
'''simple docstring'''

import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__ :			str											=		logging.getLogger(__name__)
UpperCAmelCase__ :			Dict											=		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase__ :			Optional[int]											=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class  A  :
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
	        )
	    }	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(SCREAMING_SNAKE_CASE__     )}	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'Override some existing default config settings when a model is trained from scratch. Example: '
	            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
	        )
	    }	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,   )
	snake_case__   :bool   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'}	,   )
	snake_case__   :str   =  field(
	    default='main'	,   metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}	,   )
	snake_case__   :bool   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
	            'with private models).'
	        )
	    }	,   )





	def 				__SCREAMING_SNAKE_CASE	(       self      :							str				):


				"""simple docstring"""

				if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
							raise ValueError(
							    "--config_overrides can't be used in combination with --config_name or --model_name_or_path"				)






@dataclass
class  A  :
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The name of the dataset to use (via the datasets library).'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}     )
	snake_case__   :Optional[str]   =  field(default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The input training data file (a text file).'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'}	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'}	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'}	,   )
	snake_case__   :bool   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Overwrite the cached training and evaluation sets'}     )
	snake_case__   :Optional[int]   =  field(
	    default=5	,   metadata={
	        'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The maximum total input sequence length after tokenization. Sequences longer '
	            'than this will be truncated. Default to the max input length of the model.'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The number of processes to use for the preprocessing.'}	,   )
	snake_case__   :float   =  field(
	    default=0.15	,   metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'}     )
	snake_case__   :bool   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'Whether to pad all samples to `max_seq_length`. '
	            'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
	        )
	    }	,   )





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				if self.train_file is not None:
							lowerCAmelCase__										=				self.train_file.split("."				)[-1]
							assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
				if self.validation_file is not None:
							lowerCAmelCase__										=				self.validation_file.split("."				)[-1]
							assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."







def 	A  (   UpperCamelCase_       :  Dict    ,						UpperCamelCase_       :  str   )       ->							Dict:







			'''simple docstring'''



			with open(UpperCamelCase_    ,						"r"    ,						encoding="utf-8"   ) as f:
						lowerCAmelCase__										=				[json.loads(UpperCamelCase_   ) for line in f.read().splitlines() if (len(UpperCamelCase_   ) > 0 and not line.isspace())]
			assert len(UpperCamelCase_   ) == len(UpperCamelCase_   )

			lowerCAmelCase__										=				{c: dataset[c] for c in dataset.column_names}
			lowerCAmelCase__										=				refs
			return Dataset.from_dict(UpperCamelCase_   )







def 	A  (   )       ->							Dict:







			'''simple docstring'''



			lowerCAmelCase__										=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
			if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__										=				parser.parse_args_into_dataclasses()

			# Detecting last checkpoint.
			lowerCAmelCase__										=				None
			if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
						lowerCAmelCase__										=				get_last_checkpoint(training_args.output_dir   )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir   )   ) > 0:
									raise ValueError(
									    F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
									    "Use --overwrite_output_dir to overcome."   )
						elif last_checkpoint is not None:
									logger.info(
									    F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
									    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."   )

    # Setup logging
			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,						datefmt="%m/%d/%Y %H:%M:%S"    ,						handlers=[logging.StreamHandler(sys.stdout   )]    ,						)
			logger.setLevel(logging.INFO if is_main_process(training_args.local_rank   ) else logging.WARN   )

			# Log on each process the small summary:
			logger.warning(
			    F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
			    + F"""distributed training: {bool(training_args.local_rank != -1   )}, 16-bits training: {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 before initializing model.
			set_seed(training_args.seed   )

			# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
			# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
			# (the dataset will be downloaded automatically from the datasets Hub).
			#
			# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
			# 'text' is found. You can easily tweak this behavior (see below).
			#
			# In distributed training, the load_dataset function guarantee that only one local process can concurrently
			# download the dataset.
			if data_args.dataset_name is not None:
						# Downloading and loading a dataset from the hub.
						lowerCAmelCase__										=				load_dataset(data_args.dataset_name    ,						data_args.dataset_config_name   )
						if "validation" not in datasets.keys():
									lowerCAmelCase__										=				load_dataset(
									    data_args.dataset_name    ,						data_args.dataset_config_name    ,						split=F"""train[:{data_args.validation_split_percentage}%]"""    ,						)
									lowerCAmelCase__										=				load_dataset(
									    data_args.dataset_name    ,						data_args.dataset_config_name    ,						split=F"""train[{data_args.validation_split_percentage}%:]"""    ,						)
			else:
						lowerCAmelCase__										=				{}
						if data_args.train_file is not None:
									lowerCAmelCase__										=				data_args.train_file
						if data_args.validation_file is not None:
									lowerCAmelCase__										=				data_args.validation_file
						lowerCAmelCase__										=				data_args.train_file.split("."   )[-1]
						if extension == "txt":
									lowerCAmelCase__										=				"text"
						lowerCAmelCase__										=				load_dataset(UpperCamelCase_    ,						data_files=UpperCamelCase_   )
			# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
			# https://huggingface.co/docs/datasets/loading_datasets.html.

			# Load pretrained model and tokenizer
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			lowerCAmelCase__										=				{
			    "cache_dir": model_args.cache_dir,
			    "revision": model_args.model_revision,
			    "use_auth_token": True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						lowerCAmelCase__										=				AutoConfig.from_pretrained(model_args.config_name    ,						**UpperCamelCase_   )
			elif model_args.model_name_or_path:
						lowerCAmelCase__										=				AutoConfig.from_pretrained(model_args.model_name_or_path    ,						**UpperCamelCase_   )
			else:
						lowerCAmelCase__										=				CONFIG_MAPPING[model_args.model_type]()
						logger.warning("You are instantiating a new config instance from scratch."   )
						if model_args.config_overrides is not None:
									logger.info(F"""Overriding config: {model_args.config_overrides}"""   )
									config.update_from_string(model_args.config_overrides   )
									logger.info(F"""New config: {config}"""   )

			lowerCAmelCase__										=				{
			    "cache_dir": model_args.cache_dir,
			    "use_fast": model_args.use_fast_tokenizer,
			    "revision": model_args.model_revision,
			    "use_auth_token": True if model_args.use_auth_token else None,
			}
			if model_args.tokenizer_name:
						lowerCAmelCase__										=				AutoTokenizer.from_pretrained(model_args.tokenizer_name    ,						**UpperCamelCase_   )
			elif model_args.model_name_or_path:
						lowerCAmelCase__										=				AutoTokenizer.from_pretrained(model_args.model_name_or_path    ,						**UpperCamelCase_   )
			else:
						raise ValueError(
						    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
						    "You can do it from another script, save it, and load it from here, using --tokenizer_name."   )

			if model_args.model_name_or_path:
						lowerCAmelCase__										=				AutoModelForMaskedLM.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    ,						revision=model_args.model_revision    ,						use_auth_token=True if model_args.use_auth_token else None    ,						)
			else:
						logger.info("Training new model from scratch"   )
						lowerCAmelCase__										=				AutoModelForMaskedLM.from_config(UpperCamelCase_   )

			model.resize_token_embeddings(len(UpperCamelCase_   )   )

			# Preprocessing the datasets.
			# First we tokenize all the texts.
			if training_args.do_train:
						lowerCAmelCase__										=				datasets["train"].column_names
			else:
						lowerCAmelCase__										=				datasets["validation"].column_names
			lowerCAmelCase__										=				"text" if "text" in column_names else column_names[0]

			lowerCAmelCase__										=				"max_length" if data_args.pad_to_max_length else False

			def tokenize_function(UpperCamelCase_       :  int   ):
						# Remove empty lines
						lowerCAmelCase__										=				[line for line in examples["text"] if len(UpperCamelCase_   ) > 0 and not line.isspace()]
						return tokenizer(examples["text"]    ,						padding=UpperCamelCase_    ,						truncation=UpperCamelCase_    ,						max_length=data_args.max_seq_length   )

			lowerCAmelCase__										=				datasets.map(
			    UpperCamelCase_    ,						batched=UpperCamelCase_    ,						num_proc=data_args.preprocessing_num_workers    ,						remove_columns=[text_column_name]    ,						load_from_cache_file=not data_args.overwrite_cache    ,						)

			# Add the chinese references if provided
			if data_args.train_ref_file is not None:
						lowerCAmelCase__										=				add_chinese_references(tokenized_datasets["train"]    ,						data_args.train_ref_file   )
			if data_args.validation_ref_file is not None:
						lowerCAmelCase__										=				add_chinese_references(
						    tokenized_datasets["validation"]    ,						data_args.validation_ref_file   )
			# If we have ref files, need to avoid it removed by trainer
			lowerCAmelCase__										=				data_args.train_ref_file or data_args.validation_ref_file
			if has_ref:
						lowerCAmelCase__										=				False

			# Data collator
			# This one will take care of randomly masking the tokens.
			lowerCAmelCase__										=				DataCollatorForWholeWordMask(tokenizer=UpperCamelCase_    ,						mlm_probability=data_args.mlm_probability   )

			# Initialize our Trainer
			lowerCAmelCase__										=				Trainer(
			    model=UpperCamelCase_    ,						args=UpperCamelCase_    ,						train_dataset=tokenized_datasets["train"] if training_args.do_train else None    ,						eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None    ,						tokenizer=UpperCamelCase_    ,						data_collator=UpperCamelCase_    ,						)

			# Training
			if training_args.do_train:
						if last_checkpoint is not None:
									lowerCAmelCase__										=				last_checkpoint
						elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path   ):
									lowerCAmelCase__										=				model_args.model_name_or_path
						else:
									lowerCAmelCase__										=				None
						lowerCAmelCase__										=				trainer.train(resume_from_checkpoint=UpperCamelCase_   )
						trainer.save_model()  # Saves the tokenizer too for easy upload

						lowerCAmelCase__										=				os.path.join(training_args.output_dir    ,						"train_results.txt"   )
						if trainer.is_world_process_zero():
									with open(UpperCamelCase_    ,						"w"   ) as writer:
												logger.info("***** Train results *****"   )
												for key, value in sorted(train_result.metrics.items()   ):
															logger.info(F"""  {key} = {value}"""   )
															writer.write(F"""{key} = {value}\n"""   )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
									trainer.state.save_to_json(os.path.join(training_args.output_dir    ,						"trainer_state.json"   )   )

    # Evaluation
			lowerCAmelCase__										=				{}
			if training_args.do_eval:
						logger.info("*** Evaluate ***"   )

						lowerCAmelCase__										=				trainer.evaluate()

						lowerCAmelCase__										=				math.exp(eval_output["eval_loss"]   )
						lowerCAmelCase__										=				perplexity

						lowerCAmelCase__										=				os.path.join(training_args.output_dir    ,						"eval_results_mlm_wwm.txt"   )
						if trainer.is_world_process_zero():
									with open(UpperCamelCase_    ,						"w"   ) as writer:
												logger.info("***** Eval results *****"   )
												for key, value in sorted(results.items()   ):
															logger.info(F"""  {key} = {value}"""   )
															writer.write(F"""{key} = {value}\n"""   )

			return results







def 	A  (   UpperCamelCase_       :  Optional[int]   )       ->							str:







			'''simple docstring'''



			main()


if __name__ == "__main__":
		main()









import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A				:       int      =  logging.get_logger()






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		len(list(m.modules()    )    ) == 1 or isinstance(SCREAMING_SNAKE_CASE  ,     nn.Convad    ) or isinstance(SCREAMING_SNAKE_CASE  ,     nn.BatchNormad    )
											if has_not_submodules:
																	self.traced.append(SCREAMING_SNAKE_CASE    )


					def __call__(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						List[str]:




											'''simple docstring'''

											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook    )    )
											self.module(SCREAMING_SNAKE_CASE    )
											[x.remove() for x in self.handles]
											return self






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

											return list(filter(lambda SCREAMING_SNAKE_CASE    : len(list(x.state_dict().keys()    )    ) > 0  ,     self.traced    )    )






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						0
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )


					def __call__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

											if len(SCREAMING_SNAKE_CASE    ) != len(SCREAMING_SNAKE_CASE    ):
																	raise Exception(
																	    f'''Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE    )} operations while'''
																	    f''' destination module has {len(SCREAMING_SNAKE_CASE    )}.'''    )

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	dest_m.load_state_dict(src_m.state_dict()    )
																	if self.verbose == 1:
																							print(f'''Transfered from={src_m} to={dest_m}'''    )





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)
						with torch.no_grad():
												__snake_case				=		timm.create_model(_lowerCAmelCase   ,						pretrained=_lowerCAmelCase			).eval()
												__snake_case				=		ResNetForImageClassification(_lowerCAmelCase			).eval()
												__snake_case				=		ModuleTransfer(src=_lowerCAmelCase   ,						dest=_lowerCAmelCase			)
												__snake_case				=		torch.randn((1, 3, 224, 224)			)
												module_transfer(_lowerCAmelCase			)

						assert torch.allclose(from_model(_lowerCAmelCase			)   ,						our_model(_lowerCAmelCase			).logits			), "The model logits don't match the original one."

						__snake_case				=		F'''resnet{'-'.join(name.split('resnet'			)			)}'''
						print(_lowerCAmelCase			)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add model"   ,						use_temp_dir=_lowerCAmelCase   ,						)

												# we can use the convnext one
												__snake_case				=		AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add image processor"   ,						use_temp_dir=_lowerCAmelCase   ,						)

												print(F'''Pushed {checkpoint_name}'''			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						__snake_case				=		"imagenet-1k-id2label.json"
						__snake_case				=		1000
						__snake_case				=		(1, num_labels)

						__snake_case				=		"huggingface/label-files"
						__snake_case				=		num_labels
						__snake_case				=		json.load(open(hf_hub_download(_lowerCAmelCase   ,						_lowerCAmelCase   ,						repo_type="dataset"			)   ,						"r"			)			)
						__snake_case				=		{int(_lowerCAmelCase			): v for k, v in idalabel.items()}

						__snake_case				=		idalabel
						__snake_case				=		{v: k for k, v in idalabel.items()}

						__snake_case				=		partial(_lowerCAmelCase   ,						num_labels=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						__snake_case				=		{
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						}

						if model_name:
												convert_weight_and_push(_lowerCAmelCase   ,						names_to_config[model_name]   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						return config, expected_shape


if __name__ == "__main__":
	A				:       Optional[Any]      =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default=None,
	    type=str,
	    help=(
	        'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
	        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=Path,
	    required=True,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument(
	    '--push_to_hub',
	    default=True,
	    type=bool,
	    required=False,
	    help='If True, push model and image processor to the hub.',
	)

	A				:       Union[str, Any]      =  parser.parse_args()
	A				:       Path      =  args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





