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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
SCREAMING_SNAKE_CA... | 629 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : Optional[Any] = [0 for i in range(r + 1 )]
# nc0 = 1
a__ : List[Any] = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
a__ : Dict = min(lowerCame... | 629 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ : List[Any] = """sshlei... | 629 | 1 |
def _A ( lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("Input series is not valid, valid series - [2, 4, 6]" )
if len(lowerCamelCase ) == 0:
raise ValueError("Input list must be a non empty list" )
if len(lowerCamelCase )... | 629 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 629 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_atten... | 629 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 9.80665
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volu... | 629 | 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... | 629 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__( self , snake_case = None ) -> Any:
"""simple docstring"""
a__ : Optional[int] = value
a__ : Tuple = random()
a__ : Node... | 629 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
SC... | 629 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pi... | 629 | 1 |
import logging
from transformers import PretrainedConfig
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.getLogger(__name__)
SCREAMING_SNAKE_CASE__ : Tuple = {
"""bertabs-finetuned-cnndm""": """https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstr... | 629 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""... | 629 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Tuple = TypeVar("""T""")
class __lowerCAmelCase ( Generic[T] ):
def __init__( self , snake_case ) -> Tuple:
"""si... | 629 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
a__ : A... | 629 | 1 |
# Imports
import numpy as np
class __lowerCAmelCase :
def __init__( self , snake_case=None , snake_case=None , snake_case=None , snake_case=None , snake_case=None ) -> List[str]:
"""simple docstring"""
self.set_matricies(red=snake_case ... | 629 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 | 1 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _i... | 629 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer... | 629 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer... | 629 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 629 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(... | 629 | 1 |
from __future__ import annotations
from typing import Any
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case = 0 ) -> None:
"""simple docstring"""
a__ , a__ : Optional[int] = row, column
a__ : ... | 629 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : ... | 629 | 1 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
SCREAMING_SNAKE_CASE__ : int = """\
@misc{wu2016googles,
title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
... | 629 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
SCREAMING_SNAKE_CASE__ : int = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": P... | 629 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowerCAmelCase ( _UpperCamelCase ):
def __init__( self , snake_case , snake_case ) -> List[Any]:
"""simple docstring"""
a__ : Union[str, Any] ... | 629 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(R"""([a-z\d])([A-Z])""")
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"... | 629 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _A ( ):
a__ : Optional[Any] = 9
a__ : Dict = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5... | 629 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise Optional... | 629 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
... | 629 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# U... | 629 | 1 |
from __future__ import annotations
from typing import TypedDict
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : str
_UpperCamelCase : int
def _A ( lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeE... | 629 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 629 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_UpperCamelCase ):
_UpperCamelCase : Dict = ["""speech"""]
def __init__( self , *snake_case , **snake_case ) -> Tuple:
"""simple docstring"""
require... | 629 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : ... | 629 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[str] = TypeVar("""T""")
class __lowerCAmelCase ( Gen... | 629 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.po... | 629 | 1 |
class __lowerCAmelCase :
def __init__( self ) -> None:
"""simple docstring"""
a__ : dict[str, TrieNode] = {} # Mapping from char to TrieNode
a__ : int = False
def _snake_case ( self , snake_case ) -> None:
"""simp... | 629 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 | 1 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE__ : int = {
"""susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
"""susnato/ernie-m-l... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : Dict = F"""{sampling_rate}"""
a__ : List[str] = "1"
a__ : Any = "f32le"
a__... | 629 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ : List[Any] = """sshlei... | 629 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get... | 629 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 629 | 1 |
import gc
import unittest
from transformers import CTRLConfig, 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... | 629 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 9.80665
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volu... | 629 | 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_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_l... | 629 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__( self , snake_case = None ) -> Any:
"""simple docstring"""
a__ : Optional[int] = value
a__ : Tuple = random()
a__ : Node... | 629 | 1 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_confi... | 629 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pi... | 629 | 1 |
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : Optional[int] = """"""
_UpperCamelCase : str = (
... | 629 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""... | 629 | 1 |
import unittest
from transformers import BertGenerationConfig, 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... | 629 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
a__ : A... | 629 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Dict = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blo... | 629 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _A ( lowerCamelCase ):
a__ : str = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(lowerCamelCas... | 629 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer... | 629 | 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_grap... | 629 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 629 | 1 |
import random
class __lowerCAmelCase :
@staticmethod
def _snake_case ( snake_case ) -> tuple[list[int], list[int]]:
"""simple docstring"""
a__ : Union[str, Any] = [ord(snake_case ) for i in text]
a__ : Optional[Any] = []
a... | 629 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(... | 629 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
# Initialise PyTorch model
... | 629 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : ... | 629 | 1 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
SCREAMING_SNAKE_CASE__ : int = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": P... | 629 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
SCREAMING_SNAKE_CASE__ : int = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": P... | 629 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
_UpperCamelCase : Union[str, Any] = None
def _snake_case ( self ) -> Dict:
"""simple docstring"""
a__ : List... | 629 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(R"""([a-z\d])([A-Z])""")
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"... | 629 | 1 |
import numpy as np
import qiskit
def _A ( lowerCamelCase = 8 , lowerCamelCase = None ):
a__ : Dict = np.random.default_rng(seed=lowerCamelCase )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
a__ : Dict = 6... | 629 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise Optional... | 629 | 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 import BertTokenizer
SCREAMING_SNAKE_CASE__ : List[Any] = loggin... | 629 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# U... | 629 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
SCREAMING_SNAKE_CASE__ : str = datasets.load_iris()
SCREAMING_SNAKE_CASE__ : List[Any] = np.array(data["""data"""])
SCREAMING_SNAKE_CASE... | 629 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 629 | 1 |
import json
import sys
def _A ( lowerCamelCase , lowerCamelCase ):
with open(lowerCamelCase , encoding="utf-8" ) as f:
a__ : Union[str, Any] = json.load(lowerCamelCase )
a__ : str = ["<details>", "<summary>Show updated benchmarks!</summary... | 629 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=_UpperCamelCase ):
_UpperCamelCase : Dict = ["""sentencepiece"""]
def __init__( self , *snake_case , **snake_case ) -> Optional[int]:
"""simple docstring"""
... | 629 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 | 1 |
from __future__ import annotations
from cmath import sqrt
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
a__ : Any = b * b - 4 * a * c
a__ : str = (-b + sqr... | 629 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.po... | 629 | 1 |
def _A ( lowerCamelCase ):
if num < 0:
return False
a__ : int = num
a__ : int = 0
while num > 0:
a__ : Dict = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doc... | 629 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Union[str, Any] ... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDic... | 629 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ : List[Any] = """sshlei... | 629 | 1 |
def _A ( lowerCamelCase ):
return " ".join(
"".join(word[::-1] ) if len(lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 629 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 629 | 1 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class __lowerCAmelCase ( nn.Module ):
_UpperCamelCase : int
_UpperCamelCase : jnp.dtype = jnp.floataa
def _snake_case ( self ) -> Any:
"""simple docstring"""
a__ : Dict ... | 629 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 9.80665
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volu... | 629 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__( self , snake_case = None ) -> Any:
"""simple docstring"""
a__ : Optional[int] = value
a__ : Tuple = random()
a__ : Node... | 629 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ : List[Any] = """sshlei... | 629 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pi... | 629 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
def __init__( self , *snake_case , **snak... | 629 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""... | 629 | 1 |
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,
)
SCREAMING_SNAKE_CASE__ : Dict = {
"""configu... | 629 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
a__ : A... | 629 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
... | 629 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 | 1 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 629 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer... | 629 | 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... | 629 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 629 | 1 |
SCREAMING_SNAKE_CASE__ : Tuple = """Input must be a string of 8 numbers plus letter"""
SCREAMING_SNAKE_CASE__ : str = """TRWAGMYFPDXBNJZSQVHLCKE"""
def _A ( lowerCamelCase ):
if not isinstance(lowerCamelCase , lowerCamelCase ):
a__ : ... | 629 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(... | 629 | 1 |
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE__ : Union[str, Any] = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
SCREAMING_SNAKE_CASE__ ... | 629 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : ... | 629 | 1 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
fr... | 629 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
SCREAMING_SNAKE_CASE__ : int = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": P... | 629 | 1 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __lowerCAmelCase :
_UpperCamelCase : Optional[str] = field(
default="""codeparrot/codeparrot""" ,metadata={"""help""": """Model name or path of model to be trained."""} )
_UpperCamelCase :... | 629 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(R"""([a-z\d])([A-Z])""")
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"... | 629 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
SCREAMING_SNAKE_CASE__ : Any = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.... | 629 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise Optional... | 629 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
S... | 629 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# U... | 629 | 1 |
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mod... | 629 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 629 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diff... | 629 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[int] = {"""configuration_opt""": ["""OPT_PRETRAI... | 629 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : List[str] = {
"""configuration_roberta""": ["""ROBERT... | 629 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.po... | 629 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F40... | 629 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
SCREAMING_SNAKE_CASE__ : A... | 629 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ : List[Any] = """sshlei... | 629 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
depr... | 629 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 629 | 1 |
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if index == number_of_items:
return 0
a__ : Dict = 0
a__ : Optional[int] = 0
a__ : Optional[int] = knapsack(lowerCamelCase ... | 629 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 9.80665
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volu... | 629 | 1 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__( self , snake_case = None ) -> Any:
"""simple docstring"""
a__ : Optional[int] = value
a__ : Tuple = random()
a__ : Node... | 629 | 1 |
def _A ( lowerCamelCase = 1000 ):
a__ : List[str] = -1
a__ : List[Any] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
a__ : Optional[Any] = (n * n - 2 * a * n) // (2 * n - 2 * a... | 629 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pi... | 629 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availa... | 629 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""... | 629 | 1 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _A ( ):
a__ , a__ : List[str] = 9, 14 # noqa: F841
a__ : Tuple = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 629 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
a__ : A... | 629 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ : i... | 629 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer... | 629 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""sample_size""": 3_2,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_p... | 629 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 629 | 1 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
SCREAMING_SNAKE_CASE__ : str = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
def __init__( self , *snake_case , **snake_case ... | 629 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(... | 629 | 1 |
def _A ( lowerCamelCase ):
a__ : List[str] = 0
for ch in input_str:
a__ : Optional[Any] = ord(lowerCamelCase )
a__ : Optional[int] = pow(2 , lowerCamelCase )
# If we already turned on bit for current character's unicode
... | 629 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : int = logging.get_logger(__name__)
class __lowerCAmelCase ( _UpperCamelCase ):
_UpperCamelCase : ... | 629 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __lowerCAmelCase :
_UpperCamelCase : str = field(
metadata={"""help""": ... | 629 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
SCREAMING_SNAKE_CASE__ : int = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": P... | 629 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Dict = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/mai... | 629 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE__ : List[str] = re.compile(R"""([a-z\d])([A-Z])""")
SCREAMING_SNAKE_CASE__ : Tuple = re.compile(R"... | 629 | 1 |
from math import sqrt
def _A ( lowerCamelCase ):
assert isinstance(lowerCamelCase , lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
a__ : str = True
# 0 and 1 are none primes.
if number <= 1:
a__ : Any ... | 629 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Any = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise Optional... | 629 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if n... | 629 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# U... | 629 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _A ( lowerCamelCase ):
a__ : List[str] = []
if isinstance(... | 629 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
SCREAMING_SNAKE_CASE__ : Dict = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbo... | 629 | 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 app... | 629 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( _UpperCamelCase ):
@require_torch
def _snake_case ( self ) -> str:
"""simple ... | 629 | 1 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __lowerCAmelCase ( _UpperCamelCase ):
... | 629 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 | 1 |
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncode... | 629 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.po... | 629 | 1 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor,... | 629 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE__ : Optional[Any] = logging.get_... | 629 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"""huggingface/informer-tourism-monthly""": (
... | 629 | 1 |
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, ... | 629 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
SCREAMING_SNAKE_CASE__ : List[Any] = """sshlei... | 629 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowerCAmelCase ( _UpperCamelCase ):
@staticmethod
@abstractmethod
def _snake_case ( snake_case ) -> Any:
"""simple docstring"""
raise NotImplementedError()
@abstractmeth... | 629 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
... | 629 | 1 |
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE__ : Dict = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE__ : List[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""pun... | 629 |
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 9.80665
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase = g ):
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volu... | 629 | 1 |
def _A ( ):
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def _A ( lowerCamelCase ):
a__ : Optional[int] = 1
a__ : Any = 2
while i * i <= n:
a__ : List[str] = 0
while n % i == 0:
n //= i
... | 629 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
def __init__( self , snake_case = None ) -> Any:
"""simple docstring"""
a__ : Optional[int] = value
a__ : Tuple = random()
a__ : Node... | 629 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_speech_to_text""": ["""... | 629 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pi... | 629 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 629 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"""configuration_distilbert""": [
"""... | 629 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 629 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( lowerCamelCase ):
# A local function to see if a dot lands in the circle.
def is_in_circle(lowerCamelCase , lowerCamelCase ) -> bool:
a__ : A... | 629 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : Optional[in... | 629 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientForme... | 629 | 1 |
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : str = [1]
for i in range(2 , lowerCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
a__ : Optional[int] = []
a__ : Union[str,... | 629 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
SCREAMING_SNAKE_CASE__ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer... | 629 | 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_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.g... | 629 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__... | 629 | 1 |
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