

'''simple docstring'''


import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=False  )    ->    str:
	UpperCAmelCase_		:      List[Any]       	=					OmegaConf.load(UpperCamelCase  )
	if display:
		print(yaml.dump(OmegaConf.to_container(UpperCamelCase  )  )  )
	return config







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Optional[Any]:
	if conf_path is None:
		UpperCAmelCase_		:      Any       	=					'./model_checkpoints/vqgan_only.yaml'
	UpperCAmelCase_		:      int       	=					load_config(UpperCamelCase   ,display=UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					VQModel(**config.model.params  )
	if ckpt_path is None:
		UpperCAmelCase_		:      Optional[Any]       	=					'./model_checkpoints/vqgan_only.pt'
	UpperCAmelCase_		:      Optional[int]       	=					torch.load(UpperCamelCase   ,map_location=UpperCamelCase  )
	if ".ckpt" in ckpt_path:
		UpperCAmelCase_		:      Union[str, Any]       	=					sd['state_dict']
	model.load_state_dict(UpperCamelCase   ,strict=UpperCamelCase  )
	model.to(UpperCamelCase  )
	del sd
	return model







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[str]:
	UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					model.encode(UpperCamelCase  )
	print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
	UpperCAmelCase_		:      Optional[int]       	=					model.decode(UpperCamelCase  )
	return xrec







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=False  )    ->    Optional[int]:
	UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					string.rsplit('.'   ,1  )
	if reload:
		UpperCAmelCase_		:      Dict       	=					importlib.import_module(UpperCamelCase  )
		importlib.reload(UpperCamelCase  )
	return getattr(importlib.import_module(UpperCamelCase   ,package=UpperCamelCase  )   ,cls  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Tuple:
	if "target" not in config:
		raise KeyError('Expected key `target` to instantiate.'  )
	return get_obj_from_str(config['target']  )(**config.get('params'   ,{}  )  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=True   ,UpperCamelCase=True  )    ->    Dict:
	UpperCAmelCase_		:      Dict       	=					instantiate_from_config(UpperCamelCase  )
	if sd is not None:
		model.load_state_dict(UpperCamelCase  )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_		:      Any       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
		UpperCAmelCase_		:      List[str]       	=					pl_sd['global_step']
		print(f"""loaded model from global step {global_step}."""  )
	else:
		UpperCAmelCase_		:      Union[str, Any]       	=					{'state_dict': None}
		UpperCAmelCase_		:      Any       	=					None
	UpperCAmelCase_		:      str       	=					load_model_from_config(config.model   ,pl_sd['state_dict']   ,gpu=UpperCamelCase   ,eval_mode=UpperCamelCase  )['model']
	return model, global_step









'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      List[str]=   "Wav2Vec2FeatureExtractor"
							_lowerCamelCase		:      Any=   "AutoTokenizer"


							def __init__(			self    ,			_snake_case    ,			_snake_case)							->				List[str]:
								super().__init__(_snake_case    ,			_snake_case)
								UpperCAmelCase_		:      int       	=					self.feature_extractor
								UpperCAmelCase_		:      str       	=					False


							@classmethod
							def 						_snake_case							(			cls    ,			_snake_case    ,			**_snake_case)							->				List[Any]:
								try:
									return super().from_pretrained(_snake_case    ,			**_snake_case)
								except OSError:
									warnings.warn(
									    F"""Loading a tokenizer inside {cls.__name__} from a config that does not"""
									    ' include a `tokenizer_class` attribute is deprecated and will be '
									    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
									    ' attribute to either your `config.json` or `tokenizer_config.json` '
									    'file to suppress this warning: '    ,			_snake_case    ,			)

									UpperCAmelCase_		:      Any       	=					WavaVecaFeatureExtractor.from_pretrained(_snake_case    ,			**_snake_case)
									UpperCAmelCase_		:      List[str]       	=					WavaVecaCTCTokenizer.from_pretrained(_snake_case    ,			**_snake_case)

									return cls(feature_extractor=_snake_case    ,			tokenizer=_snake_case)


							def __call__(			self    ,			*_snake_case    ,			**_snake_case)							->				Optional[Any]:
								# For backward compatibility
								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.')
									UpperCAmelCase_		:      List[str]       	=					kwargs.pop('raw_speech')
								else:
									UpperCAmelCase_		:      Dict       	=					kwargs.pop('audio'    ,			_snake_case)
								UpperCAmelCase_		:      str       	=					kwargs.pop('sampling_rate'    ,			_snake_case)
								UpperCAmelCase_		:      List[str]       	=					kwargs.pop('text'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      List[str]       	=					args[0]
									UpperCAmelCase_		:      Tuple       	=					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:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.feature_extractor(_snake_case    ,			*_snake_case    ,			sampling_rate=_snake_case    ,			**_snake_case)
								if text is not None:
									UpperCAmelCase_		:      Optional[int]       	=					self.tokenizer(_snake_case    ,			**_snake_case)

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


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				str:
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor.pad(*_snake_case    ,			**_snake_case)

								UpperCAmelCase_		:      Union[str, Any]       	=					kwargs.pop('input_features'    ,			_snake_case)
								UpperCAmelCase_		:      Dict       	=					kwargs.pop('labels'    ,			_snake_case)
								if len(_snake_case) > 0:
									UpperCAmelCase_		:      Optional[Any]       	=					args[0]
									UpperCAmelCase_		:      int       	=					args[1:]

								if input_features is not None:
									UpperCAmelCase_		:      Dict       	=					self.feature_extractor.pad(_snake_case    ,			*_snake_case    ,			**_snake_case)
								if labels is not None:
									UpperCAmelCase_		:      Union[str, Any]       	=					self.tokenizer.pad(_snake_case    ,			**_snake_case)

								if labels is None:
									return input_features
								elif input_features is None:
									return labels
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					labels['input_ids']
									return input_features


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Dict:
								return self.tokenizer.batch_decode(*_snake_case    ,			**_snake_case)


							def 						_snake_case							(			self    ,			*_snake_case    ,			**_snake_case)							->				Tuple:
								return self.tokenizer.decode(*_snake_case    ,			**_snake_case)





							@contextmanager
							def 						_snake_case							(			self)							->				Tuple:
								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.')
								UpperCAmelCase_		:      Optional[Any]       	=					True
								UpperCAmelCase_		:      Tuple       	=					self.tokenizer
								yield
								UpperCAmelCase_		:      Tuple       	=					self.feature_extractor
								UpperCAmelCase_		:      Tuple       	=					False






