


"""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()
A	:  Optional[int]            =     logging.get_logger("transformers.models.speecht5")

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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			for attribute in key.split("." ):
						__lowerCAmelCase							       =     getattr(_UpperCamelCase       ,					_UpperCamelCase )

			if weight_type is not None:
						__lowerCAmelCase							       =     getattr(_UpperCamelCase       ,					_UpperCamelCase ).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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''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 _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''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(_UpperCamelCase       ,					_UpperCamelCase ):
									logger.info(f"{name} was ignored" )
									continue

						__lowerCAmelCase							       =     False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					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(_UpperCamelCase )[0].split("." )[-2]
																		__lowerCAmelCase							       =     mapped_key.replace("*"       ,					_UpperCamelCase )
															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(_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase )
												continue
						if not is_used:
									unused_weights.append(_UpperCamelCase )

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


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase ):

			'''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(_UpperCamelCase )


@torch.no_grad()
def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase       ,					_UpperCamelCase=None       ,					_UpperCamelCase=None       ,					_UpperCamelCase=None       ,					):

			'''simple docstring'''




			if config_path is not None:
						__lowerCAmelCase							       =     SpeechTaConfig.from_pretrained(_UpperCamelCase )
			else:
						__lowerCAmelCase							       =     SpeechTaConfig()

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

			if vocab_path:
						__lowerCAmelCase							       =     SpeechTaTokenizer(_UpperCamelCase       ,					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=_UpperCamelCase       ,					rstrip=_UpperCamelCase )
						__lowerCAmelCase							       =     mask_token
						tokenizer.add_special_tokens({"mask_token": mask_token} )
						tokenizer.add_tokens(["<ctc_blank>"] )

			__lowerCAmelCase							       =     SpeechTaFeatureExtractor()
			__lowerCAmelCase							       =     SpeechTaProcessor(tokenizer=_UpperCamelCase       ,					feature_extractor=_UpperCamelCase )
			processor.save_pretrained(_UpperCamelCase )

			__lowerCAmelCase							       =     torch.load(_UpperCamelCase )
			recursively_load_weights(fairseq_checkpoint["model"]       ,					_UpperCamelCase       ,					_UpperCamelCase )

			model.save_pretrained(_UpperCamelCase )

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


if __name__ == "__main__":
	A	:  Any            =     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	:  Optional[int]            =     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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xlnet import XLNetTokenizer
else:
	A	:  Optional[Any]            =     None


A	:  Any            =     logging.get_logger(__name__)

A	:  Union[str, Any]            =     {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

A	:  List[Any]            =     {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

A	:  Union[str, Any]            =     {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

A	:  Optional[int]            =     "▁"

# Segments (not really needed)
A	:  str            =     0
A	:  Dict            =     1
A	:  List[str]            =     2
A	:  Tuple            =     3
A	:  Tuple            =     4
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[Any]							 =VOCAB_FILES_NAMES
							__UpperCAmelCase		:       Optional[int]							 =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:       Dict							 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:       List[Any]							 ="""left"""
							__UpperCAmelCase		:       List[str]							 =XLNetTokenizer

							def __init__(	self       ,  __a=None       ,  __a=None       ,  __a=False       ,  __a=True       ,  __a=False       ,  __a="<s>"       ,  __a="</s>"       ,  __a="<unk>"       ,  __a="<sep>"       ,  __a="<pad>"       ,  __a="<cls>"       ,  __a="<mask>"       ,  __a=["<eop>", "<eod>"]       ,  **__a       ,  ):
										# Mask token behave like a normal word, i.e. include the space before it
										__lowerCAmelCase							       =     AddedToken(__a       ,  lstrip=__a       ,  rstrip=__a      ) if isinstance(__a       ,  __a      ) else mask_token

										super().__init__(
										    vocab_file=__a       ,  tokenizer_file=__a       ,  do_lower_case=__a       ,  remove_space=__a       ,  keep_accents=__a       ,  bos_token=__a       ,  eos_token=__a       ,  unk_token=__a       ,  sep_token=__a       ,  pad_token=__a       ,  cls_token=__a       ,  mask_token=__a       ,  additional_special_tokens=__a       ,  **__a       ,  )

										__lowerCAmelCase							       =     3
										__lowerCAmelCase							       =     do_lower_case
										__lowerCAmelCase							       =     remove_space
										__lowerCAmelCase							       =     keep_accents
										__lowerCAmelCase							       =     vocab_file
										__lowerCAmelCase							       =     False if not self.vocab_file else True

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [self.cls_token_id]
										if token_ids_a is None:
													return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls

							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										__lowerCAmelCase							       =     [self.sep_token_id]
										__lowerCAmelCase							       =     [2]

										if token_ids_a is None:
													return len(token_ids_a + sep      ) * [0] + cls_segment_id
										return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id



							def      snake_case							(	self       ,  __a       ,  __a = None      ):
										if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."      )

										if not os.path.isdir(__a      ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
													return
										__lowerCAmelCase							       =     os.path.join(
										    __a       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

										if os.path.abspath(self.vocab_file      ) != os.path.abspath(__a      ):
													copyfile(self.vocab_file       ,  __a      )

										return (out_vocab_file,)




