
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
		import torch







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: str	=		"char"
						UpperCamelCase_							: Optional[int]	=		"bpe"
						UpperCamelCase_							: Optional[int]	=		"wp"




lowerCamelCase__	=							(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		["image_processor", "char_tokenizer"]
						UpperCamelCase_							: List[Any]	=		"ViTImageProcessor"
						UpperCamelCase_							: Union[str, Any]	=		"MgpstrTokenizer"

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      int:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							a       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

								_UpperCamelCase						    =	tokenizer
								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""gpt2"""				)
								_UpperCamelCase						    =	AutoTokenizer.from_pretrained("""bert-base-uncased"""				)

								super().__init__(a       ,							a				)

						def __call__(			self       ,							a=None       ,							a=None       ,							a=None       ,							**a				)					->      str:


								'''simple docstring'''

								if images is None and text is None:
										raise ValueError("""You need to specify either an `images` or `text` input to process."""				)

								if images is not None:
										_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a       ,							**a				)
								if text is not None:
										_UpperCamelCase						    =	self.char_tokenizer(a       ,							return_tensors=a       ,							**a				)

								if text is None:
										return inputs
								elif images is None:
										return encodings
								else:
										_UpperCamelCase						    =	encodings["""input_ids"""]
										return inputs

						def  A_				(			self       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	sequences
								_UpperCamelCase						    =	char_preds.size(0				)

								_UpperCamelCase    ,	_UpperCamelCase						    =	self._decode_helper(a       ,							"""char"""				)
								_UpperCamelCase    ,	_UpperCamelCase						    =	self._decode_helper(a       ,							"""bpe"""				)
								_UpperCamelCase    ,	_UpperCamelCase						    =	self._decode_helper(a       ,							"""wp"""				)

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	[]
								for i in range(a				):
										_UpperCamelCase						    =	[char_scores[i], bpe_scores[i], wp_scores[i]]
										_UpperCamelCase						    =	[char_strs[i], bpe_strs[i], wp_strs[i]]
										_UpperCamelCase						    =	scores.index(max(a				)				)
										final_strs.append(strs[max_score_index]				)
										final_scores.append(scores[max_score_index]				)

								_UpperCamelCase						    =	{}
								_UpperCamelCase						    =	final_strs
								_UpperCamelCase						    =	final_scores
								_UpperCamelCase						    =	char_strs
								_UpperCamelCase						    =	bpe_strs
								_UpperCamelCase						    =	wp_strs
								return out

						def  A_				(			self       ,							a       ,							a				)					->      List[str]:


								'''simple docstring'''

								if format == DecodeType.CHARACTER:
										_UpperCamelCase						    =	self.char_decode
										_UpperCamelCase						    =	1
										_UpperCamelCase						    =	"""[s]"""
								elif format == DecodeType.BPE:
										_UpperCamelCase						    =	self.bpe_decode
										_UpperCamelCase						    =	2
										_UpperCamelCase						    =	"""#"""
								elif format == DecodeType.WORDPIECE:
										_UpperCamelCase						    =	self.wp_decode
										_UpperCamelCase						    =	1_02
										_UpperCamelCase						    =	"""[SEP]"""
								else:
										raise ValueError(F'Format {format} is not supported.'				)

								_UpperCamelCase    ,	_UpperCamelCase						    =	[], []
								_UpperCamelCase						    =	pred_logits.size(0				)
								_UpperCamelCase						    =	pred_logits.size(1				)
								_UpperCamelCase    ,	_UpperCamelCase						    =	pred_logits.topk(1       ,							dim=-1       ,							largest=a       ,							sorted=a				)
								_UpperCamelCase						    =	preds_index.view(-1       ,							a				)[:, 1:]
								_UpperCamelCase						    =	decoder(a				)
								_UpperCamelCase    ,	_UpperCamelCase						    =	torch.nn.functional.softmax(a       ,							dim=2				).max(dim=2				)
								_UpperCamelCase						    =	preds_max_prob[:, 1:]

								for index in range(a				):
										_UpperCamelCase						    =	preds_str[index].find(a				)
										_UpperCamelCase						    =	preds_str[index][:pred_eos]
										_UpperCamelCase						    =	preds_index[index].cpu().tolist()
										_UpperCamelCase						    =	pred_index.index(a				) if eos_token in pred_index else -1
										_UpperCamelCase						    =	preds_max_prob[index][: pred_eos_index + 1]
										_UpperCamelCase						    =	pred_max_prob.cumprod(dim=0				)[-1] if pred_max_prob.nelement() != 0 else 0.0
										dec_strs.append(a				)
										conf_scores.append(a				)

								return dec_strs, conf_scores

						def  A_				(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	[seq.replace(""" """       ,							""""""				) for seq in self.char_tokenizer.batch_decode(a				)]
								return decode_strs

						def  A_				(			self       ,							a				)					->      Tuple:


								'''simple docstring'''

								return self.bpe_tokenizer.batch_decode(a				)







						def  A_				(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[seq.replace(""" """       ,							""""""				) for seq in self.wp_tokenizer.batch_decode(a				)]
								return decode_strs





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()
lowerCamelCase__	=							logging.get_logger()







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: List[nn.Module]	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: list	=		field(default_factory=__lowercase	)

						def  A_				(			self       ,							a       ,							a       ,							a				)					->      str:


								'''simple docstring'''

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

						def __call__(			self       ,							a				)					->      Optional[int]:


								'''simple docstring'''

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







						@property
						def  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

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







@dataclass
class        lowerCAmelCase__      :
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: nn.Module
						UpperCamelCase_							: int	=		0
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)
						UpperCamelCase_							: List	=		field(default_factory=__lowercase	)

						def __call__(			self       ,							a				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	Tracker(self.dest				)(a				).parametrized
								_UpperCamelCase						    =	Tracker(self.src				)(a				).parametrized

								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.src_skip       ,							a				)				)
								_UpperCamelCase						    =	list(filter(lambda a				: type(a				) not in self.dest_skip       ,							a				)				)

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

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




def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = True )		->				Optional[Any]:

		"""simple docstring"""






		print(F'Converting {name}...' )
		with torch.no_grad():
				_UpperCamelCase						    =	timm.create_model(lowerCAmelCase						,  pretrained=lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ResNetForImageClassification(lowerCAmelCase ).eval()
				_UpperCamelCase						    =	ModuleTransfer(src=lowerCAmelCase						,  dest=lowerCAmelCase )
				_UpperCamelCase						    =	torch.randn((1, 3, 2_2_4, 2_2_4) )
				module_transfer(lowerCAmelCase )

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

		_UpperCamelCase						    =	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
				_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase = None						,  lowerCAmelCase = True )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	"""imagenet-1k-id2label.json"""
		_UpperCamelCase						    =	1_0_0_0
		_UpperCamelCase						    =	(1, num_labels)

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

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

		_UpperCamelCase						    =	partial(lowerCAmelCase						,  num_labels=lowerCAmelCase						,  idalabel=lowerCAmelCase						,  labelaid=lowerCAmelCase )

		_UpperCamelCase						    =	{
		    """resnet18""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet26""": ImageNetPreTrainedConfig(
		        depths=[2, 2, 2, 2]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet34""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[6_4, 1_2_8, 2_5_6, 5_1_2]						,  layer_type="""basic""" ),
		    """resnet50""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet101""": ImageNetPreTrainedConfig(
		        depths=[3, 4, 2_3, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  layer_type="""bottleneck""" ),
		    """resnet152""": ImageNetPreTrainedConfig(
		        depths=[3, 8, 3_6, 3]						,  hidden_sizes=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]						,  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__":
		lowerCamelCase__	=							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.",
		)

		lowerCamelCase__	=							parser.parse_args()
		lowerCamelCase__	=							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)



