"""simple docstring"""




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      :		List[str]           =   logging.get_logger()
@dataclass
class 						__A							:
     _UpperCamelCase							:			List[Any]							=		42
     _UpperCamelCase							:			Union[str, Any]							=		field(default_factory=__UpperCAmelCase		)
     _UpperCamelCase							:			List[Any]							=		field(default_factory=__UpperCAmelCase		)






     def     __A  (		self				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       Any  			=     len(list(m.modules()  )  ) == 1 or isinstance(lowerCamelCase__				,     nn.Convad  ) or isinstance(lowerCamelCase__				,     nn.BatchNormad  )
       if has_not_submodules:
         self.traced.append(lowerCamelCase__  )






     def __call__(		self				,     a__  ):
       for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook  )  )
       self.module(lowerCamelCase__  )
       [x.remove() for x in self.handles]
       return self






     @property
     def     __A  (		self  ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda a__  : len(list(x.state_dict().keys()  )  ) > 0				,     self.traced  )  )







@dataclass
class 						__A							:
     _UpperCamelCase							:			Optional[int]							=		42
     _UpperCamelCase							:			Optional[int]							=		42
     _UpperCamelCase							:			Tuple							=		0
     _UpperCamelCase							:			Optional[Any]							=		field(default_factory=__UpperCAmelCase		)
     _UpperCamelCase							:			Union[str, Any]							=		field(default_factory=__UpperCAmelCase		)






     def __call__(		self				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     Tracker(self.dest  )(lowerCamelCase__  ).parametrized
       _lowerCAmelCase						:       str  			=     Tracker(self.src  )(lowerCamelCase__  ).parametrized

       _lowerCAmelCase						:       Dict  			=     list(filter(lambda a__  : type(lowerCamelCase__  ) not in self.src_skip				,     lowerCamelCase__  )  )
       _lowerCAmelCase						:       Dict  			=     list(filter(lambda a__  : type(lowerCamelCase__  ) not in self.dest_skip				,     lowerCamelCase__  )  )

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

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







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       Dict			,_lowerCamelCase						:       Union[str, Any] = True	)       ->      Optional[Any]:
  print(f"Converting {name}..."	)
  with torch.no_grad():
    _lowerCAmelCase						:       List[str]  			=     timm.create_model(_lowerCamelCase			,pretrained=_lowerCamelCase	).eval()
    _lowerCAmelCase						:       int  			=     ResNetForImageClassification(_lowerCamelCase	).eval()
    _lowerCAmelCase						:       Any  			=     ModuleTransfer(src=_lowerCamelCase			,dest=_lowerCamelCase	)
    _lowerCAmelCase						:       Tuple  			=     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."

  _lowerCAmelCase						:       Optional[int]  			=     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
    _lowerCAmelCase						:       int  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str			,_lowerCamelCase						:       List[Any] = None			,_lowerCamelCase						:       List[str] = True	)       ->      Union[str, Any]:
  _lowerCAmelCase						:       Optional[Any]  			=     """imagenet-1k-id2label.json"""
  _lowerCAmelCase						:       int  			=     1000
  _lowerCAmelCase						:       Dict  			=     (1, num_labels)

  _lowerCAmelCase						:       Any  			=     """huggingface/label-files"""
  _lowerCAmelCase						:       Dict  			=     num_labels
  _lowerCAmelCase						:       Optional[int]  			=     json.load(open(hf_hub_download(_lowerCamelCase			,_lowerCamelCase			,repo_type="""dataset"""	)			,"""r"""	)	)
  _lowerCAmelCase						:       Tuple  			=     {int(_lowerCamelCase	): v for k, v in idalabel.items()}

  _lowerCAmelCase						:       Optional[int]  			=     idalabel
  _lowerCAmelCase						:       Dict  			=     {v: k for k, v in idalabel.items()}

  _lowerCAmelCase						:       Dict  			=     partial(_lowerCamelCase			,num_labels=_lowerCamelCase			,idalabel=_lowerCamelCase			,labelaid=_lowerCamelCase	)

  _lowerCAmelCase						:       Tuple  			=     {
      """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      :		Tuple           =   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      :		List[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)





"""simple docstring"""






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__  :  Optional[int]						     =	logging.get_logger(__name__)
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = ["input_ids", "attention_mask"]







					def __init__(				self   :	Optional[int]    ,lowerCamelCase__   :	int="</s>"    ,lowerCamelCase__   :	str="<unk>"    ,lowerCamelCase__   :	Union[str, Any]="<pad>"    ,lowerCamelCase__   :	int=125    ,lowerCamelCase__   :	str=None    ,**lowerCamelCase__   :	Union[str, Any]    ,):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
													UpperCAmelCase__             =							[f'''<extra_id_{i}>''' for i in range(lowerCamelCase__			)]
									elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													UpperCAmelCase__             =							len(set(filter(lambda lowerCamelCase__			: bool('extra_id' in str(lowerCamelCase__			)			)    ,lowerCamelCase__			)			)			)
													if extra_tokens != extra_ids:
																	raise ValueError(
																	    f'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
																	    ' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'
																	    ' extra_ids tokens'			)

									UpperCAmelCase__             =							AddedToken(lowerCamelCase__    ,lstrip=lowerCamelCase__    ,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__    ,lowerCamelCase__			) else pad_token
									UpperCAmelCase__             =							AddedToken(lowerCamelCase__    ,lstrip=lowerCamelCase__    ,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__    ,lowerCamelCase__			) else eos_token
									UpperCAmelCase__             =							AddedToken(lowerCamelCase__    ,lstrip=lowerCamelCase__    ,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__    ,lowerCamelCase__			) else unk_token

									super().__init__(
									    eos_token=lowerCamelCase__    ,unk_token=lowerCamelCase__    ,pad_token=lowerCamelCase__    ,extra_ids=lowerCamelCase__    ,additional_special_tokens=lowerCamelCase__    ,**lowerCamelCase__    ,)

									UpperCAmelCase__             =							extra_ids

									UpperCAmelCase__             =							2**8  # utf is 8 bits

									# define special tokens dict
									UpperCAmelCase__             =							{
									    self.pad_token: 0,
									    self.eos_token: 1,
									    self.unk_token: 2,
									}
									UpperCAmelCase__             =							len(self.special_tokens_encoder			)
									UpperCAmelCase__             =							len(lowerCamelCase__			)
									for i, token in enumerate(lowerCamelCase__			):
													UpperCAmelCase__             =							self.vocab_size + i - n
									UpperCAmelCase__             =							{v: k for k, v in self.special_tokens_encoder.items()}







					@property
					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									return self._utf_vocab_size + self._num_special_tokens + self._extra_ids







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	List[int]    ,lowerCamelCase__   :	Optional[List[int]] = None    ,lowerCamelCase__   :	bool = False			):
									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=lowerCamelCase__    ,token_ids_a=lowerCamelCase__    ,already_has_special_tokens=lowerCamelCase__			)

									# normal case: some special tokens
									if token_ids_a is None:
													return ([0] * len(lowerCamelCase__			)) + [1]
									return ([0] * len(lowerCamelCase__			)) + [1] + ([0] * len(lowerCamelCase__			)) + [1]







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	List[int]			):
									if len(lowerCamelCase__			) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    f'''This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'''
													    ' eos tokens being added.'			)
													return token_ids
									else:
													return token_ids + [self.eos_token_id]







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	List[int]    ,lowerCamelCase__   :	Optional[List[int]] = None			):
									UpperCAmelCase__             =							[self.eos_token_id]

									if token_ids_a is None:
													return len(token_ids_a + eos			) * [0]
									return len(token_ids_a + eos + token_ids_a + eos			) * [0]







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	List[int]    ,lowerCamelCase__   :	Optional[List[int]] = None			):
									UpperCAmelCase__             =							self._add_eos_if_not_present(lowerCamelCase__			)
									if token_ids_a is None:
													return token_ids_a
									else:
													UpperCAmelCase__             =							self._add_eos_if_not_present(lowerCamelCase__			)
													return token_ids_a + token_ids_a







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							[chr(lowerCamelCase__			) for i in text.encode('utf-8'			)]
									return tokens







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	str			):
									if token in self.special_tokens_encoder:
													UpperCAmelCase__             =							self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
													UpperCAmelCase__             =							self.added_tokens_encoder[token]
									elif len(lowerCamelCase__			) != 1:
													UpperCAmelCase__             =							self.unk_token_id
									else:
													UpperCAmelCase__             =							ord(lowerCamelCase__			) + self._num_special_tokens
									return token_id







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]    ,lowerCamelCase__   :	Any			):
									if index in self.special_tokens_decoder:
													UpperCAmelCase__             =							self.special_tokens_decoder[index]
									else:
													UpperCAmelCase__             =							chr(index - self._num_special_tokens			)
									return token







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							b''
									for token in tokens:
													if token in self.special_tokens_decoder:
																	UpperCAmelCase__             =							self.special_tokens_decoder[token].encode('utf-8'			)
													elif token in self.added_tokens_decoder:
																	UpperCAmelCase__             =							self.special_tokens_decoder[token].encode('utf-8'			)
													elif token in self.special_tokens_encoder:
																	UpperCAmelCase__             =							token.encode('utf-8'			)
													elif token in self.added_tokens_encoder:
																	UpperCAmelCase__             =							token.encode('utf-8'			)
													else:
																	UpperCAmelCase__             =							bytes([ord(lowerCamelCase__			)]			)
													bstring += tok_string
									UpperCAmelCase__             =							bstring.decode('utf-8'    ,errors='ignore'			)
									return string







					def 		__lowerCAmelCase						(				self   :	str    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Optional[str] = None			):
									return ()



