code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 673 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 | 1 |
# 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 ap... | 673 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 | 1 |
import os
def __lowerCAmelCase ( ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.path.dirname(os.path.realpath(_UpperCamelCase ) )
SCREAMING_SNAKE_CASE = os.path.join(_UpperCamelCase , 'triangle.txt' )
with open(_UpperCamelCase ) as f:
SCREAMING_SN... | 673 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 673 | 1 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils impor... | 673 |
# 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 ap... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> str:
'''simple docstring'''
if isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('... | 673 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a_ : List[str] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a_ : Di... | 673 | 1 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging... | 673 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sig... | 673 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 673 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
... | 673 | 1 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __lowerCAmelCase ( _UpperCamelCase : List[str] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple ) -> Union[str, Any]:
'''simple docstri... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 673 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import ... | 673 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(_UpperCamelCase )
SCREAMING_SNAKE_CASE = []
for i in range(len(_UpperCamelCase ) - pat_len + 1 ):
SCREAMING_SNAKE_CASE = ... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 2**power
SCREAMING_SNAKE_CASE = str(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
for i in list_... | 673 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer... | 673 |
# 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 ap... | 673 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : Tuple = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class UpperCamelCase ( SCREAMING... | 673 |
# 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
# - generate model_cards - usef... | 673 | 1 |
# 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... | 673 |
import random
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : float , _UpperCamelCase : bool = False ) -> dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {i: [] for i in range(_UpperCamelCase )}
# if probability is greater or equal than ... | 673 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a_ : Any = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network
"scale_grad_by_std": T... | 673 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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 ... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Dict = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available()... | 673 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 673 | 1 |
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
from... | 673 |
import heapq
import sys
import numpy as np
a_ : Optional[int] = tuple[int, int]
class UpperCamelCase :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ... | 673 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import T... | 673 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils i... | 673 | 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... | 673 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a_ : List[Any] = logging.get_logger("transformers.models.speecht5")
def __lowerCAmelCase ( _UpperCamelCase : Tuple , _U... | 673 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def __lowerCAmelCase ( _UpperCamelCase : Any ) -> Tuple:
'''simple docstring'''
return x + 2
class UpperCamelCase ( unittest.TestCase ):
d... | 673 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 673 | 1 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
a_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCamel... | 673 |
#
# 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-distributed-gpu-t... | 673 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
a_ : Dict = False
class UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase ( sel... | 673 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {"vocab_file": "vocab.j... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int ) -> int:
'''simple docstring'''
while a != 0:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = b % a, a
return b
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperC... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = abs(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
while n > 0:
res += n % 10
n //= 10
return res
def __lowerCAmelCase ( _UpperCamelCase : int ) -... | 673 | 1 |
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_available
... | 673 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 | 1 |
# 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 applica... | 673 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from... | 673 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 673 | 1 |
def __lowerCAmelCase ( ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
SCREAMING_SNAKE_CASE = 6
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 19_01
SCREAMING_SNAKE_CASE = ... | 673 |
# 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 ap... | 673 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def UpperCamelCase ( snake_case__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError()
... | 673 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a_ : List[str] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a_ : Di... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : int = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
],
"proce... | 673 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sig... | 673 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
... | 673 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
... | 673 | 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,
)
a_ : str = {
"configuration_albert": ["ALBERT_PRETRAI... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 673 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 673 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 673 | 1 |
import math
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_UpperCamelCase )
def __lowerCAmelCase ( _UpperCamelCase : ... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 2**power
SCREAMING_SNAKE_CASE = str(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
for i in list_... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ : Dict = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec": ["... | 673 |
# 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 ap... | 673 | 1 |
import math
def __lowerCAmelCase ( _UpperCamelCase : int = 1_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = sum(i * i for i in range(1 , n + 1 ) )
SCREAMING_SNAKE_CASE = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_su... | 673 |
# 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
# - generate model_cards - usef... | 673 | 1 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 673 |
import random
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : float , _UpperCamelCase : bool = False ) -> dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {i: [] for i in range(_UpperCamelCase )}
# if probability is greater or equal than ... | 673 | 1 |
from __future__ import annotations
from math import gcd
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int = 2 , _UpperCamelCase : int = 1 , _UpperCamelCase : int = 3 , ) -> int | None:
'''simple docstring'''
if num < 2:
raise Value... | 673 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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 ... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : List[str] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = len(_UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorte... | 673 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 673 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from da... | 673 |
import heapq
import sys
import numpy as np
a_ : Optional[int] = tuple[int, int]
class UpperCamelCase :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ... | 673 | 1 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 673 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils i... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
a_ : Optional[Any] = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AS... | 673 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a_ : List[Any] = logging.get_logger("transformers.models.speecht5")
def __lowerCAmelCase ( _UpperCamelCase : Tuple , _U... | 673 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Tuple = logging.get_logger(__name__)
a_ : Tuple = {
"roberta-base": "https://huggingface.co/ro... | 673 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(_UpperCamelCase , _UpperCamelCase ):
return 0
elif n == 2:
return 1
else:
SCREAMING_SNAKE_CASE = [0, 1]
for i in range(2 , n + 1 ):
sequence.append... | 673 |
#
# 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-distributed-gpu-t... | 673 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class UpperCamelCase :
def __init__( self : Dict , snake_case__ : Tuple=2 , snake_case__ : int=3 , snake_case__ : Any=6_4 ... | 673 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {"vocab_file": "vocab.j... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a_ : Tuple = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is_torch_available():
... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = abs(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
while n > 0:
res += n % 10
n //= 10
return res
def __lowerCAmelCase ( _UpperCamelCase : int ) -... | 673 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
a_ : ... | 673 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 | 1 |
# 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 UpperCamelCase :
def __init__(... | 673 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 | 1 |
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_ : Dict = logging.get_logger(__name__)
a_ : Optional[int] = "▁"
a_ : ... | 673 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 673 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __lowerCAmelCase ( ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser(
description=(
... | 673 |
# 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 ap... | 673 | 1 |
from datetime import datetime as dt
import os
from github import Github
a_ : Union[str, Any] = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def __lowerCAmelCase ( ) -> Optional[int]:
'... | 673 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a_ : List[str] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a_ : Di... | 673 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
... | 673 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sig... | 673 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __lowerCAmelCase ( _UpperCamelCase : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder... | 673 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
... | 673 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Train... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Any = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_available():
... | 673 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 673 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeni... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 2**power
SCREAMING_SNAKE_CASE = str(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
for i in list_... | 673 | 1 |
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
from ...test_configuration_common import... | 673 |
# 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 ap... | 673 | 1 |
import heapq
import sys
import numpy as np
a_ : Optional[int] = tuple[int, int]
class UpperCamelCase :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ... | 673 |
# 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
# - generate model_cards - usef... | 673 | 1 |
import argparse
from collections import defaultdict
import yaml
a_ : Tuple = "docs/source/en/_toctree.yml"
def __lowerCAmelCase ( _UpperCamelCase : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_UpperCamelCase )
for doc ... | 673 |
import random
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : float , _UpperCamelCase : bool = False ) -> dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {i: [] for i in range(_UpperCamelCase )}
# if probability is greater or equal than ... | 673 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTest... | 673 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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 ... | 673 | 1 |
from __future__ import annotations
import math
def __lowerCAmelCase ( _UpperCamelCase : int ) -> list[int]:
'''simple docstring'''
if num <= 0:
SCREAMING_SNAKE_CASE = f"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(_UpperCamelCase )
SCREAMI... | 673 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str = " " ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
for index, char in enumerate(_UpperCamelCase ):
if char == separator:
split_words.append(strin... | 673 |
import heapq
import sys
import numpy as np
a_ : Optional[int] = tuple[int, int]
class UpperCamelCase :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ... | 673 | 1 |
import random
def __lowerCAmelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[int] , _UpperCamelCase : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = a[left_index]
SCREAMING_SNAKE_CASE = left_index + 1
for j in range(lef... | 673 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils i... | 673 | 1 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sig... | 673 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a_ : List[Any] = logging.get_logger("transformers.models.speecht5")
def __lowerCAmelCase ( _UpperCamelCase : Tuple , _U... | 673 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWit... | 673 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 673 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logg... | 673 |
#
# 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-distributed-gpu-t... | 673 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
a_ : int = False
class UpperCamelCase ( unittest.TestCase ):
... | 673 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {"vocab_file": "vocab.j... | 673 | 1 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
a_ : str = ... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = abs(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
while n > 0:
res += n % 10
n //= 10
return res
def __lowerCAmelCase ( _UpperCamelCase : int ) -... | 673 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a_ : Dict = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sma... | 673 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 | 1 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.p... | 673 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import Interp... | 673 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 673 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCondi... | 673 |
# 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 ap... | 673 | 1 |
def __lowerCAmelCase ( _UpperCamelCase : int = 2_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i... | 673 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a_ : List[str] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a_ : Di... | 673 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
a_ : Optional[int] = loggi... | 673 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sig... | 673 | 1 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray , _UpperCamelCase : np.ndarray , _UpperCamelCase : float = 1e-12 , _UpperCamelCase : int = 1_00 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(_UpperCamelCas... | 673 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a_ : Any = {"processing_layoutxlm": ["LayoutXLMProcessor"]}
try:
if n... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 673 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class UpperCamelCase ( unittest.TestCase , SCREAMING_SNAKE_CASE ):
def UpperCamelCase ( self : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = load... | 673 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 673 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from t... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 2**power
SCREAMING_SNAKE_CASE = str(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
for i in list_... | 673 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class UpperCamelCase ( unittest.TestCase ):
__UpperCamelCase =inspect.getfile(a... | 673 |
# 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 ap... | 673 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
a_ : List[str] = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="stor... | 673 |
# 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
# - generate model_cards - usef... | 673 | 1 |
from __future__ import annotations
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : str ) -> Union[str, Any]: # noqa: E741
'''simple docstring'''
while r - l > 1:
SCREAMING_SNAKE_CASE ... | 673 |
import random
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : float , _UpperCamelCase : bool = False ) -> dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {i: [] for i in range(_UpperCamelCase )}
# if probability is greater or equal than ... | 673 | 1 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a_ : List[Any] = logging.get_logger("transformers.models.speecht5")
def __lowerCAmelCase ( _UpperCamelCase : Tuple , _U... | 673 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, 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 ... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : int = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptTokenizer"],
}
try:... | 673 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
f... | 673 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxCo... | 673 |
import heapq
import sys
import numpy as np
a_ : Optional[int] = tuple[int, int]
class UpperCamelCase :
def __init__( self : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ... | 673 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils i... | 673 | 1 |
from functools import reduce
a_ : str = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617... | 673 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
a_ : List[Any] = logging.get_logger("transformers.models.speecht5")
def __lowerCAmelCase ( _UpperCamelCase : Tuple , _U... | 673 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def __lowerCAmelCase ( _UpperCamelCase : D... | 673 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeni... | 673 | 1 |
from __future__ import annotations
a_ : Optional[Any] = list[list[int]]
# assigning initial values to the grid
a_ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3... | 673 |
#
# 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-distributed-gpu-t... | 673 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTe... | 673 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : Optional[Any] = {"vocab_file": "vocab.j... | 673 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Any = {
"naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
# See all Donut models at https... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = abs(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
while n > 0:
res += n % 10
n //= 10
return res
def __lowerCAmelCase ( _UpperCamelCase : int ) -... | 673 | 1 |
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 ...utils import... | 673 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ : Union[str, Any] = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": ["export", "v... | 673 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
a_ : Dict = (720, 1280) # Height, Width
a_ : Tuple = (0.4, 0.6) # if height or width lower than this scale, drop it.
a_ : str = 1 / 100
a... | 673 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 673 | 1 |
import copy
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
from ..auto import CONFIG_MAPPING
a_ : int = logging.get_logger(__name__)
a_ ... | 673 |
# 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 ap... | 673 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
... | 673 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a_ : List[str] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a_ : Di... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Any = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdPegasusOnnxConfig",
... | 673 |
import numpy as np
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
'''simple docstring'''
return vector * sig... | 673 | 1 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContex... | 673 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Dict = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
),
}
... | 673 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
a_ : int = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
a_ : int ... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : int ) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 673 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecat... | 673 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_av... | 673 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase ( SCREAMING_SNAKE_CASE , unittest.TestCase ):
__UpperCamelCase =CTRLTokenizer
... | 673 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 2**power
SCREAMING_SNAKE_CASE = str(_UpperCamelCase )
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CASE = 0
for i in list_... | 673 | 1 |
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __lowerCAmelCase ... | 673 |
# 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 ap... | 673 | 1 |
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def __lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : bool = False ) -> list[float]:
'''simple docstring'... | 673 |
# 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
# - generate model_cards - usef... | 673 | 1 |
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