


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

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


lowerCAmelCase__			:      Dict								=logging.get_logger(__name__)







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""input_ids""", """attention_mask"""]






							def __init__(      self			,						lowerCAmelCase__="</s>"			,						lowerCAmelCase__="<unk>"			,						lowerCAmelCase__="<pad>"			,						lowerCAmelCase__=1_2_5			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if extra_ids > 0 and additional_special_tokens is None:
													SCREAMING_SNAKE_CASE_		:     Any					=					[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
													SCREAMING_SNAKE_CASE_		:     str					=					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'       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) else pad_token
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					AddedToken(lowerCAmelCase__			,						lstrip=lowerCAmelCase__			,						rstrip=lowerCAmelCase__       ) if isinstance(lowerCAmelCase__			,						lowerCAmelCase__       ) else eos_token
										SCREAMING_SNAKE_CASE_		:     Any					=					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__			,						)

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					extra_ids

										SCREAMING_SNAKE_CASE_		:     Dict					=					2**8  # utf is 8 bits

										# define special tokens dict
										SCREAMING_SNAKE_CASE_		:     Dict[int, str]					=					{
										    self.pad_token: 0,
										    self.eos_token: 1,
										    self.unk_token: 2,
										}
										SCREAMING_SNAKE_CASE_		:     int					=					len(self.special_tokens_encoder       )
										SCREAMING_SNAKE_CASE_		:     str					=					len(lowerCAmelCase__       )
										for i, token in enumerate(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.vocab_size + i - n
										SCREAMING_SNAKE_CASE_		:     Dict[str, int]					=					{v: k for k, v in self.special_tokens_encoder.items()}






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self._utf_vocab_size + self._num_special_tokens + self._extra_ids






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False       ):





										"""simple docstring"""




										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 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										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 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[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 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self._add_eos_if_not_present(lowerCAmelCase__       )
										if token_ids_a is None:
													return token_ids_a
										else:
													SCREAMING_SNAKE_CASE_		:     Dict					=					self._add_eos_if_not_present(lowerCAmelCase__       )
													return token_ids_a + token_ids_a






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					[chr(lowerCAmelCase__       ) for i in text.encode('utf-8'       )]
										return tokens






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if token in self.special_tokens_encoder:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
													SCREAMING_SNAKE_CASE_		:     str					=					self.added_tokens_encoder[token]
										elif len(lowerCAmelCase__       ) != 1:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.unk_token_id
										else:
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					ord(lowerCAmelCase__       ) + self._num_special_tokens
										return token_id






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if index in self.special_tokens_decoder:
													SCREAMING_SNAKE_CASE_		:     Tuple					=					self.special_tokens_decoder[index]
										else:
													SCREAMING_SNAKE_CASE_		:     List[str]					=					chr(index - self._num_special_tokens       )
										return token






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__ = None       ):





										"""simple docstring"""




										return ()








'''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[Any]							=    logging.get_logger()



@dataclass
class 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			List[nn.Module]     	= field(default_factory=a			)
  __lowerCAmelCase       :			list     	= field(default_factory=a			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Tuple:


    """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_  )				->					Union[str, Any]:


    """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     snake_case_      (  self  )				->					int:


    """simple docstring"""
    # 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 							lowercase_						:

  '''simple docstring'''





  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			nn.Module
  __lowerCAmelCase       :			int     	= 0
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  __lowerCAmelCase       :			List     	= field(default_factory=a			)
  def __call__(  self ,			a_  )				->					Dict:


    """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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : str							, SCREAMING_SNAKE_CASE   : ResNetConfig							, SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : bool = True    ):
  print(f'''Converting {name}...'''    )
  with torch.no_grad():
    UpperCAmelCase    				=						timm.create_model(SCREAMING_SNAKE_CASE							, pretrained=SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ResNetForImageClassification(SCREAMING_SNAKE_CASE    ).eval()
    UpperCAmelCase    				=						ModuleTransfer(src=SCREAMING_SNAKE_CASE							, dest=SCREAMING_SNAKE_CASE    )
    UpperCAmelCase    				=						torch.randn((1, 3, 224, 224)    )
    module_transfer(SCREAMING_SNAKE_CASE    )

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

  UpperCAmelCase    				=						f'''resnet{'-'.join(name.split('resnet'    )    )}'''
  print(SCREAMING_SNAKE_CASE    )

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

    # 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=SCREAMING_SNAKE_CASE							, )

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


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Path							, SCREAMING_SNAKE_CASE   : str = None							, SCREAMING_SNAKE_CASE   : bool = True    ):
  UpperCAmelCase    				=						'imagenet-1k-id2label.json'
  UpperCAmelCase    				=						1000
  UpperCAmelCase    				=						(1, num_labels)

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

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

  UpperCAmelCase    				=						partial(SCREAMING_SNAKE_CASE							, num_labels=SCREAMING_SNAKE_CASE							, idalabel=SCREAMING_SNAKE_CASE							, labelaid=SCREAMING_SNAKE_CASE    )

  UpperCAmelCase    				=						{
      '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(SCREAMING_SNAKE_CASE							, names_to_config[model_name]							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  return config, expected_shape


if __name__ == "__main__":
  _a					:			List[str]							=    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					:			Tuple							=    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)


