
import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      checkpoint

			_snake_case          =      {}

			_snake_case          =      vae_state_dict['encoder.conv_in.weight']
			_snake_case          =      vae_state_dict['encoder.conv_in.bias']
			_snake_case          =      vae_state_dict['encoder.conv_out.weight']
			_snake_case          =      vae_state_dict['encoder.conv_out.bias']
			_snake_case          =      vae_state_dict['encoder.norm_out.weight']
			_snake_case          =      vae_state_dict['encoder.norm_out.bias']

			_snake_case          =      vae_state_dict['decoder.conv_in.weight']
			_snake_case          =      vae_state_dict['decoder.conv_in.bias']
			_snake_case          =      vae_state_dict['decoder.conv_out.weight']
			_snake_case          =      vae_state_dict['decoder.conv_out.bias']
			_snake_case          =      vae_state_dict['decoder.norm_out.weight']
			_snake_case          =      vae_state_dict['decoder.norm_out.bias']

			_snake_case          =      vae_state_dict['quant_conv.weight']
			_snake_case          =      vae_state_dict['quant_conv.bias']
			_snake_case          =      vae_state_dict['post_quant_conv.weight']
			_snake_case          =      vae_state_dict['post_quant_conv.bias']

			# Retrieves the keys for the encoder down blocks only
			_snake_case          =      len({'.'.join(layer.split('.'			)[:3]			) for layer in vae_state_dict if 'encoder.down' in layer}			)
			_snake_case          =      {
			    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(__a			)
			}

			# Retrieves the keys for the decoder up blocks only
			_snake_case          =      len({'.'.join(layer.split('.'			)[:3]			) for layer in vae_state_dict if 'decoder.up' in layer}			)
			_snake_case          =      {
			    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(__a			)
			}

			for i in range(__a			):
						_snake_case          =      [key for key in down_blocks[i] if f'''down.{i}''' in key and f'''down.{i}.downsample''' not in key]

						if f'''encoder.down.{i}.downsample.conv.weight''' in vae_state_dict:
									_snake_case          =      vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.weight'''			)
									_snake_case          =      vae_state_dict.pop(
									    f'''encoder.down.{i}.downsample.conv.bias'''			)

						_snake_case          =      renew_vae_resnet_paths(__a			)
						_snake_case          =      {'old': f'''down.{i}.block''', 'new': f'''down_blocks.{i}.resnets'''}
						assign_to_checkpoint(__a	, __a	, __a	, additional_replacements=[meta_path]	, config=__a			)

			_snake_case          =      [key for key in vae_state_dict if 'encoder.mid.block' in key]
			_snake_case          =      2
			for i in range(1	, num_mid_res_blocks + 1			):
						_snake_case          =      [key for key in mid_resnets if f'''encoder.mid.block_{i}''' in key]

						_snake_case          =      renew_vae_resnet_paths(__a			)
						_snake_case          =      {'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(__a	, __a	, __a	, additional_replacements=[meta_path]	, config=__a			)

			_snake_case          =      [key for key in vae_state_dict if 'encoder.mid.attn' in key]
			_snake_case          =      renew_vae_attention_paths(__a			)
			_snake_case          =      {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
			assign_to_checkpoint(__a	, __a	, __a	, additional_replacements=[meta_path]	, config=__a			)
			conv_attn_to_linear(__a			)

			for i in range(__a			):
						_snake_case          =      num_up_blocks - 1 - i
						_snake_case          =      [
						    key for key in up_blocks[block_id] if f'''up.{block_id}''' in key and f'''up.{block_id}.upsample''' not in key
						]

						if f'''decoder.up.{block_id}.upsample.conv.weight''' in vae_state_dict:
									_snake_case          =      vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.weight'''
									]
									_snake_case          =      vae_state_dict[
									    f'''decoder.up.{block_id}.upsample.conv.bias'''
									]

						_snake_case          =      renew_vae_resnet_paths(__a			)
						_snake_case          =      {'old': f'''up.{block_id}.block''', 'new': f'''up_blocks.{i}.resnets'''}
						assign_to_checkpoint(__a	, __a	, __a	, additional_replacements=[meta_path]	, config=__a			)

			_snake_case          =      [key for key in vae_state_dict if 'decoder.mid.block' in key]
			_snake_case          =      2
			for i in range(1	, num_mid_res_blocks + 1			):
						_snake_case          =      [key for key in mid_resnets if f'''decoder.mid.block_{i}''' in key]

						_snake_case          =      renew_vae_resnet_paths(__a			)
						_snake_case          =      {'old': f'''mid.block_{i}''', 'new': f'''mid_block.resnets.{i - 1}'''}
						assign_to_checkpoint(__a	, __a	, __a	, additional_replacements=[meta_path]	, config=__a			)

			_snake_case          =      [key for key in vae_state_dict if 'decoder.mid.attn' in key]
			_snake_case          =      renew_vae_attention_paths(__a			)
			_snake_case          =      {'old': 'mid.attn_1', 'new': 'mid_block.attentions.0'}
			assign_to_checkpoint(__a	, __a	, __a	, additional_replacements=[meta_path]	, config=__a			)
			conv_attn_to_linear(__a			)
			return new_checkpoint






def 	a_		(	__lowercase       :     str	, __lowercase       :     str	, )			->  Any:
			# Only support V1
			_snake_case          =      requests.get(
			    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'			)
			_snake_case          =      io.BytesIO(r.content			)

			_snake_case          =      OmegaConf.load(__a			)
			_snake_case          =      512
			_snake_case          =      'cuda' if torch.cuda.is_available() else 'cpu'
			if checkpoint_path.endswith('safetensors'			):
						from safetensors import safe_open

						_snake_case          =      {}
						with safe_open(__a	, framework='pt'	, device='cpu'			) as f:
									for key in f.keys():
												_snake_case          =      f.get_tensor(__a			)
			else:
						_snake_case          =      torch.load(__a	, map_location=__a			)['state_dict']

			# Convert the VAE model.
			_snake_case          =      create_vae_diffusers_config(__a	, image_size=__a			)
			_snake_case          =      custom_convert_ldm_vae_checkpoint(__a	, __a			)

			_snake_case          =      AutoencoderKL(**__a			)
			vae.load_state_dict(__a			)
			vae.save_pretrained(__a			)


if __name__ == "__main__":
				_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()

				parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
				parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

				_lowerCamelCase	:    int          =      parser.parse_args()

				vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_snake_case          =      [
			    'encoder.version',
			    'decoder.version',
			    'model.encoder.version',
			    'model.decoder.version',
			    '_float_tensor',
			    'decoder.output_projection.weight',
			]
			for k in ignore_keys:
						state_dict.pop(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






def 	a_		(	__lowercase       :     Optional[int]	, __lowercase       :     Optional[Any]="facebook/mbart-large-en-ro"	, __lowercase       :     Union[str, Any]=False	, __lowercase       :     Optional[Any]=False			)			->  int:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model']
			remove_ignore_keys_(__lowercase			)
			_snake_case          =      state_dict['encoder.embed_tokens.weight'].shape[0]

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

			if finetuned:
						_snake_case          =      make_linear_from_emb(model.model.shared			)

			return model


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
			)
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument(
			    '''--hf_config''',
			    default='''facebook/mbart-large-cc25''',
			    type=str,
			    help='''Which huggingface architecture to use: mbart-large''',
			)
			parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
			parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			_lowerCamelCase	:    Tuple          =      convert_fairseq_mbart_checkpoint_from_disk(
			    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
			)
			model.save_pretrained(args.pytorch_dump_folder_path)