"""simple docstring"""
Uppe... | 644 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"vocab_file": "vocab.json",
"merges_f... | 254 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = ... | 254 | 1 |
"""simple docstring"""
from manim import *
class a__ ( a_ ):
def __magic_name__ ( self ):
lowercase : Union[str, Any] = Rectangle(height=0.5 , width=0.5 )
lowercase : Dict = Rectangle(height=0.2_5 , width=0.... | 518 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,... | 518 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
... | 577 |
import itertools
import math
def UpperCAmelCase ( a_ ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3... | 55 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase = {
'configuration_owlv... | 721 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedC... | 572 | 0 |
import argparse
import copy
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = {}
with open(lowercase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
SCREAMING_SNAKE_CASE : Li... | 62 |
"""simple docstring"""
def _A (__a ) -> list[list]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = current_set.copy()
for row_index, row in enumerate(__a ):
SCREAMING_SNAKE_CASE_ : Tuple = row[0]
for column_index, co... | 512 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Toke... | 718 |
"""simple docstring"""
from __future__ import annotations
__snake_case = list[tuple[int, int]]
__snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, ... | 117 | 0 |
from collections.abc import Sequence
def lowerCamelCase_ ( lowerCAmelCase__ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
A = n... | 106 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassi... | 51 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : Any ) -> Any:
"""simple docstring"""
lowerCAmelCase_ : Optional[Any] = a.name
lowerCAme... | 712 |
"""simple docstring"""
from __future__ import annotations
import bisect
def UpperCamelCase_ ( lowerCAmelCase__ : list[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : int = 0 , lowerCAmelCase__ : int = -1 ) -> in... | 317 | 0 |
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_UpperCamelCase) , _UpperCamelCase)
return number - int(_UpperCamelCase)
if __name__ == "__main__":
... | 280 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
__magic_name__ : Optional[int] = logging.get_logger(__name__)
def lowercase__ ( _UpperCamelCase) -> Dict:
""... | 280 | 1 |
from collections import Counter
from timeit import timeit
def lowerCamelCase ( SCREAMING_SNAKE_CASE = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowerCamelCase ( SCREAMING... | 452 | import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase_ ( UpperCAmelCase_ , unitt... | 452 | 1 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigb... | 549 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
_SCREAMING_SNAKE_CASE : List[str] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two dat... | 549 | 1 |
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 334 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
__A : Tuple = False
__A : Optional[int] = True
__A : Optional[Any] = False
if __name__ == "__main__":
__A : Any = argpar... | 334 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
fro... | 23 |
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( A_ ):
for i in range(len(A_ ) - 1 , 0 , -1 ):
lowerCAmelCase__ : Optional[Any] = False
for j in range(A_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
lowerCAmelCase__ ,lowerCAmelCase__ : ... | 450 | 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,
)
SCREAMING_SNAKE_CASE_ = {'''configuration_mba... | 579 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 579 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_A : List[Any] = '''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
_A : List[Any] = ... | 427 | '''simple docstring'''
import math
def UpperCamelCase_ ( snake_case_ : float , snake_case_ : float ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values o... | 427 | 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
... | 718 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ , snake_case__ , snake_case__ ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case__ ) )
def __UpperCamelCase ( snake_case__ , snake_case__ , snak... | 480 | 0 |
"""simple docstring"""
import re
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = re.compile(
R"""^(?:0|94|\+94|0{2}94)""" R"""7(0|1|2|4|5|6|7|8)""" R"""(-| |)""" R"""\d{7}$""" )
return bool(re.search(snake_case__ ,snake_case__ ) )
if... | 277 | import string
from math import logaa
def snake_case ( snake_case__ :str , snake_case__ :str) -> int:
_A = document.translate(
str.maketrans("""""" , """""" , string.punctuation)).replace("""\n""" , """""")
_A = document_with... | 401 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase : List[Any] = logging.get_logger(__name__)
UpperCamelCase : Optional[Any] = {
"ut/deta": "https://huggingfa... | 705 |
"""simple docstring"""
import numpy as np
import qiskit
def A ( snake_case :int = 8 , snake_case :int | None = None ) -> str:
__UpperCamelCase = np.random.default_rng(seed=snake_case )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than... | 293 | 0 |
from manim import *
class lowerCamelCase ( SCREAMING_SNAKE_CASE ):
def snake_case_ ( self : Optional[Any] ) -> Optional[Any]:
_a : List[Any] = Rectangle(height=0.5 , width=0.5 )
_a : Dict = Rectangle(height=0.46 ,... | 471 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__UpperCAmelCase : List[str] = '\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, ... | 471 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase( a , a , a ):
__a = list(range(len(a ) ) )
__a = [v / w for v, w in zip(a , a )]
index.sort(key=lambda a : ratio[i] , ... | 67 | """simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 67 | 1 |
"""simple docstring"""
def __lowercase ( _a ):
snake_case_ : Union[str, Any] = []
snake_case_ : Tuple = []
snake_case_ : Dict = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priority o... | 123 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowercase : Dict =HfApi()
_lowercase : str ={}
# fmt: off
_lowercase : Union[str, Any] =torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1... | 364 | 0 |
'''simple docstring'''
import math
import sys
import cva
import numpy as np
def __UpperCamelCase ( a : np.ndarray , a : float ) ->np.ndarray:
# For applying gaussian function for each element in matrix.
snake_case = math.sqrt(a )
snake_case = 1 / (sigma * math.sqrt(2 *... | 717 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=__a ):
_UpperCAmelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self , *A__ , **A__ ) -> Union... | 44 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _A ( unittest.TestCase ... | 518 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
a = ""
a = ""
a = ""
a = 1 # (0 is vertical, 1 is horizontal)
def _SCREAMING_SNAKE_CASE ( ) -> None:
_UpperCAmelCase , ... | 518 | 1 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a(lowercase__ ):
'''simple docstring'''
snake_case_ ... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
def _a ( a :List[str] , a :Optional[int] ) -> Union[str, Any]:
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
a = (boundary[1] - boundary[0]) / steps
a = boundary[0]
a = boundary[1]
a = make_points(sn... | 117 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _snake_case ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with p... | 91 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVeca... | 462 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase ={
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileBertCon... | 462 | 1 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__lowerCamelCase = HfArgumentParser(InitializationArguments)
__lowerCamelCase = parser.parse_args()
# Load ... | 288 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTeste... | 288 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a = logging.get_logger("""transformers.models.speecht5""")
def UpperCamelCase_( __magic_name__ : Tuple , ... | 708 |
from __future__ import annotations
from math import pow, sqrt
def UpperCamelCase_( __magic_name__ : float , __magic_name__ : float , __magic_name__ : float ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
... | 382 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __lowerCamelCase :
a__: List[str]
a__: Optional[str] ... | 29 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation i... | 29 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils im... | 702 |
'''simple docstring'''
from __future__ import annotations
import time
a : Dict = list[tuple[int, int]]
a : int = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0... | 593 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowercase__( _UpperCAmelCa... | 698 | 0 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__lowerCamelCase : Optional[int] = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_d... | 459 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
__lowerCamelCase : Any = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2,... | 459 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__A : List[Any] = 'naver-clova-ix/donut-base'
class __UpperCamelCase ( unittest.TestCase ):
def a__ ( self :str ):
snake_case_ : Dict = DonutProcessor.fro... | 334 |
'''simple docstring'''
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
__A : List[Any] = {
'facebook/maskformer-swin-ba... | 334 | 1 |
'''simple docstring'''
from manim import *
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __magic_name__ ( self : Dict ):
'''simple docstring'''
snake_case__ : List[str] = Rectangle(heigh... | 502 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase__ : Dict = argparse.ArgumentParser()
parser.add_argum... | 502 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditi... | 104 | """simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'AB... | 434 | 0 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _... | 533 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if n_term == "":
return []
lowerCamelCase = []
for temp in range(int(snake_case__ ) ):
series.append(F'1/{temp + 1}' if series else """1""" )
return series
if __name__ == "_... | 533 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import Gra... | 235 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class Uppe... | 568 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...tes... | 31 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _SCREAMING_SNAKE_CASE ( _lowercase : List[str] ) ->int:
... | 31 | 1 |
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
SCREAMING_SNAKE_CASE_ = yaml.safe_load(
'\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n - name: "Dataset... | 300 | import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__lowerCamelCase : str = get_logger(__name__)
class a__ ( enum.Enum ):
A = 'all_checks'
A = 'basic_checks'
A ... | 216 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( _a ):
... | 721 |
'''simple docstring'''
import sys
from collections import defaultdict
class A :
def __init__( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
_a = []
def ... | 377 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class A (datasets.BeamBasedBuilder ):
'''simple docstring'''
def a_ (... | 176 |
def __lowerCamelCase ( __a :str ) -> bool:
"""simple docstring"""
A__ = 0
for ch in input_str:
A__ = ord(__a )
A__ = pow(2 , __a )
# If we already turned on bit for current character's unicode
if bitmap >>... | 176 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConf... | 596 |
from pathlib import Path
import fire
from tqdm import tqdm
def snake_case( __magic_name__="ro" , __magic_name__="en" , __magic_name__="wmt16" , __magic_name__=None ) -> None:
'''simple docstring'''
try:
import datasets
... | 596 | 1 |
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
return "".join(chr(ord(__A ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 445 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class __snake_case ( snake_case__ ):
"""simple docstring"""
def __init__( self , *_UpperCamelCase ... | 268 | 0 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_ca... | 713 |
'''simple docstring'''
from typing import Any
class a :
'''simple docstring'''
def __init__( self , lowerCamelCase_ ) -> Dict:
_a : int = data
_a : Any = None
def __repr__( self ) -> str:
return F'''Node({self.... | 424 | 0 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_SCREAMING_SNAKE_CASE = """src/... | 502 |
'''simple docstring'''
import math
import qiskit
def _a ( lowerCamelCase_ = 1 , lowerCamelCase_ = 1 , lowerCamelCase_ = 1 ):
if (
isinstance(lowerCamelCase_ , lowerCamelCase_ )
or isinstance(lowerCamelCase_ , lowerCamelCase_ )
or isinstance(lowerCamelCase_ ,... | 349 | 0 |
from __future__ import annotations
def _a ( UpperCAmelCase = 4 ) -> list[list[int]]:
"""simple docstring"""
lowerCamelCase__ : str = abs(UpperCAmelCase ) or 4
return [[1 + x + y * row_size for x in range(UpperCAmelCase )] for y in range(UpperCAmelCase ... | 130 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Any = ["image_processor", "tokenizer"]
_UpperCAmelCase : Dict =... | 130 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
A = logging.get_logger(__name__)
def lowerCamelCase ( UpperCamelCase : Dict , UpperCamelCase : Union[str, Any] ) -> Optional[i... | 544 |
import math
from datetime import datetime, timedelta
def __magic_name__ ( __lowerCAmelCase : int ) -> datetime:
__lowerCamelCase = year % 19
__lowerCamelCase = year % 4
__lowerCamelCase = year % 7
__lowerCamelCase = mat... | 298 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
_lowercase : Dict... | 661 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available()... | 661 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def a_ ( UpperCamelCas... | 246 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
imp... | 133 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffus... | 480 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 480 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transfor... | 160 |
"""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 _snake_case ( UpperCamelCase : List[str] , UpperCamelCase : Any ... | 160 | 1 |
'''simple docstring'''
def _lowerCAmelCase ():
"""simple docstring"""
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(_lowercase , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
... | 716 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single... | 394 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def lowercase_ ( __UpperCAmelCase = 100_0000 , __UpperCAmelCase = 10 ) -> int:
lowerCAmelCase__ : defaultdict = defaultdict(__UpperCAmelCase )
for outer_width in range(3... | 299 |
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 170 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _A( unittest.TestCase... | 77 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 | 1 |
'''simple docstring'''
from __future__ import annotations
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Optional[int] = len(__UpperCAmelCase )
# We need to create solution object to save path.
lowerCamelCase_ : Optional[int] ... | 501 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __snake_case (__UpperCAmelCase , __UpperCAmelCase=7 ):
"""simple docstring"""
lowerCamelCase_ : List[Any] = None
if tok... | 501 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a : Any = 200
# Number of elements selected in every generation of evolution. The selection takes
# place... | 85 |
"""simple docstring"""
from __future__ import annotations
def lowercase__(A ) ->list[int]: # This function is recursive
"""simple docstring"""
lowercase__ : int= len(A )
# If the array contains only one element, we return it (... | 85 | 1 |
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, UNetaDConditionModel
from diffusers.utils import ... | 428 |
import functools
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = len(lowerCAmelCase__ )
lowercase = len(lowerCAmelCase__ )
@functools.cache
def min_distance(lowerCAmelCase__ ,lowerCAmelCase__ ) -> int:
# if first word in... | 428 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common ... | 698 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, Bl... | 698 | 1 |
from __future__ import annotations
import math
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % ... | 469 |
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if len(__snake_case ) == 0:
return []
snake_case__ , snake_case__ :Union[str, Any] = min(__snake_case ), max(_... | 241 | 0 |
import math
class UpperCamelCase__ :
"""simple docstring"""
def snake_case__ ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> int:
A__ = 0.0
A__ = 0.0
for i in range(len(SCREAMING_SNAKE_CASE__ ) ):
da... | 711 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_d... | 562 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import j... | 122 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow... | 122 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
cla... | 211 |
'''simple docstring'''
import math
def _lowerCAmelCase( UpperCAmelCase_ : int ) -> bool:
return math.sqrt(UpperCAmelCase_ ) * math.sqrt(UpperCAmelCase_ ) == num
def _lowerCAmelCase( UpperCAmelCase_ : int ) -> bool:
lowerCAmelCase__ = 0
... | 211 | 1 |
"""simple docstring"""
def __magic_name__ ( __snake_case : int ) -> int:
if not isinstance(__snake_case , __snake_case ) or number < 0:
raise ValueError("Input must be a non-negative integer" )
lowercase : int = 0... | 361 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A : str = logging.get_logger(__name__)
_A : Tuple = {
"""goog... | 361 | 1 |
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : int = []
__SCREAMING_SNAKE_CASE : Optional[Any] = input_list[low:mid], input_list[mid : high + 1]
while left and right:
... | 721 |
def _UpperCamelCase ( lowercase__ , lowercase__ ):
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=1_0))
| 260 | 0 |
"""simple docstring"""
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def lowerCamelCase_ (... | 142 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowercase : int = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": oper... | 142 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...te... | 579 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils ... | 579 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase = {
"configuration_swiftformer": [
"SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SwiftFormerConfig",
"SwiftFormerOnnxConfig"... | 66 | """simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXL... | 516 | 0 |
"""simple docstring"""
def __magic_name__ ( __snake_case : int = 10 ) -> str:
if not isinstance(__snake_case , __snake_case ) or n < 0:
raise ValueError("Invalid input" )
lowercase : Tuple = 10**n
lowercase ... | 713 |
"""simple docstring"""
# flake8: noqa
# Lint as: python3
_A : Dict = [
"""VerificationMode""",
"""Version""",
"""disable_progress_bar""",
"""enable_progress_bar""",
"""is_progress_bar_enabled""",
"""experimental""",
]
from .info_utils import VerificationMode
from ... | 518 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class _snake_case :
def __init__( self ):
UpperCAmelCase_ : Union[str, Any] = {}
def UpperCamelCase__ ( self ,_snake_cas... | 71 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
f... | 319 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ , lowercase__ : Tuple = position
lowercase__ : int = [
(y + 1, x + 2),
(y - 1, x + 2),
(y + 1, x - 2),
(y ... | 428 | '''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerT... | 428 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple do... | 51 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
_lowerCamelCase =["image_processor", "tokenizer"]
_lowerCame... | 51 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_lowercase = logging.getLogger(__name__)
class _lowercase :
... | 44 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from... | 44 | 1 |
def lowerCamelCase__ ( _lowercase , _lowercase , _lowercase ):
'''simple docstring'''
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowercase ) )
def lowerCamelCase__ ( _lowercase , _lowercase , _lo... | 30 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 198 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __a ( __UpperCamelCase ):
__lowercase : Unio... | 702 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''m... | 335 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case : int = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowerCAmelCase_... | 81 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_nump... | 269 | 0 |
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_CLASS... | 284 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 284 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 644 | """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 ... | 644 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
i... | 707 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple... | 658 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLD... | 3 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
... | 3 | 1 |
def __a ( A__ : list[int] , A__ : int ):
SCREAMING_SNAKE_CASE = len(A__ )
SCREAMING_SNAKE_CASE = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# h... | 698 |
from manim import *
class _SCREAMING_SNAKE_CASE ( __snake_case ):
'''simple docstring'''
def _snake_case ( self : List[Any] ):
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = R... | 698 | 1 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : int = 6008_5147_5143 ) ->int:
try:
_SCREAMING_SNAKE_CASE = int(__lowerCamelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
... | 314 |
'''simple docstring'''
def lowerCamelCase ( __lowerCamelCase : str , __lowerCamelCase : str ) ->float:
def get_matched_characters(__lowerCamelCase : str , __lowerCamelCase : str ) -> str:
_SCREAMING_SNAKE_CASE = []
_S... | 314 | 1 |
'''simple docstring'''
def __magic_name__( lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = len(snake_case_)
__lowerCAmelCase = []
for i in range(len(snake_case_) - pat_len + 1):
__lowerCAmelCase = True
... | 719 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__( lowerCamelCase, lowerCamelCase):
__lowerCAmelCase = sorted(numsa + numsa)
__lowerCAmelCase , __lowerCAmelCase = divmod(len(lowerCamelCase), 2)
if mod... | 474 | 0 |
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