






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

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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Union[str, Any]					,  snake_case_ :							Dict					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]					,  snake_case_ :							List[Any]       ):
	for attribute in key.split("."       ):
		snake_case__			:				Dict       =  getattr(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_       )

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

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







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







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							Any					,  snake_case_ :							str       ):
	snake_case__			:				Optional[Any]       =  []

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

		snake_case__			:				List[str]       =  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"					,  )
			snake_case__			:				Optional[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:
					snake_case__,						snake_case__			:				Optional[Any]       =  key.split(".*."       )
					if prefix in name and suffix in name:
						snake_case__			:				Optional[Any]       =  suffix

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

	if task == "s2t":
		snake_case__			:				str       =  config.max_text_positions
		snake_case__			:				Tuple       =  SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE_       )
	elif task == "t2s":
		snake_case__			:				Union[str, Any]       =  1876
		snake_case__			:				Dict       =  600
		snake_case__			:				str       =  config.max_speech_positions
		snake_case__			:				str       =  SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE_       )
	elif task == "s2s":
		snake_case__			:				List[str]       =  1876
		snake_case__			:				Union[str, Any]       =  config.max_speech_positions
		snake_case__			:				List[str]       =  SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE_       )
	else:
		raise ValueError(F'''Unknown task name: {task}'''       )

	if vocab_path:
		snake_case__			:				str       =  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
		snake_case__			:				int       =  AddedToken("<mask>"					,  lstrip=SCREAMING_SNAKE_CASE_					,  rstrip=SCREAMING_SNAKE_CASE_       )
		snake_case__			:				List[Any]       =  mask_token
		tokenizer.add_special_tokens({"mask_token": mask_token}       )
		tokenizer.add_tokens(["<ctc_blank>"]       )

	snake_case__			:				List[Any]       =  SpeechTaFeatureExtractor()
	snake_case__			:				Union[str, Any]       =  SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE_					,  feature_extractor=SCREAMING_SNAKE_CASE_       )
	processor.save_pretrained(SCREAMING_SNAKE_CASE_       )

	snake_case__			:				List[str]       =  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__":
	__lowerCamelCase :      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."""
	)

	__lowerCamelCase :      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,
	)











def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float(moles / volume       ) * nfactor       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (volume)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((moles * 0.08_21 * temperature) / (pressure)       )       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							float					,  snake_case_ :							float					,  snake_case_ :							float       ):
	return round(float((pressure * volume) / (0.08_21 * moles)       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()



