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 |
|---|---|---|---|---|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__snake_case :Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__snake_case :Any = [file for fil... | 49 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __a ( __UpperCamelCase ... | 196 | 0 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import... | 41 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp... | 41 | 1 |
"""simple docstring"""
from collections import defaultdict
from math import gcd
def a_ ( _lowerCAmelCase : int = 150_0000 ):
'''simple docstring'''
lowercase__ : defaultdict = defaultdict(_lowerCAmelCase )
lowercase__ : int = 2
while 2 ... | 77 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
""... | 97 | 0 |
"""simple docstring"""
import random
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) -> dict:
snake_case_ = {i: [] for i in range(_SCREAMING_SNAKE_CASE )}
# if probability is greater or equal than 1, then generate a compl... | 233 |
"""simple docstring"""
import math
class __A :
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : Tuple=0 ) ->Optional[int]: # a graph with Node 0,1,...,N-1
"""simple docstring"""
snake_case_ ... | 233 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:Tuple = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniS... | 116 |
from __future__ import annotations
from math import ceil, floor, sqrt
def __UpperCamelCase ( _lowerCAmelCase = 200_0000 ) -> int:
"""simple docstring"""
A : list[int] = [0]
A : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
... | 116 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
__lowerCamelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__lowerCamelCase = n - k
... | 348 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 348 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor,... | 128 |
UpperCAmelCase : dict[tuple[int, int, int], int] ={}
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
... | 128 | 1 |
'''simple docstring'''
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = "" , _lowercase = False ):
"""simple docstring"""
_lowerCAmelCase = {}
# A node will be a leaf if the tree contai... | 229 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import Fl... | 229 | 1 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig... | 41 |
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> float:
return sum(c * (x**i) for i, c in enumerate(UpperCamelCase ) )
def SCREAMING_SNAKE_CASE_ ... | 41 | 1 |
import argparse
import os
import re
import packaging.version
_lowerCamelCase = '''examples/'''
_lowerCamelCase = {
'''examples''': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'^_... | 369 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : float , __UpperCamelCase : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
... | 177 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
) | 233 |
lowerCamelCase : Dict = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import s... | 233 | 1 |
from PIL import Image
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Image:
"""simple docstring"""
lowercase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_UpperCamelCase ) -> int:
return int(128 + ... | 355 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
lowercase : List[str] = [
'''encoder.version''',
... | 173 | 0 |
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Optional[int] =1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase : Any ... | 348 | import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348 | 1 |
'''simple docstring'''
import math
def __a ( _UpperCamelCase: list , _UpperCamelCase: int = 0 , _UpperCamelCase: int = 0 ) -> list:
"""simple docstring"""
_snake_case = end or len(_UpperCamelCase )
for i in range(_UpperCamelCase , ... | 142 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _a ( __lowerCAmelCase ... | 142 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import chec... | 229 | '''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter ... | 229 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_avail... | 370 | """simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 149 | 0 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __A ( unittest.TestCase ):
... | 16 | """simple docstring"""
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():
... | 177 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase : Tuple = {"vocab_file": "vocab.txt", "tokenizer_file": "tok... | 364 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
Upper... | 66 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import Te... | 46 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
_UpperCAmelCase = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase = re.compile(r"""([a-z\d])([A-Z])""")
_UpperCAmelCase = re.compile(r"""(?<!_)_(?!_)""")
_UpperCAmelCase = re.compile(r"""(_{2,})""")
_... | 173 | 0 |
"""simple docstring"""
__snake_case = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
"""cook... | 112 |
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VAR... | 112 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any = logging.get_logger(__name__)
_A : Dict = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
# See all SEW models... | 142 |
import datasets
from .evaluate import evaluate
_A : Optional[int] = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016... | 142 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( __A ):
def __init__( self : str , ... | 359 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...... | 202 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTester... | 105 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
... | 149 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _snake_case ( unittest.Test... | 363 |
import argparse
import json
import subprocess
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = []
lowercase__ = (
f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
" https://api.github... | 224 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_... | 313 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_d... | 66 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 249 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
@staticmethod
@abstractmethod
def A ( UpperCamelCase__ : ArgumentParser ):
"""si... | 249 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
'''BAAI/AltCLIP''': '''https://huggingface... | 112 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list ):
if any(not isinstance(_lowerCamelCase , _lowerCamelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowerCamelCase )... | 112 | 1 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = '▁'
A_ : int = {'voc... | 371 |
import requests
A_ : List[Any] = 'YOUR API KEY'
def UpperCamelCase (lowercase_: str , lowercase_: str = giphy_api_key ) -> list:
A__ : Dict = """+""".join(query.split() )
A__ : Optional[int] = f"""https://api.giphy.com/v1/gifs/search?q={format... | 141 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin... | 5 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[int] = {
"""configuration_llama""": [""... | 202 | 0 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokeniza... | 356 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_... | 251 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 30 |
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
... | 224 | 0 |
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_loggi... | 23 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = (PNDMScheduler,)
SCREAMING_SNAKE_CASE__ : str ... | 23 | 1 |
"""simple docstring"""
import sys
a_ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'6689664895044... | 249 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedu... | 249 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list ):
UpperCAmelCase : Any = len(UpperCamelCase )
for i in range(1 , UpperCamelCase ):
UpperCAmelCase : Dict = collection[i]
UpperCAmelCase : Optional[int] = 0
UpperCAmelC... | 357 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase :... | 76 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto impor... | 250 |
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/... | 141 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax... | 369 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Union[str, Any] = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de... | 57 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( __UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = 0.00
lowerCAmelCase_ : Tuple = 0
for resistor in resistors:
if re... | 241 |
'''simple docstring'''
from itertools import product
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sides_number
SCREAMING_SNAKE_CASE : str = max_face_number * dic... | 251 | 0 |
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def __UpperCamelCase ( UpperCAmelCase ):
def decorator(UpperCAmelCase ):
lowercase__ : Dict = getattr(UpperCAmelCase , '''handle_key''' , [] )
handle += [key]
setattr(UpperCAmel... | 214 | '''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UN... | 214 | 1 |
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCamelCase__: Any = logging.get_logger(__name__)
... | 23 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
Wav... | 23 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ... | 371 |
"""simple docstring"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
if "model" in orig_key:
lowerCamelCase__ : Optional[int] =orig_key.replace('''model.''' ... | 272 | 0 |
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowercase_ = re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_ordering
@datacl... | 45 |
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to... | 76 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCamelCase_ : List[Any] = datasets.utils.logging.get_logger(__name__)
class _a ( folder_based_build... | 142 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __a ( _UpperCamelCase: Tuple ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = os.path.j... | 142 | 1 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import l... | 26 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : Any = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 0 |
"""simple docstring"""
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE__ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
SCREAMING_SNAKE_CASE__ = "\... | 149 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ... | 149 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNext... | 214 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case_ = logging.getLogger(__name__)
snake_case_ = 50 # max width of layer names
snake_cas... | 214 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils impor... | 307 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked bef... | 307 | 1 |
"""simple docstring"""
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__A = "Run commands across TPU VMs for initial setup before running `accelerate la... | 217 | '''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def ... | 272 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class lowerCAmelCase__( __l... | 325 |
import os
import unicodedata
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
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ... | 325 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 142 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : Dict = [
'''encoder.version''',
... | 142 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_M... | 234 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokeniz... | 234 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql impo... | 149 |
from torch import nn
def lowerCAmelCase_ ( A_):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"Unsupported activation funct... | 149 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
def A(__a: Dict ):
lowerCAmelCase_ = r"\w+[.]\d+"
lowerCAmelCase_ = ... | 351 |
import string
from math import logaa
def A(__a: str , __a: str ):
lowerCAmelCase_ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
lowerCAmelCase_ = document_without_punctuation.split(" " ) # word tokeniza... | 22 | 0 |
def a_ ( _A = 1000 ) -> int:
"""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() = }''')
| 307 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""Jukeb... | 307 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Tuple = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
... | 150 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import Auto... | 150 | 1 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
__lowercase = [0] * len(SCREAMING_SNAKE_CASE )
__lowercase = []
__lowercase = [1] * len(SCREAMING_SNAKE_CASE )
for values in graph.values():
for i ... | 325 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class A__ :
def __init__( se... | 325 | 1 |
import string
from math import logaa
def _UpperCamelCase ( snake_case__, snake_case__ ) -> int:
__UpperCAmelCase : Optional[Any] = document.translate(
str.maketrans("", "", string.punctuation ) ).replace("\n", "" ... | 360 | import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import... | 342 | 0 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGeneration... | 234 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 234 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtracto... | 228 |
'''simple docstring'''
def _snake_case ( A = 10 ) -> str:
if not isinstance(A , A ) or n < 0:
raise ValueError('''Invalid input''' )
lowerCAmelCase__ = 10**n
lowerCAmelCase__ = 28433 * (pow(2 , 7830457 , A )) +... | 228 | 1 |
"""simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int ):
if not isinstance(__lowercase , __lowercase ):
lowercase_ : Any = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowercase )
... | 213 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floa... | 371 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mode... | 259 | 0 |
"""simple docstring"""
import math
def lowerCAmelCase__ ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' ... | 150 | """simple docstring"""
def lowerCAmelCase__ ( _UpperCamelCase : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(_UpperCamelCase , (list, tuple) ) or not all(
isinstance(_UpperCamelCase ... | 150 | 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 transformers.utils import... | 323 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfi... | 323 | 1 |
"""simple docstring"""
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
a__ : Tuple = logging.get_logger(__name__)
a__ : O... | 54 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils i... | 342 | 0 |
def snake_case () -> int:
return 1
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(UpperCAmelCase__ )
def snake_case (U... | 292 |
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 snake_case (UpperCAmelCase__ ) -> tuple:
return (data["data... | 292 | 1 |
def __A ( __lowerCamelCase = 100 ) -> int:
a = n * (n + 1) * (2 * n + 1) / 6
a = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }')
| 228 |
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__... | 228 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
#... | 356 | """simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_ten... | 149 | 0 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is... | 36 |
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] ):
UpperCamelCase :Tuple = len(SCREAMING_SNAKE_CASE__ )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCamelCase ... | 259 | 0 |
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
def count_of_possible_combinations(_A ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - i... | 353 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_SCREAMING_SNAKE_CASE : str = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
... | 218 | 0 |
'''simple docstring'''
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_ ):
... | 323 |
'''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
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCa... | 323 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A_ : str = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFor... | 368 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = ... | 349 | 0 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : List[Any] ) -> str:
'''simple docstring'... | 339 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 291 | 0 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__UpperCAmelCase : int = {
... | 315 |
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int... | 315 | 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 UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperC... | 69 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Any = logging.get_logger(__name__)
A__: List[str] = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS mod... | 149 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common impo... | 172 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = '... | 172 | 1 |
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import h... | 108 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_lowerCAmelCase : str = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__snake_case... | 218 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( a ) -> Optional[int]:
# getting number of pixels in the image
_A: Any = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(a ):
for j in range... | 354 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxMode... | 301 | 0 |
snake_case : List[Any] = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first... | 94 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelo... | 349 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
... | 362 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large ... | 71 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''c... | 315 |
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
... | 315 | 1 |
'''simple docstring'''
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ... | 135 |
'''simple docstring'''
def a ( ):
'''simple docstring'''
A_ : Optional[int] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A_ : Dict = 6
A_ : List[Any] = 1
A_ : Optional[Any] = 19_01
A_ : Tuple ... | 135 | 1 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class UpperCamelCase ( lowercase ):
def __in... | 172 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int= logging.get_logger(__name__)
_a : Optional[Any]= {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/reso... | 172 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Any ={
"configuration_xlm_roberta": [
"XL... | 357 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__snake_case : Dict =HfArgumentParser(InitializationArguments)
__snake_case : Tuple =parser.parse_args()
# Load codeparrot tokenizer trained for Pyth... | 94 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : str = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class snake_case__ (_Up... | 107 |
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lower... | 301 | 0 |
'''simple docstring'''
from collections.abc import Sequence
def A_( A : Sequence[float] , A : float):
return sum(c * (x**i) for i, c in enumerate(A))
def A_( A : Sequence[float] , A : float):
UpperCamelCas... | 359 |
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
imp... | 251 | 0 |
from torch import nn
def UpperCAmelCase__ ( lowerCamelCase ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"Unsupported activation function: {act_fn}" )
| 236 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Ev... | 71 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def UpperCAmelCase__ ( lowerCAmelCase__ :Dict , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Optional[Any] ) -> tuple:
'''simple docstring'''
lowercase ... | 369 | """simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from... | 32 | 0 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _snake_case :
def __init__( self : Dict ):
__lowerCamelCase : List[Any] = {}
def lowerCamelCase__ ( ... | 135 | """simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_to... | 135 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case :List[str] = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerC... | 131 |
def __snake_case ( _UpperCAmelCase = 1000000 ):
__a = limit + 1
__a = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = first... | 131 | 1 |
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 :int = logging.get_logger(__name__)
__snake_case :Union[str, Any] = '''▁'''... | 49 |
import math
class _snake_case :
def __init__( self , _lowerCamelCase=0 ): # a graph with Node 0,1,...,N-1
a :Optional[int] = n
a :Union[str, Any] = [
[math.inf for j in range(0 , _lowerCamelCase )] for i in range(0 , _... | 94 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
snake_case__ : List[str] = len(_lowerCAmelCase )
snake_case__ : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each ar... | 43 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 1 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
A__ : int ='''\\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 t... | 70 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/git-base": "https://huggingface... | 251 | 0 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__lowerCAmelCase = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__lowerCAmelCase ... | 367 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
for i in range(0 , lowerCAmelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in... | 107 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matr... | 107 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__versi... | 369 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any]... | 78 | 0 |
from math import isclose, sqrt
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = point_y / 4 / point_x
lowerCAmelCase__ : int = 2 * normal_gradient / (1 + normal_gradient... | 131 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase = collections.namedtuple('''_Datasets''', ['''trai... | 131 | 1 |
"""simple docstring"""
UpperCamelCase_ = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_fir... | 362 |
"""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 im... | 303 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_t... | 43 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | 43 | 1 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 147 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a_ ... | 147 | 1 |
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE : Union[str, Any] = 300 # TEMPERATURE (unit = K)
def lowercase ( _snake_case : float , _snake_case : float , _snake_case :... | 102 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : List[Any] = get_tests_dir('fixtures/test_sentencepiece_wit... | 107 | 0 |
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __lowerCamelCase ( lowerCAmelCase_ ) -> Tuple:
... | 370 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __magic_name__ :
def __init__( self : Dict ,_UpperCAmelCase : Any ):
_a : Any = data
_a : Node | None = ... | 107 | 0 |
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