code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def _UpperCAmelCase ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict ) -> str:
_snake_case = [0 for i in range(r + 1 )]
# nc0 = 1
_snake_case = 1
for i in range(1 , n + 1 ):
# to compu... | 288 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ ... | 288 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
),
# ... | 29 |
def lowerCamelCase__ ( A__ : list ):
'''simple docstring'''
for i in range(len(A__ ) - 1 , 0 , -1 ):
__lowerCamelCase = False
for j in range(A__ , 0 , -1 ):
if unsorted[j] < unsorted[j -... | 29 | 1 |
from ... import PretrainedConfig
lowerCamelCase_ = {
'''sijunhe/nezha-cn-base''': '''https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json''',
}
class __A( __lowerCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
SCRE... | 244 |
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 __magic_name__ ( __a : Dict[str, torch.Tensor] ):
'''simple docstring'''
UpperCamelCase__ = []
UpperCamelCase__... | 244 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_... | 362 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUE... | 104 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : int ) -> bool:
UpperCAmelCase : Tuple = str(_lowerCAmelCase )
return len(_lowerCAmelCase ) == 9 and set(_lowerCAmelCase ) == set('''123456789'''... | 23 |
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_ = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')
... | 340 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available... | 178 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCamelCase_ = {'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
lowerCamelCase_ = _L... | 178 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ : int = """▁"""
A__ : Any = {"""vocab_file""": ... | 185 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
i... | 185 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
lowerCAmelCase_ = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Res... | 260 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
lowe... | 260 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tok... | 305 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
f... | 188 | 0 |
'''simple docstring'''
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nn... | 8 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def UpperCAmelCase ( lowerCamelCase_ :str ):
''... | 8 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
lowerCAmelCase : str ={
# 1536-... | 223 |
"""simple docstring"""
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return round(float(moles / volume ) * nfactor )
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
return round(float((moles * 0.0821 * te... | 86 | 0 |
from __future__ import annotations
from typing import Any
class _a :
"""simple docstring"""
def __init__( self : str , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : float = 0 ):
A_ , A_ ... | 329 |
from __future__ import annotations
def __snake_case ( __UpperCamelCase : int = 4 ):
"""simple docstring"""
A_ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def ... | 329 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeq... | 88 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_av... | 88 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'huggingface/time-series-transfo... | 302 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = ... | 302 | 1 |
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ):
"""simple docstring"""
_A: str = ''''''
_A: Union[str, Any] = ''''''
_A: Optional[int] = []
def __magic_name__ ( self : L... | 121 |
# 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
#
# Unless required by applicab... | 121 | 1 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _snake_case ( lowercase__ : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
lowerCAmelCase_ :Tuple ... | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP',... | 1 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : List[Any] = logging.get_logger(__name__)
lowerCAmelCase : int = ... | 13 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
... | 13 | 1 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowerCamelCase_ = '''.'''
if __name__ == "__main__":
lowerCamelCase_ = os.path.join(REPO_PATH, '''utils/documentation_tests.txt''')
low... | 34 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 1 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def __lowerCAmelCase ( a__ , a__ , a__ ) -> Optional[int]:
# Initialise PyTorch model
__a ... | 6 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = an... | 37 | 0 |
_snake_case : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def a_ ( lowerCAmelCase_ : bytes ):
# Make sure the supplied data is a bytes-like object
if not isinstance(lowerCAmelCase_, lowerCAmelCase_ ):
__lowerCAmelCase = ... | 207 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_snake_case : Union[str, Any] = False
try:
_snake_case ... | 207 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/c... | 29 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ... | 29 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor... | 272 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
... | 272 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def a ( __a , __a = True , __a = math.inf , __a = -math.inf , __a = math.inf , __a = -math.inf , __a = False , __a = 100 , __a = 0.0_1 , __a = 1 , ) -... | 97 |
'''simple docstring'''
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_avai... | 104 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you ... | 363 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except... | 299 | 0 |
def __UpperCAmelCase ( a_): # noqa: E741
snake_case_ = len(a_)
snake_case_ = 0
snake_case_ = [0] * n
snake_case_ = [False] * n
snake_case_ = [False] * n
def dfs(a_ , a_ , a_ , a_):
... | 178 |
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase = "src/transformers"
# Matches is_xxx_available()
lowercase = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowercase = re.compile(r"^_imp... | 178 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _snake_case ( snake_case ):
UpperCamelCase__ = CustomTokenizer
pass
| 41 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : List[Any] = {
"junnyu/roformer_chin... | 41 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A : Optional[int] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
t... | 260 |
"""simple docstring"""
def lowercase ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
_UpperCAm... | 260 | 1 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def _lowerCAmelCase ( A__: int ):
'''simple docstring'''
UpperCAmelCase = test... | 152 |
# Function to print upper half of diamond (pyramid)
def _lowerCAmelCase ( A__: str ):
'''simple docstring'''
for i in range(0 , A__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
fo... | 152 | 1 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distribut... | 8 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=__A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = ["note_seq"]
def __init__( self : Optional[int] , *_UpperCamelCase : ... | 8 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class a__ ( lowerCamelCase_ ):
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
return [
{"co... | 371 | '''simple docstring'''
import os
import re
import warnings
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_ta import T... | 237 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json"... | 343 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.js... | 343 | 1 |
'''simple docstring'''
def UpperCamelCase_( snake_case : list ):
'''simple docstring'''
if any(not isinstance(snake_case , snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
... | 92 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int , snake_case : int ):
'''simple docstring'''
while b:
snake_case_ , snake_case_ = b, a % b
return a
def UpperCamelCase_( snake_case : int ,... | 92 | 1 |
import numpy as np
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return np.where(vector > 0 , _SCREAMING_SNAKE_CASE , (alpha * (np.exp(_SCREAMING_SNAKE_CASE ) - 1)) )
if __name__ == "__main__":
... | 302 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransform... | 302 | 1 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_l... | 371 |
'''simple docstring'''
from PIL import Image
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ , lowerCAmelCase__ : Dict = image.size
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : int = imag... | 184 | 0 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase_ ( snake_case_ : str = "isbn/0140328726" ) -> dict:
'''simple docstring'''
UpperCAmelCase_ = olid.strip().strip(... | 1 | '''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 | 1 |
'''simple docstring'''
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class lowerCAmelCase_( tf.keras.optimizers.schedules.Learnin... | 368 |
'''simple docstring'''
import os
def _SCREAMING_SNAKE_CASE ( UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = len(grid[0] )
lowerCAmelCase__ : int = len(UpperCamelCase )
lowerCAmelCase__ : Optional[int] ... | 184 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization... | 34 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 1 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def _UpperCAmelCase ( __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : Optional[int] , __lowerCamelCase : str ) -> str:
_snake_case = sorted(z... | 40 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class lowerCAmelCase__ :
__a = 42
__a = 42
class lowerCAmelCase__ :
... | 40 | 1 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
# Initi... | 207 |
from math import asin, atan, cos, radians, sin, sqrt, tan
A__ : Optional[int] = 637_8137.0
A__ : List[str] = 635_6752.31_4245
A__ : Union[str, Any] = 6_37_81_37
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
'''simple do... | 207 | 1 |
"""simple docstring"""
from typing import Any
def _A ( UpperCamelCase_ : list) -> list[Any]:
'''simple docstring'''
if not input_list:
return []
__lowercase = [input_list.count(UpperCamelCase_) for value in input_list]
__lowercase = max(UpperCamelC... | 366 |
"""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_chan... | 144 | 0 |
a : Optional[int] = 8.3_1_4_4_5_9_8
def lowerCAmelCase_ (lowerCAmelCase__: float , lowerCAmelCase__: float ):
"""simple docstring"""
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if m... | 147 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmel... | 271 | 0 |
"""simple docstring"""
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.tran... | 203 | """simple docstring"""
import unittest
from knapsack import knapsack as k
class __snake_case ( unittest.TestCase ):
def lowerCamelCase_ ( self) -> Dict:
'''simple docstring'''
a__: List[Any] = 0
a__: Dict ... | 203 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accele... | 324 |
'''simple docstring'''
import os
import re
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
lowercase__ : Tuple = logging.get_logger(__name... | 324 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowerCamelCase ( snake_case_ , snake_case_ ):
"""s... | 297 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipe... | 297 | 1 |
'''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... | 41 |
'''simple docstring'''
from __future__ import annotations
import requests
_A : str =set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post catego... | 41 | 1 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 354 |
'''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
_lowerCAmelCase = logging.get_logge... | 184 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {}
try:
if not is_sentencepiece_available():
rai... | 152 |
'''simple docstring'''
def _a( UpperCamelCase__ : int = 1_0_0_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =2**power
SCREAMING_SNAKE_CASE__ : Optional[Any] =0
while n:
SCREAMIN... | 152 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 359 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP... | 120 | 0 |
from collections.abc import Sequence
def UpperCamelCase( __UpperCamelCase : Sequence[float] ,__UpperCamelCase : bool = False ):
if not arr:
return 0
lowerCAmelCase_ : Tuple = 0 if allow_empty_subarrays else float('''-inf''' )
lowerCAmelCase_ : Optional[Any] ... | 103 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from tor... | 237 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
... | 362 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_spee... | 230 | 0 |
UpperCamelCase__ = """Input must be a string of 8 numbers plus letter"""
UpperCamelCase__ = """TRWAGMYFPDXBNJZSQVHLCKE"""
def _a ( SCREAMING_SNAKE_CASE_ : str ):
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
__lowerCA... | 92 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ = get_tests_dir("""fixtures/spiece.mode... | 92 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_to... | 228 |
'''simple docstring'''
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_c... | 228 | 1 |
"""simple docstring"""
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _snake_case ( lowercase__ , lowercase__ , lowercase__ ):
_lowerCamelCase : Dict = {
"en": "Machine learning is great, is... | 96 |
from collections import defaultdict
def lowercase_ ( _A : int ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = 1
lowerCamelCase__ : Dict = True
for v in tree[start]:
if v not in visited:
ret +=... | 184 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
__lowerCamelCase : Any = ["""onnx"""]
def __init__( self, *lowerCamelCase__, **lowerCamelCase__ ):
requires_backe... | 357 |
import os
from pathlib import Path
def __UpperCamelCase ( ) -> Any:
"""simple docstring"""
from torch.utils.cpp_extension import load
A : Any = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
A : int ... | 115 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase (__A):
"""simple docstring"""
if not nums:
return 0
_a = nums[0]
_a = 0
for num in nums[1:]:
_a = (
max_excluding + num,
max(_A , _... | 211 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A : List[str] = logging.get_logger(__name__)
class _lowercase ( lowercase__):
"""simple docstring"""
def __init__( self : Optional[... | 184 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect numbe... | 302 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase = "cpu" , __lowerCamelCase = None ) -> None:
lowercase__ : List[str] = ... | 302 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=_a ):
"""simple docstring"""
UpperCAmelCase : Any = ["""torch""", """torchsde"""]
def __init__( self : Di... | 40 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
__lowercase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 40 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a_ ( ) -> str:
_snake_case = ArgumentParser(
description=(
'PyTorch TPU distributed training launch helper utili... | 130 |
from __future__ import annotations
import requests
_lowerCamelCase : List[str] = set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories... | 130 | 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,
Wa... | 160 |
"""simple docstring"""
import math
def _snake_case ( lowerCamelCase__ : list , lowerCamelCase__ : int ) -> int:
lowerCamelCase_ : int =len(lowerCamelCase__ )
lowerCamelCase_ : List[Any] =int(math.floor(math.sqrt(lowerCamelCase__... | 144 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :List[str] = {
'''microsoft/swinv2-tiny-patch4-window8-256''': (
'''https://huggingface.co/microsoft... | 362 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 60 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import requi... | 203 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
snake_case : List[str] = sum(lowercase ) / len(lowerca... | 203 | 1 |
"""simple docstring"""
def _A ( _a : int = 1_0_0_0 ):
"""simple docstring"""
return sum(e for e in range(3 , _a ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 77 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 1 |
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def lowerCamelCase__ ( _A , _A , _A ):
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
a : int = b * b - 4 * a * c
a : List[str] = ... | 297 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoe... | 297 | 1 |
def lowerCAmelCase__ ( lowerCamelCase_ : list):
'''simple docstring'''
if len(lowerCamelCase_) <= 1:
return [tuple(lowerCamelCase_)]
lowerCAmelCase__ : List[str] = []
def generate(lowerCamelCase_ : int ,lowerCamelCase_ : list):
lowerCAmelCa... | 94 |
def lowerCAmelCase__ ( lowerCamelCase_ : int = 1000):
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ : int = 1, 1
lowerCAmelCase__ : Any = 2
while True:
lowerCAmelCase__ : Optional[Any] = 0
... | 94 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE :Union[str, Any] = logging.get_logger(__name__)
__SCREAM... | 22 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def lowercase_ ( _A : Optional[int] , _A : bool = True , _A : float = math.inf , _A : float = -math.inf , _A : float = math.inf , _A : float = ... | 184 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
_A = logging.get_logger(__name__)
class lowerCamelCase ( _snake_case ):
'''simple docstring'''
def __init__(self , *_lowerCamelCase , **_lowerCam... | 363 |
"""simple docstring"""
_A = range(2, 20 + 1)
_A = [10**k for k in range(ks[-1] + 1)]
_A = {}
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> int:
UpperCAmelCase__ : List[str] = sum(a_i[j] for j in... | 166 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_availa... | 289 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecu... | 120 | 0 |
'''simple docstring'''
from string import ascii_uppercase
lowerCAmelCase_ : Optional[Any] = {char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_ : Any = dict(enumerate(ascii_uppercase))
def __A ( lowerCAmelCase_ , lowerCAmel... | 170 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( __a ):
def __init_... | 170 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if i... | 150 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ... | 230 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 34 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 1 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : Tuple = ... | 228 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 228 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def lowerCAmelCase_ (lowerCAmelCase__: NDArray[floataa] , lowerCAmelCase__: NDArray[floataa] , lowerCAmelCase__: ... | 366 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : List[... | 82 | 0 |
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_t... | 92 |
"""simple docstring"""
from collections.abc import Callable
def lowerCamelCase ( _UpperCamelCase : Callable[[float], float] , _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
'''simple docstring'''
__UpperCAmelCase :... | 115 | 0 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 142 |
'''simple docstring'''
def __a ( _UpperCamelCase: int ) -> None:
"""simple docstring"""
_snake_case = generate_pascal_triangle(_UpperCamelCase )
for row_idx in range(_UpperCamelCase ):
# Print left spaces
for _ in range(num_rows - ... | 142 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
"""configuration_owlvit""": [
"""OWLVIT_P... | 302 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransform... | 302 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowercase : Optional[int] = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {Fr... | 359 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def lowerCAmelCase_ ( snake_case__ = "laptop" ):
'''simple docstring'''
A : Tuple = F'https://www.amazon.i... | 311 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
fr... | 130 |
# flake8: noqa
# Lint as: python3
lowerCAmelCase__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar... | 130 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 18 | '''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fla... | 18 | 1 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_UpperCamelCase : List[Any] = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S ... | 77 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> int:
if not isinstance(_lowerCamelCase ,_lowerCamelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicat... | 126 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class __A ( metaclass=SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Optional[Any] = ["sentencepiece"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , ["""se... | 126 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 77 | """simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
... | 77 | 1 |
# Algorithm for the pigeonhole sorting
def a ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase : Optional[int] = min(SCREAMING_SNAKE_CASE_ ) # min() finds the minimum value
UpperCamelC... | 315 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def a ( SCREAMING_SNAKE_CASE_ : dict ):
... | 315 | 1 |
snake_case : Any = '''Input must be a string of 8 numbers plus letter'''
snake_case : Any = '''TRWAGMYFPDXBNJZSQVHLCKE'''
def __lowerCamelCase ( UpperCAmelCase_ : str ):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , UpperC... | 94 |
def __lowerCamelCase ( UpperCAmelCase_ : list , UpperCAmelCase_ : list , UpperCAmelCase_ : int ):
"""simple docstring"""
if len(UpperCAmelCase_ ) != len(UpperCAmelCase_ ):
raise ValueError('''The length of profit and weight must be same.'''... | 94 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def A ( _lowerCamelCase = 8 ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ascii_letters + digits + punctuation
... | 300 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class UpperCAmelCase_ ( datasets.BuilderConfig):
lowerCamelCase__ = None
class UpperCAmelCase_ ... | 300 | 1 |
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = '''▁'''... | 97 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = ... | 166 | 0 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def __lowerCAmelCase ( a__ , a__ , a__ , a__ , a__ ) -> np.ndarray:
__a = cva.getAffineTransform(a__ , a__ )
return cva.warpAffine(a__ , a__ ... | 33 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MA... | 33 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def lowerCAmelCase_ ( _lowercase : Iterable[str] , _lowercase : int) -> Generator[tuple[str, ...], None, None]:
"""simple docstring"""
a__ : Dict = ite... | 170 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_b... | 170 | 1 |
import math
def lowerCamelCase_ ( _a : list , _a : int = 0 , _a : int = 0 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = end or len(_a )
for i in range(_a , _a ):
UpperCAmelCase_ : str = i... | 59 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'''junnyu/roformer_chinese_small''': '''https://... | 59 | 1 |
'''simple docstring'''
def snake_case_ (_a : int , _a : int ):
while a != 0:
UpperCAmelCase , UpperCAmelCase = b % a, a
return b
def snake_case_ (_a : int , _a : int ):
if gcd(_a , _a ) ... | 34 |
'''simple docstring'''
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test im... | 34 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : list ) -> float:
if not nums:
raise ValueError('''List is empty''' )
return sum(lowerCAmelCase__ ) / len(lowerCAmelCase__ )
if __name__ == "__main__":
... | 352 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 11 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.u... | 10 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
A__ = logging.get_logger... | 82 | 0 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case = 4_00_00_00 ) -> int:
"""simple docstring"""
_UpperCamelCase = [0, 1]
_UpperCamelCase = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
... | 100 |
"""simple docstring"""
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_a = namedtuple(
"""_TestComma... | 100 | 1 |
import warnings
from .generation import TFGenerationMixin
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
# warning at import time
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in ... | 142 |
import argparse
import os
import re
import packaging.version
_A : Optional[int] = 'examples/'
_A : str = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULT... | 142 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def snake_case ( A__ = "AAPL" ):
UpperCAmelCase_ : str = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
UpperCAmelCase_ : int = BeautifulSoup(requests.get(A__ ).text ... | 253 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingS... | 253 | 1 |
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def snake_case__ ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
... | 214 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
a : List[Any] = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improve... | 311 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __lowerCamelCase ( __a :bool = True , *__a :str , **__a :Optional[int] ) -> Optional[Any]:
... | 371 |
import math
def __lowerCamelCase ( __a :int ) -> bool:
"""simple docstring"""
A__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__a )
def __lowerCamelCase ( _... | 276 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_d... | 18 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : Union[str, Any] = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ChineseCLIPConfig''... | 18 | 1 |
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _a ( lowerCamelCase: List[str] ) -> ... | 250 |
from __future__ import annotations
from math import pi, sqrt
def _a ( lowerCamelCase: float , lowerCamelCase: float ) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('''Inductance cannot be ... | 250 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
lowerCAmelCase = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARC... | 126 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, To... | 126 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
'tokeniza... | 29 |
from math import ceil, sqrt
def lowerCamelCase__ ( A__ : int = 1000000 ):
'''simple docstring'''
__lowerCamelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCamelCase ... | 29 | 1 |
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