


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=1						)     ->			Any:







 """simple docstring"""


 if n_shave_prefix_segments >= 0:
  return ".".join(path.split('''.'''						)[n_shave_prefix_segments:]						)
 else:
  return ".".join(path.split('''.'''						)[:n_shave_prefix_segments]						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=0						)     ->			str:







 """simple docstring"""


 _lowercase							      =[]
 for old_item in old_list:
  _lowercase							      =old_item.replace('''in_layers.0'''     ,						'''norm1'''						)
  _lowercase							      =new_item.replace('''in_layers.2'''     ,						'''conv1'''						)

  _lowercase							      =new_item.replace('''out_layers.0'''     ,						'''norm2'''						)
  _lowercase							      =new_item.replace('''out_layers.3'''     ,						'''conv2'''						)

  _lowercase							      =new_item.replace('''emb_layers.1'''     ,						'''time_emb_proj'''						)
  _lowercase							      =new_item.replace('''skip_connection'''     ,						'''conv_shortcut'''						)

  _lowercase							      =shave_segments(__snake_case     ,						n_shave_prefix_segments=__snake_case						)

  mapping.append({'''old''': old_item, '''new''': new_item}						)

 return mapping
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case=0						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =[]
 for old_item in old_list:
  _lowercase							      =old_item

  _lowercase							      =new_item.replace('''norm.weight'''     ,						'''group_norm.weight'''						)
  _lowercase							      =new_item.replace('''norm.bias'''     ,						'''group_norm.bias'''						)

  _lowercase							      =new_item.replace('''proj_out.weight'''     ,						'''proj_attn.weight'''						)
  _lowercase							      =new_item.replace('''proj_out.bias'''     ,						'''proj_attn.bias'''						)

  _lowercase							      =shave_segments(__snake_case     ,						n_shave_prefix_segments=__snake_case						)

  mapping.append({'''old''': old_item, '''new''': new_item}						)

 return mapping
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=None						)     ->			Any:







 """simple docstring"""


 assert isinstance(__snake_case     ,						__snake_case						), "Paths should be a list of dicts containing 'old' and 'new' keys."

 # Splits the attention layers into three variables.
 if attention_paths_to_split is not None:
  for path, path_map in attention_paths_to_split.items():
   _lowercase							      =old_checkpoint[path]
   _lowercase							      =old_tensor.shape[0] // 3

   _lowercase							      =(-1, channels) if len(old_tensor.shape						) == 3 else (-1)

   _lowercase							      =old_tensor.shape[0] // config['''num_head_channels'''] // 3

   _lowercase							      =old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]						)
   _lowercase      ,    _lowercase      ,    _lowercase							      =old_tensor.split(channels // num_heads     ,						dim=1						)

   _lowercase							      =query.reshape(__snake_case						)
   _lowercase							      =key.reshape(__snake_case						)
   _lowercase							      =value.reshape(__snake_case						)

 for path in paths:
  _lowercase							      =path['''new''']

  # These have already been assigned
  if attention_paths_to_split is not None and new_path in attention_paths_to_split:
   continue

  # Global renaming happens here
  _lowercase							      =new_path.replace('''middle_block.0'''     ,						'''mid_block.resnets.0'''						)
  _lowercase							      =new_path.replace('''middle_block.1'''     ,						'''mid_block.attentions.0'''						)
  _lowercase							      =new_path.replace('''middle_block.2'''     ,						'''mid_block.resnets.1'''						)

  if additional_replacements is not None:
   for replacement in additional_replacements:
    _lowercase							      =new_path.replace(replacement['''old''']     ,						replacement['''new''']						)

        # proj_attn.weight has to be converted from conv 1D to linear
  if "proj_attn.weight" in new_path:
   _lowercase							      =old_checkpoint[path['''old''']][:, :, 0]
  else:
   _lowercase							      =old_checkpoint[path['''old''']]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			str:







 """simple docstring"""


 _lowercase							      ={}

 _lowercase							      =checkpoint['''time_embed.0.weight''']
 _lowercase							      =checkpoint['''time_embed.0.bias''']
 _lowercase							      =checkpoint['''time_embed.2.weight''']
 _lowercase							      =checkpoint['''time_embed.2.bias''']

 _lowercase							      =checkpoint['''input_blocks.0.0.weight''']
 _lowercase							      =checkpoint['''input_blocks.0.0.bias''']

 _lowercase							      =checkpoint['''out.0.weight''']
 _lowercase							      =checkpoint['''out.0.bias''']
 _lowercase							      =checkpoint['''out.2.weight''']
 _lowercase							      =checkpoint['''out.2.bias''']

 # Retrieves the keys for the input blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''input_blocks''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 # Retrieves the keys for the middle blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''middle_block''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 # Retrieves the keys for the output blocks only
 _lowercase							      =len({'''.'''.join(layer.split('''.'''						)[:2]						) for layer in checkpoint if '''output_blocks''' in layer}						)
 _lowercase							      ={
     layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
     for layer_id in range(__snake_case						)
 }

 for i in range(1     ,						__snake_case						):
  _lowercase							      =(i - 1) // (config['''num_res_blocks'''] + 1)
  _lowercase							      =(i - 1) % (config['''num_res_blocks'''] + 1)

  _lowercase							      =[key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
  _lowercase							      =[key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

  if F"input_blocks.{i}.0.op.weight" in checkpoint:
   _lowercase							      =checkpoint[
       F"input_blocks.{i}.0.op.weight"
   ]
   _lowercase							      =checkpoint[
       F"input_blocks.{i}.0.op.bias"
   ]
   continue

  _lowercase							      =renew_resnet_paths(__snake_case						)
  _lowercase							      ={'''old''': F"input_blocks.{i}.0", '''new''': F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
  _lowercase							      ={'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
  assign_to_checkpoint(
      __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path, resnet_op]     ,						config=__snake_case						)

  if len(__snake_case						):
   _lowercase							      =renew_attention_paths(__snake_case						)
   _lowercase							      ={
       '''old''': F"input_blocks.{i}.1",
       '''new''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
   }
   _lowercase							      ={
       F"input_blocks.{i}.1.qkv.bias": {
           '''key''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
           '''query''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
           '''value''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
       },
       F"input_blocks.{i}.1.qkv.weight": {
           '''key''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
           '''query''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
           '''value''': F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
       },
   }
   assign_to_checkpoint(
       __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						attention_paths_to_split=__snake_case     ,						config=__snake_case     ,						)

 _lowercase							      =middle_blocks[0]
 _lowercase							      =middle_blocks[1]
 _lowercase							      =middle_blocks[2]

 _lowercase							      =renew_resnet_paths(__snake_case						)
 assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						config=__snake_case						)

 _lowercase							      =renew_resnet_paths(__snake_case						)
 assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						config=__snake_case						)

 _lowercase							      =renew_attention_paths(__snake_case						)
 _lowercase							      ={
     '''middle_block.1.qkv.bias''': {
         '''key''': '''mid_block.attentions.0.key.bias''',
         '''query''': '''mid_block.attentions.0.query.bias''',
         '''value''': '''mid_block.attentions.0.value.bias''',
     },
     '''middle_block.1.qkv.weight''': {
         '''key''': '''mid_block.attentions.0.key.weight''',
         '''query''': '''mid_block.attentions.0.query.weight''',
         '''value''': '''mid_block.attentions.0.value.weight''',
     },
 }
 assign_to_checkpoint(
     __snake_case     ,						__snake_case     ,						__snake_case     ,						attention_paths_to_split=__snake_case     ,						config=__snake_case						)

 for i in range(__snake_case						):
  _lowercase							      =i // (config['''num_res_blocks'''] + 1)
  _lowercase							      =i % (config['''num_res_blocks'''] + 1)
  _lowercase							      =[shave_segments(__snake_case     ,						2						) for name in output_blocks[i]]
  _lowercase							      ={}

  for layer in output_block_layers:
   _lowercase      ,    _lowercase							      =layer.split('''.'''						)[0], shave_segments(__snake_case     ,						1						)
   if layer_id in output_block_list:
    output_block_list[layer_id].append(__snake_case						)
   else:
    _lowercase							      =[layer_name]

  if len(__snake_case						) > 1:
   _lowercase							      =[key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
   _lowercase							      =[key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

   _lowercase							      =renew_resnet_paths(__snake_case						)
   _lowercase							      =renew_resnet_paths(__snake_case						)

   _lowercase							      ={'''old''': F"output_blocks.{i}.0", '''new''': F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
   assign_to_checkpoint(__snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						config=__snake_case						)

   if ["conv.weight", "conv.bias"] in output_block_list.values():
    _lowercase							      =list(output_block_list.values()						).index(['''conv.weight''', '''conv.bias''']						)
    _lowercase							      =checkpoint[
        F"output_blocks.{i}.{index}.conv.weight"
    ]
    _lowercase							      =checkpoint[
        F"output_blocks.{i}.{index}.conv.bias"
    ]

    # Clear attentions as they have been attributed above.
    if len(__snake_case						) == 2:
     _lowercase							      =[]

   if len(__snake_case						):
    _lowercase							      =renew_attention_paths(__snake_case						)
    _lowercase							      ={
        '''old''': F"output_blocks.{i}.1",
        '''new''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
    }
    _lowercase							      ={
        F"output_blocks.{i}.1.qkv.bias": {
            '''key''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
            '''query''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
            '''value''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
        },
        F"output_blocks.{i}.1.qkv.weight": {
            '''key''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
            '''query''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
            '''value''': F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
        },
    }
    assign_to_checkpoint(
        __snake_case     ,						__snake_case     ,						__snake_case     ,						additional_replacements=[meta_path]     ,						attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions						) else None     ,						config=__snake_case     ,						)
  else:
   _lowercase							      =renew_resnet_paths(__snake_case     ,						n_shave_prefix_segments=1						)
   for path in resnet_0_paths:
    _lowercase							      ='''.'''.join(['''output_blocks''', str(__snake_case						), path['''old''']]						)
    _lowercase							      ='''.'''.join(['''up_blocks''', str(__snake_case						), '''resnets''', str(__snake_case						), path['''new''']]						)

    _lowercase							      =checkpoint[old_path]

 return new_checkpoint


if __name__ == "__main__":
  UpperCAmelCase__   	=							argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )

  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the architecture.''',
  )

  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

  UpperCAmelCase__   	=							parser.parse_args()

  UpperCAmelCase__   	=							torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    UpperCAmelCase__   	=							json.loads(f.read())

  UpperCAmelCase__   	=							convert_ldm_checkpoint(checkpoint, config)

  if "ldm" in config:
    del config["ldm"]

  UpperCAmelCase__   	=							UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    UpperCAmelCase__   	=							DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
    UpperCAmelCase__   	=							VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

    UpperCAmelCase__   	=							LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)









"""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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





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





  '''simple docstring'''





  lowercase							    =       []
  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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

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

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

  lowercase							    =       lowercase							    =       CvtConfig(num_labels=__snake_case		,					idalabel=__snake_case		,					labelaid=__snake_case    )

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

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

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowercase							    =       [2, 2, 20]
    lowercase							    =       [3, 12, 16]
    lowercase							    =       [1_92, 7_68, 10_24]

  lowercase							    =       CvtForImageClassification(__snake_case    )
  lowercase							    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
  lowercase							    =       image_size
  lowercase							    =       torch.load(__snake_case		,					map_location=torch.device('cpu'    )    )

  lowercase							    =       OrderedDict()
  lowercase							    =       []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      lowercase							    =       list_of_state_dict + cls_token(__snake_case    )
    lowercase							    =       list_of_state_dict + embeddings(__snake_case    )
    for cnt in range(config.depth[idx]    ):
      lowercase							    =       list_of_state_dict + attention(__snake_case		,					__snake_case    )

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__snake_case    )
  model.save_pretrained(__snake_case    )
  image_processor.save_pretrained(__snake_case    )


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

if __name__ == "__main__":
    _UpperCamelCase		:  Optional[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.'
    )

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


