


'''simple docstring'''







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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					str:
     """simple docstring"""




     __a       =   checkpoint

     __a       =   {}

     __a       =   vae_state_dict["""encoder.conv_in.weight"""]
     __a       =   vae_state_dict["""encoder.conv_in.bias"""]
     __a       =   vae_state_dict["""encoder.conv_out.weight"""]
     __a       =   vae_state_dict["""encoder.conv_out.bias"""]
     __a       =   vae_state_dict["""encoder.norm_out.weight"""]
     __a       =   vae_state_dict["""encoder.norm_out.bias"""]

     __a       =   vae_state_dict["""decoder.conv_in.weight"""]
     __a       =   vae_state_dict["""decoder.conv_in.bias"""]
     __a       =   vae_state_dict["""decoder.conv_out.weight"""]
     __a       =   vae_state_dict["""decoder.conv_out.bias"""]
     __a       =   vae_state_dict["""decoder.norm_out.weight"""]
     __a       =   vae_state_dict["""decoder.norm_out.bias"""]

     __a       =   vae_state_dict["""quant_conv.weight"""]
     __a       =   vae_state_dict["""quant_conv.bias"""]
     __a       =   vae_state_dict["""post_quant_conv.weight"""]
     __a       =   vae_state_dict["""post_quant_conv.bias"""]

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

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

     for i in range(__SCREAMING_SNAKE_CASE		):
          __a       =   [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       =   vae_state_dict.pop(
                   F'''encoder.down.{i}.downsample.conv.weight'''		)
               __a       =   vae_state_dict.pop(
                   F'''encoder.down.{i}.downsample.conv.bias'''		)

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

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

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

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

     for i in range(__SCREAMING_SNAKE_CASE		):
          __a       =   num_up_blocks - 1 - i
          __a       =   [
              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       =   vae_state_dict[
                   F'''decoder.up.{block_id}.upsample.conv.weight'''
               ]
               __a       =   vae_state_dict[
                   F'''decoder.up.{block_id}.upsample.conv.bias'''
               ]

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

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

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

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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  )				->					Union[str, Any]:
     """simple docstring"""




     __a       =   requests.get(
         """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""		)
     __a       =   io.BytesIO(r.content		)

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

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

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

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			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.')

 SCREAMING_SNAKE_CASE_      =			parser.parse_args()

 vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










'''simple docstring'''







import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


SCREAMING_SNAKE_CASE_      =			[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
SCREAMING_SNAKE_CASE_      =			[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

SCREAMING_SNAKE_CASE_      =			(
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE_      =			(
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

SCREAMING_SNAKE_CASE_      =			[
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




     for tf_name, hf_name in patterns:
          __a       =   k.replace(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     return k




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					BigBirdPegasusForConditionalGeneration:
     """simple docstring"""




     __a       =   BigBirdPegasusConfig(**__SCREAMING_SNAKE_CASE		)
     __a       =   BigBirdPegasusForConditionalGeneration(__SCREAMING_SNAKE_CASE		)
     __a       =   torch_model.state_dict()
     __a       =   {}

     # separating decoder weights
     __a       =   {k: tf_weights[k] for k in tf_weights if k.startswith("""pegasus/decoder"""		)}
     __a       =   {k: tf_weights[k] for k in tf_weights if not k.startswith("""pegasus/decoder"""		)}

     for k, v in tqdm(decoder_weights.items()				,  """tf -> hf conversion"""		):
          __a       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   DECODER_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          if new_k not in state_dict:
               raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''		)
          if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]		):
               __a       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

     for k, v in tqdm(remaining_weights.items()				,  """tf -> hf conversion"""		):
          __a       =   [k.endswith(__SCREAMING_SNAKE_CASE		) for ending in KEYS_TO_IGNORE]
          if any(__SCREAMING_SNAKE_CASE		):
               continue
          __a       =   REMAINING_PATTERNS
          __a       =   rename_state_dict_key(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''		)
          if any(True if i in k else False for i in ["""dense""", """query""", """key""", """value"""]		):
               __a       =   v.T
          __a       =   torch.from_numpy(__SCREAMING_SNAKE_CASE		)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

     __a       =   mapping["""model.embed_positions.weight"""]
     __a       =   mapping.pop("""model.embed_positions.weight"""		)
     __a ,						__a       =   torch_model.load_state_dict(__SCREAMING_SNAKE_CASE				,  strict=__SCREAMING_SNAKE_CASE		)
     __a       =   [
         k
         for k in missing
         if k
         not in [
             """final_logits_bias""",
             """model.encoder.embed_tokens.weight""",
             """model.decoder.embed_tokens.weight""",
             """lm_head.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        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					Dict:
     """simple docstring"""




     __a       =   tf.train.list_variables(__SCREAMING_SNAKE_CASE		)
     __a       =   {}
     __a       =   ["""global_step"""]
     for name, shape in tqdm(__SCREAMING_SNAKE_CASE				,  desc="""converting tf checkpoint to dict"""		):
          __a       =   any(pat in name for pat in ignore_name		)
          if skip_key:
               continue
          __a       =   tf.train.load_variable(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
          __a       =   array
     return tf_weights




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """simple docstring"""




     __a       =   get_tf_weights_as_numpy(__SCREAMING_SNAKE_CASE		)
     __a       =   convert_bigbird_pegasus(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)
     torch_model.save_pretrained(__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			argparse.ArgumentParser()
 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.')
 SCREAMING_SNAKE_CASE_      =			parser.parse_args()
 SCREAMING_SNAKE_CASE_      =			{}
 convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






