
'''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()
_snake_case				:   List[str]     =				logging.get_logger("""transformers.models.speecht5""")

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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Dict	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   str  ):
						for attribute in key.split("."  ):
												_SCREAMING_SNAKE_CASE				= getattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )

						if weight_type is not None:
												_SCREAMING_SNAKE_CASE				= getattr(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ).shape
						else:
												_SCREAMING_SNAKE_CASE				= 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":
												_SCREAMING_SNAKE_CASE				= value
						elif weight_type == "weight_g":
												_SCREAMING_SNAKE_CASE				= value
						elif weight_type == "weight_v":
												_SCREAMING_SNAKE_CASE				= value
						elif weight_type == "bias":
												_SCREAMING_SNAKE_CASE				= value
						elif weight_type == "running_mean":
												_SCREAMING_SNAKE_CASE				= value
						elif weight_type == "running_var":
												_SCREAMING_SNAKE_CASE				= value
						elif weight_type == "num_batches_tracked":
												_SCREAMING_SNAKE_CASE				= value
						else:
												_SCREAMING_SNAKE_CASE				= value

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



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



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   Tuple	,			_SCREAMING_SNAKE_CASE		:   List[str]  ):
						_SCREAMING_SNAKE_CASE				= []

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

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

												_SCREAMING_SNAKE_CASE				= False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			hf_model.config.feat_extract_norm == "group"	,			)
																		_SCREAMING_SNAKE_CASE				= 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:
																														_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= key.split(".*."  )
																														if prefix in name and suffix in name:
																																				_SCREAMING_SNAKE_CASE				= suffix

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

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



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



@torch.no_grad()
def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   List[str]	,			_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   str=None	,			_SCREAMING_SNAKE_CASE		:   Any=None	,			_SCREAMING_SNAKE_CASE		:   Dict=None	,			):
						if config_path is not None:
												_SCREAMING_SNAKE_CASE				= SpeechTaConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
						else:
												_SCREAMING_SNAKE_CASE				= SpeechTaConfig()

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

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

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

						_SCREAMING_SNAKE_CASE				= SpeechTaFeatureExtractor()
						_SCREAMING_SNAKE_CASE				= SpeechTaProcessor(tokenizer=_SCREAMING_SNAKE_CASE	,			feature_extractor=_SCREAMING_SNAKE_CASE  )
						processor.save_pretrained(_SCREAMING_SNAKE_CASE  )

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

						model.save_pretrained(_SCREAMING_SNAKE_CASE  )

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


if __name__ == "__main__":
						_snake_case				:   Dict     =				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."""
						)

						_snake_case				:   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'''







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
_snake_case				:   Optional[Any]     =				abspath(join(dirname(dirname(dirname(__file__))), """src"""))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Tuple  ):
						from transformers.testing_utils import pytest_addoption_shared

						pytest_addoption_shared(_SCREAMING_SNAKE_CASE  )




def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						from transformers.testing_utils import pytest_terminal_summary_main

						_SCREAMING_SNAKE_CASE				= terminalreporter.config.getoption("--make-reports"  )
						if make_reports:
												pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE	,			id=_SCREAMING_SNAKE_CASE  )