
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      __lowerCAmelCase						(							UpperCAmelCase__				:      Optional[Any]			,				UpperCAmelCase__				:      Any       )  ->    int:
							lowerCamelCase_							  =					checkpoint

							lowerCamelCase_							  =					{}

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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



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

							lowerCamelCase_							  =					OmegaConf.load(UpperCAmelCase__       )
							lowerCamelCase_							  =					5_1_2
							lowerCamelCase_							  =					"""cuda""" if torch.cuda.is_available() else """cpu"""
							if checkpoint_path.endswith("""safetensors"""       ):
														from safetensors import safe_open

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

							# Convert the VAE model.
							lowerCamelCase_							  =					create_vae_diffusers_config(UpperCAmelCase__			,				image_size=UpperCAmelCase__       )
							lowerCamelCase_							  =					custom_convert_ldm_vae_checkpoint(UpperCAmelCase__			,				UpperCAmelCase__       )

							lowerCamelCase_							  =					AutoencoderKL(**UpperCAmelCase__       )
							vae.load_state_dict(UpperCAmelCase__       )
							vae.save_pretrained(UpperCAmelCase__       )


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

		lowercase        =  parser.parse_args()

		vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''image_processor''', '''tokenizer''']
					SCREAMING_SNAKE_CASE			    =    '''BlipImageProcessor'''
					SCREAMING_SNAKE_CASE			    =    '''AutoTokenizer'''

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					False
												super().__init__(__UpperCamelCase   ,					__UpperCamelCase   )
												lowerCamelCase_							  =					self.image_processor

					def __call__(	self					:     Any   ,					__UpperCamelCase					:     ImageInput = None   ,					__UpperCamelCase					:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[bool, str, PaddingStrategy] = False   ,					__UpperCamelCase					:     Union[bool, str, TruncationStrategy] = None   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     int = 0   ,					__UpperCamelCase					:     Optional[int] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					**__UpperCamelCase					:     Optional[Any]   ,					):
												if images is None and text is None:
																			raise ValueError("""You have to specify either images or text."""   )

												# Get only text
												if images is None:
																			lowerCamelCase_							  =					self.tokenizer
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
																			return text_encoding

												# add pixel_values
												lowerCamelCase_							  =					self.image_processor(__UpperCamelCase   ,					return_tensors=__UpperCamelCase   )

												if text is not None:
																			lowerCamelCase_							  =					self.tokenizer(
																			    text=__UpperCamelCase   ,					add_special_tokens=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					truncation=__UpperCamelCase   ,					max_length=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					pad_to_multiple_of=__UpperCamelCase   ,					return_attention_mask=__UpperCamelCase   ,					return_overflowing_tokens=__UpperCamelCase   ,					return_special_tokens_mask=__UpperCamelCase   ,					return_offsets_mapping=__UpperCamelCase   ,					return_token_type_ids=__UpperCamelCase   ,					return_length=__UpperCamelCase   ,					verbose=__UpperCamelCase   ,					return_tensors=__UpperCamelCase   ,					**__UpperCamelCase   ,					)
												else:
																			lowerCamelCase_							  =					None

												if text_encoding is not None:
																			encoding_image_processor.update(__UpperCamelCase   )

												return encoding_image_processor

					def    lowercase__      (	self					:     Any   ,					*__UpperCamelCase					:     Union[str, Any]   ,					**__UpperCamelCase					:     List[Any]   ):
												return self.tokenizer.batch_decode(*__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					*__UpperCamelCase					:     List[Any]   ,					**__UpperCamelCase					:     int   ):
												return self.tokenizer.decode(*__UpperCamelCase   ,					**__UpperCamelCase   )



					@property
					# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.tokenizer.model_input_names
												lowerCamelCase_							  =					self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )





