code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
UpperCamelCase__ = False
UpperCamelCase__ = True
UpperCamelCase__ = False
if __name__ == "__main__":
UpperCamelCase... | 619 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=_a ):
snake_case : Tuple = ["""speech"""]
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
requires_backends(self , ["""speech"""]... | 619 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase__ = False
class __SCREAMING_SNAKE_CASE ( unittest.T... | 619 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_t... | 619 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 619 | 1 |
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
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ ... | 619 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CA... | 619 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class __SCREAMING_SNAKE_CA... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __SCREAMING_SNAKE_CASE ( _a ):
@staticmethod
@abstractmethod
def _lowerCamelCase ( __lowerCAmelCase ):
raise NotImplementedError()
@abstractmethod
... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
UpperCamelCase__ = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( sel... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class __SCREAMING_SNAKE_CASE ( _a , _a ):
... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils imp... | 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 619 |
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 i... | 619 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAdded... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
from math import isclose, sqrt
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
UpperCamelCase__ = point_y / 4 / point_x
UpperCamelCase__ = 2 * normal_gradient / (1 + normal_gradient * normal_gradie... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase__ = "src/transformers"
# This is to make sure the t... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase__ = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2StructConfig",
"Pix2StructTextCo... | 619 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from t... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_... | 619 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 1 |
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 619 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 | 1 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
UpperCamelCase__ = HfArgumentParser(InitializationArguments)
UpperCamelCase__ = parser.parse_args()
# Load codeparrot tokenizer train... | 619 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 619 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCamelCase__ = logging.get_logger(__name__) # pylint: disable=invalid-name
def _UpperCamelCase ... | 619 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 | 1 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformer... | 619 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 619 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
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_vision, slow, t... | 619 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase__ = False
class __SCREAMING_SNAKE_CASE ( unittest.T... | 619 | 1 |
def _UpperCamelCase ():
"""simple docstring"""
UpperCamelCase__ = []
UpperCamelCase__ = 1
while len(a__ ) < 1e6:
constant.append(str(a__ ) )
i += 1
UpperCamelCase__ = """""".join(a_... | 619 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 619 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavL... | 619 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CA... | 619 | 1 |
UpperCamelCase__ = 65_521
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = 1
UpperCamelCase__ = 0
for plain_chr in plain_text:
UpperCamelCase__ = (a + ord(a__ )) % MOD... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}
try:
if not is_torch_available():
raise... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Dict = (EulerDiscreteScheduler,)
snake_case : List[Any] =... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {name: getattr(transformers, name + "Fast") for name i... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
UpperCamelCase__ = {
"gwf-440k": {
... | 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTok... | 619 |
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 i... | 619 | 1 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str,... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
import math
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
UpperCamelCase__ = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(a__ )
def _UpperCamelCase (a__ :float = 1 / 1_2345 ... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 1 |
def _UpperCamelCase (a__ :int = 100 ):
"""simple docstring"""
UpperCamelCase__ = set()
UpperCamelCase__ = 0
UpperCamelCase__ = n + 1 # maximum limit
for a in range(2 , a__ ):
for b in range(2 , ... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],... | 619 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__ = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models fo... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
W... | 619 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.... | 619 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
"to... | 619 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 619 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
UpperCamelCase__ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-cla... | 619 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 | 1 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 | 1 |
import math
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multipl... | 619 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..... | 619 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
UpperCamelCase__ = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _UpperCamelCase (a__ :str ):
"""simple do... | 619 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase__ = False
class __SCREAMING_SNAKE_CASE ( unittest.T... | 619 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
UpperCamelCase__ = "1"
UpperCamelCase__ = "0"
UpperCamelCase__ = "1"
UpperCamelCase__ = ort.SessionOptions()
UpperCamelCase__ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Creat... | 619 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 619 | 1 |
from itertools import product
def _UpperCamelCase (a__ :int , a__ :int ):
"""simple docstring"""
UpperCamelCase__ = sides_number
UpperCamelCase__ = max_face_number * dice_number
UpperCamelCase__ = [0] * (max_total + 1)
... | 619 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CA... | 619 | 1 |
import re
import string
import numpy as np
import datasets
UpperCamelCase__ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCamelCase__ = "\nArgs:\n predictions: Lis... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase__ = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tok... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = [int(a__ ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(a__ ) == 4 and all(0 <= int(a__ ) <= 254 for octet in octets )
if __name__ ... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mi... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
UpperCamelCase__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
UpperCamelCase__ = [ord(letter) for letter in string.ascii_lowercase]
Up... | 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _UpperCamelCase (a__ :List[Any] ):
"""simple docstring"""
if not is_accelerate_available():
return method
Upper... | 619 |
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 i... | 619 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
import math
def _UpperCamelCase (a__ :float , a__ :float ):
"""simple docstring"""
return math.pow(a__ , 2 ) - a
def _UpperCamelCase (a__ :float ):
"""simple docstring"""
return 2 * x
def _UpperCamelCase ... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 1 |
from __future__ import annotations
import pandas as pd
def _UpperCamelCase (a__ :list[int] , a__ :list[int] , a__ :int ):
"""simple docstring"""
UpperCamelCase__ = [0] * no_of_processes
UpperCamelCase__ = [0] * no_of_processes
... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
import os
def _UpperCamelCase ():
"""simple docstring"""
with open(os.path.dirname(a__ ) + """/grid.txt""" ) as f:
UpperCamelCase__ = [] # noqa: E741
for _ in range(20 ):
l.append([int(a__ ) fo... | 619 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
from __future__ import annotations
from math import pow, sqrt
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argu... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
A... | 619 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __SCREAMING_SNAKE_CASE :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=0.2 , __lowerCAmelCase=0.2 ):
... | 619 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
... | 619 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 619 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=_a )
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : str = field(default="""autom... | 619 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 | 1 |
from __future__ import annotations
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError("""days_between_payments must be > 0""" )
if daily_interest_rate < 0... | 619 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase__ = {"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
UpperCamelCase__ = _Laz... | 619 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional... | 619 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase__ = False
class __SCREAMING_SNAKE_CASE ( unittest.T... | 619 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 619 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 619 | 1 |
from datetime import datetime
import requests
def _UpperCamelCase (a__ :str ):
"""simple docstring"""
UpperCamelCase__ = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
UpperCamelCase__ = requests.get(base_url + url... | 619 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CA... | 619 | 1 |
import cva
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ):
if k in (0.04, 0.06):
UpperCamelCase__ = k
UpperCamelCase__ = window_size
... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
import socket
def _UpperCamelCase ():
"""simple docstring"""
UpperCamelCase__ = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
UpperCamelCase__ = socket.gethostname()
UpperCamelCase__ = 1_2312
sock.connect((... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
import onnxruntime as... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Any = ["""image_processor""", """tokenizer"""]
snake_case : Optional[int] = """CLIPI... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
return sum(e for e in range(3 , a__ ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 |
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 i... | 619 | 1 |
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 SPIECE_UNDERLINE, logging
UpperCamelCase__ = logging.get_logger(__name__)
... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird i... | 619 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDC... | 619 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
r... | 619 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
UpperCamelCase__ = 10
def _UpperCamelCase (a__ :int , a__ :int , a__ :list[int] , a__ :int ... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "Vi... | 619 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impo... | 619 | 1 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 619 |
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class __SCREAMING_SNAKE_CASE ( _a ):
snake_ca... | 619 | 1 |
from __future__ import annotations
class __SCREAMING_SNAKE_CASE :
def __init__( self , __lowerCAmelCase ):
UpperCamelCase__ = TypeError(
"""Matrices must be formed from a list of zero or more lists containing at """
"""le... | 619 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
UpperCamelCase__ = "\nimport os\n"
UpperCamelCase__ = "\ndef foo():\n import os\n return False\n"
UpperCamelCase__ = "\ndef foo():\n def bar():\n if True:\n import... | 619 | 1 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils imp... | 619 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required b... | 619 | 1 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCamelCase__ = {"UserAgent": UserAgent().random}
def _UpperCamelCase (a__ :Union[str, Any] ):
"""simple docstring"""
UpperC... | 619 |
def _UpperCamelCase (a__ :int = 1000 ):
"""simple docstring"""
UpperCamelCase__ = 2**power
UpperCamelCase__ = 0
while n:
UpperCamelCase__ , UpperCamelCase__ = r + n % 10, n // 10
return r
if __name__ == "_... | 619 | 1 |
def _UpperCamelCase ():
"""simple docstring"""
return 1
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _UpperCamelCase (a__ :int ):
"""si... | 619 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer
... | 619 | 1 |
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
f... | 619 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# sinc... | 619 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironmen... | 619 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRo... | 619 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
UpperCamelCase__ = False
class __SCREAMING_SNAKE_CASE ( unittest.T... | 619 | 1 |
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if number < 0:
raise ValueError("""number must not be negative""" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 619 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 619 | 1 |
from collections.abc import Sequence
from queue import Queue
class __SCREAMING_SNAKE_CASE :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=None , __lowerCAmelCase=None ):
UpperCamelCase__ = start
... | 619 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CA... | 619 | 1 |
import argparse
import os
import re
import packaging.version
UpperCamelCase__ = "examples/"
UpperCamelCase__ = {
"examples": (re.compile(r"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(r"^__version__\s+=\s+\"([^\"]... | 619 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMi... | 619 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING... | 619 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_b... | 619 | 1 |
from __future__ import annotations
def _UpperCamelCase (a__ :list[int] , a__ :list[int] , a__ :list[int] , a__ :list[list[str]] , a__ :int , ):
"""simple docstring"""
UpperCamelCase__ = len(a__ )
# If row is equal to the size of... | 619 |
from __future__ import annotations
from math import pi
def _UpperCamelCase (a__ :float , a__ :float , a__ :float ):
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and only one argument mu... | 619 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingfac... | 619 |
import copy
import random
from transformers import CLIPTokenizer
class __SCREAMING_SNAKE_CASE ( _a ):
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
super().__init__(*__lowerCAmelCase , **__lowerCAmelCase )
UpperCamelCase__ ... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__ = {
"configuration_encodec": [
"ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP",
"EncodecConfig",
],
"feature_extraction_encodec... | 619 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : Optional[Any] = """encoder-decoder"""
snake_case : Optio... | 619 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCamelCase__ = logging.getLogger(__name__)
if __name__ == "__main__":
Upper... | 619 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class __SCREAMING_SNAKE_CASE :
... | 619 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
"configuration_megatron_bert": ["MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegatronBertConfig"],
}
try:
if not is_torch_available():
ra... | 619 |
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 i... | 619 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCamelCase__ = "src/transformers"
UpperCamelCase__ = "doc... | 619 |
from manim import *
class __SCREAMING_SNAKE_CASE ( _a ):
def _lowerCamelCase ( self ):
UpperCamelCase__ = Rectangle(height=0.5 , width=0.5 )
UpperCamelCase__ = Rectangle(height=0.25 , width=0.25 )
UpperC... | 619 | 1 |
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