





'''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      __A						(							a_						:  int      ,a_						:  Dict	):
		lowerCAmelCase     :    int						=   checkpoint

		lowerCAmelCase     :    Tuple						=   {}

		lowerCAmelCase     :    Tuple						=   vae_state_dict["encoder.conv_in.weight"]
		lowerCAmelCase     :    List[str]						=   vae_state_dict["encoder.conv_in.bias"]
		lowerCAmelCase     :    Any						=   vae_state_dict["encoder.conv_out.weight"]
		lowerCAmelCase     :    Optional[Any]						=   vae_state_dict["encoder.conv_out.bias"]
		lowerCAmelCase     :    Tuple						=   vae_state_dict["encoder.norm_out.weight"]
		lowerCAmelCase     :    Dict						=   vae_state_dict["encoder.norm_out.bias"]

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

		lowerCAmelCase     :    str						=   vae_state_dict["quant_conv.weight"]
		lowerCAmelCase     :    str						=   vae_state_dict["quant_conv.bias"]
		lowerCAmelCase     :    Dict						=   vae_state_dict["post_quant_conv.weight"]
		lowerCAmelCase     :    int						=   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     :    int						=   {
		    layer_id: [key for key in vae_state_dict if f'''down.{layer_id}''' in key] for layer_id in range(a_	)
		}

		# Retrieves the keys for the decoder up blocks only
		lowerCAmelCase     :    Dict						=   len({".".join(layer.split("."	)[:3]	) for layer in vae_state_dict if "decoder.up" in layer}	)
		lowerCAmelCase     :    Union[str, Any]						=   {
		    layer_id: [key for key in vae_state_dict if f'''up.{layer_id}''' in key] for layer_id in range(a_	)
		}

		for i in range(a_	):
				lowerCAmelCase     :    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:
						lowerCAmelCase     :    Optional[int]						=   vae_state_dict.pop(
						    f'''encoder.down.{i}.downsample.conv.weight'''	)
						lowerCAmelCase     :    int						=   vae_state_dict.pop(
						    f'''encoder.down.{i}.downsample.conv.bias'''	)

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

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

				lowerCAmelCase     :    List[Any]						=   renew_vae_resnet_paths(a_	)
				lowerCAmelCase     :    Union[str, Any]						=   {"old": f'''mid.block_{i}''', "new": f'''mid_block.resnets.{i - 1}'''}
				assign_to_checkpoint(a_      ,a_      ,a_      ,additional_replacements=[meta_path]      ,config=a_	)

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

		for i in range(a_	):
				lowerCAmelCase     :    Union[str, Any]						=   num_up_blocks - 1 - i
				lowerCAmelCase     :    Optional[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:
						lowerCAmelCase     :    List[str]						=   vae_state_dict[
						    f'''decoder.up.{block_id}.upsample.conv.weight'''
						]
						lowerCAmelCase     :    Tuple						=   vae_state_dict[
						    f'''decoder.up.{block_id}.upsample.conv.bias'''
						]

				lowerCAmelCase     :    Tuple						=   renew_vae_resnet_paths(a_	)
				lowerCAmelCase     :    Tuple						=   {"old": f'''up.{block_id}.block''', "new": f'''up_blocks.{i}.resnets'''}
				assign_to_checkpoint(a_      ,a_      ,a_      ,additional_replacements=[meta_path]      ,config=a_	)

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

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

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






def      __A						(							a_						:  str      ,a_						:  str      ,):
		# Only support V1
		lowerCAmelCase     :    Any						=   requests.get(
		    " https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"	)
		lowerCAmelCase     :    Any						=   io.BytesIO(r.content	)

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

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

		# Convert the VAE model.
		lowerCAmelCase     :    Any						=   create_vae_diffusers_config(a_      ,image_size=a_	)
		lowerCAmelCase     :    Any						=   custom_convert_ldm_vae_checkpoint(a_      ,a_	)

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


if __name__ == "__main__":
					lowerCAmelCase       		=      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       		=      parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)










'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
	    "text to read (in English) and returns a waveform object containing the sound."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   self.pre_processor(text=a_		,      return_tensors="pt"		,      truncation=a_	)

			if speaker_embeddings is None:
					if not is_datasets_available():
							raise ImportError("Datasets needs to be installed if not passing speaker embeddings."	)

					lowerCAmelCase     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

			return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



