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 |
|---|---|---|---|---|
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is... | 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 unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
req... | 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 : int ) -> bool:
'''simple docstring'''
if number < 0:
raise ValueError('number must not be negative' )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 : list[list[int | float]] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(_UpperCamelCase )
SCREAMING_SNAKE_CASE = len(matrix[0] )
SCREAMING_SNAKE_CASE = min(_UpperCamelCase , _UpperCamelCase )
for ro... | 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 math
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return Fa... | 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 unittest
import numpy as np
from transformers import AlbertConfig, 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.numpy as jnp
from ... | 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 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [0] * len(_UpperCamelCase )
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [1] * len(_UpperCamelCase )
for values in graph.values():
for i in v... | 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 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 |
#
# 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 json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : ... | 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 = 1_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ =... | 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 sys
a_ : Tuple = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6689664895044524452316173185640309871112... | 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 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE = int(_UpperCamelCase )
if n_element < 1:
SCREAMING_SNAKE_CASE = ValueError('a should be a positive number' )
raise my_error
SCREAMING_SNAKE_CASE = [1]
SC... | 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 unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def __lowerCAmelCase ( ) -> List[Any]:
'''simple docstring'''
raise RuntimeError('CUDA out of memory.' )
cl... | 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 subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
@require_torch
def UpperCamelCase ( self : Optional[int] ):
... | 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 importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from tra... | 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 |
#
# 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 |
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 os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
from... | 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_ : Union[str, Any] = {"configuration_xglm": ["XGLM_PRET... | 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 math
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
return math.sqrt(_UpperCamelCase ) * math.sqrt(_UpperCamelCase ) == num
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
SCREAMING_SNAK... | 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 |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str , _UpperCamelCase : Optional[str] = None ) -> str:
'''simple docstring'''
if ver... | 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 operator
def __lowerCAmelCase ( _UpperCamelCase : list , _UpperCamelCase : bool = False , _UpperCamelCase : list | None = None ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE = operator.lt if reverse else operator.gt
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 |
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 |
# 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
import os
from typing import Any
import requests
a_ : Any = "https://api.github.com"
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
a_ : List[Any] = BASE_URL + "/user"
# https://github.com/se... | 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 |
# 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 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 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.se... | 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 |
# 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 |
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 |
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(_UpperCamelCase ) + 1
SCREAMING_SNAKE_CASE = len(_UpperCamelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix ... | 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird impo... | 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 functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
a_ : List[str] = {
"asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
# ... | 700 |
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 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( _UpperCamelCase : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
SCREAMING_SNAKE_CASE = hex_num... | 701 |
#
# 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 | 0 |
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 before to... | 702 |
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 | 0 |
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, ... | 703 |
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 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils ... | 704 |
# 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 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase ( unittest.TestCase... | 705 |
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 | 0 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
a_ : Any = 4
a_ : int = 3
class UpperCamelCase ( __lowerCamelCase ):... | 706 |
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 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
a_ : Any = logging.get_logger(__name__)
class UpperCamelCase ( UpperCAmelCase_ ):
def __init__( self : Optional[Any] , *snake_case__ : Optional[int... | 707 |
# 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 | 0 |
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 import FlaxAutoModel
... | 708 |
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 | 0 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __lowerCAmelCase ( *_UpperCamelCase : int ) -> Dict:
'''simple docstring'''
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ):
SCREAMING_SNAKE_CASE ... | 709 |
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 | 0 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCamelCase ( pl.LightningModule ):
def __init__( self : Optional[Any] , snake_case__ : str ):
"""simpl... | 710 |
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 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available()... | 711 |
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 | 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,
ftp_head,
... | 712 |
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 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCamelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 713 |
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 | 0 |
from __future__ import annotations
import math
def __lowerCAmelCase ( _UpperCamelCase : Any , _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple , _UpperCamelCase : Tuple ) -> List[Any]:
'''simple docstring'''
... | 714 |
# 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 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import To... | 715 |
# 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 | 0 |
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:
import jax
... | 716 |
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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : str ) -> Union[str, Any]:
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
a... | 717 |
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 | 0 |
from __future__ import annotations
def __lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple[str, float]:
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You can... | 718 |
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 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, Rando... | 719 |
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 | 0 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase :
__UpperCamelCase =None
def UpperCamelCase ( self : Optional[int] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = self... | 720 |
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 | 0 |
from __future__ import annotations
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int ) -> Any:
'''simple docstring'''
if b == 0:
return (1, 0)
(SCREAMING_SNAKE_CASE) = extended_euclid(__UpperCamelCase , a % b )
SCREAMING_SNAKE_CASE ... | 721 |
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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : list[list[int]] , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : set ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = len(snake_case__ ), len(grid[0] )
if ... | 700 |
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 | 0 |
'''simple docstring'''
import re
def __lowerCAmelCase ( _UpperCamelCase : Any ) -> list:
'''simple docstring'''
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def __lowerCAmelCase ( _UpperCamelCase : List[str] ) -> str:
... | 701 |
#
# 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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : int ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(_UpperCamelCase )
SCREAMING_SNAKE_CASE = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each ar... | 702 |
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 | 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 ... | 703 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNo... | 704 |
# 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 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from trans... | 705 |
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 | 0 |
import os
import sys
import transformers
a_ : Tuple = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
prin... | 706 |
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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : int = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
... | 707 |
# 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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int ) -> str:
'''simple docstring'''
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number... | 708 |
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 | 0 |
import math
import qiskit
def __lowerCAmelCase ( _UpperCamelCase : int = 1 , _UpperCamelCase : int = 1 , _UpperCamelCase : int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(_lowercase , _lowercase )
or isinstance(_lowerc... | 709 |
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 | 0 |
from math import isclose, sqrt
def __lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = point_y / 4 / point_x
SCREAMING_SNAKE_CASE = 2 * normal_gradien... | 710 |
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 | 0 |
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... | 711 |
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 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCamelCase ( UpperCamelCase_ ):
def __init__( self : str , snake_case__ : Optional[int] , snake_case__ : Optional[int] ... | 712 |
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 | 0 |
from __future__ import annotations
def __lowerCAmelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : List[str] ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only... | 713 |
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 | 0 |
from scipy.stats import spearmanr
import datasets
a_ : Any = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations im... | 714 |
# 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 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
a_ : Opti... | 715 |
# 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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : List[str] ) -> int:
'''simple docstring'''
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError('only integers accepted as input' )
else:
SCREAMING_SNAKE_CASE = str(abs(_lowerCAmelCase ) )
SCREAMING_SNAKE_CAS... | 716 |
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 | 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
a_ : List[str] = {
'''gwf-440k''':... | 717 |
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 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from ... | 718 |
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 | 0 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
a_ : Tuple = None
try:
import msvcrt
except ImportError:
a_ : List[Any] = None
try:
import fcntl
except ImportError:
a_ : List[Any] = None
# Back... | 719 |
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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : int = 10 ) -> str:
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or n < 0:
raise ValueError('Invalid input' )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , ... | 720 |
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 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = list(_UpperCamelCase )
SCREAMING_SNAKE_CAS... | 721 |
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 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
... | 700 |
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 | 0 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCamelCase ( SCREAMING_SNAKE_C... | 701 |
#
# 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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {"configuration_yolos": ["YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP", "YolosConfig", "YolosOnnxConfig"]}
try:
if not is_vision_avail... | 702 |
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 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_tor... | 703 |
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 | 0 |
import random
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = num - 1
SCREAMING_SNAKE_CASE = 0
while s % 2 == 0:
SCREAMING_SNAKE_CASE = s // 2
t += 1
for _ in range(5 ):
SCREAMING_SNAKE_CASE ... | 704 |
# 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 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ : Dict = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
e... | 705 |
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 | 0 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,... | 706 |
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 | 0 |
import argparse
import json
from tqdm import tqdm
def __lowerCAmelCase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=lowercase_ , default='biencoder-nq-dev... | 707 |
# 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 | 0 |
import math
def __lowerCAmelCase ( _UpperCamelCase : List[Any] ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
ret... | 708 |
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 | 0 |
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... | 709 |
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 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : Tuple = {
"facebook/xmod-base": "https://huggingface.c... | 710 |
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 | 0 |
a_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ : Union[str, Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
a_ : List[Any] = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def ... | 711 |
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 | 0 |
from __future__ import annotations
from random import choice
def __lowerCAmelCase ( _UpperCamelCase : str ) -> List[str]:
'''simple docstring'''
return choice(lowerCamelCase_ )
def __lowerCAmelCase ( _UpperCamelCase : list[int] , _UpperCamelCase : int )... | 712 |
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 | 0 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def __lowerCAmelCase ( _UpperCamelCase : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = analyze_text(__snake_case )
SCREAMING_SNAKE_CASE = ... | 713 |
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 | 0 |
import os
a_ : Union[str, Any] = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def __lowerCAmelCase ( _UpperCamelCase : str ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
while index < len(a... | 714 |
# 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 | 0 |
import itertools
import string
from collections.abc import Generator, Iterable
def __lowerCAmelCase ( _UpperCamelCase : Iterable[str] , _UpperCamelCase : int ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = iter(UpperCamelCase__ )
while True:
SCREAMING_... | 715 |
# 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 | 0 |
import heapq
import sys
import numpy as np
a_ : List[Any] = tuple[int, int]
class UpperCamelCase :
def __init__( self : str ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = set()
def UpperCamelCase ( ... | 716 |
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 | 0 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transform... | 717 |
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 | 0 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Acceler... | 718 |
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 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 719 |
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 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a_ : ... | 720 |
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 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE )
class UpperCamelCase ( __SCREAMING_SNAKE_CASE ):
__UpperCamelCase =field(def... | 721 |
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 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 700 |
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 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils ... | 701 |
#
# 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 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : ... | 702 |
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 | 0 |
def __lowerCAmelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Tuple ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for ea... | 703 |
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 | 0 |
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_availa... | 704 |
# 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 | 0 |
from torch import nn
def __lowerCAmelCase ( _UpperCamelCase : str ) -> Union[str, Any]:
'''simple docstring'''
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"""Unsupport... | 705 |
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 | 0 |
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