'''simple docstring'''




import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__lowercase          =  logging.get_logger('''transformers.models.speecht5''')

__lowercase          =  {
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__lowercase          =  {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__lowercase          =  {
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__lowercase          =  {
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__lowercase          =  {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__lowercase          =  {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__lowercase          =  {
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__lowercase          =  {
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__lowercase          =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowercase          =  {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowercase          =  {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowercase          =  []
__lowercase          =  [
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
__lowercase          =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__lowercase          =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__lowercase          =  IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]






def 				snake_case__							(			_A:					List[str]      ,   _A:					Union[str, Any]      ,   _A:					int      ,   _A:					Union[str, Any]      ,   _A:					Optional[Any] )    ->	Dict:

		'''simple docstring'''


		for attribute in key.split(""".""" ):
				lowerCAmelCase   					=  getattr(_A      ,   _A )

		if weight_type is not None:
				lowerCAmelCase   					=  getattr(_A      ,   _A ).shape
		else:
				lowerCAmelCase   					=  hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
				    f" {value.shape} for {full_name}" )

		if weight_type == "weight":
				lowerCAmelCase   					=  value
		elif weight_type == "weight_g":
				lowerCAmelCase   					=  value
		elif weight_type == "weight_v":
				lowerCAmelCase   					=  value
		elif weight_type == "bias":
				lowerCAmelCase   					=  value
		elif weight_type == "running_mean":
				lowerCAmelCase   					=  value
		elif weight_type == "running_var":
				lowerCAmelCase   					=  value
		elif weight_type == "num_batches_tracked":
				lowerCAmelCase   					=  value
		else:
				lowerCAmelCase   					=  value

		logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}." )






def 				snake_case__							(			_A:					Any      ,   _A:					Optional[int] )    ->	Optional[Any]:

		'''simple docstring'''


		for key in ignore_keys:
				if key.endswith(""".*""" ):
						if name.startswith(key[:-1] ):
								return True
				elif ".*." in key:
						lowerCAmelCase					,     lowerCAmelCase   					=  key.split(""".*.""" )
						if prefix in name and suffix in name:
								return True
				elif key in name:
						return True
		return False






def 				snake_case__							(			_A:					Any      ,   _A:					Optional[Any]      ,   _A:					Tuple )    ->	Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase   					=  []

		if task == "s2t":
				lowerCAmelCase   					=  hf_model.speechta.encoder.prenet.feature_encoder
				lowerCAmelCase   					=  MAPPING_S2T
				lowerCAmelCase   					=  IGNORE_KEYS_S2T
		elif task == "t2s":
				lowerCAmelCase   					=  None
				lowerCAmelCase   					=  MAPPING_T2S
				lowerCAmelCase   					=  IGNORE_KEYS_T2S
		elif task == "s2s":
				lowerCAmelCase   					=  hf_model.speechta.encoder.prenet.feature_encoder
				lowerCAmelCase   					=  MAPPING_S2S
				lowerCAmelCase   					=  IGNORE_KEYS_S2S
		else:
				raise ValueError(f"Unsupported task: {task}" )

		for name, value in fairseq_dict.items():
				if should_ignore(_A      ,   _A ):
						logger.info(f"{name} was ignored" )
						continue

				lowerCAmelCase   					=  False
				if "conv_layers" in name:
						load_conv_layer(
						    _A      ,   _A      ,   _A      ,   _A      ,   hf_model.config.feat_extract_norm == """group"""      ,   )
						lowerCAmelCase   					=  True
				else:
						for key, mapped_key in MAPPING.items():
								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

								if "*" in key:
										lowerCAmelCase					,     lowerCAmelCase   					=  key.split(""".*.""" )
										if prefix in name and suffix in name:
												lowerCAmelCase   					=  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
								if key in name:
										lowerCAmelCase   					=  True
										if "*" in mapped_key:
												lowerCAmelCase   					=  name.split(_A )[0].split(""".""" )[-2]
												lowerCAmelCase   					=  mapped_key.replace("""*"""      ,   _A )
										if "weight_g" in name:
												lowerCAmelCase   					=  """weight_g"""
										elif "weight_v" in name:
												lowerCAmelCase   					=  """weight_v"""
										elif "bias" in name:
												lowerCAmelCase   					=  """bias"""
										elif "weight" in name:
												lowerCAmelCase   					=  """weight"""
										elif "running_mean" in name:
												lowerCAmelCase   					=  """running_mean"""
										elif "running_var" in name:
												lowerCAmelCase   					=  """running_var"""
										elif "num_batches_tracked" in name:
												lowerCAmelCase   					=  """num_batches_tracked"""
										else:
												lowerCAmelCase   					=  None
										set_recursively(_A      ,   _A      ,   _A      ,   _A      ,   _A )
								continue
				if not is_used:
						unused_weights.append(_A )

		logger.warning(f"Unused weights: {unused_weights}" )






def 				snake_case__							(			_A:					str      ,   _A:					Tuple      ,   _A:					Optional[Any]      ,   _A:					Tuple      ,   _A:					Union[str, Any] )    ->	Optional[int]:

		'''simple docstring'''


		lowerCAmelCase   					=  full_name.split("""conv_layers.""" )[-1]
		lowerCAmelCase   					=  name.split(""".""" )
		lowerCAmelCase   					=  int(items[0] )
		lowerCAmelCase   					=  int(items[1] )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f"{full_name} has size {value.shape}, but"
								    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found." )
						lowerCAmelCase   					=  value
						logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f"{full_name} has size {value.shape}, but"
								    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found." )
						lowerCAmelCase   					=  value
						logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f"{full_name} has size {value.shape}, but"
								    f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
						lowerCAmelCase   					=  value
						logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f"{full_name} has size {value.shape}, but"
								    f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
						lowerCAmelCase   					=  value
						logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
		else:
				unused_weights.append(_A )






@torch.no_grad()
def 				snake_case__							(			_A:					Any      ,   _A:					Optional[Any]      ,   _A:					Tuple      ,   _A:					List[Any]=None      ,   _A:					int=None      ,   _A:					List[str]=None      ,   )    ->	Dict:

		'''simple docstring'''


		if config_path is not None:
				lowerCAmelCase   					=  SpeechTaConfig.from_pretrained(_A )
		else:
				lowerCAmelCase   					=  SpeechTaConfig()

		if task == "s2t":
				lowerCAmelCase   					=  config.max_text_positions
				lowerCAmelCase   					=  SpeechTaForSpeechToText(_A )
		elif task == "t2s":
				lowerCAmelCase   					=  1876
				lowerCAmelCase   					=  600
				lowerCAmelCase   					=  config.max_speech_positions
				lowerCAmelCase   					=  SpeechTaForTextToSpeech(_A )
		elif task == "s2s":
				lowerCAmelCase   					=  1876
				lowerCAmelCase   					=  config.max_speech_positions
				lowerCAmelCase   					=  SpeechTaForSpeechToSpeech(_A )
		else:
				raise ValueError(f"Unknown task name: {task}" )

		if vocab_path:
				lowerCAmelCase   					=  SpeechTaTokenizer(_A      ,   model_max_length=config.max_text_positions )

				# Mask token behaves like a normal word, i.e. include the space before it
				lowerCAmelCase   					=  AddedToken("""<mask>"""      ,   lstrip=_A      ,   rstrip=_A )
				lowerCAmelCase   					=  mask_token
				tokenizer.add_special_tokens({"""mask_token""": mask_token} )
				tokenizer.add_tokens(["""<ctc_blank>"""] )

		lowerCAmelCase   					=  SpeechTaFeatureExtractor()
		lowerCAmelCase   					=  SpeechTaProcessor(tokenizer=_A      ,   feature_extractor=_A )
		processor.save_pretrained(_A )

		lowerCAmelCase   					=  torch.load(_A )
		recursively_load_weights(fairseq_checkpoint["""model"""]      ,   _A      ,   _A )

		model.save_pretrained(_A )

		if repo_id:
				print("""Pushing to the hub...""" )
				processor.push_to_hub(_A )
				model.push_to_hub(_A )


if __name__ == "__main__":
						__lowercase          =  argparse.ArgumentParser()
						parser.add_argument(
						    '''--task''',
						    default='''s2t''',
						    type=str,
						    help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
						)
						parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
						parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
						)

						__lowercase          =  parser.parse_args()
						convert_speechta_checkpoint(
						    args.task,
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.config_path,
						    args.vocab_path,
						    args.push_to_hub,
						)





'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''EncodecFeatureExtractor'''
			UpperCAmelCase_			:     Dict      =       ('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  self.feature_extractor
					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True):




					"""simple docstring"""


					return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,    language=__lowerCAmelCase       ,    no_timestamps=__lowerCAmelCase)



			def __call__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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

					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""sampling_rate"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""text"""       ,    __lowerCAmelCase)
					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  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 text is not None:
							lowerCAmelCase   					=  self.tokenizer(__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is not None:
							lowerCAmelCase   					=  self.feature_extractor(__lowerCAmelCase       ,    *__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is None:
							return inputs

					elif text is None:
							return audio_inputs

					else:
							lowerCAmelCase   					=  audio_inputs["""input_values"""]
							if "padding_mask" in audio_inputs:
									lowerCAmelCase   					=  audio_inputs["""padding_mask"""]
							return inputs



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""padding_mask"""       ,    __lowerCAmelCase)

					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio_values is not None:
							return self._decode_audio(__lowerCAmelCase       ,    padding_mask=__lowerCAmelCase)
					else:
							return self.tokenizer.batch_decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  audio_values.shape

					if padding_mask is None:
							return list(__lowerCAmelCase)

					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)

					# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
					# token (so that the generated audio values are **not** treated as padded tokens)
					lowerCAmelCase   					=  seq_len - padding_mask.shape[-1]
					lowerCAmelCase   					=  1 - self.feature_extractor.padding_value
					lowerCAmelCase   					=  np.pad(__lowerCAmelCase       ,    ((0, 0), (0, difference))       ,    """constant"""       ,    constant_values=__lowerCAmelCase)

					lowerCAmelCase   					=  audio_values.tolist()
					for i in range(__lowerCAmelCase):
							lowerCAmelCase   					=  np.asarray(audio_values[i])[
							    padding_mask[i][None, :] != self.feature_extractor.padding_value
							]
							lowerCAmelCase   					=  sliced_audio.reshape(__lowerCAmelCase       ,    -1)

					return audio_values




