

"""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 					a__						(						lowerCAmelCase			,	lowerCAmelCase    )	-> List[Any]:
      UpperCAmelCase__			:							List[str]							=    checkpoint

      UpperCAmelCase__			:							Tuple							=    {}

      UpperCAmelCase__			:							Tuple							=    vae_state_dict["""encoder.conv_in.weight"""]
      UpperCAmelCase__			:							Any							=    vae_state_dict["""encoder.conv_in.bias"""]
      UpperCAmelCase__			:							Optional[int]							=    vae_state_dict["""encoder.conv_out.weight"""]
      UpperCAmelCase__			:							int							=    vae_state_dict["""encoder.conv_out.bias"""]
      UpperCAmelCase__			:							int							=    vae_state_dict["""encoder.norm_out.weight"""]
      UpperCAmelCase__			:							Dict							=    vae_state_dict["""encoder.norm_out.bias"""]

      UpperCAmelCase__			:							Optional[int]							=    vae_state_dict["""decoder.conv_in.weight"""]
      UpperCAmelCase__			:							int							=    vae_state_dict["""decoder.conv_in.bias"""]
      UpperCAmelCase__			:							Optional[Any]							=    vae_state_dict["""decoder.conv_out.weight"""]
      UpperCAmelCase__			:							str							=    vae_state_dict["""decoder.conv_out.bias"""]
      UpperCAmelCase__			:							Union[str, Any]							=    vae_state_dict["""decoder.norm_out.weight"""]
      UpperCAmelCase__			:							Optional[int]							=    vae_state_dict["""decoder.norm_out.bias"""]

      UpperCAmelCase__			:							List[str]							=    vae_state_dict["""quant_conv.weight"""]
      UpperCAmelCase__			:							Dict							=    vae_state_dict["""quant_conv.bias"""]
      UpperCAmelCase__			:							Any							=    vae_state_dict["""post_quant_conv.weight"""]
      UpperCAmelCase__			:							Tuple							=    vae_state_dict["""post_quant_conv.bias"""]

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

      # Retrieves the keys for the decoder up blocks only
      UpperCAmelCase__			:							Optional[Any]							=    len({""".""".join(layer.split("""."""    )[:3]    ) for layer in vae_state_dict if """decoder.up""" in layer}    )
      UpperCAmelCase__			:							Tuple							=    {
          layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(lowerCAmelCase    )
      }

      for i in range(lowerCAmelCase    ):
            UpperCAmelCase__			:							Optional[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:
                  UpperCAmelCase__			:							List[str]							=    vae_state_dict.pop(
                      F"""encoder.down.{i}.downsample.conv.weight"""    )
                  UpperCAmelCase__			:							Optional[Any]							=    vae_state_dict.pop(
                      F"""encoder.down.{i}.downsample.conv.bias"""    )

            UpperCAmelCase__			:							Dict							=    renew_vae_resnet_paths(lowerCAmelCase    )
            UpperCAmelCase__			:							Optional[int]							=    {"""old""": F"""down.{i}.block""", """new""": F"""down_blocks.{i}.resnets"""}
            assign_to_checkpoint(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	additional_replacements=[meta_path]			,	config=lowerCAmelCase    )

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

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

      UpperCAmelCase__			:							List[str]							=    [key for key in vae_state_dict if """encoder.mid.attn""" in key]
      UpperCAmelCase__			:							List[Any]							=    renew_vae_attention_paths(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[Any]							=    {"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
      assign_to_checkpoint(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	additional_replacements=[meta_path]			,	config=lowerCAmelCase    )
      conv_attn_to_linear(lowerCAmelCase    )

      for i in range(lowerCAmelCase    ):
            UpperCAmelCase__			:							Any							=    num_up_blocks - 1 - i
            UpperCAmelCase__			:							Optional[Any]							=    [
                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:
                  UpperCAmelCase__			:							Optional[int]							=    vae_state_dict[
                      F"""decoder.up.{block_id}.upsample.conv.weight"""
                  ]
                  UpperCAmelCase__			:							str							=    vae_state_dict[
                      F"""decoder.up.{block_id}.upsample.conv.bias"""
                  ]

            UpperCAmelCase__			:							List[Any]							=    renew_vae_resnet_paths(lowerCAmelCase    )
            UpperCAmelCase__			:							Any							=    {"""old""": F"""up.{block_id}.block""", """new""": F"""up_blocks.{i}.resnets"""}
            assign_to_checkpoint(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	additional_replacements=[meta_path]			,	config=lowerCAmelCase    )

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

            UpperCAmelCase__			:							Any							=    renew_vae_resnet_paths(lowerCAmelCase    )
            UpperCAmelCase__			:							Dict							=    {"""old""": F"""mid.block_{i}""", """new""": F"""mid_block.resnets.{i - 1}"""}
            assign_to_checkpoint(lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	additional_replacements=[meta_path]			,	config=lowerCAmelCase    )

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




def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	)	-> Tuple:
      # Only support V1
      UpperCAmelCase__			:							int							=    requests.get(
          """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""    )
      UpperCAmelCase__			:							int							=    io.BytesIO(r.content    )

      UpperCAmelCase__			:							Union[str, Any]							=    OmegaConf.load(lowerCAmelCase    )
      UpperCAmelCase__			:							List[Any]							=    5_12
      UpperCAmelCase__			:							Optional[Any]							=    """cuda""" if torch.cuda.is_available() else """cpu"""
      if checkpoint_path.endswith("""safetensors"""    ):
            from safetensors import safe_open

            UpperCAmelCase__			:							Tuple							=    {}
            with safe_open(lowerCAmelCase			,	framework="""pt"""			,	device="""cpu"""    ) as f:
                  for key in f.keys():
                        UpperCAmelCase__			:							str							=    f.get_tensor(lowerCAmelCase    )
      else:
            UpperCAmelCase__			:							Optional[Any]							=    torch.load(lowerCAmelCase			,	map_location=lowerCAmelCase    )["""state_dict"""]

      # Convert the VAE model.
      UpperCAmelCase__			:							Optional[Any]							=    create_vae_diffusers_config(lowerCAmelCase			,	image_size=lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    custom_convert_ldm_vae_checkpoint(lowerCAmelCase			,	lowerCAmelCase    )

      UpperCAmelCase__			:							Dict							=    AutoencoderKL(**lowerCAmelCase    )
      vae.load_state_dict(lowerCAmelCase    )
      vae.save_pretrained(lowerCAmelCase    )


if __name__ == "__main__":
    _A							=  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.""")

    _A							=  parser.parse_args()

    vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken




@torch.no_grad()
def 					a__						(						lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase			,	lowerCAmelCase    )	-> Tuple:
      # Load configuration defined in the metadata file
      with open(lowerCAmelCase    ) as metadata_file:
            UpperCAmelCase__			:							Optional[int]							=    json.load(lowerCAmelCase    )
      UpperCAmelCase__			:							Optional[int]							=    LukeConfig(use_entity_aware_attention=lowerCAmelCase			,	**metadata["""model_config"""]    )

      # Load in the weights from the checkpoint_path
      UpperCAmelCase__			:							Dict							=    torch.load(lowerCAmelCase			,	map_location="""cpu"""    )

      # Load the entity vocab file
      UpperCAmelCase__			:							List[str]							=    load_entity_vocab(lowerCAmelCase    )

      UpperCAmelCase__			:							Union[str, Any]							=    RobertaTokenizer.from_pretrained(metadata["""model_config"""]["""bert_model_name"""]    )

      # Add special tokens to the token vocabulary for downstream tasks
      UpperCAmelCase__			:							Any							=    AddedToken("""<ent>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      UpperCAmelCase__			:							Tuple							=    AddedToken("""<ent2>"""			,	lstrip=lowerCAmelCase			,	rstrip=lowerCAmelCase    )
      tokenizer.add_special_tokens({"""additional_special_tokens""": [entity_token_a, entity_token_a]}    )
      config.vocab_size += 2

      print(F"""Saving tokenizer to {pytorch_dump_folder_path}"""    )
      tokenizer.save_pretrained(lowerCAmelCase    )
      with open(os.path.join(lowerCAmelCase			,	LukeTokenizer.vocab_files_names["""entity_vocab_file"""]    )			,	"""w"""    ) as f:
            json.dump(lowerCAmelCase			,	lowerCAmelCase    )

      UpperCAmelCase__			:							int							=    LukeTokenizer.from_pretrained(lowerCAmelCase    )

      # Initialize the embeddings of the special tokens
      UpperCAmelCase__			:							Any							=    state_dict["""embeddings.word_embeddings.weight"""]
      UpperCAmelCase__			:							Any							=    word_emb[tokenizer.convert_tokens_to_ids(["""@"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    word_emb[tokenizer.convert_tokens_to_ids(["""#"""]    )[0]].unsqueeze(0    )
      UpperCAmelCase__			:							List[Any]							=    torch.cat([word_emb, ent_emb, enta_emb]    )

      # Initialize the query layers of the entity-aware self-attention mechanism
      for layer_index in range(config.num_hidden_layers    ):
            for matrix_name in ["query.weight", "query.bias"]:
                  UpperCAmelCase__			:							Any							=    F"""encoder.layer.{layer_index}.attention.self."""
                  UpperCAmelCase__			:							List[Any]							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Dict							=    state_dict[prefix + matrix_name]
                  UpperCAmelCase__			:							Optional[int]							=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
      UpperCAmelCase__			:							List[str]							=    state_dict["""entity_embeddings.entity_embeddings.weight"""]
      UpperCAmelCase__			:							int							=    entity_emb[entity_vocab["""[MASK]"""]]

      UpperCAmelCase__			:							Tuple							=    LukeModel(config=lowerCAmelCase    ).eval()

      UpperCAmelCase__					,		UpperCAmelCase__			:							Optional[Any]							=    model.load_state_dict(lowerCAmelCase			,	strict=lowerCAmelCase    )
      if not (len(lowerCAmelCase    ) == 1 and missing_keys[0] == "embeddings.position_ids"):
            raise ValueError(F"""Missing keys {', '.join(lowerCAmelCase    )}. Expected only missing embeddings.position_ids"""    )
      if not (all(key.startswith("""entity_predictions"""    ) or key.startswith("""lm_head"""    ) for key in unexpected_keys    )):
            raise ValueError(
                """Unexpected keys"""
                F""" {', '.join([key for key in unexpected_keys if not (key.startswith('entity_predictions'    ) or key.startswith('lm_head'    ))]    )}"""    )

      # Check outputs
      UpperCAmelCase__			:							Optional[Any]							=    LukeTokenizer.from_pretrained(lowerCAmelCase			,	task="""entity_classification"""    )

      UpperCAmelCase__			:							Dict							=    (
          """Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the"""
          """ new world number one avoid a humiliating second- round exit at Wimbledon ."""
      )
      UpperCAmelCase__			:							List[Any]							=    (39, 42)
      UpperCAmelCase__			:							str							=    tokenizer(lowerCAmelCase			,	entity_spans=[span]			,	add_prefix_space=lowerCAmelCase			,	return_tensors="""pt"""    )

      UpperCAmelCase__			:							List[str]							=    model(**lowerCAmelCase    )

      # Verify word hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Optional[Any]							=    torch.Size((1, 42, 10_24)    )
            UpperCAmelCase__			:							Union[str, Any]							=    torch.tensor(
                [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]    )
      else:  # base
            UpperCAmelCase__			:							Optional[int]							=    torch.Size((1, 42, 7_68)    )
            UpperCAmelCase__			:							Optional[Any]							=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]    )

      if not (outputs.last_hidden_state.shape == expected_shape):
            raise ValueError(
                F"""Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}"""    )
      if not torch.allclose(outputs.last_hidden_state[0, :3, :3]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Verify entity hidden states
      if model_size == "large":
            UpperCAmelCase__			:							Union[str, Any]							=    torch.Size((1, 1, 10_24)    )
            UpperCAmelCase__			:							str							=    torch.tensor([[0.0466, -0.0106, -0.0179]]    )
      else:  # base
            UpperCAmelCase__			:							Tuple							=    torch.Size((1, 1, 7_68)    )
            UpperCAmelCase__			:							List[str]							=    torch.tensor([[0.1457, 0.1044, 0.0174]]    )

      if not (outputs.entity_last_hidden_state.shape != expected_shape):
            raise ValueError(
                F"""Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"""
                F""" {expected_shape}"""    )
      if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]			,	lowerCAmelCase			,	atol=1E-4    ):
            raise ValueError

      # Finally, save our PyTorch model and tokenizer
      print("""Saving PyTorch model to {}""".format(lowerCAmelCase    )    )
      model.save_pretrained(lowerCAmelCase    )




def 					a__						(						lowerCAmelCase    )	-> Union[str, Any]:
      UpperCAmelCase__			:							List[str]							=    {}
      with open(lowerCAmelCase			,	"""r"""			,	encoding="""utf-8"""    ) as f:
            for index, line in enumerate(lowerCAmelCase    ):
                  UpperCAmelCase__					,		UpperCAmelCase__			:							Tuple							=    line.rstrip().split("""\t"""    )
                  UpperCAmelCase__			:							int							=    index

      return entity_vocab


if __name__ == "__main__":
    _A							=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--checkpoint_path""", type=str, help="""Path to a pytorch_model.bin file.""")
    parser.add_argument(
        """--metadata_path""", default=None, type=str, help="""Path to a metadata.json file, defining the configuration."""
    )
    parser.add_argument(
        """--entity_vocab_path""",
        default=None,
        type=str,
        help="""Path to an entity_vocab.tsv file, containing the entity vocabulary.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to where to dump the output PyTorch model."""
    )
    parser.add_argument(
        """--model_size""", default="""base""", type=str, choices=["""base""", """large"""], help="""Size of the model to be converted."""
    )
    _A							=  parser.parse_args()
    convert_luke_checkpoint(
        args.checkpoint_path,
        args.metadata_path,
        args.entity_vocab_path,
        args.pytorch_dump_folder_path,
        args.model_size,
    )

