
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=1				):
	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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=0				):
	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(_UpperCamelCase					,				n_shave_prefix_segments=_UpperCamelCase				)

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

	return mapping






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=0				):
	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(_UpperCamelCase					,				n_shave_prefix_segments=_UpperCamelCase				)

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

	return mapping






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=None				):
	assert isinstance(_UpperCamelCase					,				_UpperCamelCase				), "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(_UpperCamelCase				)
			lowercase	    =       key.reshape(_UpperCamelCase				)
			lowercase	    =       value.reshape(_UpperCamelCase				)

	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_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	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(_UpperCamelCase				)
	}

	# 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(_UpperCamelCase				)
	}

	# 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(_UpperCamelCase				)
	}

	for i in range(1					,				_UpperCamelCase				):
		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(_UpperCamelCase				)
		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(
		    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				additional_replacements=[meta_path, resnet_op]					,				config=_UpperCamelCase				)

		if len(_UpperCamelCase				):
			lowercase	    =       renew_attention_paths(_UpperCamelCase				)
			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(
			    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				additional_replacements=[meta_path]					,				attention_paths_to_split=_UpperCamelCase					,				config=_UpperCamelCase					,				)

	lowercase	    =       middle_blocks[0]
	lowercase	    =       middle_blocks[1]
	lowercase	    =       middle_blocks[2]

	lowercase	    =       renew_resnet_paths(_UpperCamelCase				)
	assign_to_checkpoint(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				config=_UpperCamelCase				)

	lowercase	    =       renew_resnet_paths(_UpperCamelCase				)
	assign_to_checkpoint(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				config=_UpperCamelCase				)

	lowercase	    =       renew_attention_paths(_UpperCamelCase				)
	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(
	    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				attention_paths_to_split=_UpperCamelCase					,				config=_UpperCamelCase				)

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

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

		if len(_UpperCamelCase				) > 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(_UpperCamelCase				)
			lowercase	    =       renew_resnet_paths(_UpperCamelCase				)

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

			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(_UpperCamelCase				) == 2:
					lowercase	    =       []

			if len(_UpperCamelCase				):
				lowercase	    =       renew_attention_paths(_UpperCamelCase				)
				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(
				    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				additional_replacements=[meta_path]					,				attention_paths_to_split=to_split if any('qkv' in key for key in attentions				) else None					,				config=_UpperCamelCase					,				)
		else:
			lowercase	    =       renew_resnet_paths(_UpperCamelCase					,				n_shave_prefix_segments=1				)
			for path in resnet_0_paths:
				lowercase	    =       '.'.join(['output_blocks', str(_UpperCamelCase				), path['old']]				)
				lowercase	    =       '.'.join(['up_blocks', str(_UpperCamelCase				), 'resnets', str(_UpperCamelCase				), 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)






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor





def 		a__      (		_UpperCamelCase      :			List[Any]				):
   if "cls_token" in name:
      __lowerCamelCase    				=     name.replace('''cls_token'''			,'''vit.embeddings.cls_token'''				)
   if "mask_token" in name:
      __lowerCamelCase    				=     name.replace('''mask_token'''			,'''decoder.mask_token'''				)
   if "decoder_pos_embed" in name:
      __lowerCamelCase    				=     name.replace('''decoder_pos_embed'''			,'''decoder.decoder_pos_embed'''				)
   if "pos_embed" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''pos_embed'''			,'''vit.embeddings.position_embeddings'''				)
   if "patch_embed.proj" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.proj'''			,'''vit.embeddings.patch_embeddings.projection'''				)
   if "patch_embed.norm" in name:
      __lowerCamelCase    				=     name.replace('''patch_embed.norm'''			,'''vit.embeddings.norm'''				)
   if "decoder_blocks" in name:
      __lowerCamelCase    				=     name.replace('''decoder_blocks'''			,'''decoder.decoder_layers'''				)
   if "blocks" in name:
      __lowerCamelCase    				=     name.replace('''blocks'''			,'''vit.encoder.layer'''				)
   if "attn.proj" in name:
      __lowerCamelCase    				=     name.replace('''attn.proj'''			,'''attention.output.dense'''				)
   if "attn" in name:
      __lowerCamelCase    				=     name.replace('''attn'''			,'''attention.self'''				)
   if "norm1" in name:
      __lowerCamelCase    				=     name.replace('''norm1'''			,'''layernorm_before'''				)
   if "norm2" in name:
      __lowerCamelCase    				=     name.replace('''norm2'''			,'''layernorm_after'''				)
   if "mlp.fc1" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc1'''			,'''intermediate.dense'''				)
   if "mlp.fc2" in name:
      __lowerCamelCase    				=     name.replace('''mlp.fc2'''			,'''output.dense'''				)
   if "decoder_embed" in name:
      __lowerCamelCase    				=     name.replace('''decoder_embed'''			,'''decoder.decoder_embed'''				)
   if "decoder_norm" in name:
      __lowerCamelCase    				=     name.replace('''decoder_norm'''			,'''decoder.decoder_norm'''				)
   if "decoder_pred" in name:
      __lowerCamelCase    				=     name.replace('''decoder_pred'''			,'''decoder.decoder_pred'''				)
   if "norm.weight" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''norm.weight'''			,'''vit.layernorm.weight'''				)
   if "norm.bias" in name and "decoder" not in name:
      __lowerCamelCase    				=     name.replace('''norm.bias'''			,'''vit.layernorm.bias'''				)

   return name





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict				):
   for key in orig_state_dict.copy().keys():
      __lowerCamelCase    				=     orig_state_dict.pop(_UpperCamelCase				)

      if "qkv" in key:
         __lowerCamelCase    				=     key.split('''.'''				)
         __lowerCamelCase    				=     int(key_split[1]				)
         if "decoder_blocks" in key:
            __lowerCamelCase    				=     config.decoder_hidden_size
            __lowerCamelCase    				=     '''decoder.decoder_layers.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]
         else:
            __lowerCamelCase    				=     config.hidden_size
            __lowerCamelCase    				=     '''vit.encoder.layer.'''
            if "weight" in key:
               __lowerCamelCase    				=     val[:dim, :]
               __lowerCamelCase    				=     val[dim : dim * 2, :]
               __lowerCamelCase    				=     val[-dim:, :]
            elif "bias" in key:
               __lowerCamelCase    				=     val[:dim]
               __lowerCamelCase    				=     val[dim : dim * 2]
               __lowerCamelCase    				=     val[-dim:]

      else:
         __lowerCamelCase    				=     val

   return orig_state_dict





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     ViTMAEConfig()
   if "large" in checkpoint_url:
      __lowerCamelCase    				=     10_24
      __lowerCamelCase    				=     40_96
      __lowerCamelCase    				=     24
      __lowerCamelCase    				=     16
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     14
      __lowerCamelCase    				=     12_80
      __lowerCamelCase    				=     51_20
      __lowerCamelCase    				=     32
      __lowerCamelCase    				=     16

   __lowerCamelCase    				=     ViTMAEForPreTraining(_UpperCamelCase				)

   __lowerCamelCase    				=     torch.hub.load_state_dict_from_url(_UpperCamelCase			,map_location='''cpu'''				)['''model''']

   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)

   __lowerCamelCase    				=     convert_state_dict(_UpperCamelCase			,_UpperCamelCase				)

   model.load_state_dict(_UpperCamelCase				)
   model.eval()

   __lowerCamelCase    				=     '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				)
   __lowerCamelCase    				=     ViTMAEImageProcessor(size=config.image_size				)
   __lowerCamelCase    				=     image_processor(images=_UpperCamelCase			,return_tensors='''pt'''				)

   # forward pass
   torch.manual_seed(2				)
   __lowerCamelCase    				=     model(**_UpperCamelCase				)
   __lowerCamelCase    				=     outputs.logits

   if "large" in checkpoint_url:
      __lowerCamelCase    				=     torch.tensor(
          [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]				)
   elif "huge" in checkpoint_url:
      __lowerCamelCase    				=     torch.tensor(
          [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]				)
   else:
      __lowerCamelCase    				=     torch.tensor(
          [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]				)

   # verify logits
   assert torch.allclose(logits[0, :3, :3]			,_UpperCamelCase			,atol=1e-4				)

   print(F"""Saving model to {pytorch_dump_folder_path}"""				)
   model.save_pretrained(_UpperCamelCase				)

   print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
   image_processor.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  a_								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






