



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 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		Any:







						"""simple docstring"""

						with open(__lowercase       ) as metadata_file:
												A__	:							str						    = json.load(__lowercase       )
						A__	:							Any						    = LukeConfig(use_entity_aware_attention=__lowercase							,     **metadata["model_config"]       )

						# Load in the weights from the checkpoint_path
						A__	:							Union[str, Any]						    = torch.load(__lowercase							,     map_location="cpu"       )

						# Load the entity vocab file
						A__	:							Optional[int]						    = load_entity_vocab(__lowercase       )

						A__	:							int						    = RobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"]       )

						# Add special tokens to the token vocabulary for downstream tasks
						A__	:							Dict						    = AddedToken("<ent>"							,     lstrip=__lowercase							,     rstrip=__lowercase       )
						A__	:							int						    = AddedToken("<ent2>"							,     lstrip=__lowercase							,     rstrip=__lowercase       )
						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(__lowercase       )
						with open(os.path.join(__lowercase							,     LukeTokenizer.vocab_files_names["entity_vocab_file"]       )							,     "w"       ) as f:
												json.dump(__lowercase							,     __lowercase       )

						A__	:							Dict						    = LukeTokenizer.from_pretrained(__lowercase       )

						# Initialize the embeddings of the special tokens
						A__	:							Union[str, Any]						    = state_dict["embeddings.word_embeddings.weight"]
						A__	:							str						    = word_emb[tokenizer.convert_tokens_to_ids(["@"]       )[0]].unsqueeze(0       )
						A__	:							Any						    = word_emb[tokenizer.convert_tokens_to_ids(["#"]       )[0]].unsqueeze(0       )
						A__	:							Optional[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"]:
																		A__	:							Optional[Any]						    = F'encoder.layer.{layer_index}.attention.self.'
																		A__	:							List[str]						    = state_dict[prefix + matrix_name]
																		A__	:							Optional[Any]						    = state_dict[prefix + matrix_name]
																		A__	:							Optional[Any]						    = state_dict[prefix + matrix_name]

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

						A__	:							Union[str, Any]						    = LukeModel(config=__lowercase       ).eval()

						A__    ,       A__	:							Dict						    = model.load_state_dict(__lowercase							,     strict=__lowercase       )
						if not (len(__lowercase       ) == 1 and missing_keys[0] == "embeddings.position_ids"):
												raise ValueError(F'Missing keys {", ".join(__lowercase       )}. 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
						A__	:							List[str]						    = LukeTokenizer.from_pretrained(__lowercase							,     task="entity_classification"       )

						A__	:							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 ."
						)
						A__	:							List[str]						    = (3_9, 4_2)
						A__	:							str						    = tokenizer(__lowercase							,     entity_spans=[span]							,     add_prefix_space=__lowercase							,     return_tensors="pt"       )

						A__	:							Optional[Any]						    = model(**__lowercase       )

						# Verify word hidden states
						if model_size == "large":
												A__	:							Union[str, Any]						    = torch.Size((1, 4_2, 1_0_2_4)       )
												A__	:							List[str]						    = torch.tensor(
												    [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]       )
						else:  # base
												A__	:							List[Any]						    = torch.Size((1, 4_2, 7_6_8)       )
												A__	:							List[str]						    = 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]							,     __lowercase							,     atol=1E-4       ):
												raise ValueError

						# Verify entity hidden states
						if model_size == "large":
												A__	:							Dict						    = torch.Size((1, 1, 1_0_2_4)       )
												A__	:							Union[str, Any]						    = torch.tensor([[0.0466, -0.0106, -0.0179]]       )
						else:  # base
												A__	:							int						    = torch.Size((1, 1, 7_6_8)       )
												A__	:							Any						    = 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]							,     __lowercase							,     atol=1E-4       ):
												raise ValueError

						# Finally, save our PyTorch model and tokenizer
						print("Saving PyTorch model to {}".format(__lowercase       )       )
						model.save_pretrained(__lowercase       )
def 	snake_case__       (					__lowercase       )		->		List[Any]:







						"""simple docstring"""

						A__	:							Tuple						    = {}
						with open(__lowercase							,     "r"							,     encoding="utf-8"       ) as f:
												for index, line in enumerate(__lowercase       ):
																		A__    ,       A__	:							List[str]						    = line.rstrip().split("\t"       )
																		A__	:							List[str]						    = index

						return entity_vocab


if __name__ == "__main__":
		snake_case    :						Union[str, Any]  =						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.'
		)
		snake_case    :						List[str]  =						parser.parse_args()
		convert_luke_checkpoint(
		    args.checkpoint_path,
		    args.metadata_path,
		    args.entity_vocab_path,
		    args.pytorch_dump_folder_path,
		    args.model_size,
		)




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 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							List[str]						    = checkpoint

						A__	:							Dict						    = {}

						A__	:							Union[str, Any]						    = 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__	:							Optional[int]						    = vae_state_dict["encoder.conv_out.bias"]
						A__	:							int						    = vae_state_dict["encoder.norm_out.weight"]
						A__	:							Optional[int]						    = vae_state_dict["encoder.norm_out.bias"]

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

						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.weight"]
						A__	:							Optional[Any]						    = vae_state_dict["quant_conv.bias"]
						A__	:							Dict						    = vae_state_dict["post_quant_conv.weight"]
						A__	:							Union[str, Any]						    = vae_state_dict["post_quant_conv.bias"]

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

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

						for i in range(__lowercase       ):
												A__	:							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__	:							Any						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.weight'       )
																		A__	:							Optional[int]						    = vae_state_dict.pop(
																		    F'encoder.down.{i}.downsample.conv.bias'       )

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

						A__	:							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__	:							Optional[int]						    = [key for key in mid_resnets if F'encoder.mid.block_{i}' in key]

												A__	:							Optional[Any]						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

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

						for i in range(__lowercase       ):
												A__	:							Dict						    = num_up_blocks - 1 - i
												A__	:							int						    = [
												    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__	:							Tuple						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.weight'
																		]
																		A__	:							List[Any]						    = vae_state_dict[
																		    F'decoder.up.{block_id}.upsample.conv.bias'
																		]

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

						A__	:							int						    = [key for key in vae_state_dict if "decoder.mid.block" in key]
						A__	:							Optional[int]						    = 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__	:							Dict						    = renew_vae_resnet_paths(__lowercase       )
												A__	:							int						    = {"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
												assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )

						A__	:							Optional[Any]						    = [key for key in vae_state_dict if "decoder.mid.attn" in key]
						A__	:							Union[str, Any]						    = renew_vae_attention_paths(__lowercase       )
						A__	:							List[str]						    = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
						assign_to_checkpoint(__lowercase							,     __lowercase							,     __lowercase							,     additional_replacements=[meta_path]							,     config=__lowercase       )
						conv_attn_to_linear(__lowercase       )
						return new_checkpoint
def 	snake_case__       (					__lowercase							,     __lowercase							,     )		->		Tuple:







						"""simple docstring"""

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

						A__	:							str						    = OmegaConf.load(__lowercase       )
						A__	:							Any						    = 5_1_2
						A__	:							Optional[Any]						    = "cuda" if torch.cuda.is_available() else "cpu"
						if checkpoint_path.endswith("safetensors"       ):
												from safetensors import safe_open

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

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

						A__	:							Dict						    = AutoencoderKL(**__lowercase       )
						vae.load_state_dict(__lowercase       )
						vae.save_pretrained(__lowercase       )


if __name__ == "__main__":
		snake_case    :						List[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.')

		snake_case    :						int  =						parser.parse_args()

		vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)