
"""simple docstring"""




import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Any   			=  []
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
          f'''stage{idx}.patch_embed.proj.weight''',
      )    )
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
          f'''stage{idx}.patch_embed.proj.bias''',
      )    )
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
          f'''stage{idx}.patch_embed.norm.weight''',
      )    )
  embed.append(
      (
          f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
          f'''stage{idx}.patch_embed.norm.bias''',
      )    )
  return embed




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  []
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
          f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
          f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
      )    )
  attention_weights.append(
      (
          f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
          f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
      )    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''')    )
  attention_weights.append(
      (f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''')    )
  return attention_weights




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      Tuple   			=  []
  token.append((f'''cvt.encoder.stages.{idx}.cls_token''', '''stage2.cls_token''')    )
  return token




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      int   			=  []
  head.append(('''layernorm.weight''', '''norm.weight''')    )
  head.append(('''layernorm.bias''', '''norm.bias''')    )
  head.append(('''classifier.weight''', '''head.weight''')    )
  head.append(('''classifier.bias''', '''head.bias''')    )
  return head




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      List[str]   			=  """imagenet-1k-id2label.json"""
  _lowercase:      Optional[int]   			=  1_000

  _lowercase:      Union[str, Any]   			=  """huggingface/label-files"""
  _lowercase:      Optional[Any]   			=  num_labels
  _lowercase:      Optional[Any]   			=  json.load(open(cached_download(hf_hub_url(A__			,					A__			,					repo_type='''dataset'''    )    )			,					'''r'''    )    )
  _lowercase:      str   			=  {int(A__    ): v for k, v in idalabel.items()}

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

  _lowercase:      Optional[Any]   			=  CvtConfig(num_labels=A__			,					idalabel=A__			,					labelaid=A__    )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('''/'''			,					1    )[-1][4:6] == "13":
    _lowercase:      Dict   			=  [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('''/'''			,					1    )[-1][4:6] == "21":
    _lowercase:      Any   			=  [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    _lowercase:      Optional[Any]   			=  [2, 2, 20]
    _lowercase:      Optional[Any]   			=  [3, 12, 16]
    _lowercase:      Dict   			=  [192, 768, 1_024]

  _lowercase:      Any   			=  CvtForImageClassification(A__    )
  _lowercase:      Optional[int]   			=  AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''    )
  _lowercase:      Tuple   			=  image_size
  _lowercase:      Optional[Any]   			=  torch.load(A__			,					map_location=torch.device('''cpu'''    )    )

  _lowercase:      List[Any]   			=  OrderedDict()
  _lowercase:      Union[str, Any]   			=  []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      _lowercase:      List[Any]   			=  list_of_state_dict + cls_token(A__    )
    _lowercase:      Any   			=  list_of_state_dict + embeddings(A__    )
    for cnt in range(config.depth[idx]    ):
      _lowercase:      Union[str, Any]   			=  list_of_state_dict + attention(A__			,					A__    )

  _lowercase:      Optional[Any]   			=  list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(A__    )
  for i in range(len(A__    )    ):
    _lowercase:      str   			=  original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(A__    )
  model.save_pretrained(A__    )
  image_processor.save_pretrained(A__    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
  A__ :       List[Any]				= argparse.ArgumentParser()
  parser.add_argument(
      '--cvt_model',
      default='cvt-w24',
      type=str,
      help='Name of the cvt model you\'d like to convert.',
  )
  parser.add_argument(
      '--image_size',
      default=3_8_4,
      type=int,
      help='Input Image Size',
  )
  parser.add_argument(
      '--cvt_file_name',
      default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
      type=str,
      help='Input Image Size',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
  )

  A__ :       Tuple				= parser.parse_args()
  convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




from dataclasses import dataclass, field
from typing import Optional

from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser





@dataclass
class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       _a   							=							field(
           metadata={"help": "The output directory where the model will be written."}						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The encoder model checkpoint for weights initialization."
                   "Don't set if you want to train an encoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           metadata={
               "help": (
                   "The decoder model checkpoint for weights initialization."
                   "Don't set if you want to train a decoder model from scratch."
               )
           }						,       )
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained encoder config name or path if not the same as encoder_model_name"}						)
       _a   							=							field(
           default=a__						,       metadata={"help": "Pretrained decoder config name or path if not the same as decoder_model_name"}						)







def    __lowerCamelCase   (      )     ->    Optional[Any]:
   lowerCamelCase_						:   List[Any]		= HfArgumentParser((ModelArguments,)					)
   ((lowerCamelCase_),	)						:   List[Any]		= parser.parse_args_into_dataclasses()

   # Load pretrained model and tokenizer

   # Use explicit specified encoder config
   if model_args.encoder_config_name:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.encoder_config_name					)
   # Use pretrained encoder model's config
   else:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.encoder_model_name_or_path					)

   # Use explicit specified decoder config
   if model_args.decoder_config_name:
      lowerCamelCase_						:   Dict		= AutoConfig.from_pretrained(model_args.decoder_config_name					)
   # Use pretrained decoder model's config
   else:
      lowerCamelCase_						:   Any		= AutoConfig.from_pretrained(model_args.decoder_model_name_or_path					)

   # necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
   lowerCamelCase_						:   Optional[Any]		= True
   lowerCamelCase_						:   List[Any]		= True

   lowerCamelCase_						:   List[Any]		= FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
       encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path						,						decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path						,						encoder_config=A__						,						decoder_config=A__						,						)

   # GPT2 only has bos/eos tokens but not decoder_start/pad tokens
   lowerCamelCase_						:   Tuple		= decoder_config.decoder_start_token_id
   lowerCamelCase_						:   int		= decoder_config.pad_token_id
   if decoder_start_token_id is None:
      lowerCamelCase_						:   Any		= decoder_config.bos_token_id
   if pad_token_id is None:
      lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id

   # This is necessary to make Flax's generate() work
   lowerCamelCase_						:   List[str]		= decoder_config.eos_token_id
   lowerCamelCase_						:   Tuple		= decoder_start_token_id
   lowerCamelCase_						:   Optional[Any]		= pad_token_id

   lowerCamelCase_						:   str		= AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path					)

   lowerCamelCase_						:   Any		= AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path					)
   lowerCamelCase_						:   Dict		= tokenizer.convert_ids_to_tokens(model.config.pad_token_id					)

   model.save_pretrained(model_args.output_dir					)
   image_processor.save_pretrained(model_args.output_dir					)
   tokenizer.save_pretrained(model_args.output_dir					)


if __name__ == "__main__":
      main()





