code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def A (__A : str , __A : int ) -> list:
"""simple docstring"""
UpperCAmelCase_ = word.split()
def justify(__A : list , __A : int , __A : int ) -> str:
UpperCAmelCase_ = ... | 51 |
snake_case_ : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingfa... | 51 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.rou... | 51 |
from datetime import datetime
import requests
def A (__A : str ) -> bytes:
"""simple docstring"""
UpperCAmelCase_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase_ = r... | 51 | 1 |
import baseaa
def A (__A : str ) -> bytes:
"""simple docstring"""
return baseaa.baaencode(string.encode('''utf-8''' ) )
def A (__A : bytes ) -> str:
"""simple docstring"""
retu... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b... | 51 | 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 __snake_case ( a ):
U... | 51 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 | 1 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def A (__A : str , __A : float | Decimal , __A : float = 10**-10 ) -> float:
"""simple docstring"""
Upp... | 51 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 51 | 1 |
from collections import deque
from .hash_table import HashTable
class __snake_case ( a ):
def __init__( self : Union[str, Any] , *_snake_case : List[str] , **_snake_case : Tuple):
"""simple docstring"""
s... | 51 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A (__A : Optional[int] , __A : int , __A ... | 51 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 51 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from dif... | 51 | 1 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_... | 51 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging... | 51 | 1 |
from manim import *
class __snake_case ( a ):
def lowerCamelCase ( self : str):
"""simple docstring"""
UpperCAmelCase_ = Rectangle(height=0.5 , width=0.5)
UpperCAmelCase_ = Rectangle(h... | 51 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 51 | 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_funnel import FunnelTokenizer
snake_case_ : Optional[int] = logging.get_logger(__name_... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 1 |
from math import ceil, sqrt
def A (__A : int = 1000000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
Uppe... | 51 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 1 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
snake_case_ : Union[str, Any] = TypeVar("T")
class __snake_case ( Generic[T] ):
UpperCAmelCase__ : deque[T] # Cache store of keys
UpperCAmelCase__... | 51 |
def A (__A : list , __A : int , __A : int = 0 , __A : int = 0 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = right or len(__A ) - 1
if left > right:
return -1
elif list_data[le... | 51 | 1 |
from __future__ import annotations
def A (__A : int ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = str(__A )
return len(__A ) == 9 and set(__A ) == set('''123456789''' )
def A () -> ... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''llama'''
UpperCAmelCase__ : ... | 51 | 1 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def A (__A : Optional[int] , __A : Any , __A : str=1024 , __A : Tuple=1024 , __A : int=... | 51 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
snake_case_ ... | 51 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
snake_case_ : Tuple = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
... | 51 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Any = PhobertTo... | 51 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
snake_case_ : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm",... | 51 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 51 | 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 impo... | 51 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Toke... | 51 | 1 |
from __future__ import annotations
def A (__A : list[list[int]] ) -> bool:
"""simple docstring"""
UpperCAmelCase_ = len(__A )
# We need to create solution object to save path.
UpperCAmelCase_ = [[0 for... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {"configuration_mbart"... | 51 | 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
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMi... | 51 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 51 | 1 |
def A (__A : list[int] , __A : list[int] , __A : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__A ) )
def A... | 51 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 | 1 |
from collections.abc import Iterable
from typing import Any
class __snake_case :
def __init__( self : Tuple , _snake_case : int | None = None):
"""simple docstring"""
UpperCAmelCase_ = value
UpperCAme... | 51 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 51 | 1 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 | 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 req... | 51 |
snake_case_ : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingfa... | 51 | 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()... | 51 |
from datetime import datetime
import requests
def A (__A : str ) -> bytes:
"""simple docstring"""
UpperCAmelCase_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase_ = r... | 51 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b... | 51 | 1 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : int = CTRLT... | 51 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching... | 51 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 51 | 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... | 51 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A (__A : Optional[int] , __A : int , __A ... | 51 | 1 |
from __future__ import annotations
class __snake_case :
def __init__( self : Tuple , _snake_case : Dict=None):
"""simple docstring"""
UpperCAmelCase_ = data
UpperCAmelCase_ = None
... | 51 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from dif... | 51 | 1 |
from copy import deepcopy
class __snake_case :
def __init__( self : List[str] , _snake_case : list[int] | None = None , _snake_case : int | None = None):
"""simple docstring"""
if arr is None and size is not No... | 51 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging... | 51 | 1 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 51 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
snake_case_ : str = logging.get_logger(__name__)
class __snake_case ( a ):
def __init__( self : Optional[Any] , _snake_case : List[str]=None , ... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 1 |
import pytest
import datasets
# Import fixture modules as plugins
snake_case_ : Tuple = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def A (__A : Optional[int] , __A : Any ) -> Optional[int]:
"""simple ... | 51 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipe... | 51 |
def A (__A : list , __A : int , __A : int = 0 , __A : int = 0 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = right or len(__A ) - 1
if left > right:
return -1
elif list_data[le... | 51 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''llama'''
UpperCAmelCase__ : ... | 51 | 1 |
from __future__ import annotations
def A (__A : str ) -> list[int]:
"""simple docstring"""
return [ord(__A ) - 96 for elem in plain]
def A (__A : list[int] ) -> str:
"""simple docstring"""
... | 51 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
snake_case_ ... | 51 | 1 |
import math
import sys
def A (__A : int ) -> int:
"""simple docstring"""
if number != int(__A ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the val... | 51 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Any = PhobertTo... | 51 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
... | 51 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 51 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import P... | 51 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Toke... | 51 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as ... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {"configuration_mbart"... | 51 | 1 |
import os
import sys
snake_case_ : str = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceC... | 51 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 51 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerat... | 51 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 51 | 1 |
import numpy as np
def A (__A : Dict , __A : str , __A : int , __A : str , __A : int ) -> List[str]:
"""simple docstring"""
UpperCAmelCase_ = int(np.ceil((x_end - xa) / h ) )
U... | 51 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 | 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,
... | 51 |
snake_case_ : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingfa... | 51 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __snake_case ( a ):
def __init__( self : ... | 51 |
from datetime import datetime
import requests
def A (__A : str ) -> bytes:
"""simple docstring"""
UpperCAmelCase_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase_ = r... | 51 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def A (__A : Optiona... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b... | 51 | 1 |
def A (__A : bytes ) -> str:
"""simple docstring"""
return "".join([hex(__A )[2:].zfill(2 ).upper() for byte in list(__A )] )
def A (__A : str ) -> bytes:
"""simple docstring"""
... | 51 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transforme... | 51 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 51 | 1 |
import copy
import random
from transformers import CLIPTokenizer
class __snake_case ( a ):
def __init__( self : List[Any] , *_snake_case : List[str] , **_snake_case : List[str]):
"""simple docstring"""
s... | 51 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A (__A : Optional[int] , __A : int , __A ... | 51 | 1 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if i... | 51 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from dif... | 51 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __snake_case ( a ):
UpperCAm... | 51 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging... | 51 | 1 |
import argparse
import os
import re
snake_case_ : Union[str, Any] = "src/transformers/models/auto"
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
snake_case_ : Any = re.compile(r"[A-Z_]+_MAP... | 51 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 51 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
snake_case_ : Optional[Any] = 3
def A (__A : int ) -> int:
"""simple docstring"""
print('''Generating primitive root of p... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 1 |
from datetime import datetime
import requests
def A (__A : str ) -> bytes:
"""simple docstring"""
UpperCAmelCase_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase_ = r... | 51 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 1 |
import doctest
from collections import deque
import numpy as np
class __snake_case :
def __init__( self : int):
"""simple docstring"""
UpperCAmelCase_ = [2, 1, 2, -1]
UpperCAmelCase_ = [1, 2, 3, 4]... | 51 |
def A (__A : list , __A : int , __A : int = 0 , __A : int = 0 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = right or len(__A ) - 1
if left > right:
return -1
elif list_data[le... | 51 | 1 |
def A (__A : int = 1000000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = set(range(3 , __A , 2 ) )
primes.add(2 )
for p in range(3 , __A , 2 ):
if p not in primes:
... | 51 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''llama'''
UpperCAmelCase__ : ... | 51 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version... | 51 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
snake_case_ ... | 51 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ : int = get_tests_... | 51 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Any = PhobertTo... | 51 | 1 |
import unittest
import numpy as np
def A (__A : np.ndarray , __A : np.ndarray , __A : np.ndarray , __A : np.ndarray | None = None , ) -> np.ndarray:
"""simple docstring"""
UpperCAmelCase_ = np.shape(__A... | 51 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 51 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLB... | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Toke... | 51 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
SCREAMING_SNAKE_CASE_: int ={
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
t... | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {"configuration_mbart"... | 51 | 0 |
'''simple docstring'''
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils i... | 2 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 51 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(snake_case__ ) == 0:
... | 3 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoMode... | 4 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 51 | 0 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = OrderedDict(
[
... | 5 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 6 |
snake_case_ : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingfa... | 51 | 0 |
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
lowercase_ = get_tests_dir("fixtures/spiece.model")
... | 7 |
from datetime import datetime
import requests
def A (__A : str ) -> bytes:
"""simple docstring"""
UpperCAmelCase_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase_ = r... | 51 | 0 |
import json
import sys
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
with open(SCREAMING_SNAKE_CASE__ , encoding='''utf-8''' ) as f:
snake_case_ = json.load(SCREAMING_SNAKE_CASE__ )
snake_case_ = ['''<details>''', ''... | 8 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b... | 51 | 0 |
def _UpperCamelCase ( lowercase__ = 10 , lowercase__ = 1000 , lowercase__ = True ):
assert (
isinstance(lowercase__ , lowercase__ )
and isinstance(lowercase__ , lowercase__ )
and isinstance(lowercase__ , ... | 9 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 | 0 |
def lowerCAmelCase_ ( __a ) -> int:
"""simple docstring"""
if not isinstance(__a , __a ):
raise TypeError("only integers accepted as input" )
else:
lowerCamelCase__: Tuple =str(abs(__a ) )
lowerCamelCase__: Union[str, Any] =[list(__a ) for c... | 10 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 51 | 0 |
from __future__ import annotations
def _UpperCAmelCase (UpperCamelCase__ : list ):
if not nums:
raise ValueError("List is empty" )
return sum(UpperCamelCase__ ) / len(UpperCamelCase__ )
if __name__ == "__main__":
import doctest
doctest.testmod... | 11 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A (__A : Optional[int] , __A : int , __A ... | 51 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blende... | 12 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from dif... | 51 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_se... | 13 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging... | 51 | 0 |
import datasets
from .evaluate import evaluate
_lowerCamelCase : Any = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year=... | 14 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 51 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE :str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE :Tuple = {
'roberta-base':... | 15 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
return int((input_a, input_a).count(0 ) != 0 )
def __UpperCAmelCase ( ) -> None:
assert nand_gate(0 , 0 ) == 1
assert nand_... | 16 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 0 |
"""simple docstring"""
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def _A ( *UpperCamelCase_ : List[Any]) -> List[str]:
'''simple docstring'''
if not isinstance(UpperCamelCase_, UpperCamelCase_):
__lowerca... | 17 |
def A (__A : list , __A : int , __A : int = 0 , __A : int = 0 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = right or len(__A ) - 1
if left > right:
return -1
elif list_data[le... | 51 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import Big... | 18 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : int = logging.get_logger(__name__)
snake_case_ : str = {}
class __snake_case ( a ):
UpperCAmelCase__ : str = '''llama'''
UpperCAmelCase__ : ... | 51 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
__A =logging.get_logger(__name__) # pylint: disable=invalid-name
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
... | 19 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
snake_case_ ... | 51 | 0 |
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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 20 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( a , unittest.TestCase ):
UpperCAmelCase__ : Any = PhobertTo... | 51 | 0 |
import torch
from diffusers import StableDiffusionPipeline
SCREAMING_SNAKE_CASE : str = "path-to-your-trained-model"
SCREAMING_SNAKE_CASE : Optional[int] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
SCREAMING_SNAKE_CASE ... | 21 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 51 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
__SCREAMING_SNAKE_CASE :str = [
'''good first issue''',
'''feature request''',
'''wip''',
]
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
... | 22 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Toke... | 51 | 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 SCREAMING_SNAKE_CASE( datasets.BeamBasedBuilder ):
... | 23 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {"configuration_mbart"... | 51 | 0 |
class SCREAMING_SNAKE_CASE__ :
def __init__(self : Union[str, Any] , a__ : int ):
"""simple docstring"""
__snake_case = n
__snake_case = [None] * self.n
__snake_case ... | 24 |
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 51 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCAmelCase__ : ... | 25 |
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __snake_case :
pass
| 51 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visualbert-vqa-pre": "htt... | 26 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 51 | 0 |
'''simple docstring'''
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if n == 1 or not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return 0
elif n == 2:
return 1
else:
__a : int = [0, 1]
for i ... | 27 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.s... | 51 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_lowerCamelCase : Tuple = {"vocab_file": "vocab.txt", "to... | 28 |
snake_case_ : Dict = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingfa... | 51 | 0 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state impor... | 29 |
from datetime import datetime
import requests
def A (__A : str ) -> bytes:
"""simple docstring"""
UpperCAmelCase_ = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
UpperCAmelCase_ = r... | 51 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Tuple ... | 30 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : Any = logging.get_logger(__name__)
snake_case_ : Optional[Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b... | 51 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = ... | 31 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ : str = 0
snake_case_ : Union[str, Any] = [
[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... | 51 | 0 |
def SCREAMING_SNAKE_CASE_ ( __A : list ) -> list:
"""simple docstring"""
a_ : Any = len(__A )
for i in range(1 , __A ):
a_ : Optional[Any] = collection[i]
a_ : Tuple = 0
a_ ... | 32 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_visi... | 51 | 0 |
"""simple docstring"""
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
cla... | 33 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def A (__A : Optional[int] , __A : int , __A ... | 51 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_visi... | 34 |
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from dif... | 51 | 0 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acce... | 35 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging... | 51 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ... | 36 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
... | 51 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : int = """"""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase ):
... | 37 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : int = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Deber... | 51 | 0 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[Any] ) ... | 38 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Union[str, Any] = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
... | 51 | 0 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self , UpperCAmelCase ):
"""simple docstring"""
_UpperCAmelCase = val
_UpperCAmelCase = None
_UpperCAmelCase = None
def ... | 39 |
def A (__A : list , __A : int , __A : int = 0 , __A : int = 0 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = right or len(__A ) - 1
if left > right:
return -1
elif list_data[le... | 51 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.