





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__			(__lowerCamelCase,       __lowerCamelCase	):
     _SCREAMING_SNAKE_CASE       :	int						    =							checkpoint

     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{}

     _SCREAMING_SNAKE_CASE       :	Any						    =							vae_state_dict["encoder.conv_in.weight"]
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							vae_state_dict["encoder.conv_in.bias"]
     _SCREAMING_SNAKE_CASE       :	int						    =							vae_state_dict["encoder.conv_out.weight"]
     _SCREAMING_SNAKE_CASE       :	int						    =							vae_state_dict["encoder.conv_out.bias"]
     _SCREAMING_SNAKE_CASE       :	Any						    =							vae_state_dict["encoder.norm_out.weight"]
     _SCREAMING_SNAKE_CASE       :	Dict						    =							vae_state_dict["encoder.norm_out.bias"]

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

     _SCREAMING_SNAKE_CASE       :	Dict						    =							vae_state_dict["quant_conv.weight"]
     _SCREAMING_SNAKE_CASE       :	Dict						    =							vae_state_dict["quant_conv.bias"]
     _SCREAMING_SNAKE_CASE       :	int						    =							vae_state_dict["post_quant_conv.weight"]
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							vae_state_dict["post_quant_conv.bias"]

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

          _SCREAMING_SNAKE_CASE       :	int						    =							renew_vae_resnet_paths(__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Optional[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       :	Optional[int]						    =							[key for key in vae_state_dict if "encoder.mid.block" in key]
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							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       :	str						    =							renew_vae_resnet_paths(__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Optional[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	)

     _SCREAMING_SNAKE_CASE       :	Dict						    =							[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       :	Union[str, 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       :	Tuple						    =							num_up_blocks - 1 - i
          _SCREAMING_SNAKE_CASE       :	List[str]						    =							[
              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       :	Optional[Any]						    =							vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.weight"""
               ]
               _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							vae_state_dict[
                   f"""decoder.up.{block_id}.upsample.conv.bias"""
               ]

          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							renew_vae_resnet_paths(__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							{"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       :	List[Any]						    =							[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       :	int						    =							[key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

          _SCREAMING_SNAKE_CASE       :	Tuple						    =							renew_vae_resnet_paths(__lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	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	)

     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							[key for key in vae_state_dict if "decoder.mid.attn" in key]
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							renew_vae_attention_paths(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	int						    =							{"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     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       ):
     # Only support V1
     _SCREAMING_SNAKE_CASE       :	int						    =							requests.get(
         " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							io.BytesIO(r.content	)

     _SCREAMING_SNAKE_CASE       :	Dict						    =							OmegaConf.load(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							512
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							"cuda" if torch.cuda.is_available() else "cpu"
     if checkpoint_path.endswith("safetensors"	):
          from safetensors import safe_open

          _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{}
          with safe_open(__lowerCamelCase,       framework="pt",       device="cpu"	) as f:
               for key in f.keys():
                    _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							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       :	List[Any]						    =							create_vae_diffusers_config(__lowerCamelCase,       image_size=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							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__":
       UpperCamelCase__      =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.')

       UpperCamelCase__      =parser.parse_args()

       vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							"ylacombe/bark-small"
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							tempfile.mkdtemp()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"en_speaker_1"
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"This is a test string"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							"speaker_embeddings_path.json"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"speaker_embeddings"




       def     UpperCamelCase_			(      self    ,						**__lowerCamelCase							)			->     str:
            return AutoTokenizer.from_pretrained(self.checkpoint    ,						**__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            shutil.rmtree(self.tmpdirname							)




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            processor.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(self.tmpdirname							)

            self.assertEqual(processor.tokenizer.get_vocab()    ,						tokenizer.get_vocab()							)




       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)
            processor.save_pretrained(
                self.tmpdirname    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						speaker_embeddings_directory=self.speaker_embeddings_directory    ,						)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							self.get_tokenizer(bos_token="(BOS)"    ,						eos_token="(EOS)"							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							BarkProcessor.from_pretrained(
                self.tmpdirname    ,						self.speaker_embeddings_dict_path    ,						bos_token="(BOS)"    ,						eos_token="(EOS)"    ,						)

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




       def     UpperCamelCase_			(      self							)			->     str:
            _SCREAMING_SNAKE_CASE       :	Any						    =							BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,						)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							3_5
            _SCREAMING_SNAKE_CASE       :	Any						    =							2
            _SCREAMING_SNAKE_CASE       :	int						    =							8

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							{
                "semantic_prompt": np.ones(__lowerCamelCase							),
                "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)							),
                "fine_prompt": np.ones((nb_codebooks_total, seq_len)							),
            }

            # test providing already loaded voice_preset
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							inputs["history_prompt"]
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from npz file
            _SCREAMING_SNAKE_CASE       :	Any						    =							os.path.join(self.tmpdirname    ,						"file.npz"							)
            np.savez(__lowerCamelCase    ,						**__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							processor(text=self.input_string    ,						voice_preset=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							inputs["history_prompt"]

            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()    ,						processed_voice_preset.get(__lowerCamelCase    ,						np.array([]							)							).tolist()							)

            # test loading voice preset from the hub
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							processor(text=self.input_string    ,						voice_preset=self.voice_preset							)







       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkProcessor(tokenizer=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE       :	str						    =							processor(text=self.input_string							)

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							tokenizer(
                self.input_string    ,						padding="max_length"    ,						max_length=2_5_6    ,						add_special_tokens=__lowerCamelCase    ,						return_attention_mask=__lowerCamelCase    ,						return_token_type_ids=__lowerCamelCase    ,						)

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