






"""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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    Optional[int]:

		"""simple docstring"""







		lowerCAmelCase_   :							str									=			checkpoint

		lowerCAmelCase_   :							str									=			{}

		lowerCAmelCase_   :							Optional[int]									=			vae_state_dict["encoder.conv_in.weight"]
		lowerCAmelCase_   :							List[Any]									=			vae_state_dict["encoder.conv_in.bias"]
		lowerCAmelCase_   :							int									=			vae_state_dict["encoder.conv_out.weight"]
		lowerCAmelCase_   :							str									=			vae_state_dict["encoder.conv_out.bias"]
		lowerCAmelCase_   :							Union[str, Any]									=			vae_state_dict["encoder.norm_out.weight"]
		lowerCAmelCase_   :							Any									=			vae_state_dict["encoder.norm_out.bias"]

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

		lowerCAmelCase_   :							Optional[int]									=			vae_state_dict["quant_conv.weight"]
		lowerCAmelCase_   :							List[Any]									=			vae_state_dict["quant_conv.bias"]
		lowerCAmelCase_   :							Any									=			vae_state_dict["post_quant_conv.weight"]
		lowerCAmelCase_   :							str									=			vae_state_dict["post_quant_conv.bias"]

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

				lowerCAmelCase_   :							Tuple									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							str									=			{"old": f'''down.{i}.block''', "new": f'''down_blocks.{i}.resnets'''}
				assign_to_checkpoint(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       additional_replacements=[meta_path]      ,       config=__UpperCamelCase       )

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

				lowerCAmelCase_   :							Union[str, Any]									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							Union[str, Any]									=			{"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_   :							Tuple									=			[key for key in vae_state_dict if "encoder.mid.attn" in key]
		lowerCAmelCase_   :							Union[str, Any]									=			renew_vae_attention_paths(__UpperCamelCase       )
		lowerCAmelCase_   :							List[Any]									=			{"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_   :							Optional[Any]									=			num_up_blocks - 1 - i
				lowerCAmelCase_   :							List[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:
						lowerCAmelCase_   :							Union[str, Any]									=			vae_state_dict[
						    f'''decoder.up.{block_id}.upsample.conv.weight'''
						]
						lowerCAmelCase_   :							Union[str, Any]									=			vae_state_dict[
						    f'''decoder.up.{block_id}.upsample.conv.bias'''
						]

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

				lowerCAmelCase_   :							List[Any]									=			renew_vae_resnet_paths(__UpperCamelCase       )
				lowerCAmelCase_   :							List[Any]									=			{"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_   :							List[Any]									=			[key for key in vae_state_dict if "decoder.mid.attn" in key]
		lowerCAmelCase_   :							List[str]									=			renew_vae_attention_paths(__UpperCamelCase       )
		lowerCAmelCase_   :							List[str]									=			{"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      ,       __UpperCamelCase      ,       )						->    Union[str, Any]:

		"""simple docstring"""







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

		lowerCAmelCase_   :							Union[str, Any]									=			OmegaConf.load(__UpperCamelCase       )
		lowerCAmelCase_   :							List[str]									=			512
		lowerCAmelCase_   :							int									=			"cuda" if torch.cuda.is_available() else "cpu"
		if checkpoint_path.endswith("safetensors"       ):
				from safetensors import safe_open

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

		# Convert the VAE model.
		lowerCAmelCase_   :							Dict									=			create_vae_diffusers_config(__UpperCamelCase      ,       image_size=__UpperCamelCase       )
		lowerCAmelCase_   :							Dict									=			custom_convert_ldm_vae_checkpoint(__UpperCamelCase      ,       __UpperCamelCase       )

		lowerCAmelCase_   :							str									=			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)








"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Optional[int]									=			tempfile.mkdtemp()

				lowerCAmelCase_   :							Optional[int]									=			BlipImageProcessor()
				lowerCAmelCase_   :							Dict									=			BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"	)

				lowerCAmelCase_   :							str									=			BlipProcessor(a_						,			a_	)

				processor.save_pretrained(self.tmpdirname	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			**a_							:	Union[str, Any]	):
				return AutoProcessor.from_pretrained(self.tmpdirname						,			**a_	).tokenizer





		def    lowerCamelCase       (   self							:	int						,			**a_							:	int	):
				return AutoProcessor.from_pretrained(self.tmpdirname						,			**a_	).image_processor





		def    lowerCamelCase       (   self							:	List[str]	):
				shutil.rmtree(self.tmpdirname	)





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta	)]

				lowerCAmelCase_   :							List[str]									=			[Image.fromarray(np.moveaxis(a_						,			0						,			-1	)	) for x in image_inputs]

				return image_inputs





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Dict									=			BlipProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				lowerCAmelCase_   :							Optional[Any]									=			self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"	)
				lowerCAmelCase_   :							Dict									=			self.get_image_processor(do_normalize=a_						,			padding_value=1.0	)

				lowerCAmelCase_   :							str									=			BlipProcessor.from_pretrained(
				    self.tmpdirname						,			bos_token="(BOS)"						,			eos_token="(EOS)"						,			do_normalize=a_						,			padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()						,			tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer						,			a_	)

				self.assertEqual(processor.image_processor.to_json_string()						,			image_processor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.image_processor						,			a_	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							List[str]									=			self.get_image_processor()
				lowerCAmelCase_   :							Tuple									=			self.get_tokenizer()

				lowerCAmelCase_   :							str									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							Any									=			self.prepare_image_inputs()

				lowerCAmelCase_   :							Any									=			image_processor(a_						,			return_tensors="np"	)
				lowerCAmelCase_   :							List[Any]									=			processor(images=a_						,			return_tensors="np"	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()						,			input_processor[key].sum()						,			delta=1e-2	)





		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Dict									=			self.get_image_processor()
				lowerCAmelCase_   :							int									=			self.get_tokenizer()

				lowerCAmelCase_   :							Tuple									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							Dict									=			"lower newer"

				lowerCAmelCase_   :							List[str]									=			processor(text=a_	)

				lowerCAmelCase_   :							int									=			tokenizer(a_						,			return_token_type_ids=a_	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]						,			encoded_processor[key]	)





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							str									=			self.get_image_processor()
				lowerCAmelCase_   :							Dict									=			self.get_tokenizer()

				lowerCAmelCase_   :							Optional[int]									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							Any									=			"lower newer"
				lowerCAmelCase_   :							str									=			self.prepare_image_inputs()

				lowerCAmelCase_   :							Any									=			processor(text=a_						,			images=a_	)

				self.assertListEqual(list(inputs.keys()	)						,			["pixel_values", "input_ids", "attention_mask"]	)

				# test if it raises when no input is passed
				with pytest.raises(a_	):
						processor()





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							int									=			self.get_image_processor()
				lowerCAmelCase_   :							List[Any]									=			self.get_tokenizer()

				lowerCAmelCase_   :							str									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							int									=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				lowerCAmelCase_   :							List[str]									=			processor.batch_decode(a_	)
				lowerCAmelCase_   :							List[str]									=			tokenizer.batch_decode(a_	)

				self.assertListEqual(a_						,			a_	)





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							Optional[Any]									=			self.get_image_processor()
				lowerCAmelCase_   :							List[str]									=			self.get_tokenizer()

				lowerCAmelCase_   :							str									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							List[str]									=			"lower newer"
				lowerCAmelCase_   :							Optional[int]									=			self.prepare_image_inputs()

				lowerCAmelCase_   :							Optional[int]									=			processor(text=a_						,			images=a_	)

				# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
				self.assertListEqual(list(inputs.keys()	)						,			["pixel_values", "input_ids", "attention_mask"]	)
