




import importlib

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







def 		lowercase__   (		__A:	str							,__A:	int=False				):





					'''simple docstring'''






					__magic_name__							:	List[Any]    							=							OmegaConf.load(__A				)
					if display:
										print(yaml.dump(OmegaConf.to_container(__A				)				)				)
					return config







def 		lowercase__   (		__A:	int							,__A:	Optional[int]=None							,__A:	Union[str, Any]=None				):





					'''simple docstring'''






					if conf_path is None:
										__magic_name__							:	Tuple    							=							'''./model_checkpoints/vqgan_only.yaml'''
					__magic_name__							:	List[str]    							=							load_config(__A							,display=__A				)
					__magic_name__							:	Dict    							=							VQModel(**config.model.params				)
					if ckpt_path is None:
										__magic_name__							:	Tuple    							=							'''./model_checkpoints/vqgan_only.pt'''
					__magic_name__							:	Any    							=							torch.load(__A							,map_location=__A				)
					if ".ckpt" in ckpt_path:
										__magic_name__							:	Optional[int]    							=							sd['''state_dict''']
					model.load_state_dict(__A							,strict=__A				)
					model.to(__A				)
					del sd
					return model







def 		lowercase__   (		__A:	Tuple							,__A:	str				):





					'''simple docstring'''






					__magic_name__							,    __magic_name__							,    __magic_name__							:	int    							=							model.encode(__A				)
					print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''				)
					__magic_name__							:	List[Any]    							=							model.decode(__A				)
					return xrec







def 		lowercase__   (		__A:	Dict							,__A:	Union[str, Any]=False				):





					'''simple docstring'''






					__magic_name__							,    __magic_name__							:	List[Any]    							=							string.rsplit('''.'''							,1				)
					if reload:
										__magic_name__							:	Dict    							=							importlib.import_module(__A				)
										importlib.reload(__A				)
					return getattr(importlib.import_module(__A							,package=__A				)							,cls				)







def 		lowercase__   (		__A:	Optional[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	str							,__A:	Union[str, Any]							,__A:	List[Any]=True							,__A:	Optional[int]=True				):





					'''simple docstring'''






					__magic_name__							:	Union[str, Any]    							=							instantiate_from_config(__A				)
					if sd is not None:
										model.load_state_dict(__A				)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def 		lowercase__   (		__A:	Optional[Any]							,__A:	int							,__A:	int							,__A:	str				):





					'''simple docstring'''






					if ckpt:
										__magic_name__							:	str    							=							torch.load(__A							,map_location='''cpu'''				)
										__magic_name__							:	Any    							=							pl_sd['''global_step''']
										print(F'''loaded model from global step {global_step}.'''				)
					else:
										__magic_name__							:	Any    							=							{'''state_dict''': None}
										__magic_name__							:	Tuple    							=							None
					__magic_name__							:	str    							=							load_model_from_config(config.model							,pl_sd['''state_dict''']							,gpu=__A							,eval_mode=__A				)['''model''']
					return model, global_step









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[Any]				=			{
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			['''Speech2TextTokenizer''']

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			['''Speech2TextFeatureExtractor''']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSpeech2TextForConditionalGeneration''',
	    '''TFSpeech2TextModel''',
	    '''TFSpeech2TextPreTrainedModel''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''Speech2TextForConditionalGeneration''',
	    '''Speech2TextModel''',
	    '''Speech2TextPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
	from .processing_speech_to_text import SpeechaTextProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speech_to_text import SpeechaTextTokenizer

	try:
		if not is_speech_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_speech_to_text import (
		    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSpeechaTextForConditionalGeneration,
		    TFSpeechaTextModel,
		    TFSpeechaTextPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_to_text import (
		    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechaTextForConditionalGeneration,
		    SpeechaTextModel,
		    SpeechaTextPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						List[str]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



