code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers... | 433 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def __UpperCamelCase ( lowercase__ : str, lowercase__ : Dict, lowercase__ : Any ):
'''simple docstring'''
__lowercase ={
"en": "Machine learning... | 119 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase ) -> bool:
'''simple docstring'''
_lowerCamelCase : List[Any] = 0
for ch in input_str:
_lowerCamelCase : Optional[Any] = ord(_lowerCAmelCase )
_lowerCamelCase : Op... | 46 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : ... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_log... | 197 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowercase = logging.get_logger(__name__)
class _lowercase ( __snake_case ):
def __init__( self : int , *lowerCamelCase__ : str , **lowerCamelCase__ : List[st... | 203 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 629 | 0 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformer... | 692 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
... | 652 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json"""... | 161 |
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
__lo... | 629 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _lowercase ( __lowerCamelCase : Union[str, Any] = True ,*__lowerCamelCase : List[str] ,**__lowerCamelCase : Optional[int] ... | 344 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
'''simple docstring'''
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class a__( un... | 370 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 433 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
UpperCAmelCase = logging.get_logger(__n... | 119 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : List[Any] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available()... | 46 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowercase : Any ="""sshleifer/bart-tiny-random"""
... | 305 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : int = {
"""configurat... | 197 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
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.configuration_utils import FrozenDict
fr... | 203 |
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, logging
__low... | 629 | 0 |
'''simple docstring'''
lowerCAmelCase_ : Any = 9.80_665
def _lowerCamelCase ( lowercase : Dict , lowercase : List[str] , lowercase : Any = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
... | 692 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
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,
SegformerForSemanticSegmenta... | 652 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_lowerCAmelCase = True
except (ImportError, ModuleNotFoundError):
_lowerCAmelCase = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
... | 161 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : Union[str, Any] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
... | 344 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import ... | 370 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_... | 433 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
'''simple docstring'''
UpperCAmelCase = """
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/... | 119 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterT... | 46 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
... | 197 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__lowercase = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfi... | 203 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 629 | 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-... | 692 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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 ...t... | 652 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_c... | 161 |
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
__lo... | 629 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class UpperCamelCase__ ( enum.Enum ):
a__ : Tuple = 'all_checks'
a__ : Any ... | 344 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class a__( __snake_case ):
'''simple docstring'''
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase):
"""simple docstring"""
super().__init__(*A_ , ... | 370 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H... | 433 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class lowerCAmelCase ( unittest.TestCase ):
... | 119 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils impo... | 46 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
import random
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE_ ( SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Tuple:
A : Dict =[ord(A_ ) for i in text]
A : Union[str, Any] =[]
... | 305 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 197 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class _lowercase ( __snake_case ):
@require_torch
def UpperCamelCase ( self : int ) -> Optional[Any]:
... | 203 |
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, logging
__low... | 629 | 0 |
'''simple docstring'''
import math
def _lowerCamelCase ( lowercase : str , lowercase : Optional[Any] ) -> int:
_a = len(_lowerCAmelCase )
_a = int(math.floor(math.sqrt(_lowerCAmelCase ) ) )
_a = 0
while ar... | 692 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a =logging.get_logger(__name__)
class A_ ( __snake_case ):
_UpperCAmelCase : ... | 652 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : Tuple , lowercase : List[str] , lowercase : Optional[int] ) ->int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = ... | 161 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
import baseaa
def _lowercase ( __lowerCamelCase : List[str] ) -> bytes:
'''simple docstring'''
return baseaa.baaencode(string.encode('''utf-8''' ) )
def _lowercase ( __lowerCamelCase : Optional[Any] ) -> str:
... | 344 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
'''simple docstring'''
from __future__ import annotations
class a__:
'''simple docstring'''
def __init__( self , __lowerCAmelCase = 0):
"""simple docstring"""
lowerCAmelCase = key
def a_ ( self , __lowerCAmelCase , __lowerCAmelC... | 370 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available... | 433 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
'''simple docstring'''
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# C... | 119 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .uti... | 46 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHE... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/mai... | 197 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
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 ):
'''simple docstring'''
... | 203 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 629 | 0 |
'''simple docstring'''
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 P... | 692 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
a =logging.get_logger(__name__)
class A_ ( __snake_case ):
def __init__( self : Tuple ,*SCREAMING_SNAKE_CASE__ : int ,**SCREAMING_SNAKE_CAS... | 652 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
'''simple docstring'''
from typing import Any
def _lowerCAmelCase ( lowercase : Optional[Any] ) ->list[Any]:
"""simple docstring"""
if not input_list:
return []
lowercase__ = [input_list.count(_lowerCAmelCase ) for value i... | 161 |
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
__lo... | 629 | 0 |
from random import shuffle
import tensorflow as tf
from numpy import array
def _lowercase ( __lowerCamelCase : int ,__lowerCamelCase : List[Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : List[Any] = int(_lowerCAmelCas... | 344 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
'''simple docstring'''
def snake_case__ ( _A: Any = 1000000 ) -> int:
'''simple docstring'''
lowerCAmelCase = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , _lowerCAmelCase... | 370 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...te... | 433 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""... | 119 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_lowerCAmelCase ... | 46 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
Efficien... | 305 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
def __A ( _A , _A , _A ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def __A ( _A , _A , _A ):
"""simple docstring"""
return round(float((moles * 0.0821 * temperature) / (volume) ) ... | 197 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
__lowercase = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def _lowerCamelCase ( SCREAM... | 203 |
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, logging
__low... | 629 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE (metaclass=__snake_case ):
"""simple docstring"""
__a =['sentencepiece']
def __init__( self : Union[str, Any] , *__a : Union[str, Any] , **__... | 692 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
a =logging.get_logger(__name__)
a ={
"""Salesfor... | 652 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowerCAmelCase ( lowercase : Union[str, Any] ) ->Any:
"""simple docstring"""
... | 161 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 344 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
'''simple docstring'''
def snake_case__ ( _A: List[str] = 1000 ) -> int:
'''simple docstring'''
return sum(e for e in range(3 , _lowerCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'{solution() = }')
| 370 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
'''simple docstring'''
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
UpperCAmelCase = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machin... | 433 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""xlm-roberta-base""": """https://h... | 119 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : str = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/conf... | 46 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_g... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( __snake_case ... | 197 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A_ = f"{sampling_rate}"
A_ = "1"
... | 203 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 629 | 0 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
lowerCAmelCase_ : Any = logging.getLogger(__name__)
lowerCAmelCase_ : Tuple = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstr... | 692 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""huggingface/informer-tourism-monthly""": (
"""https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main... | 652 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowerCAmelCase = """src/transformers"""
# This is to make ... | 161 |
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
__lo... | 629 | 0 |
from __future__ import annotations
def _lowercase ( __lowerCamelCase : Dict ,__lowerCamelCase : Dict ,__lowerCamelCase : Optional[Any] ,) -> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
... | 344 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowercase = {
"""sample_size""": 3_2,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
... | 370 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
'''simple docstring'''
def _snake_case ( _SCREAMING_SNAKE_CASE : Union[str, Any] ) -> bool:
"""simple docstring"""
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def _snake_case ( _SCREAMING_SNAKE_CASE : List[Any] ) ... | 433 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_ut... | 119 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class A__ :
_UpperCAmelCase :Union[str, Any] = None
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int ... | 629 | 0 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_lowerCAmelCase : Optional[int] = [
# tf -> hf
("""/""", """."""),
("""... | 46 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( __snake_case ):
def __init__( self , *A_ , **A_ ):
'''simple docstring'''
... | 629 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between c... | 305 |
from __future__ import annotations
import math
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> float:
UpperCamelCase : Tuple = u
for i in range(1 , _lowerCAmelCase ):
UpperCamelCase : Any = temp * (u - i)
return temp
def A_ ( ) -> ... | 629 | 0 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __A ( ):
"""simple docstring"""
__a = 9, 14 # noqa: F841
__a = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, ... | 197 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
__lowerCamelCase : str = TypeVar("""T""")
class A__ ( Generic[T] ):
def __init__( ... | 629 | 0 |
from __future__ import annotations
from math import gcd
def _lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 2 , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 3 , ):
'''simple docstring'''
if num < 2:
raise ValueError('''The input va... | 203 |
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, logging
__low... | 629 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
... | 692 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils... | 629 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a ={
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available()... | 652 |
__lowerCamelCase : Any = 9.8_0_6_6_5
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = g ) -> float:
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
raise... | 629 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase : Optional[Any] , lowercase : Optional[int] , lowercase : int , lowercase : List[Any] , lowercase : str ) ->int:
"""simple docstring"""
if index == number_of_items:
... | 161 |
import numpy as np
import qiskit
def A_ ( _lowerCAmelCase = 8 , _lowerCAmelCase = None ) -> str:
UpperCamelCase : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
UpperCamelCase... | 629 | 0 |
import numpy as np
import qiskit
def _lowercase ( __lowerCamelCase : Tuple = 8 ,__lowerCamelCase : List[str] = None ) -> str:
'''simple docstring'''
UpperCamelCase__ : Tuple = np.random.default_rng(seed=_lowerCAmelCase )
# Roug... | 344 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCamelCase : str = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
__lowerCamelCase : List[str] = _LazyModule(__name__, glo... | 629 | 0 |
'''simple docstring'''
class a__:
'''simple docstring'''
def __init__( self):
"""simple docstring"""
lowerCAmelCase = {} # Mapping from char to TrieNode
lowerCAmelCase = False
def a_ ( self , __lowerCAmelCase):
"""simple docstri... | 370 |
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> str:
UpperCamelCase : Union[str, Any] = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - эт... | 629 | 0 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase = 5_0000
UpperCAmelCase = 5000
UpperCAmelCase = os.path.split(__file__)
UpperCAmelCase ... | 433 |
def A_ ( _lowerCAmelCase = 1000 ) -> int:
UpperCamelCase : Optional[int] = -1
UpperCamelCase : int = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
UpperCamelCase : Optional[Any] = (n *... | 629 | 0 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def __UpperCamelCase ( ):
'''simple docstring'''
__lowercase =9
__lowercase =[
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
... | 119 |
def A_ ( _lowerCAmelCase ) -> bool:
UpperCamelCase : List[Any] = 0
for ch in input_str:
UpperCamelCase : Optional[Any] = ord(_lowerCAmelCase )
UpperCamelCase : Optional[Any] = pow(2 , _lowerCAmelCase )
# If we already turned on bit for ... | 629 | 0 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase_( _lowerCamelCase ) -> int:
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" ... | 46 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 | 0 |
from __future__ import annotations
from cmath import sqrt
def A__ ( lowercase: List[Any], lowercase: Tuple, lowercase: List[Any] ) -> tuple[complex, complex]:
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
A : Union[str, Any]... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig"""... | 629 | 0 |
from math import factorial
class A_ :
def __init__( self : List[str] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Optional[Any] ):
__a = real
if isinstance(A_ , A_ ):
__a = [1] * rank
else:
__a = rank
def __repr__( ... | 197 |
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[int]:
UpperCamelCase : List[Any] = [1]
for i in range(2 , _lowerCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase : Tuple =... | 629 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContex... | 203 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_confi... | 629 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def _lowerCamelCase ( lowercase : List[Any] = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(_lowerCAmelCase ):
_a = [d for d in dir_names if d != "scripts... | 692 |
def A_ ( _lowerCAmelCase ) -> bool:
return str(_lowerCAmelCase ) == str(_lowerCAmelCase )[::-1]
def A_ ( _lowerCAmelCase ) -> int:
return int(_lowerCAmelCase ) + int(str(_lowerCAmelCase )[::-1] )
def A_ ( _lowerCAmelCase = 1_0000 ) -> int:
UpperCamelCase... | 629 | 0 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
a =logging.get_logger(__name__)
class A_ :
_UpperCAmelCase : List[str] = None
@experimental
def SCREAMING_SNAKE_CASE... | 652 |
__lowerCamelCase : str = 6_5521
def A_ ( _lowerCAmelCase ) -> int:
UpperCamelCase : Any = 1
UpperCamelCase : str = 0
for plain_chr in plain_text:
UpperCamelCase : List[Any] = (a + ord(_lowerCAmelCase )) % MOD_ADLER
UpperCamelCase... | 629 | 0 |
'''simple docstring'''
# Imports
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None , _lowerCamelCase=None )-> Any:
self.... | 161 |
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
__lo... | 629 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : List[Any] = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLI... | 344 |
from typing import Any
def A_ ( _lowerCAmelCase ) -> list[Any]:
if not input_list:
return []
UpperCamelCase : List[str] = [input_list.count(_lowerCAmelCase ) for value in input_list]
UpperCamelCase : Dict = max(_lowerCAmelCase ) # Gets the maximum count in... | 629 | 0 |
'''simple docstring'''
from PIL import Image
def snake_case__ ( _A: Dict , _A: Tuple ) -> Image:
'''simple docstring'''
def brightness(_A: Union[str, Any] ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""l... | 370 |
from random import shuffle
import tensorflow as tf
from numpy import array
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> Optional[Any]:
UpperCamelCase : List[Any] = int(_lowerCAmelCase )
assert noofclusters < len(_lowerCAmelCase )
# Find out the dimensionality
Upper... | 629 | 0 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _snake_case ( _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Tuple ) ... | 433 |
import os
def A_ ( ) -> Union[str, Any]:
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCamelCase : Optional[Any] = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
UpperCamelCase : ... | 629 | 0 |
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