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	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    checkpoint

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {}

		SCREAMING_SNAKE_CASE_  :		Any				      =    vae_state_dict["encoder.conv_in.weight"]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    vae_state_dict["encoder.conv_in.bias"]
		SCREAMING_SNAKE_CASE_  :		int				      =    vae_state_dict["encoder.conv_out.weight"]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    vae_state_dict["encoder.conv_out.bias"]
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    vae_state_dict["encoder.norm_out.weight"]
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    vae_state_dict["encoder.norm_out.bias"]

		SCREAMING_SNAKE_CASE_  :		int				      =    vae_state_dict["decoder.conv_in.weight"]
		SCREAMING_SNAKE_CASE_  :		Any				      =    vae_state_dict["decoder.conv_in.bias"]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    vae_state_dict["decoder.conv_out.weight"]
		SCREAMING_SNAKE_CASE_  :		Any				      =    vae_state_dict["decoder.conv_out.bias"]
		SCREAMING_SNAKE_CASE_  :		Any				      =    vae_state_dict["decoder.norm_out.weight"]
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    vae_state_dict["decoder.norm_out.bias"]

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

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

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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [key for key in vae_state_dict if "encoder.mid.block" in key]
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    2
		for i in range(1   , num_mid_res_blocks + 1	):
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in mid_resnets if f'encoder.mid.block_{i}' in key]

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

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

				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    renew_vae_resnet_paths(lowerCAmelCase__	)
				SCREAMING_SNAKE_CASE_  :		List[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__	)

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

				SCREAMING_SNAKE_CASE_  :		Dict				      =    renew_vae_resnet_paths(lowerCAmelCase__	)
				SCREAMING_SNAKE_CASE_  :		int				      =    {"old": f'mid.block_{i}', "new": f'mid_block.resnets.{i - 1}'}
				assign_to_checkpoint(lowerCAmelCase__   , lowerCAmelCase__   , lowerCAmelCase__   , additional_replacements=[meta_path]   , config=lowerCAmelCase__	)

		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in vae_state_dict if "decoder.mid.attn" in key]
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    renew_vae_attention_paths(lowerCAmelCase__	)
		SCREAMING_SNAKE_CASE_  :		str				      =    {"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       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Tuple   , ):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		str				      =    OmegaConf.load(lowerCAmelCase__	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    5_1_2
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    "cuda" if torch.cuda.is_available() else "cpu"
		if checkpoint_path.endswith("safetensors"	):
				from safetensors import safe_open

				SCREAMING_SNAKE_CASE_  :		int				      =    {}
				with safe_open(lowerCAmelCase__   , framework="pt"   , device="cpu"	) as f:
						for key in f.keys():
								SCREAMING_SNAKE_CASE_  :		str				      =    f.get_tensor(lowerCAmelCase__	)
		else:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.load(lowerCAmelCase__   , map_location=lowerCAmelCase__	)["state_dict"]

		# Convert the VAE model.
		SCREAMING_SNAKE_CASE_  :		int				      =    create_vae_diffusers_config(lowerCAmelCase__   , image_size=lowerCAmelCase__	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    custom_convert_ldm_vae_checkpoint(lowerCAmelCase__   , lowerCAmelCase__	)

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoencoderKL(**lowerCAmelCase__	)
		vae.load_state_dict(lowerCAmelCase__	)
		vae.save_pretrained(lowerCAmelCase__	)


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 os
import sys

lowercase__		:Tuple   =    os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase__		:List[Any]   =    [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]



@add_start_docstrings(AutoConfig.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoConfig.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoTokenizer.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoTokenizer.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModel.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModel.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForCausalLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForCausalLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForMaskedLM.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForMaskedLM.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForSequenceClassification.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__		)
def        UpperCamelCase    (							*lowerCAmelCase__ ,			**lowerCAmelCase__		):
					'''simple docstring'''
					return AutoModelForQuestionAnswering.from_pretrained(*lowerCAmelCase__ ,			**lowerCAmelCase__		)




