

'''simple docstring'''


import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional

from torch.utils.data import ConcatDataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_WITH_LM_HEAD_MAPPING,
    AutoConfig,
    AutoModelWithLMHead,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    LineByLineTextDataset,
    LineByLineWithRefDataset,
    PreTrainedTokenizer,
    TextDataset,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__										= logging.getLogger(__name__)


lowerCAmelCase__										= list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowerCAmelCase__										= tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)



@dataclass
class 					lowercase						:
							_lowerCamelCase		:      Optional[int]=   field(
							    default=a_,					metadata={
							        "help": (
							            "The model checkpoint for weights initialization. Leave None if you want to train a model from"
							            " scratch."
							        )
							    },					)
							_lowerCamelCase		:      Optional[int]=   field(
							    default=a_,					metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a_ )},					)
							_lowerCamelCase		:      Any=   field(
							    default=a_,					metadata={"help": "Pretrained config name or path if not the same as model_name"} )
							_lowerCamelCase		:      Tuple=   field(
							    default=a_,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
							_lowerCamelCase		:      Tuple=   field(
							    default=a_,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},					)



@dataclass
class 					lowercase						:
							_lowerCamelCase		:      Optional[int]=   field(
							    default=a_,					metadata={"help": "The input training data file (a text file)."} )
							_lowerCamelCase		:      Union[str, Any]=   field(
							    default=a_,					metadata={
							        "help": (
							            "The input training data files (multiple files in glob format). "
							            "Very often splitting large files to smaller files can prevent tokenizer going out of memory"
							        )
							    },					)
							_lowerCamelCase		:      Any=   field(
							    default=a_,					metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},					)
							_lowerCamelCase		:      int=   field(
							    default=a_,					metadata={"help": "An optional input train ref data file for whole word mask in Chinese."},					)
							_lowerCamelCase		:      Tuple=   field(
							    default=a_,					metadata={"help": "An optional input eval ref data file for whole word mask in Chinese."},					)
							_lowerCamelCase		:      str=   field(
							    default=a_,					metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},					)

							_lowerCamelCase		:      Any=   field(
							    default=a_,					metadata={"help": "Train with masked-language modeling loss instead of language modeling."} )
							_lowerCamelCase		:      Any=   field(default=a_,					metadata={"help": "Whether ot not to use whole word mask."} )
							_lowerCamelCase		:      Dict=   field(
							    default=0.15,					metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} )
							_lowerCamelCase		:      Union[str, Any]=   field(
							    default=1 / 6,					metadata={
							        "help": (
							            "Ratio of length of a span of masked tokens to surrounding context length for permutation language"
							            " modeling."
							        )
							    },					)
							_lowerCamelCase		:      List[Any]=   field(
							    default=5,					metadata={"help": "Maximum length of a span of masked tokens for permutation language modeling."} )

							_lowerCamelCase		:      Optional[int]=   field(
							    default=-1,					metadata={
							        "help": (
							            "Optional input sequence length after tokenization."
							            "The training dataset will be truncated in block of this size for training."
							            "Default to the model max input length for single sentence inputs (take into account special tokens)."
							        )
							    },					)
							_lowerCamelCase		:      List[Any]=   field(
							    default=a_,					metadata={"help": "Overwrite the cached training and evaluation sets"} )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase = False   ,UpperCamelCase = None   ,)    ->    Tuple:

	'''simple docstring'''





	def _dataset(UpperCamelCase   ,UpperCamelCase=None  ):
		if args.line_by_line:
			if ref_path is not None:
				if not args.whole_word_mask or not args.mlm:
					raise ValueError('You need to set world whole masking and mlm to True for Chinese Whole Word Mask'  )
				return LineByLineWithRefDataset(
				    tokenizer=UpperCamelCase   ,file_path=UpperCamelCase   ,block_size=args.block_size   ,ref_path=UpperCamelCase   ,)

			return LineByLineTextDataset(tokenizer=UpperCamelCase   ,file_path=UpperCamelCase   ,block_size=args.block_size  )
		else:
			return TextDataset(
			    tokenizer=UpperCamelCase   ,file_path=UpperCamelCase   ,block_size=args.block_size   ,overwrite_cache=args.overwrite_cache   ,cache_dir=UpperCamelCase   ,)

	if evaluate:
		return _dataset(args.eval_data_file   ,args.eval_ref_file  )
	elif args.train_data_files:
		return ConcatDataset([_dataset(UpperCamelCase  ) for f in glob(args.train_data_files  )]  )
	else:
		return _dataset(args.train_data_file   ,args.train_ref_file  )







def 						SCREAMING_SNAKE_CASE(  )    ->    str:

	'''simple docstring'''





	UpperCAmelCase_		:      str       	=					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
	UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					parser.parse_args_into_dataclasses()

	if data_args.eval_data_file is None and training_args.do_eval:
		raise ValueError(
		    'Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '
		    'or remove the --do_eval argument.'  )
	if (
	    os.path.exists(training_args.output_dir  )
	    and os.listdir(training_args.output_dir  )
	    and training_args.do_train
	    and not training_args.overwrite_output_dir
	):
		raise ValueError(
		    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
		    ' --overwrite_output_dir to overcome.'  )

	# Setup logging
	logging.basicConfig(
	    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,datefmt='%m/%d/%Y %H:%M:%S'   ,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,)
	logger.warning(
	    'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'   ,training_args.local_rank   ,training_args.device   ,training_args.n_gpu   ,bool(training_args.local_rank != -1  )   ,training_args.fpaa   ,)
	# Set the verbosity to info of the Transformers logger (on main process only):
	if is_main_process(training_args.local_rank  ):
		transformers.utils.logging.set_verbosity_info()
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
	logger.info('Training/evaluation parameters %s'   ,UpperCamelCase  )

	# Set seed
	set_seed(training_args.seed  )

	# Load pretrained model and tokenizer
	#
	# Distributed training:
	# The .from_pretrained methods guarantee that only one local process can concurrently
	# download model & vocab.

	if model_args.config_name:
		UpperCAmelCase_		:      Tuple       	=					AutoConfig.from_pretrained(model_args.config_name   ,cache_dir=model_args.cache_dir  )
	elif model_args.model_name_or_path:
		UpperCAmelCase_		:      Tuple       	=					AutoConfig.from_pretrained(model_args.model_name_or_path   ,cache_dir=model_args.cache_dir  )
	else:
		UpperCAmelCase_		:      Tuple       	=					CONFIG_MAPPING[model_args.model_type]()
		logger.warning('You are instantiating a new config instance from scratch.'  )

	if model_args.tokenizer_name:
		UpperCAmelCase_		:      Tuple       	=					AutoTokenizer.from_pretrained(model_args.tokenizer_name   ,cache_dir=model_args.cache_dir  )
	elif model_args.model_name_or_path:
		UpperCAmelCase_		:      Dict       	=					AutoTokenizer.from_pretrained(model_args.model_name_or_path   ,cache_dir=model_args.cache_dir  )
	else:
		raise ValueError(
		    'You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'
		    ' script, save it,and load it from here, using --tokenizer_name'  )

	if model_args.model_name_or_path:
		UpperCAmelCase_		:      Dict       	=					AutoModelWithLMHead.from_pretrained(
		    model_args.model_name_or_path   ,from_tf=bool('.ckpt' in model_args.model_name_or_path  )   ,config=UpperCamelCase   ,cache_dir=model_args.cache_dir   ,)
	else:
		logger.info('Training new model from scratch'  )
		UpperCAmelCase_		:      Optional[int]       	=					AutoModelWithLMHead.from_config(UpperCamelCase  )

	model.resize_token_embeddings(len(UpperCamelCase  )  )

	if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
		raise ValueError(
		    'BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'
		    '--mlm flag (masked language modeling).'  )

	if data_args.block_size <= 0:
		UpperCAmelCase_		:      Any       	=					tokenizer.max_len
		# Our input block size will be the max possible for the model
	else:
		UpperCAmelCase_		:      List[Any]       	=					min(data_args.block_size   ,tokenizer.max_len  )

	# Get datasets

	UpperCAmelCase_		:      List[Any]       	=					(
	    get_dataset(UpperCamelCase   ,tokenizer=UpperCamelCase   ,cache_dir=model_args.cache_dir  ) if training_args.do_train else None
	)
	UpperCAmelCase_		:      Dict       	=					(
	    get_dataset(UpperCamelCase   ,tokenizer=UpperCamelCase   ,evaluate=UpperCamelCase   ,cache_dir=model_args.cache_dir  )
	    if training_args.do_eval
	    else None
	)
	if config.model_type == "xlnet":
		UpperCAmelCase_		:      Optional[int]       	=					DataCollatorForPermutationLanguageModeling(
		    tokenizer=UpperCamelCase   ,plm_probability=data_args.plm_probability   ,max_span_length=data_args.max_span_length   ,)
	else:
		if data_args.mlm and data_args.whole_word_mask:
			UpperCAmelCase_		:      str       	=					DataCollatorForWholeWordMask(
			    tokenizer=UpperCamelCase   ,mlm_probability=data_args.mlm_probability  )
		else:
			UpperCAmelCase_		:      Any       	=					DataCollatorForLanguageModeling(
			    tokenizer=UpperCamelCase   ,mlm=data_args.mlm   ,mlm_probability=data_args.mlm_probability  )

    # Initialize our Trainer
	UpperCAmelCase_		:      int       	=					Trainer(
	    model=UpperCamelCase   ,args=UpperCamelCase   ,data_collator=UpperCamelCase   ,train_dataset=UpperCamelCase   ,eval_dataset=UpperCamelCase   ,prediction_loss_only=UpperCamelCase   ,)

	# Training
	if training_args.do_train:
		UpperCAmelCase_		:      Union[str, Any]       	=					(
		    model_args.model_name_or_path
		    if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path  )
		    else None
		)
		trainer.train(model_path=UpperCamelCase  )
		trainer.save_model()
		# For convenience, we also re-save the tokenizer to the same directory,
		# so that you can share your model easily on huggingface.co/models =)
		if trainer.is_world_master():
			tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
	UpperCAmelCase_		:      Optional[int]       	=					{}
	if training_args.do_eval:
		logger.info('*** Evaluate ***'  )

		UpperCAmelCase_		:      Union[str, Any]       	=					trainer.evaluate()

		UpperCAmelCase_		:      List[Any]       	=					math.exp(eval_output['eval_loss']  )
		UpperCAmelCase_		:      Optional[int]       	=					{'perplexity': perplexity}

		UpperCAmelCase_		:      List[Any]       	=					os.path.join(training_args.output_dir   ,'eval_results_lm.txt'  )
		if trainer.is_world_master():
			with open(UpperCamelCase   ,'w'  ) as writer:
				logger.info('***** Eval results *****'  )
				for key in sorted(result.keys()  ):
					logger.info('  %s = %s'   ,UpperCamelCase   ,str(result[key]  )  )
					writer.write('%s = %s\n' % (key, str(result[key]  ))  )

		results.update(UpperCamelCase  )

	return results







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Tuple:

	'''simple docstring'''





	main()


if __name__ == "__main__":
						main()









'''simple docstring'''


from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


lowerCAmelCase__										= logging.get_logger(__name__)

# General docstring
lowerCAmelCase__										= "RegNetConfig"

# Base docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= [1, 1088, 7, 7]

# Image classification docstring
lowerCAmelCase__										= "facebook/regnet-y-040"
lowerCAmelCase__										= "tabby, tabby cat"

lowerCAmelCase__										= [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 3    ,			_snake_case = 1    ,			_snake_case = 1    ,			_snake_case = "relu"    ,			)							->				int:
								super().__init__()
								UpperCAmelCase_		:      str       	=					nn.Convad(
								    _snake_case    ,			_snake_case    ,			kernel_size=_snake_case    ,			stride=_snake_case    ,			padding=kernel_size // 2    ,			groups=_snake_case    ,			bias=_snake_case    ,			)
								UpperCAmelCase_		:      List[Any]       	=					nn.BatchNormad(_snake_case)
								UpperCAmelCase_		:      Tuple       	=					ACTaFN[activation] if activation is not None else nn.Identity()





							def 						_snake_case							(			self    ,			_snake_case)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      int       	=					self.normalization(_snake_case)
								UpperCAmelCase_		:      Optional[int]       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      Tuple       	=					RegNetConvLayer(
								    config.num_channels    ,			config.embedding_size    ,			kernel_size=3    ,			stride=2    ,			activation=config.hidden_act)
								UpperCAmelCase_		:      Optional[Any]       	=					config.num_channels





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      List[Any]       	=					pixel_values.shape[1]
								if num_channels != self.num_channels:
									raise ValueError(
									    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.')
								UpperCAmelCase_		:      Any       	=					self.embedder(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2)							->				Optional[Any]:
								super().__init__()
								UpperCAmelCase_		:      Any       	=					nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			stride=_snake_case    ,			bias=_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.BatchNormad(_snake_case)





							def 						_snake_case							(			self    ,			_snake_case)							->				Tensor:
								UpperCAmelCase_		:      Optional[Any]       	=					self.convolution(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.normalization(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				Any:
								super().__init__()

								UpperCAmelCase_		:      Tuple       	=					nn.AdaptiveAvgPoolad((1, 1))
								UpperCAmelCase_		:      int       	=					nn.Sequential(
								    nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.ReLU()    ,			nn.Convad(_snake_case    ,			_snake_case    ,			kernel_size=1)    ,			nn.Sigmoid()    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								# b c h w -> b c 1 1
								UpperCAmelCase_		:      Union[str, Any]       	=					self.pooler(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.attention(_snake_case)
								UpperCAmelCase_		:      Optional[Any]       	=					hidden_state * attention
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				str:
								super().__init__()
								UpperCAmelCase_		:      Optional[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Any       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      str       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[int]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      int       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      str       	=					hidden_state
								UpperCAmelCase_		:      List[Any]       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Dict       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 1)							->				int:
								super().__init__()
								UpperCAmelCase_		:      List[Any]       	=					in_channels != out_channels or stride != 1
								UpperCAmelCase_		:      Optional[Any]       	=					max(1    ,			out_channels // config.groups_width)
								UpperCAmelCase_		:      Optional[int]       	=					(
								    RegNetShortCut(_snake_case    ,			_snake_case    ,			stride=_snake_case) if should_apply_shortcut else nn.Identity()
								)
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=config.hidden_act)    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			groups=_snake_case    ,			activation=config.hidden_act)    ,			RegNetSELayer(_snake_case    ,			reduced_channels=int(round(in_channels / 4)))    ,			RegNetConvLayer(_snake_case    ,			_snake_case    ,			kernel_size=1    ,			activation=_snake_case)    ,			)
								UpperCAmelCase_		:      Any       	=					ACTaFN[config.hidden_act]





							def 						_snake_case							(			self    ,			_snake_case)							->				Union[str, Any]:
								UpperCAmelCase_		:      List[str]       	=					hidden_state
								UpperCAmelCase_		:      int       	=					self.layer(_snake_case)
								UpperCAmelCase_		:      Any       	=					self.shortcut(_snake_case)
								hidden_state += residual
								UpperCAmelCase_		:      Any       	=					self.activation(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case    ,			_snake_case    ,			_snake_case    ,			_snake_case = 2    ,			_snake_case = 2    ,			)							->				Optional[int]:
								super().__init__()

								UpperCAmelCase_		:      str       	=					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

								UpperCAmelCase_		:      Dict       	=					nn.Sequential(
								    # downsampling is done in the first layer with stride of 2
								    layer(
								        _snake_case    ,			_snake_case    ,			_snake_case    ,			stride=_snake_case    ,			)    ,			*[layer(_snake_case    ,			_snake_case    ,			_snake_case) for _ in range(depth - 1)]    ,			)





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict:
								UpperCAmelCase_		:      Optional[Any]       	=					self.layers(_snake_case)
								return hidden_state



class 					lowercase						(  nn.Module ):


							def __init__(			self    ,			_snake_case)							->				List[Any]:
								super().__init__()
								UpperCAmelCase_		:      List[str]       	=					nn.ModuleList([])
								# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
								self.stages.append(
								    RegNetStage(
								        _snake_case    ,			config.embedding_size    ,			config.hidden_sizes[0]    ,			stride=2 if config.downsample_in_first_stage else 1    ,			depth=config.depths[0]    ,			))
								UpperCAmelCase_		:      Dict       	=					zip(config.hidden_sizes    ,			config.hidden_sizes[1:])
								for (in_channels, out_channels), depth in zip(_snake_case    ,			config.depths[1:]):
									self.stages.append(RegNetStage(_snake_case    ,			_snake_case    ,			_snake_case    ,			depth=_snake_case))





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = False    ,			_snake_case = True)							->				BaseModelOutputWithNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					() if output_hidden_states else None

								for stage_module in self.stages:
									if output_hidden_states:
										UpperCAmelCase_		:      int       	=					hidden_states + (hidden_state,)

									UpperCAmelCase_		:      Tuple       	=					stage_module(_snake_case)

								if output_hidden_states:
									UpperCAmelCase_		:      Union[str, Any]       	=					hidden_states + (hidden_state,)

								if not return_dict:
									return tuple(v for v in [hidden_state, hidden_states] if v is not None)

								return BaseModelOutputWithNoAttention(last_hidden_state=_snake_case    ,			hidden_states=_snake_case)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   RegNetConfig
							_lowerCamelCase		:      Optional[int]=   "regnet"
							_lowerCamelCase		:      Union[str, Any]=   "pixel_values"
							_lowerCamelCase		:      List[str]=   True


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								if isinstance(_snake_case    ,			nn.Convad):
									nn.init.kaiming_normal_(module.weight    ,			mode='fan_out'    ,			nonlinearity='relu')
								elif isinstance(_snake_case    ,			(nn.BatchNormad, nn.GroupNorm)):
									nn.init.constant_(module.weight    ,			1)
									nn.init.constant_(module.bias    ,			0)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case=False)							->				List[Any]:
								if isinstance(_snake_case    ,			_snake_case):
									UpperCAmelCase_		:      str       	=					value


lowerCAmelCase__										= r"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowerCAmelCase__										= r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top.",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      str       	=					config
								UpperCAmelCase_		:      Optional[int]       	=					RegNetEmbeddings(_snake_case)
								UpperCAmelCase_		:      int       	=					RegNetEncoder(_snake_case)
								UpperCAmelCase_		:      Any       	=					nn.AdaptiveAvgPoolad((1, 1))
								# Initialize weights and apply final processing
								self.post_init()





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			modality='vision'    ,			expected_output=_EXPECTED_OUTPUT_SHAPE    ,			)
							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None    ,			_snake_case = None)							->				BaseModelOutputWithPoolingAndNoAttention:
								UpperCAmelCase_		:      List[Any]       	=					(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								UpperCAmelCase_		:      Optional[Any]       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      int       	=					self.embedder(_snake_case)

								UpperCAmelCase_		:      str       	=					self.encoder(
								    _snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					encoder_outputs[0]

								UpperCAmelCase_		:      str       	=					self.pooler(_snake_case)

								if not return_dict:
									return (last_hidden_state, pooled_output) + encoder_outputs[1:]

								return BaseModelOutputWithPoolingAndNoAttention(
								    last_hidden_state=_snake_case    ,			pooler_output=_snake_case    ,			hidden_states=encoder_outputs.hidden_states    ,			)



@add_start_docstrings(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ",					a_,					)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					lowercase						(  a_ ):


							def __init__(			self    ,			_snake_case)							->				Union[str, Any]:
								super().__init__(_snake_case)
								UpperCAmelCase_		:      List[str]       	=					config.num_labels
								UpperCAmelCase_		:      Optional[int]       	=					RegNetModel(_snake_case)
								# classification head
								UpperCAmelCase_		:      Optional[Any]       	=					nn.Sequential(
								    nn.Flatten()    ,			nn.Linear(config.hidden_sizes[-1]    ,			config.num_labels) if config.num_labels > 0 else nn.Identity()    ,			)
								# initialize weights and apply final processing
								self.post_init()





							@add_start_docstrings_to_model_forward(_snake_case)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,			output_type=_snake_case    ,			config_class=_CONFIG_FOR_DOC    ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,			)
							def 						_snake_case							(			self    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			_snake_case = None    ,			)							->				ImageClassifierOutputWithNoAttention:
								UpperCAmelCase_		:      str       	=					return_dict if return_dict is not None else self.config.use_return_dict

								UpperCAmelCase_		:      Optional[Any]       	=					self.regnet(_snake_case    ,			output_hidden_states=_snake_case    ,			return_dict=_snake_case)

								UpperCAmelCase_		:      Dict       	=					outputs.pooler_output if return_dict else outputs[1]

								UpperCAmelCase_		:      int       	=					self.classifier(_snake_case)

								UpperCAmelCase_		:      List[Any]       	=					None

								if labels is not None:
									if self.config.problem_type is None:
										if self.num_labels == 1:
											UpperCAmelCase_		:      Optional[int]       	=					'regression'
										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											UpperCAmelCase_		:      Union[str, Any]       	=					'single_label_classification'
										else:
											UpperCAmelCase_		:      Tuple       	=					'multi_label_classification'
									if self.config.problem_type == "regression":
										UpperCAmelCase_		:      int       	=					MSELoss()
										if self.num_labels == 1:
											UpperCAmelCase_		:      Dict       	=					loss_fct(logits.squeeze()    ,			labels.squeeze())
										else:
											UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(_snake_case    ,			_snake_case)
									elif self.config.problem_type == "single_label_classification":
										UpperCAmelCase_		:      Optional[int]       	=					CrossEntropyLoss()
										UpperCAmelCase_		:      Union[str, Any]       	=					loss_fct(logits.view(-1    ,			self.num_labels)    ,			labels.view(-1))
									elif self.config.problem_type == "multi_label_classification":
										UpperCAmelCase_		:      int       	=					BCEWithLogitsLoss()
										UpperCAmelCase_		:      Tuple       	=					loss_fct(_snake_case    ,			_snake_case)

								if not return_dict:
									UpperCAmelCase_		:      Union[str, Any]       	=					(logits,) + outputs[2:]
									return (loss,) + output if loss is not None else output

								return ImageClassifierOutputWithNoAttention(loss=_snake_case    ,			logits=_snake_case    ,			hidden_states=outputs.hidden_states)






