



'''simple docstring'''



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 _UpperCamelCase						(   unittest.TestCase						):





				'''simple docstring'''





				def  __lowerCamelCase   (							self     :     int):







											'''simple docstring'''

											__lowercase						="ylacombe/bark-small"
											__lowercase						=tempfile.mkdtemp()
											__lowercase						="en_speaker_1"
											__lowercase						="This is a test string"
											__lowercase						="speaker_embeddings_path.json"
											__lowercase						="speaker_embeddings"

				def  __lowerCamelCase   (							self     :     Optional[Any]     ,				**_lowerCAmelCase     :     int):







											'''simple docstring'''

											return AutoTokenizer.from_pretrained(self.checkpoint     ,				**_a)

				def  __lowerCamelCase   (							self     :     Tuple):







											'''simple docstring'''

											shutil.rmtree(self.tmpdirname)

				def  __lowerCamelCase   (							self     :     Optional[int]):







											'''simple docstring'''

											__lowercase						=self.get_tokenizer()

											__lowercase						=BarkProcessor(tokenizer=_a)

											processor.save_pretrained(self.tmpdirname)
											__lowercase						=BarkProcessor.from_pretrained(self.tmpdirname)

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

				@slow
				def  __lowerCamelCase   (							self     :     str):







											'''simple docstring'''

											__lowercase						=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     ,				)

											__lowercase						=self.get_tokenizer(bos_token='(BOS)'     ,				eos_token='(EOS)')

											__lowercase						=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  __lowerCamelCase   (							self     :     Union[str, Any]):







											'''simple docstring'''

											__lowercase						=BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint     ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,				)

											__lowercase						=3_5
											__lowercase						=2
											__lowercase						=8

											__lowercase						={
											    "semantic_prompt": np.ones(_a),
											    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)),
											    "fine_prompt": np.ones((nb_codebooks_total, seq_len)),
											}

											# test providing already loaded voice_preset
											__lowercase						=processor(text=self.input_string     ,				voice_preset=_a)

											__lowercase						=inputs["history_prompt"]
											for key in voice_preset:
																		self.assertListEqual(voice_preset[key].tolist()     ,				processed_voice_preset.get(_a     ,				np.array([])).tolist())

											# test loading voice preset from npz file
											__lowercase						=os.path.join(self.tmpdirname     ,				'file.npz')
											np.savez(_a     ,				**_a)
											__lowercase						=processor(text=self.input_string     ,				voice_preset=_a)
											__lowercase						=inputs["history_prompt"]

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

											# test loading voice preset from the hub
											__lowercase						=processor(text=self.input_string     ,				voice_preset=self.voice_preset)

				def  __lowerCamelCase   (							self     :     Any):







											'''simple docstring'''

											__lowercase						=self.get_tokenizer()

											__lowercase						=BarkProcessor(tokenizer=_a)

											__lowercase						=processor(text=self.input_string)

											__lowercase						=tokenizer(
											    self.input_string     ,				padding='max_length'     ,				max_length=2_5_6     ,				add_special_tokens=_a     ,				return_attention_mask=_a     ,				return_token_type_ids=_a     ,				)

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





"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							DDIMScheduler(
           timesteps=config.model.params.timesteps     , beta_schedule="scaled_linear"     , beta_start=config.model.params.linear_start     , beta_end=config.model.params.linear_end     , clip_sample=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", type=str, required=True)
   parser.add_argument("""--config_path""", type=str, required=True)
   parser.add_argument("""--output_path""", type=str, required=True)
   _A			:       Dict   		=       parser.parse_args()

   convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


