



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Optional[Any]      							=     "Speech2TextFeatureExtractor"
					A__					:		Optional[Any]      							=     "Speech2TextTokenizer"



					def __init__(  self       :							Tuple					,       _snake_case       :							List[Any]					,       _snake_case       :							List[Any]       ):






								"""simple docstring"""


								super().__init__(_snake_case					,       _snake_case       )
								A__	  =     self.feature_extractor
								A__	  =     False



					def __call__(  self       :							Optional[int]					,       *_snake_case       :							Any					,       **_snake_case       :							int       ):






								"""simple docstring"""


								if self._in_target_context_manager:
											return self.current_processor(*_snake_case					,       **_snake_case       )

								if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
											A__	  =     kwargs.pop('raw_speech'       )
								else:
											A__	  =     kwargs.pop('audio'					,       _snake_case       )
								A__	  =     kwargs.pop('sampling_rate'					,       _snake_case       )
								A__	  =     kwargs.pop('text'					,       _snake_case       )
								if len(_snake_case       ) > 0:
											A__	  =     args[0]
											A__	  =     args[1:]

								if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'       )

								if audio is not None:
											A__	  =     self.feature_extractor(_snake_case					,       *_snake_case					,       sampling_rate=_snake_case					,       **_snake_case       )
								if text is not None:
											A__	  =     self.tokenizer(_snake_case					,       **_snake_case       )

								if text is None:
											return inputs
								elif audio is None:
											return encodings
								else:
											A__	  =     encodings['input_ids']
											return inputs



					def      _a				(  self       :							Optional[Any]					,       *_snake_case       :							Dict					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


								return self.tokenizer.batch_decode(*_snake_case					,       **_snake_case       )



					def      _a				(  self       :							Optional[Any]					,       *_snake_case       :							Optional[Any]					,       **_snake_case       :							List[str]       ):






								"""simple docstring"""


								return self.tokenizer.decode(*_snake_case					,       **_snake_case       )



					@contextmanager
					def      _a				(  self       :							Any       ):






								"""simple docstring"""


								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'       )
								A__	  =     True
								A__	  =     self.tokenizer
								yield
								A__	  =     self.feature_extractor
								A__	  =     False









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     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=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


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

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




