


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  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    List[str]       )			->	Optional[Any]:

       """simple docstring"""

       a_      :	Union[str, Any]							=						checkpoint

       a_      :	List[Any]							=						{}

       a_      :	Dict							=						vae_state_dict['encoder.conv_in.weight']
       a_      :	int							=						vae_state_dict['encoder.conv_in.bias']
       a_      :	List[Any]							=						vae_state_dict['encoder.conv_out.weight']
       a_      :	Any							=						vae_state_dict['encoder.conv_out.bias']
       a_      :	int							=						vae_state_dict['encoder.norm_out.weight']
       a_      :	Dict							=						vae_state_dict['encoder.norm_out.bias']

       a_      :	Optional[Any]							=						vae_state_dict['decoder.conv_in.weight']
       a_      :	int							=						vae_state_dict['decoder.conv_in.bias']
       a_      :	List[Any]							=						vae_state_dict['decoder.conv_out.weight']
       a_      :	Tuple							=						vae_state_dict['decoder.conv_out.bias']
       a_      :	str							=						vae_state_dict['decoder.norm_out.weight']
       a_      :	Optional[Any]							=						vae_state_dict['decoder.norm_out.bias']

       a_      :	Tuple							=						vae_state_dict['quant_conv.weight']
       a_      :	List[str]							=						vae_state_dict['quant_conv.bias']
       a_      :	str							=						vae_state_dict['post_quant_conv.weight']
       a_      :	int							=						vae_state_dict['post_quant_conv.bias']

       # Retrieves the keys for the encoder down blocks only
       a_      :	Dict							=						len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'encoder.down' in layer}       )
       a_      :	Union[str, Any]							=						{
           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
       a_      :	str							=						len({'.'.join(layer.split('.'       )[:3]       ) for layer in vae_state_dict if 'decoder.up' in layer}       )
       a_      :	Optional[int]							=						{
           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       ):
              a_      :	List[Any]							=						[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:
                     a_      :	int							=						vae_state_dict.pop(
                         F"""encoder.down.{i}.downsample.conv.weight"""       )
                     a_      :	Optional[Any]							=						vae_state_dict.pop(
                         F"""encoder.down.{i}.downsample.conv.bias"""       )

              a_      :	Optional[Any]							=						renew_vae_resnet_paths(__A       )
              a_      :	Union[str, Any]							=						{'old': F"""down.{i}.block""", 'new': F"""down_blocks.{i}.resnets"""}
              assign_to_checkpoint(__A						,					__A						,					__A						,					additional_replacements=[meta_path]						,					config=__A       )

       a_      :	Optional[int]							=						[key for key in vae_state_dict if 'encoder.mid.block' in key]
       a_      :	Any							=						2
       for i in range(1						,					num_mid_res_blocks + 1       ):
              a_      :	Tuple							=						[key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]

              a_      :	List[str]							=						renew_vae_resnet_paths(__A       )
              a_      :	int							=						{'old': F"""mid.block_{i}""", 'new': F"""mid_block.resnets.{i - 1}"""}
              assign_to_checkpoint(__A						,					__A						,					__A						,					additional_replacements=[meta_path]						,					config=__A       )

       a_      :	int							=						[key for key in vae_state_dict if 'encoder.mid.attn' in key]
       a_      :	Tuple							=						renew_vae_attention_paths(__A       )
       a_      :	List[str]							=						{'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       ):
              a_      :	List[str]							=						num_up_blocks - 1 - i
              a_      :	Dict							=						[
                  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:
                     a_      :	str							=						vae_state_dict[
                         F"""decoder.up.{block_id}.upsample.conv.weight"""
                     ]
                     a_      :	Union[str, Any]							=						vae_state_dict[
                         F"""decoder.up.{block_id}.upsample.conv.bias"""
                     ]

              a_      :	Optional[int]							=						renew_vae_resnet_paths(__A       )
              a_      :	int							=						{'old': F"""up.{block_id}.block""", 'new': F"""up_blocks.{i}.resnets"""}
              assign_to_checkpoint(__A						,					__A						,					__A						,					additional_replacements=[meta_path]						,					config=__A       )

       a_      :	int							=						[key for key in vae_state_dict if 'decoder.mid.block' in key]
       a_      :	List[str]							=						2
       for i in range(1						,					num_mid_res_blocks + 1       ):
              a_      :	Union[str, Any]							=						[key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]

              a_      :	Union[str, Any]							=						renew_vae_resnet_paths(__A       )
              a_      :	List[str]							=						{'old': F"""mid.block_{i}""", 'new': F"""mid_block.resnets.{i - 1}"""}
              assign_to_checkpoint(__A						,					__A						,					__A						,					additional_replacements=[meta_path]						,					config=__A       )

       a_      :	int							=						[key for key in vae_state_dict if 'decoder.mid.attn' in key]
       a_      :	Union[str, Any]							=						renew_vae_attention_paths(__A       )
       a_      :	Tuple							=						{'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  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str						,					)			->	List[str]:

       """simple docstring"""

       a_      :	int							=						requests.get(
           ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'       )
       a_      :	List[str]							=						io.BytesIO(r.content       )

       a_      :	str							=						OmegaConf.load(__A       )
       a_      :	str							=						5_12
       a_      :	str							=						'cuda' if torch.cuda.is_available() else 'cpu'
       if checkpoint_path.endswith('safetensors'       ):
              from safetensors import safe_open

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

       # Convert the VAE model.
       a_      :	List[Any]							=						create_vae_diffusers_config(__A						,					image_size=__A       )
       a_      :	str							=						custom_convert_ldm_vae_checkpoint(__A						,					__A       )

       a_      :	Optional[Any]							=						AutoencoderKL(**__A       )
       vae.load_state_dict(__A       )
       vae.save_pretrained(__A       )


if __name__ == "__main__":
   UpperCAmelCase_   :				Any						      = 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.')

   UpperCAmelCase_   :				Optional[int]						      = parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase_   :				Optional[int]						      = [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Union[str, Any]:

       """simple docstring"""

       for pegasus_name, hf_name in PATTERNS:
              a_      :	Union[str, Any]							=						k.replace(__A						,					__A       )
       return k







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    dict       )			->	PegasusForConditionalGeneration:

       """simple docstring"""

       a_      :	int							=						DEFAULTS.copy()
       cfg_kwargs.update(__A       )
       a_      :	List[str]							=						PegasusConfig(**__A       )
       a_      :	List[Any]							=						PegasusForConditionalGeneration(__A       )
       a_      :	List[str]							=						torch_model.model.state_dict()
       a_      :	Optional[int]							=						{}
       for k, v in tf_weights.items():
              a_      :	str							=						rename_state_dict_key(__A       )
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})"""       )

              if "dense" in k or "proj" in new_k:
                     a_      :	Union[str, Any]							=						v.T
              a_      :	List[str]							=						torch.tensor(__A						,					dtype=sd[new_k].dtype       )
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       a_      :	int							=						torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]       )
       a_      :	Optional[Any]							=						mapping['shared.weight']
       a_      :	Any							=						mapping['shared.weight']
       a_      :	Optional[int]							=						{k: torch.zeros_like(__A       ) for k, v in sd.items() if k.endswith('bias'       ) and k not in mapping}
       mapping.update(**__A       )
       a_							,	a_      :	Optional[Any]							=						torch_model.model.load_state_dict(__A						,					strict=__A       )
       a_      :	Optional[Any]							=						[
           k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict="./ckpt/aeslc/model.ckpt-32000"       )			->	Dict:

       """simple docstring"""

       a_      :	List[Any]							=						tf.train.list_variables(__A       )
       a_      :	Optional[Any]							=						{}
       a_      :	int							=						['Adafactor', 'global_step']
       for name, shape in tqdm(__A						,					desc='converting tf checkpoint to dict'       ):
              a_      :	Optional[int]							=						any(pat in name for pat in ignore_name       )
              if skip_key:
                     continue
              a_      :	List[str]							=						tf.train.load_variable(__A						,					__A       )
              a_      :	Optional[Any]							=						array
       return tf_weights







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    str       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Any							=						Path(__A       ).parent.name
       a_      :	Dict							=						task_specific_params[F"""summarization_{dataset}"""]['max_position_embeddings']
       a_      :	Union[str, Any]							=						PegasusTokenizer.from_pretrained('sshleifer/pegasus'						,					model_max_length=__A       )
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(__A       )

       # convert model
       a_      :	List[Any]							=						get_tf_weights_as_numpy(__A       )
       a_      :	List[str]							=						task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              a_      :	Union[str, Any]							=						task_specific_params
       a_      :	Optional[int]							=						convert_pegasus(__A						,					__A       )
       torch_model.save_pretrained(__A       )
       a_      :	Dict							=						torch_model.state_dict()
       sd.pop('model.decoder.embed_positions.weight'       )
       sd.pop('model.encoder.embed_positions.weight'       )
       torch.save(__A						,					Path(__A       ) / 'pytorch_model.bin'       )


if __name__ == "__main__":
   UpperCAmelCase_   :				Dict						      = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   UpperCAmelCase_   :				str						      = parser.parse_args()
   if args.save_dir is None:
      UpperCAmelCase_   :				str						      = Path(args.tf_ckpt_path).parent.name
      UpperCAmelCase_   :				Optional[int]						      = os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)


