






'''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()
UpperCamelCase__:     Dict										=    logging.get_logger("transformers.models.speecht5")

UpperCamelCase__:     Optional[int]										=    {
    "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",
}
UpperCamelCase__:     Dict										=    {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
UpperCamelCase__:     Optional[Any]										=    {
    "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",
}
UpperCamelCase__:     Optional[int]										=    {
    "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",
}
UpperCamelCase__:     Any										=    {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
UpperCamelCase__:     Optional[Any]										=    {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
UpperCamelCase__:     Dict										=    {
    "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",
}
UpperCamelCase__:     Optional[Any]										=    {
    "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",
}
UpperCamelCase__:     Optional[int]										=    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
UpperCamelCase__:     Optional[int]										=    {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__:     Optional[int]										=    {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
UpperCamelCase__:     str										=    []
UpperCamelCase__:     Union[str, Any]										=    [
    "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",
]
UpperCamelCase__:     Tuple										=    IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
UpperCamelCase__:     List[Any]										=    IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
UpperCamelCase__:     Any										=    IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Tuple						)   ->     Optional[Any]:

							for attribute in key.split('''.'''						):
														UpperCAmelCase				: List[Any]											=	getattr(_lowerCAmelCase     ,   _lowerCAmelCase						)

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

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


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

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


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		List[str]						)   ->     List[str]:
							UpperCAmelCase				: int											=	[]

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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																												if key in name:
																																			UpperCAmelCase				: List[Any]											=	True
																																			if "*" in mapped_key:
																																										UpperCAmelCase				: str											=	name.split(_lowerCAmelCase						)[0].split('''.'''						)[-2]
																																										UpperCAmelCase				: List[Any]											=	mapped_key.replace('''*'''     ,   _lowerCAmelCase						)
																																			if "weight_g" in name:
																																										UpperCAmelCase				: Optional[int]											=	'''weight_g'''
																																			elif "weight_v" in name:
																																										UpperCAmelCase				: List[Any]											=	'''weight_v'''
																																			elif "bias" in name:
																																										UpperCAmelCase				: Union[str, Any]											=	'''bias'''
																																			elif "weight" in name:
																																										UpperCAmelCase				: Optional[Any]											=	'''weight'''
																																			elif "running_mean" in name:
																																										UpperCAmelCase				: Optional[int]											=	'''running_mean'''
																																			elif "running_var" in name:
																																										UpperCAmelCase				: int											=	'''running_var'''
																																			elif "num_batches_tracked" in name:
																																										UpperCAmelCase				: Optional[Any]											=	'''num_batches_tracked'''
																																			else:
																																										UpperCAmelCase				: str											=	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 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Optional[Any]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		int						)   ->     Optional[int]:
							UpperCAmelCase				: str											=	full_name.split('''conv_layers.'''						)[-1]
							UpperCAmelCase				: Optional[int]											=	name.split('''.'''						)
							UpperCAmelCase				: Optional[Any]											=	int(items[0]						)
							UpperCAmelCase				: Dict											=	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."""						)
																					UpperCAmelCase				: Tuple											=	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."""						)
																					UpperCAmelCase				: List[Any]											=	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."""						)
																					UpperCAmelCase				: 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."""						)
																					UpperCAmelCase				: str											=	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 				snake_case_  (      _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		Union[str, Any]=None     ,   _lowerCAmelCase						:		Dict=None     ,   _lowerCAmelCase						:		List[Any]=None     ,   )   ->     List[Any]:

							if config_path is not None:
														UpperCAmelCase				: Any											=	SpeechTaConfig.from_pretrained(_lowerCAmelCase						)
							else:
														UpperCAmelCase				: str											=	SpeechTaConfig()

							if task == "s2t":
														UpperCAmelCase				: List[str]											=	config.max_text_positions
														UpperCAmelCase				: int											=	SpeechTaForSpeechToText(_lowerCAmelCase						)
							elif task == "t2s":
														UpperCAmelCase				: List[str]											=	1876
														UpperCAmelCase				: Any											=	600
														UpperCAmelCase				: List[str]											=	config.max_speech_positions
														UpperCAmelCase				: Tuple											=	SpeechTaForTextToSpeech(_lowerCAmelCase						)
							elif task == "s2s":
														UpperCAmelCase				: int											=	1876
														UpperCAmelCase				: List[str]											=	config.max_speech_positions
														UpperCAmelCase				: int											=	SpeechTaForSpeechToSpeech(_lowerCAmelCase						)
							else:
														raise ValueError(f"""Unknown task name: {task}"""						)

							if vocab_path:
														UpperCAmelCase				: Tuple											=	SpeechTaTokenizer(_lowerCAmelCase     ,   model_max_length=config.max_text_positions						)

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

							UpperCAmelCase				: int											=	SpeechTaFeatureExtractor()
							UpperCAmelCase				: str											=	SpeechTaProcessor(tokenizer=_lowerCAmelCase     ,   feature_extractor=_lowerCAmelCase						)
							processor.save_pretrained(_lowerCAmelCase						)

							UpperCAmelCase				: Optional[int]											=	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__":
						UpperCamelCase__:     Optional[int]										=    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."
						)

						UpperCamelCase__:     Optional[Any]										=    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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





