



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()
lowerCAmelCase__	    =    logging.get_logger('''transformers.models.speecht5''')

lowerCAmelCase__	    =    {
    '''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''',
}
lowerCAmelCase__	    =    {
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
lowerCAmelCase__	    =    {
    '''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''',
}
lowerCAmelCase__	    =    {
    '''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''',
}
lowerCAmelCase__	    =    {
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
lowerCAmelCase__	    =    {
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
lowerCAmelCase__	    =    {
    '''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''',
}
lowerCAmelCase__	    =    {
    '''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''',
}
lowerCAmelCase__	    =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase__	    =    {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__	    =    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__	    =    []
lowerCAmelCase__	    =    [
    '''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''',
]
lowerCAmelCase__	    =    IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
lowerCAmelCase__	    =    IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
lowerCAmelCase__	    =    IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]


def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				for attribute in key.split("."		):
								lowercase__	:       Tuple						=	getattr(lowerCAmelCase_	,					lowerCAmelCase_		)

				if weight_type is not None:
								lowercase__	:       List[Any]						=	getattr(lowerCAmelCase_	,					lowerCAmelCase_		).shape
				else:
								lowercase__	:       Optional[int]						=	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":
								lowercase__	:       Dict						=	value
				elif weight_type == "weight_g":
								lowercase__	:       List[Any]						=	value
				elif weight_type == "weight_v":
								lowercase__	:       Optional[int]						=	value
				elif weight_type == "bias":
								lowercase__	:       str						=	value
				elif weight_type == "running_mean":
								lowercase__	:       Any						=	value
				elif weight_type == "running_var":
								lowercase__	:       Any						=	value
				elif weight_type == "num_batches_tracked":
								lowercase__	:       Tuple						=	value
				else:
								lowercase__	:       Tuple						=	value

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


def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


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


def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       Tuple						=	[]

				if task == "s2t":
								lowercase__	:       Optional[int]						=	hf_model.speechta.encoder.prenet.feature_encoder
								lowercase__	:       Tuple						=	MAPPING_S2T
								lowercase__	:       Union[str, Any]						=	IGNORE_KEYS_S2T
				elif task == "t2s":
								lowercase__	:       List[str]						=	None
								lowercase__	:       Any						=	MAPPING_T2S
								lowercase__	:       str						=	IGNORE_KEYS_T2S
				elif task == "s2s":
								lowercase__	:       Any						=	hf_model.speechta.encoder.prenet.feature_encoder
								lowercase__	:       Optional[Any]						=	MAPPING_S2S
								lowercase__	:       Dict						=	IGNORE_KEYS_S2S
				else:
								raise ValueError(F"""Unsupported task: {task}"""		)

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

								lowercase__	:       Tuple						=	False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCAmelCase_	,					lowerCAmelCase_	,					lowerCAmelCase_	,					lowerCAmelCase_	,					hf_model.config.feat_extract_norm == "group"	,					)
												lowercase__	:       int						=	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:
																				lowercase__				,				lowercase__	:       str						=	key.split(".*."		)
																				if prefix in name and suffix in name:
																								lowercase__	:       Dict						=	suffix

																				 # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				lowercase__	:       int						=	True
																				if "*" in mapped_key:
																								lowercase__	:       List[Any]						=	name.split(lowerCAmelCase_		)[0].split("."		)[-2]
																								lowercase__	:       Union[str, Any]						=	mapped_key.replace("*"	,					lowerCAmelCase_		)
																				if "weight_g" in name:
																								lowercase__	:       Any						=	"weight_g"
																				elif "weight_v" in name:
																								lowercase__	:       Dict						=	"weight_v"
																				elif "bias" in name:
																								lowercase__	:       Tuple						=	"bias"
																				elif "weight" in name:
																								lowercase__	:       Optional[Any]						=	"weight"
																				elif "running_mean" in name:
																								lowercase__	:       List[str]						=	"running_mean"
																				elif "running_var" in name:
																								lowercase__	:       Union[str, Any]						=	"running_var"
																				elif "num_batches_tracked" in name:
																								lowercase__	:       Optional[int]						=	"num_batches_tracked"
																				else:
																								lowercase__	:       Any						=	None
																				set_recursively(lowerCAmelCase_	,					lowerCAmelCase_	,					lowerCAmelCase_	,					lowerCAmelCase_	,					lowerCAmelCase_		)
																continue
								if not is_used:
												unused_weights.append(lowerCAmelCase_		)

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


def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__		):

				"""simple docstring"""


				lowercase__	:       Optional[Any]						=	full_name.split("conv_layers."		)[-1]
				lowercase__	:       List[str]						=	name.split("."		)
				lowercase__	:       Any						=	int(items[0]		)
				lowercase__	:       Tuple						=	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."""		)
												lowercase__	:       Optional[Any]						=	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."""		)
												lowercase__	:       Dict						=	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."""		)
												lowercase__	:       Optional[Any]						=	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."""		)
												lowercase__	:       Optional[int]						=	value
												logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
				else:
								unused_weights.append(lowerCAmelCase_		)


@torch.no_grad()
def    __lowerCamelCase   (  lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					):

				"""simple docstring"""


				if config_path is not None:
								lowercase__	:       Optional[int]						=	SpeechTaConfig.from_pretrained(lowerCAmelCase_		)
				else:
								lowercase__	:       str						=	SpeechTaConfig()

				if task == "s2t":
								lowercase__	:       Optional[Any]						=	config.max_text_positions
								lowercase__	:       Any						=	SpeechTaForSpeechToText(lowerCAmelCase_		)
				elif task == "t2s":
								lowercase__	:       Optional[int]						=	1_876
								lowercase__	:       int						=	600
								lowercase__	:       Optional[int]						=	config.max_speech_positions
								lowercase__	:       Any						=	SpeechTaForTextToSpeech(lowerCAmelCase_		)
				elif task == "s2s":
								lowercase__	:       Dict						=	1_876
								lowercase__	:       List[str]						=	config.max_speech_positions
								lowercase__	:       Tuple						=	SpeechTaForSpeechToSpeech(lowerCAmelCase_		)
				else:
								raise ValueError(F"""Unknown task name: {task}"""		)

				if vocab_path:
								lowercase__	:       int						=	SpeechTaTokenizer(lowerCAmelCase_	,					model_max_length=config.max_text_positions		)

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

				lowercase__	:       List[Any]						=	SpeechTaFeatureExtractor()
				lowercase__	:       Any						=	SpeechTaProcessor(tokenizer=lowerCAmelCase_	,					feature_extractor=lowerCAmelCase_		)
				processor.save_pretrained(lowerCAmelCase_		)

				lowercase__	:       Optional[Any]						=	torch.load(lowerCAmelCase_		)
				recursively_load_weights(fairseq_checkpoint["model"]	,					lowerCAmelCase_	,					lowerCAmelCase_		)

				model.save_pretrained(lowerCAmelCase_		)

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


if __name__ == "__main__":
				lowerCAmelCase__	    =    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.'''
				)

				lowerCAmelCase__	    =    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,
				)








from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




