





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

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

def     __A      (			a_						:Any   ,       a_						:List[str]   ,       a_						:str   ,       a_						:Optional[Any]   ,       a_						:Optional[Any])		->		str:
				for attribute in key.split('''.'''):
								__a :						Optional[Any]			       =  getattr(a_   ,       a_)

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

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

def     __A      (			a_						:str   ,       a_						:Optional[int])		->		int:
				for key in ignore_keys:
								if key.endswith('''.*'''):
												if name.startswith(key[:-1]):
																return True
								elif ".*." in key:
												__a :						Optional[Any]			       =  key.split('''.*.''')
												if prefix in name and suffix in name:
																return True
								elif key in name:
												return True
				return False

def     __A      (			a_						:str   ,       a_						:str   ,       a_						:Union[str, Any])		->		Dict:
				__a :						List[Any]			       =  []

				if task == "s2t":
								__a :						Optional[int]			       =  hf_model.speechta.encoder.prenet.feature_encoder
								__a :						int			       =  MAPPING_S2T
								__a :						List[str]			       =  IGNORE_KEYS_S2T
				elif task == "t2s":
								__a :						int			       =  None
								__a :						List[Any]			       =  MAPPING_T2S
								__a :						List[Any]			       =  IGNORE_KEYS_T2S
				elif task == "s2s":
								__a :						Dict			       =  hf_model.speechta.encoder.prenet.feature_encoder
								__a :						int			       =  MAPPING_S2S
								__a :						Dict			       =  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

								__a :						int			       =  False
								if "conv_layers" in name:
												load_conv_layer(
												    a_   ,       a_   ,       a_   ,       a_   ,       hf_model.config.feat_extract_norm == '''group'''   ,       )
												__a :						str			       =  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:
																				__a :						List[Any]			       =  key.split('''.*.''')
																				if prefix in name and suffix in name:
																								__a :						List[Any]			       =  suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																if key in name:
																				__a :						List[Any]			       =  True
																				if "*" in mapped_key:
																								__a :						Union[str, Any]			       =  name.split(a_)[0].split('''.''')[-2]
																								__a :						Optional[Any]			       =  mapped_key.replace('''*'''   ,       a_)
																				if "weight_g" in name:
																								__a :						Dict			       =  '''weight_g'''
																				elif "weight_v" in name:
																								__a :						List[str]			       =  '''weight_v'''
																				elif "bias" in name:
																								__a :						List[Any]			       =  '''bias'''
																				elif "weight" in name:
																								__a :						List[Any]			       =  '''weight'''
																				elif "running_mean" in name:
																								__a :						Tuple			       =  '''running_mean'''
																				elif "running_var" in name:
																								__a :						Tuple			       =  '''running_var'''
																				elif "num_batches_tracked" in name:
																								__a :						Tuple			       =  '''num_batches_tracked'''
																				else:
																								__a :						Union[str, Any]			       =  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     __A      (			a_						:int   ,       a_						:str   ,       a_						:Union[str, Any]   ,       a_						:List[Any]   ,       a_						:int)		->		List[Any]:
				__a :						List[Any]			       =  full_name.split('''conv_layers.''')[-1]
				__a :						int			       =  name.split('''.''')
				__a :						Optional[Any]			       =  int(items[0])
				__a :						Union[str, Any]			       =  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.""")
												__a :						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.""")
												__a :						Optional[int]			       =  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.""")
												__a :						Union[str, 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.""")
												__a :						List[str]			       =  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     __A      (			a_						:str   ,       a_						:str   ,       a_						:Tuple   ,       a_						:Union[str, Any]=None   ,       a_						:Any=None   ,       a_						:Union[str, Any]=None   ,       )		->		Optional[Any]:
				if config_path is not None:
								__a :						int			       =  SpeechTaConfig.from_pretrained(a_)
				else:
								__a :						List[str]			       =  SpeechTaConfig()

				if task == "s2t":
								__a :						Any			       =  config.max_text_positions
								__a :						Optional[Any]			       =  SpeechTaForSpeechToText(a_)
				elif task == "t2s":
								__a :						Dict			       =  18_76
								__a :						str			       =  6_00
								__a :						str			       =  config.max_speech_positions
								__a :						List[Any]			       =  SpeechTaForTextToSpeech(a_)
				elif task == "s2s":
								__a :						List[Any]			       =  18_76
								__a :						Optional[int]			       =  config.max_speech_positions
								__a :						Optional[int]			       =  SpeechTaForSpeechToSpeech(a_)
				else:
								raise ValueError(F"""Unknown task name: {task}""")

				if vocab_path:
								__a :						Any			       =  SpeechTaTokenizer(a_   ,       model_max_length=config.max_text_positions)

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

				__a :						int			       =  SpeechTaFeatureExtractor()
				__a :						Dict			       =  SpeechTaProcessor(tokenizer=a_   ,       feature_extractor=a_)
				processor.save_pretrained(a_)

				__a :						str			       =  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__":
		A 			=  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.'''
		)

		A 			=  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"""






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
		import bsa
		from bsa import BeautifulSoup


A 			=  logging.get_logger(__name__)




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			**_UpperCAmelCase     ):
									requires_backends(self	,			['''bs4''']     )
									super().__init__(**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Tuple			       =  []
									__a :						Dict			       =  []
									__a :						int			       =  element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
													__a :						Dict			       =  parent.find_all(child.name	,			recursive=_UpperCAmelCase     )
													xpath_tags.append(child.name     )
													xpath_subscripts.append(
													    0 if 1 == len(_UpperCAmelCase     ) else next(i for i, s in enumerate(_UpperCAmelCase	,			1     ) if s is child     )     )
													__a :						Any			       =  parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Dict			       =  BeautifulSoup(_UpperCAmelCase	,			'''html.parser'''     )

									__a :						Any			       =  []
									__a :						Dict			       =  []
									__a :						Optional[int]			       =  []

									for element in html_code.descendants:
													if type(_UpperCAmelCase     ) == bsa.element.NavigableString:
																	if type(element.parent     ) != bsa.element.Tag:
																					continue

																	__a :						Dict			       =  html.unescape(_UpperCAmelCase     ).strip()
																	if not text_in_this_tag:
																					continue

																	all_doc_strings.append(_UpperCAmelCase     )

																	__a       ,						__a :						List[Any]			       =  self.xpath_soup(_UpperCAmelCase     )
																	stringaxtag_seq.append(_UpperCAmelCase     )
																	stringaxsubs_seq.append(_UpperCAmelCase     )

									if len(_UpperCAmelCase     ) != len(_UpperCAmelCase     ):
													raise ValueError('''Number of doc strings and xtags does not correspond'''     )
									if len(_UpperCAmelCase     ) != len(_UpperCAmelCase     ):
													raise ValueError('''Number of doc strings and xsubs does not correspond'''     )

									return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						List[str]			       =  ''''''
									for tagname, subs in zip(_UpperCAmelCase	,			_UpperCAmelCase     ):
													xpath += f"""/{tagname}"""
													if subs != 0:
																	xpath += f"""[{subs}]"""
									return xpath







					def __call__(						self	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  False

									# Check that strings has a valid type
									if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
													__a :						str			       =  True
									elif isinstance(_UpperCAmelCase	,			(list, tuple)     ):
													if len(_UpperCAmelCase     ) == 0 or isinstance(html_strings[0]	,			_UpperCAmelCase     ):
																	__a :						Optional[Any]			       =  True

									if not valid_strings:
													raise ValueError(
													    '''HTML strings must of type `str`, `List[str]` (batch of examples), '''
													    f"""but is of type {type(_UpperCAmelCase     )}."""     )

									__a :						List[str]			       =  bool(isinstance(_UpperCAmelCase	,			(list, tuple)     ) and (isinstance(html_strings[0]	,			_UpperCAmelCase     ))     )

									if not is_batched:
													__a :						List[Any]			       =  [html_strings]

									# Get nodes + xpaths
									__a :						Tuple			       =  []
									__a :						int			       =  []
									for html_string in html_strings:
													__a       ,						__a       ,						__a :						Any			       =  self.get_three_from_single(_UpperCAmelCase     )
													nodes.append(_UpperCAmelCase     )
													__a :						List[Any]			       =  []
													for node, tag_list, sub_list in zip(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
																	__a :						Optional[int]			       =  self.construct_xpath(_UpperCAmelCase	,			_UpperCAmelCase     )
																	xpath_strings.append(_UpperCAmelCase     )
													xpaths.append(_UpperCAmelCase     )

									# return as Dict
									__a :						int			       =  {'''nodes''': nodes, '''xpaths''': xpaths}
									__a :						Any			       =  BatchFeature(data=_UpperCAmelCase	,			tensor_type=_UpperCAmelCase     )

									return encoded_inputs