'''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()
__lowerCAmelCase	:       Any						       =	logging.get_logger("transformers.models.speecht5")

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





def       lowerCAmelCase					(		UpperCamelCase__				:   Union[str, Any]    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Optional[int]       ):


	"""simple docstring"""
	for attribute in key.split('''.'''       ):
		__UpperCAmelCase							=			getattr(UpperCamelCase__    ,					UpperCamelCase__       )

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

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





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Any       ):


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





def       lowerCAmelCase					(		UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			[]

	if task == "s2t":
		__UpperCAmelCase							=			hf_model.speechta.encoder.prenet.feature_encoder
		__UpperCAmelCase							=			MAPPING_S2T
		__UpperCAmelCase							=			IGNORE_KEYS_S2T
	elif task == "t2s":
		__UpperCAmelCase							=			None
		__UpperCAmelCase							=			MAPPING_T2S
		__UpperCAmelCase							=			IGNORE_KEYS_T2S
	elif task == "s2s":
		__UpperCAmelCase							=			hf_model.speechta.encoder.prenet.feature_encoder
		__UpperCAmelCase							=			MAPPING_S2S
		__UpperCAmelCase							=			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

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

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
				if key in name:
					__UpperCAmelCase							=			True
					if "*" in mapped_key:
						__UpperCAmelCase							=			name.split(UpperCamelCase__       )[0].split('''.'''       )[-2]
						__UpperCAmelCase							=			mapped_key.replace('''*'''    ,					UpperCamelCase__       )
					if "weight_g" in name:
						__UpperCAmelCase							=			'''weight_g'''
					elif "weight_v" in name:
						__UpperCAmelCase							=			'''weight_v'''
					elif "bias" in name:
						__UpperCAmelCase							=			'''bias'''
					elif "weight" in name:
						__UpperCAmelCase							=			'''weight'''
					elif "running_mean" in name:
						__UpperCAmelCase							=			'''running_mean'''
					elif "running_var" in name:
						__UpperCAmelCase							=			'''running_var'''
					elif "num_batches_tracked" in name:
						__UpperCAmelCase							=			'''num_batches_tracked'''
					else:
						__UpperCAmelCase							=			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__				:   List[str]    ,					UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   str       ):


	"""simple docstring"""
	__UpperCAmelCase							=			full_name.split('''conv_layers.'''       )[-1]
	__UpperCAmelCase							=			name.split('''.'''       )
	__UpperCAmelCase							=			int(items[0]       )
	__UpperCAmelCase							=			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							=			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							=			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							=			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							=			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__				:   List[str]    ,					UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Tuple    ,					UpperCamelCase__				:   Optional[Any]=None    ,					UpperCamelCase__				:   List[str]=None    ,					UpperCamelCase__				:   Tuple=None    ,					):


	"""simple docstring"""
	if config_path is not None:
		__UpperCAmelCase							=			SpeechTaConfig.from_pretrained(UpperCamelCase__       )
	else:
		__UpperCAmelCase							=			SpeechTaConfig()

	if task == "s2t":
		__UpperCAmelCase							=			config.max_text_positions
		__UpperCAmelCase							=			SpeechTaForSpeechToText(UpperCamelCase__       )
	elif task == "t2s":
		__UpperCAmelCase							=			1_8_7_6
		__UpperCAmelCase							=			6_0_0
		__UpperCAmelCase							=			config.max_speech_positions
		__UpperCAmelCase							=			SpeechTaForTextToSpeech(UpperCamelCase__       )
	elif task == "s2s":
		__UpperCAmelCase							=			1_8_7_6
		__UpperCAmelCase							=			config.max_speech_positions
		__UpperCAmelCase							=			SpeechTaForSpeechToSpeech(UpperCamelCase__       )
	else:
		raise ValueError(f"""Unknown task name: {task}"""       )

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

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

	__UpperCAmelCase							=			SpeechTaFeatureExtractor()
	__UpperCAmelCase							=			SpeechTaProcessor(tokenizer=UpperCamelCase__    ,					feature_extractor=UpperCamelCase__       )
	processor.save_pretrained(UpperCamelCase__       )

	__UpperCAmelCase							=			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__":
					__lowerCAmelCase	:       List[str]						       =	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	:       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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


__lowerCAmelCase	:       List[Any]						       =	{
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       str						       =	[
					    "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ASTForAudioClassification",
					    "ASTModel",
					    "ASTPreTrainedModel",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCAmelCase	:       Union[str, Any]						       =	["ASTFeatureExtractor"]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					__lowerCAmelCase	:       Optional[Any]						       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
