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 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class UpperCamelCase_ :
def __init__( self , A ) -> None:
UpperCAmelCase : Optional[Any] = value
UpperCAmelCase : Node | None = None
UpperCAmelCa... | 714 |
'''simple docstring'''
a : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def __lowerCamelCase ( _lowercase ) -> int:
UpperCAmelCase : str = 0
while number:
# Increased Speed Slightly by checking ev... | 672 | 0 |
from __future__ import annotations
def __lowerCamelCase ( _lowercase ) -> list[int]:
UpperCAmelCase : Optional[int] = 2
UpperCAmelCase : Optional[Any] = []
while i * i <= n:
if n % i:
i += 1
else:
... | 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
... | 672 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCamelCase_ ( datasets.BeamBasedBuilder ):
def _lowercase( ... | 716 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def __lowerCamelCase ( _lowercase , _lowercase = True , _lowercase = math.inf , _lowercase = -math.inf , _lowercase = math.inf , _lowercase = ... | 672 | 0 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
a : List[Any] = logging.get_logger(__name__)
a : str ... | 717 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
... | 672 | 0 |
'''simple docstring'''
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a = get_tests_dir("""fixtures... | 718 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a : Tuple = False
class UpperCamelCase_ ( unit... | 672 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoF... | 719 |
'''simple docstring'''
# 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 TensorF... | 672 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_... | 720 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
a : int = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def __lowerCamelCase... | 672 | 0 |
'''simple docstring'''
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : str = logging.get_logger(__name__)
... | 721 |
'''simple docstring'''
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... | 672 | 0 |
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 |
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 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class UpperCame... | 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 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
'''simple docstring'''
if num < 0:
return False
SCREAMING_SNAKE_CASE = num
SCREAMING_SNAKE_CASE = 0
while num > 0:
SCREAMING_SNAKE_CASE = rev_num * 10 + (num % 10)
num //= 10
return num_copy == ... | 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 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
a_ : Dict = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
"mod... | 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 flax.linen as nn
import jax
import jax.numpy as jnp
class UpperCamelCase ( nn.Module ):
__UpperCamelCase =42
__UpperCamelCase =jnp.floataa
def UpperCamelCase ( self : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = nn.Conv(
... | 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 socket
def __lowerCAmelCase ( ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE = socket.gethostname()
SCREAMING_SNAKE_CASE = 1_23_12
sock.connect((host, port) )
so... | 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 datetime import datetime
import matplotlib.pyplot as plt
import torch
def __lowerCAmelCase ( _UpperCamelCase : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
for param in module.parameters():
SCREAMING_SNAKE_CASE = False
def __lowerCAmelCase ( ... | 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 |
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 |
# 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 ( ) -> int:
'''simple docstring'''
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(_UpperCamelCase , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F"""{solutio... | 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 collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : int = {
"andreasmadsen/efficient_mlm_m0.40":... | 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 |
from __future__ import annotations
from cmath import sqrt
def __lowerCAmelCase ( _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zer... | 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 torch
from diffusers import StableDiffusionPipeline
a_ : int = "path-to-your-trained-model"
a_ : List[str] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
a_ : Dict = "A photo of sks dog in a bucket"
a_ : ... | 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 functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __lowerCAmelCase ( *_UpperCamelCase : int ) -> Tuple:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
SCREAMING_SNAKE_CASE =... | 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 : int , _UpperCamelCase : bool = False ) -> bool:
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70... | 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 Dict, Optional
import numpy as np
import datasets
a_ : List[Any] = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-cla... | 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 glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ : List[Any] = "... | 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 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 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
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
f... | 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 collections import defaultdict
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = True
for v in tree[start]:
if v not in visited:
ret += dfs(_UpperCamelCase )
if ret % 2 == 0:
cut... | 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 : int = 1_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = set(range(3 , _UpperCamelCase , 2 ) )
primes.add(2 )
for p in range(3 , _UpperCamelCase , 2 ):
if p not in primes:
continue
prime... | 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 argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
a_ : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, ty... | 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 dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Dict = logging.get_logger(__name__)
# TODO Update this
a_ : List[str] = {
"facebook/esm-1b": "https://huggingface.co/f... | 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 math
class UpperCamelCase :
def __init__( self : List[str] , snake_case__ : Optional[int]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
SCREAMING_SNAKE_CASE = n
SCREAMING_SNAKE_CASE = [
[math.inf for j in rang... | 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 os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
a_ : List[Any] = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
... | 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 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since th... | 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, is_vision_available
a_ : Tuple = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxConfig... | 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 cva import destroyAllWindows, imread, imshow, waitKey
def __lowerCAmelCase ( _UpperCamelCase : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = img.shape[0], img.shape[1]
# converting each pixel's color to its negati... | 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 __future__ import annotations
from collections import deque
class UpperCamelCase :
def __init__( self : Optional[Any] , snake_case__ : list[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = []
self.adlist.append(
{'value': '', 'ne... | 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 typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
a_ : int = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if not is_v... | 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 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 DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verbosit... | 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 manim import *
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
def UpperCamelCase ( self : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = Rectangle(height=0.5 , width=0.5 )
SCREAMING_SNAKE_CASE = Rectangle(height=0.46 ... | 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 os
from collections import deque
import torch
from torch.utils.data import Dataset
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
def __init__( self : Tuple , snake_case__ : Any="" , snake_case__ : Tuple="train" ):
"""simple docstring"""
... | 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 unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import tor... | 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 Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase ( SCR... | 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 ....utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
def __init__( self : str , snake_case__ : List[Any] , snake_case__ : Tuple=None , snake_case__ : Opt... | 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 Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configurat... | 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 __future__ import annotations
def __lowerCAmelCase ( _UpperCamelCase : list , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : int ) -> list:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE , SCREAMING... | 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 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 |
# 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 time
a_ : str = list[tuple[int, int]]
a_ : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, ... | 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 LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_... | 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 ) -> 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 |
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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaT... | 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 unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inp... | 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 |
def __lowerCAmelCase ( _UpperCamelCase : int ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError('Input must be a positive integer' )
SCREAMING_SNAKE_CASE = [True] * (num + 1)
SCREAMING_SNAKE_CASE = 2
while p * p <= num:
if primes[p]:
for i i... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : str = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
),
... | 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 math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
__Up... | 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 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_ : Any = "▁"
a_ : List[str] = {"vocab_file": "spiece.model"}
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 |
import numpy
class UpperCamelCase :
def __init__( self : Union[str, Any] , snake_case__ : numpy.ndarray , snake_case__ : numpy.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = input_array
# Random initial weights are assigned ... | 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 time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
a_ : List[str] = "__DUMMY_TRANSFORMERS_USER__"
a_ : str = "Dummy User"
a_ : List[str] = "hf_hZEmnoOEYISjr... | 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 functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Optional[Any] = logging.get_logger(__name__)
a_ : List[Any] = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/... | 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 collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPR... | 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 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 |
# 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_ : Optional[int] = logging.get_logger(__name__)
a_ : Any = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c... | 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 warnings
from functools import wraps
from typing import Callable
def __lowerCAmelCase ( _UpperCamelCase : Callable ) -> Callable:
'''simple docstring'''
@wraps(_UpperCamelCase )
def _inner_fn(*_UpperCamelCase : List[str] , **_UpperCamelCase : List[str] ):
warnings.w... | 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 typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a_ : Union[str, Any] = logging.getLogger(__name__)
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
def _... | 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 copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import ca... | 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 |
a_ : Union[str, Any] = "Tobias Carryer"
from time import time
class UpperCamelCase :
def __init__( self : Optional[Any] , snake_case__ : int , snake_case__ : Dict , snake_case__ : Dict , snake_case__ : Optional[int]=int(ti... | 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 |
# 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 |
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 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 |
# 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 typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class... | 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 os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
a_ : Union[str, Any] ... | 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 MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 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 datetime
def __lowerCAmelCase ( _UpperCamelCase : str ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
'0': 'Sunday',
'1': 'Monday',
'2': 'Tuesday',
'3': 'Wednesday',
'4': 'Thursday',
'5': 'Friday',
'6': 'Saturd... | 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 dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
sma... | 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 : List[str] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(_UpperCamelCase )
SCREAMING_SNAKE_CASE = sum(_UpperCamelCase )
SCREAMING_SNAKE_CASE = [[False for x in range(s + 1 )] for y in range(n + 1... | 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 shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTok... | 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 pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
a_ : List[str] = logging.get_logger(__name__)
def __lowerCAmelCase ( _Upper... | 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 Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENA... | 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 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : int = logging.get_logger(__name__)
a_ : Any = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN models at https://h... | 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 Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : Optional[int] = logging.get_logger(__name__)
a_ : Union[str, Any] = {
"facebook/xmod-base": "h... | 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 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 |
# 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 re
from filelock import FileLock
try:
import nltk
a_ : List[str] = True
except (ImportError, ModuleNotFoundError):
a_ : Dict = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __... | 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 collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class UpperCamelCase ( yaml.SafeLoader ):
def UpperCamelCase ( self : List[str] , snake_case__ : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE ... | 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 dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Option... | 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
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 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 functools import lru_cache
@lru_cache
def __lowerCAmelCase ( _UpperCamelCase : int ) -> int:
'''simple docstring'''
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import... | 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 |
from __future__ import annotations
from typing import Any
def __lowerCAmelCase ( _UpperCamelCase : list[Any] ) -> None:
'''simple docstring'''
create_state_space_tree(_UpperCamelCase , [] , 0 )
def __lowerCAmelCase ( _UpperCamelCase : list[Any] , ... | 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 sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class UpperCamelCase :
def __init__( self : Dict , snake_case__ : int , s... | 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 diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class UpperCamelCase ( SCREAM... | 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 os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase ( self : Dict ):
... | 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 json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers 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 os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and no... | 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 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 |
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 |
def __lowerCAmelCase ( _UpperCamelCase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = set()
# Replace all the whitespace in our sentence
SCREAMING_SNAKE_CASE = input_str.replace(' ' , '' )... | 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 os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import requir... | 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
def __lowerCAmelCase ( _UpperCamelCase : int | float | str , _UpperCamelCase : int | float | str ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
SCREAMING_SNAKE_CASE = int(_UpperCamelCase )
SCREAMING_SNAKE_CA... | 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 hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils imp... | 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 |
def __lowerCAmelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : Tuple , _UpperCamelCase : List[Any] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] ) -> Any:
'''simple docstring'''
if index == r:
... | 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 logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
a_ : Tuple = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classification",
... | 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 logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
a_ : Optional[int] = logging.getLogger(__name__... | 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 math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class UpperCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__Upp... | 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 dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
__UpperCame... | 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 |
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