code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' , [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokeni... | 617 | 0 |
'''simple docstring'''
from statistics import mean, stdev
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = 3 )-> list:
__UpperCAmelCase : Any = min(_lowerCAmelCase )
__UpperCAmelCase : Optional[Any] = max(_lowerCAmelCase )
# normalize d... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self ,... | 617 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeli... | 703 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A: Union[s... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
... | 704 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 617 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTran... | 705 |
'''simple docstring'''
from collections.abc import Sequence
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase... | 617 | 0 |
'''simple docstring'''
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 ...tes... | 706 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 617 | 0 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class UpperCAmelCase ( unittest.TestCase ):
def __lowerCamelCase ( self ):
__UpperCAmelCase = 0
__UpperCAmelCase = [0]
__UpperCAmelCase = [0]
__UpperCAmelCas... | 707 |
'''simple docstring'''
from string import ascii_uppercase
_A: Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
_A: str = dict(enumerate(ascii_uppercase))
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAm... | 617 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ImageProcessingSav... | 708 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_A: Tuple = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
... | 617 | 0 |
'''simple docstring'''
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A: Optional[Any] = logging.get_logger(__name__)
_A: Dict ... | 709 |
'''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
_A: List[str] = logging.... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> int | float:
if len(_lowerCAmelCase ) == 0:
raise ValueError('find_max() arg is an empty sequence' )
if (
left >= len(_lower... | 710 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 617 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.u... | 711 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less t... | 617 | 0 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 712 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 617 | 0 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class UpperCAmelCase ( unittest.TestCase ):
def __lowerCamelCase ( self ):
__UpperCAmelCase = [10, 20, 30, 40, 50, 60]
__UpperCAmelCase = [2, 4, 6, 8, 10, 12]
... | 713 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if not nums:
return 0
__UpperCAmelCase = nums[0]
__UpperCAmelCase = 0
for num in nums[1:]:
__UpperCAmelCase , __UpperCAmelCase = (
max_excludin... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> int:
'''simple docstring'''
__UpperCAmelCase = [0 for i in range(r + 1 )]
# nc0 = 1
__UpperCAmelCase = 1
for i in range(1 , n + 1 ):
# to compute current row from... | 714 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 0 |
'''simple docstring'''
from string import ascii_uppercase
_A: Optional[int] = {str(ord(c) - 55): c for c in ascii_uppercase}
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise Ty... | 715 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A: int = logging.getLogger(__name__)
class UpperCAmelCase :
def __init__( self ... | 617 | 0 |
'''simple docstring'''
import qiskit
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> qiskit.result.counts.Counts:
__UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
__UpperCAmelCase = qi... | 716 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggi... | 617 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> List[Any]:
__UpperCAmelCase = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Маш... | 717 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffus... | 617 | 0 |
'''simple docstring'''
import comet # From: unbabel-comet
import torch
import datasets
_A: List[str] = datasets.logging.get_logger(__name__)
_A: int = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, Alon}... | 718 |
'''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 ...test_config... | 617 | 0 |
'''simple docstring'''
import math
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = 0 , _lowerCAmelCase = 0 )-> list:
__UpperCAmelCase = end or len(_lowerCAmelCase )
for i in range(_lowerCAmelCase , _lowerCAmelCase ):
__UpperCAmelCase =... | 719 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self , ... | 720 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str | Literal[False]:
__UpperCAmelCase = list(_lowerCAmelCase )
__UpperCAmelCase ... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if not postfix_notation:
return 0
__UpperCAmelCase = {'+', '-', '*', '/'}
__UpperCAmelCase = []
for token in postfix_notation:
if token ... | 721 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A: str = {
"""configuration_whisper""": ["""WHISPER_PRETRA... | 617 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
fr... | 700 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 617 | 0 |
'''simple docstring'''
from manim import *
class UpperCAmelCase ( UpperCAmelCase_ ):
def __lowerCamelCase ( self ):
__UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase = Rectangle(height=0.4_6 , width=0.4_6 ).... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokeni... | 617 | 0 |
'''simple docstring'''
_A: int = """Tobias Carryer"""
from time import time
class UpperCAmelCase :
def __init__( self , __A , __A , __A , __A=int(time() ) ): # noqa: B008
__UpperCAmelCase : int = multiplier
__UpperCAmelCa... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self ,... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
return "".join(sorted(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAmelCase )-> list[str]:
re... | 703 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A: Union[s... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Optional[Any]:
print('\nThe shortest path matrix using Floyd Warshall algorithm\n' )
for i in range(_lowerCAmelCase ):
for j in range(_lowerCAmelCase ):
if dist[i][j] != float('inf' ):
... | 704 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 617 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAmelCase = BeautifulSoup(requests.get(_lowerCAmelCase , params=_lowerCAmelCase ).content , 'html.parser' ... | 705 |
'''simple docstring'''
from collections.abc import Sequence
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase... | 617 | 0 |
'''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
cl... | 706 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 617 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Any = True
except (ImportError, ModuleNotFoundError):
_A: List[str] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
... | 707 |
'''simple docstring'''
from string import ascii_uppercase
_A: Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
_A: str = dict(enumerate(ascii_uppercase))
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAm... | 617 | 0 |
'''simple docstring'''
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: Union[str, Any] = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingf... | 708 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_A: Tuple = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
... | 617 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: str = {
"""xlm-roberta-... | 709 |
'''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
_A: List[str] = logging.... | 617 | 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 import is... | 710 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Any = logging.get_logger(__name__)
_A: List[str] = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_unif... | 711 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less t... | 617 | 0 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRob... | 712 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 617 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase_ ):
_A : str = ["""sentencepiece"""]
def __init__( self , *__A , **__A ):
requires_backends(self , ['sentencepiece... | 713 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if not nums:
return 0
__UpperCAmelCase = nums[0]
__UpperCAmelCase = 0
for num in nums[1:]:
__UpperCAmelCase , __UpperCAmelCase = (
max_excludin... | 617 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_A: int = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": ... | 714 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if not nums:
return 0
__UpperCAmelCase = nums[0]
__UpperCAmelCase = 0
for num in nums[1:]:
__UpperCAmelCase , __UpperCAmelCase = (
max_excludin... | 715 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A: int = logging.getLogger(__name__)
class UpperCAmelCase :
def __init__( self ... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase )-> float:
return 10 - x * x
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(_lowerCAmelCase ) ... | 716 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggi... | 617 | 0 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _lowerCAmelCase ( _lowerCAmelCase )-> Dict[str, torch.Tensor]:
__UpperCAmelCase = []
__UpperCAmelCase ... | 717 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffus... | 617 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from tran... | 718 |
'''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 ...test_config... | 617 | 0 |
'''simple docstring'''
import os
def _lowerCAmelCase ( _lowerCAmelCase = "matrix.txt" )-> int:
with open(os.path.join(os.path.dirname(_lowerCAmelCase ) , _lowerCAmelCase ) ) as in_file:
__UpperCAmelCase = in_file.read()
__UpperCAmelCase = [[int(_lowerCAmelC... | 719 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 617 | 0 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config i... | 720 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str | Literal[False]:
__UpperCAmelCase = list(_lowerCAmelCase )
__UpperCAmelCase ... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1_00 , )-> float:
__UpperCAmelCase = x_start
... | 721 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A: str = {
"""configuration_whisper""": ["""WHISPER_PRETRA... | 617 | 0 |
'''simple docstring'''
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 UpperCAmelCase ... | 700 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 617 | 0 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A: Union[st... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokeni... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self ,... | 617 | 0 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_tra... | 703 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A: Union[s... | 617 | 0 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> tuple[float | int, list[tuple[int, int]]]:
__UpperCAmelCase , __UpperCA... | 704 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 617 | 0 |
'''simple docstring'''
import random
class UpperCAmelCase :
@staticmethod
def __lowerCamelCase ( __A ):
__UpperCAmelCase = [ord(__A ) for i in text]
__UpperCAmelCase = []
__UpperCAmelCase = []
for i in plain:
__UpperCAmelCas... | 705 |
'''simple docstring'''
from collections.abc import Sequence
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase... | 617 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 706 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 617 | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 707 |
'''simple docstring'''
from string import ascii_uppercase
_A: Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
_A: str = dict(enumerate(ascii_uppercase))
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAm... | 617 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
im... | 708 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_A: Tuple = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
... | 617 | 0 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> List[str]:
__UpperCAmelCase = AutoConfig.from_pretra... | 709 |
'''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
_A: List[str] = logging.... | 617 | 0 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _lowerCAmelCase ( _lowerCAmelCase )-> Dict:
__UpperCAmelCase = FileLock(str(tmpdir / 'foo.lock' ) )
__UpperCAmelCase = FileLock(str(tmpdir / 'foo.lock' ... | 710 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 617 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {"""vocab_file""": """vocab.json"""}
_A: Tuple ... | 711 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less t... | 617 | 0 |
'''simple docstring'''
import math
_A: Tuple = 10
_A: Union[str, Any] = 7
_A: Optional[Any] = BALLS_PER_COLOUR * NUM_COLOURS
def _lowerCAmelCase ( _lowerCAmelCase = 20 )-> str:
__UpperCAmelCase = math.comb(_lowerCAmelCase , _lowerC... | 712 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 617 | 0 |
'''simple docstring'''
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _lowerCAmelCase ( *_lowerCAmelCase )-> Optional[Any]:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__UpperCAmelCase = ... | 713 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if not nums:
return 0
__UpperCAmelCase = nums[0]
__UpperCAmelCase = 0
for num in nums[1:]:
__UpperCAmelCase , __UpperCAmelCase = (
max_excludin... | 617 | 0 |
'''simple docstring'''
import os
import time
import numpy as np
import onnxruntime as ort
_A: Tuple = """1"""
_A: Optional[int] = """0"""
_A: Union[str, Any] = """1"""
_A: Union[str, Any] = ort.SessionOptions()
_A: List[str] = ort.GraphOptim... | 714 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoi... | 715 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A: int = logging.getLogger(__name__)
class UpperCAmelCase :
def __init__( self ... | 617 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 716 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggi... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def _lowerCAmelCase ( _lowerCAmelCase )-> typing.Counter[int]:
__UpperCAmelCase = Counter()
for base in range(1 , max_perimeter + 1 ):
for perpendicular in rang... | 717 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffus... | 617 | 0 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
__UpperCAmelCase = prime_factors(_lowerCAmelCase )
if is_square_free(_lowerCAmelCase ):
return -1 if len(_l... | 718 |
'''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 ...test_config... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokenizer""... | 719 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 617 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common... | 720 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str | Literal[False]:
__UpperCAmelCase = list(_lowerCAmelCase )
__UpperCAmelCase ... | 617 | 0 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import Tokenizer... | 721 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A: str = {
"""configuration_whisper""": ["""WHISPER_PRETRA... | 617 | 0 |
'''simple docstring'''
import heapq
def _lowerCAmelCase ( _lowerCAmelCase )-> set[int]:
__UpperCAmelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works wi... | 700 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: int = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokeni... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Any = logging.get_logger(__name__)
_A: Dict = {
"""facebook/s2t-small-librispeech-asr""": (
"""https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self ,... | 617 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.u... | 703 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A: Union[s... | 617 | 0 |
'''simple docstring'''
_A: int = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_A: int ... | 704 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 617 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise Op... | 705 |
'''simple docstring'''
from collections.abc import Sequence
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase... | 617 | 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,
)
_A: str = {
"""configuration_whisper""": ["""WHISPER_PRETRA... | 706 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 617 | 0 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 707 |
'''simple docstring'''
from string import ascii_uppercase
_A: Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
_A: str = dict(enumerate(ascii_uppercase))
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAm... | 617 | 0 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class Uppe... | 708 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_A: Tuple = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 709 |
'''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
_A: List[str] = logging.... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Tuple:
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(_lowerCAmelCase ):
print(F'{i}\t\t{d}' )
def _lowerCAmelCase ... | 710 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 617 | 0 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Tuple = CustomTokenizer
pass
| 711 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less t... | 617 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is... | 712 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 617 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_c... | 713 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if not nums:
return 0
__UpperCAmelCase = nums[0]
__UpperCAmelCase = 0
for num in nums[1:]:
__UpperCAmelCase , __UpperCAmelCase = (
max_excludin... | 617 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
'''simple docstring'''
__UpperCAmelCase = sorted(numsa + numsa )
__UpperCAmelCase , __UpperCAmelCase = divmod(len(_lowerCA... | 714 |
'''simple docstring'''
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _lowerCAmelCase ( _lowerCAmelCase = 3 )-> qiskit.result.counts.Counts:
if isinstance(_lowerCAmelCase , _lowerCAmelCase ... | 617 | 0 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Any = """Speech2TextFeatureExtractor"""
_A : List[Any] = """Speech2TextToke... | 715 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A: int = logging.getLogger(__name__)
class UpperCAmelCase :
def __init__( self ... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase ) + 1
__UpperCAmelCase = len(_lowerCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of i... | 716 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggi... | 617 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeniz... | 717 |
'''simple docstring'''
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffus... | 617 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
... | 718 |
'''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 ...test_config... | 617 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
_A: int = 3
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
print('Generating primitive root of p' )
while True:
__UpperCAmelCase = ra... | 719 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils import Generatio... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_lowerCAmelCase ) == 0:
raise ValueError('Input list must be a n... | 720 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str | Literal[False]:
__UpperCAmelCase = list(_lowerCAmelCase )
__UpperCAmelCase ... | 617 | 0 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenizatio... | 721 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A: str = {
"""configuration_whisper""": ["""WHISPER_PRETRA... | 617 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class UpperCAmelCase ( unittest.TestCase ):
_A : O... | 700 |
'''simple docstring'''
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
... | 617 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_A: int = logging.get_logger(__name__)
_A: Optional[A... | 701 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokeni... | 617 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArg... | 702 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """timm_backbone"""
def __init__( self ,... | 617 | 0 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: str = logging.get_logger(__name__)
_A: Optional[Any] = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingfac... | 703 |
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_A: Union[s... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A: Any = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 704 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = " " )-> list:
__UpperCAmelCase = []
__UpperCAmelCase = 0
for index, char in enumerate(_lowerCAmelCase ):
if char == separator:
split_words.append(string[last_index:index] )
_... | 705 |
'''simple docstring'''
from collections.abc import Sequence
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase... | 617 | 0 |
'''simple docstring'''
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import fro... | 706 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 617 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 707 |
'''simple docstring'''
from string import ascii_uppercase
_A: Union[str, Any] = {char: i for i, char in enumerate(ascii_uppercase)}
_A: str = dict(enumerate(ascii_uppercase))
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAm... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> str:
__UpperCAmelCase = 0
__UpperCAmelCase = len(_lowerCAmelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collecti... | 708 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_A: Tuple = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
... | 617 | 0 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_A: Union[str, Any] = get_tests_d... | 709 |
'''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
_A: List[str] = logging.... | 617 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Dict = logging.get_logger(__name__)
_A: str = {
"""google/vivit-b-16x2-kinetics400""": (
"""https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.... | 710 |
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 617 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( )-> Tuple:
__UpperCAmelCase = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_lowerCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 711 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError('You cannot supply more or less t... | 617 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Tuple = {
"""configuration_informer""": [
"""INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""I... | 712 |
'''simple docstring'''
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 617 | 0 |
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