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 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 ):
... | 126 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
__UpperCAmelCase = []
__UpperCAmelCase = []
__UpperCAmelCase = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+': 1,
'-': 1,
} # Priority of each operator
__UpperCAmelCase = len(_lower... | 126 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 1 |
'''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..tabl... | 126 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = False , )-> tuple[int, float, str]:
__UpperCAmelCase = cipher_alphabet or [chr(_lowerC... | 126 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffus... | 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 1 |
'''simple docstring'''
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_A: str = logging.getL... | 126 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 1 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 |
'''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] )
__Uppe... | 126 | 1 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transform... | 126 |
'''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, ... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A: List[str] = logging.get_logger(__name__)
_A: ... | 126 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 1 |
'''simple docstring'''
# 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
_A: Optional[Any] = TypeVar("""T""")
class UpperCAmelCase ( Ge... | 126 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not is_torch_av... | 126 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 1 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
_A: Optional[int] = logging.get_logger(__name__)
_A: Union[str, An... | 126 |
'''simple docstring'''
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: List[str] = logging.get_... | 126 | 1 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import cl... | 126 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 1 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
_A: Any = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), ... | 126 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 1 |
'''simple docstring'''
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
_A: Dict = [
"""kernels/rwkv/wkv_cuda.cu""",
"""kernels/rwkv/wkv_op.cpp""",
"""kernels/deformable_detr/ms_deform_attn.h""",
"""kernels/deformable_d... | 126 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase_ ):
_A : List[Any] = ["""onnx"""]
def __init__( self , *__A , **__A ):
requires_backends(self , ['onnx'] )
... | 126 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 1 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_A: List[Any] = parse(importlib.metadata.version("""torch"""))
def _lowerCAmelCase ( _lowerCAmelCase , ... | 126 |
'''simple docstring'''
# Copyright 2022 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... | 126 | 1 |
'''simple docstring'''
_A: List[str] = """0.18.2"""
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_... | 126 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( UpperCAmelCase_ ):
... | 126 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase )-> None:
__UpperCAmelCase = generate_pascal_triangle(_lowerCAmelCase )
for row_idx in range(_lowerCAmelCase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=' ' )
# Print row values
... | 126 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 1 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCAmelCase ( UpperCAmelCase_ ):
@staticmethod
@abstractmethod
def __lowerCamelCase ( __A ):
raise NotImplementedError()
@abstractmethod
def ... | 126 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> list[int]:
__UpperCAmelCase = 0
__UpperCAmelCase = len(_lowerCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif... | 126 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_A: int = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormer... | 126 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
_A: str = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A )... | 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase_ ):
_A : Optional[Any] = ["""torch""", """scipy"""]
def __init__( self , *__A , **__A ):
requires_backends(self , ['to... | 126 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: List[str] = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : List[Any] = """encoder-decoder"""
_A : ... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 1 |
'''simple docstring'''
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: Tuple = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase... | 126 |
'''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] )
__Uppe... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> int:
return x if y == 0 else greatest_common_divisor(_lowerCAmelCase , x % y )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> int:
return (x * y) // greate... | 126 |
'''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, ... | 126 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 1 |
'''simple docstring'''
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
def wrapper(*_lowerCAmelCase , **_lowerCAmelCase ):
__UpperCAme... | 126 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Dict = logging.get_logger(__name__)
_A: int = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.js... | 126 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 | 1 |
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors... | 126 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 1 |
'''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: int ... | 126 |
'''simple docstring'''
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: List[str] = logging.get_... | 126 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 1 |
'''simple docstring'''
from collections.abc import Callable
class UpperCAmelCase :
def __init__( self , __A = None ):
# Stores actual heap items.
__UpperCAmelCase = []
# Stores indexes of each item for supporting updates and deletion.
__UpperCAmelCase ... | 126 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
_A: Opt... | 126 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def _lowerCAmelCase ( _lowerCAmelCase )-> int:
if "model" in orig_key:
__UpperCAmelCase = orig_key.replace('model.' , '' )
if "norm1" in orig_key:
__UpperCAmelCas... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 |
'''simple docstring'''
# Copyright 2022 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... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_A: Dict = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import s... | 126 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if... | 126 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 1 |
'''simple docstring'''
class UpperCAmelCase :
def __init__( self , __A ):
__UpperCAmelCase = val
__UpperCAmelCase = None
__UpperCAmelCase = None
def __lowerCamelCase ( self , __A ):
if self.val:
if val < self.val:
... | 126 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: int = logging.get_logger(__name__)
_A: Dict = {
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decis... | 126 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_A: List[str] = {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 126 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 1 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_A: Dict = {
"""User-Agent""": """Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"""
""" (KHTML, like Gecko) Chrome/70.0.3538.102 Safa... | 126 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Dict = logging.get_logger(__name__)
_A: Dict = {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
# Se... | 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 1 |
'''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_common import ... | 126 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: List[Any] = logging.get_logger(__name__)
_A: Optional[int] = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}
class Upp... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 1 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch... | 126 |
'''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] )
__Uppe... | 126 | 1 |
'''simple docstring'''
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCAmelCase :
_A : int = None
def __lowerCamelCase ( self ):
__UpperCAmelCase = self.feature_extracti... | 126 |
'''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, ... | 126 | 1 |
'''simple docstring'''
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data... | 126 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , __A , __A ):
__UpperCAmelCase = params
__UpperCAmelCase = np.array(_... | 126 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> list:
if len(_lowerCAmelCase ) == 0:
return []
__UpperCAmelCase , __UpperCAmelCase = min(_lowerCAmelCase ), max(_lowerCAmelCase )
__UpperCAmelCase = int(max... | 126 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 | 1 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
_A: List[Any] = """\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and ... | 126 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_A: Dict = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correla... | 126 |
'''simple docstring'''
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: List[str] = logging.get_... | 126 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
@... | 126 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase_ ):
_A : Union[str, Any] = ["""speech"""]
def __init__( self , *__A , **__A ):
requires_backends(self , ['speech'] ... | 126 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 1 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils imp... | 126 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 1 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_A: Optional[int] = logging.getLogger()
def... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 1 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
... | 126 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
_A: Any = logging... | 126 |
'''simple docstring'''
# Copyright 2022 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... | 126 | 1 |
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_A: Optional[int] = get_logger(__name__)
_A: List[Any] = r"""
Args:
input_ids (... | 126 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 1 |
'''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
# encoder.embeddings are doubl... | 126 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> float:
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def _lowerCAmelCase ( ... | 126 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A: Optional[int] = {
"""configuration_rembe... | 126 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Dict = ["""image_processor""", """tokenizer"""]
_A : Union[str, Any] = """AutoImagePr... | 126 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 1 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 126 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVe... | 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 1 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class UpperCAmelCase :
def __init__( self , __A , __A , __A = 0 ):
__UpperCAmelCase , __UpperCAmelCase = row, column
__UpperCAmelCase = [[default_value for ... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def _lowerCAmelCase ( _lowerCAmelCase = 1_00_00_00 )-> int:
__UpperCAmelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__UpperCAmelCase = max(ceil(sqrt(outer_width**2 - lim... | 126 |
'''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] )
__Uppe... | 126 | 1 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 |
'''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, ... | 126 | 1 |
'''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_excluding + n... | 126 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 1 |
'''simple docstring'''
from manim import *
class UpperCAmelCase ( UpperCAmelCase_ ):
def __lowerCamelCase ( self ):
__UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase = Rectangle(height=0.2_5 , width=0.2_5 )
... | 126 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 1 |
'''simple docstring'''
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import T... | 126 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_com... | 126 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
__UpperCAmelCase = len(_lowerCAmelCase )
# We need to create solution object to save path.
__UpperCAmelCase = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lo... | 126 | 1 |
'''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] )
__Uppe... | 126 |
'''simple docstring'''
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: List[str] = logging.get_... | 126 | 1 |
'''simple docstring'''
from math import log
from scipy.constants import Boltzmann, physical_constants
_A: List[Any] = 300 # TEMPERATURE (unit = K)
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )-> float:
if donor_co... | 126 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_A: Optional[Any] = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R... | 126 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_on... | 126 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Optional[int] = CustomTokenizer
pass
| 126 | 1 |
'''simple docstring'''
import numpy as np
import qiskit
def _lowerCAmelCase ( _lowerCAmelCase = 8 , _lowerCAmelCase = None )-> str:
__UpperCAmelCase = np.random.default_rng(seed=_lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more t... | 126 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
_A: Optional[int] = True
except (ImportError, ModuleNotFoundError):
_A: Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"... | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_mod... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase = 2**power
__UpperCAmelCase = 0
while n:
__UpperCAmelCase , __UpperCAmelCase = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).... | 126 | 1 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumen... | 126 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase )-> bool:
return np.array_equal(_lowerCAmelCase , matrix.conjugate().T )
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase )-> Any:
__UpperCAmel... | 126 | 1 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_A: str = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
from nltk import ... | 126 |
'''simple docstring'''
# Copyright 2022 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... | 126 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
_A: Tuple = """http:... | 126 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( UpperCAmelCase_ ... | 126 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_A: Tuple = {
"""configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
"""tok... | 126 |
'''simple docstring'''
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
_A: List[Any] = datasets.load_iris()
_A: Union[str, Any] = np.array(data["""data"""])
_A: Union[str, Any] ... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase = 10_00 )-> int:
__UpperCAmelCase , __UpperCAmelCase = 1, 1
__UpperCAmelCase = []
for i in range(1 , n + 1 ):
__UpperCAmelCase = prev_numerator + 2 * prev_denominator
__UpperCAmelCase = ... | 126 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _lowerCAmelCase ( _lowerCAmelCase="ro" , _lowerCAmelCase="en" , _lowerCAmelCase="wmt16" , _lowerCAmelCase=None )-> None:
try:
import datasets
except (ModuleNotFoundError, Imp... | 126 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : Dict = ["""image_processor""", """feature_extractor"""]
_A : Optional[int] = """TvltImageProcessor"""
_A : Union[str, Any] ... | 126 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase ( UpperCAmelCase_ ):
_A : int = ["""image_processor""", """tokenizer"""]
_A : Union[str, Any] = ... | 126 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_confi... | 126 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_doc... | 126 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: Optional[int] = {
"""configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}
try:
if not is_torch_avail... | 126 | 1 |
'''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, ... | 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , ... | 126 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
... | 126 | 1 |
'''simple docstring'''
import string
def _lowerCAmelCase ( _lowerCAmelCase )-> str:
__UpperCAmelCase = ''
for i in sequence:
__UpperCAmelCase = ord(_lowerCAmelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1_22:
output += ch... | 126 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> bool:
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(_lowerCAmelCase ) )
def _lowerCAmelCase ( _lowerCAme... | 126 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> List[str]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
__UpperCAmelCase = mf_knapsack(i - 1 , _lo... | 126 |
'''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] )
__Uppe... | 126 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accele... | 126 |
'''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, ... | 126 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _lowerCAmelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )-> np.array:
__UpperCAmelCase = int(np.ceil((x_end - xa) ... | 126 |
'''simple docstring'''
from __future__ import annotations
_A: Tuple = list[list[int]]
# assigning initial values to the grid
_A: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0]... | 126 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
_A: List[str] = logging.get_logger(__name__) # pylint: disable=invalid-name
class Upp... | 126 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_A: Any = logging.get_logger(__name__)
class UpperCAmelCase ( UpperCAmelCase_ ):
def __init__( self , *__A , **__A ):
... | 126 | 1 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_... | 126 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 | 1 |
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