

import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :      List[Any]			 =logging.get_logger(__name__)


_lowerCAmelCase  :      str			 =["""model.decoder.embed_positions.weights"""]



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if "emb" in name:
		UpperCAmelCase__:	Any									= name.replace("emb"	,"model.decoder.embed_tokens"      )
	if "transformer" in name:
		UpperCAmelCase__:	int									= name.replace("transformer"	,"model.decoder"      )
	if "cross_attention" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("cross_attention"	,"encoder_attn"      )
	if "linear1" in name:
		UpperCAmelCase__:	List[str]									= name.replace("linear1"	,"fc1"      )
	if "linear2" in name:
		UpperCAmelCase__:	List[Any]									= name.replace("linear2"	,"fc2"      )
	if "norm1" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("norm1"	,"self_attn_layer_norm"      )
	if "norm_cross" in name:
		UpperCAmelCase__:	Union[str, Any]									= name.replace("norm_cross"	,"encoder_attn_layer_norm"      )
	if "norm2" in name:
		UpperCAmelCase__:	Any									= name.replace("norm2"	,"final_layer_norm"      )
	if "out_norm" in name:
		UpperCAmelCase__:	str									= name.replace("out_norm"	,"model.decoder.layer_norm"      )
	if "linears" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("linears"	,"lm_heads"      )
	if "condition_provider.conditioners.description.output_proj" in name:
		UpperCAmelCase__:	Optional[int]									= name.replace("condition_provider.conditioners.description.output_proj"	,"enc_to_dec_proj"      )
	return name



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	int									= list(state_dict.keys()      )
	UpperCAmelCase__:	int									= {}
	for key in keys:
		UpperCAmelCase__:	Tuple									= state_dict.pop(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	List[Any]									= rename_keys(SCREAMING_SNAKE_CASE      )
		if "in_proj_weight" in key:
			# split fused qkv proj
			UpperCAmelCase__:	List[str]									= val[:hidden_size, :]
			UpperCAmelCase__:	int									= val[hidden_size : 2 * hidden_size, :]
			UpperCAmelCase__:	Optional[Any]									= val[-hidden_size:, :]
		elif "enc_to_dec_proj" in key:
			UpperCAmelCase__:	int									= val
		else:
			UpperCAmelCase__:	List[str]									= val
	return state_dict, enc_dec_proj_state_dict



def 			_A	(      SCREAMING_SNAKE_CASE      ):
	if checkpoint == "small":
		# default config values
		UpperCAmelCase__:	Dict									= 1_0_2_4
		UpperCAmelCase__:	Optional[Any]									= 2_4
		UpperCAmelCase__:	Union[str, Any]									= 1_6
	elif checkpoint == "medium":
		UpperCAmelCase__:	Optional[int]									= 1_5_3_6
		UpperCAmelCase__:	Optional[Any]									= 4_8
		UpperCAmelCase__:	Tuple									= 2_4
	elif checkpoint == "large":
		UpperCAmelCase__:	Dict									= 2_0_4_8
		UpperCAmelCase__:	Optional[Any]									= 4_8
		UpperCAmelCase__:	Union[str, Any]									= 3_2
	else:
		raise ValueError(f"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."      )
	UpperCAmelCase__:	Tuple									= MusicgenDecoderConfig(
	    hidden_size=SCREAMING_SNAKE_CASE	,ffn_dim=hidden_size * 4	,num_hidden_layers=SCREAMING_SNAKE_CASE	,num_attention_heads=SCREAMING_SNAKE_CASE	,)
	return config



@torch.no_grad()
def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE="cpu"      ):
	UpperCAmelCase__:	Optional[int]									= MusicGen.get_pretrained(SCREAMING_SNAKE_CASE	,device=SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Dict									= fairseq_model.lm.state_dict()
	UpperCAmelCase__							,						UpperCAmelCase__:	List[Any]									= rename_state_dict(
	    SCREAMING_SNAKE_CASE	,hidden_size=decoder_config.hidden_size      )

	UpperCAmelCase__:	Dict									= TaEncoderModel.from_pretrained("t5-base"      )
	UpperCAmelCase__:	Dict									= EncodecModel.from_pretrained("facebook/encodec_32khz"      )
	UpperCAmelCase__:	Tuple									= MusicgenForCausalLM(SCREAMING_SNAKE_CASE      ).eval()

	# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
	UpperCAmelCase__							,						UpperCAmelCase__:	Optional[int]									= decoder.load_state_dict(SCREAMING_SNAKE_CASE	,strict=SCREAMING_SNAKE_CASE      )

	for key in missing_keys.copy():
		if key.startswith(("text_encoder", "audio_encoder")      ) or key in EXPECTED_MISSING_KEYS:
			missing_keys.remove(SCREAMING_SNAKE_CASE      )

	if len(SCREAMING_SNAKE_CASE      ) > 0:
		raise ValueError(f"Missing key(s) in state_dict: {missing_keys}"      )

	if len(SCREAMING_SNAKE_CASE      ) > 0:
		raise ValueError(f"Unexpected key(s) in state_dict: {unexpected_keys}"      )

	# init the composite model
	UpperCAmelCase__:	Dict									= MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE	,audio_encoder=SCREAMING_SNAKE_CASE	,decoder=SCREAMING_SNAKE_CASE      )

	# load the pre-trained enc-dec projection (from the decoder state dict)
	model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE      )

	# check we can do a forward pass
	UpperCAmelCase__:	Dict									= torch.arange(0	,8	,dtype=torch.long      ).reshape(2	,-1      )
	UpperCAmelCase__:	Union[str, Any]									= input_ids.reshape(2 * 4	,-1      )

	with torch.no_grad():
		UpperCAmelCase__:	str									= model(input_ids=SCREAMING_SNAKE_CASE	,decoder_input_ids=SCREAMING_SNAKE_CASE      ).logits

	if logits.shape != (8, 1, 2_0_4_8):
		raise ValueError("Incorrect shape for logits"      )

	# now construct the processor
	UpperCAmelCase__:	Tuple									= AutoTokenizer.from_pretrained("t5-base"      )
	UpperCAmelCase__:	Union[str, Any]									= AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"	,padding_side="left"      )

	UpperCAmelCase__:	Tuple									= MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE	,tokenizer=SCREAMING_SNAKE_CASE      )

	# set the appropriate bos/pad token ids
	UpperCAmelCase__:	Tuple									= 2_0_4_8
	UpperCAmelCase__:	List[str]									= 2_0_4_8

	# set other default generation config params
	UpperCAmelCase__:	Union[str, Any]									= int(3_0 * audio_encoder.config.frame_rate      )
	UpperCAmelCase__:	Tuple									= True
	UpperCAmelCase__:	str									= 3.0

	if pytorch_dump_folder is not None:
		Path(SCREAMING_SNAKE_CASE      ).mkdir(exist_ok=SCREAMING_SNAKE_CASE      )
		logger.info(f"Saving model {checkpoint} to {pytorch_dump_folder}"      )
		model.save_pretrained(SCREAMING_SNAKE_CASE      )
		processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	if repo_id:
		logger.info(f"Pushing model {checkpoint} to {repo_id}"      )
		model.push_to_hub(SCREAMING_SNAKE_CASE      )
		processor.push_to_hub(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
					_lowerCAmelCase  :      str			 =argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--checkpoint""",
					    default="""small""",
					    type=str,
					    help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder""",
					    required=True,
					    default=None,
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					parser.add_argument(
					    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
					)
					parser.add_argument(
					    """--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
					)

					_lowerCAmelCase  :      Union[str, Any]			 =parser.parse_args()
					convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)


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

_lowerCAmelCase  :      List[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  :      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  :      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""",
}
_lowerCAmelCase  :      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""",
}
_lowerCAmelCase  :      List[Any]			 ={
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_lowerCAmelCase  :      Any			 ={
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_lowerCAmelCase  :      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""",
}
_lowerCAmelCase  :      List[str]			 ={
    """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  :      List[Any]			 ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowerCAmelCase  :      int			 ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase  :      Dict			 ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowerCAmelCase  :      Optional[Any]			 =[]
_lowerCAmelCase  :      int			 =[
    """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  :      Any			 =IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
_lowerCAmelCase  :      int			 =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 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for attribute in key.split("."      ):
		UpperCAmelCase__:	List[Any]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	if weight_type is not None:
		UpperCAmelCase__:	Optional[int]									= getattr(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ).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__:	str									= value
	elif weight_type == "weight_v":
		UpperCAmelCase__:	Optional[int]									= value
	elif weight_type == "bias":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "running_mean":
		UpperCAmelCase__:	Optional[Any]									= value
	elif weight_type == "running_var":
		UpperCAmelCase__:	Union[str, Any]									= value
	elif weight_type == "num_batches_tracked":
		UpperCAmelCase__:	Optional[int]									= value
	else:
		UpperCAmelCase__:	Union[str, Any]									= value

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



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key in ignore_keys:
		if key.endswith(".*"      ):
			if name.startswith(key[:-1]      ):
				return True
		elif ".*." in key:
			UpperCAmelCase__							,						UpperCAmelCase__:	Union[str, Any]									= 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	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[Any]									= []

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

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

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

	if task == "s2t":
		UpperCAmelCase__:	List[Any]									= config.max_text_positions
		UpperCAmelCase__:	List[str]									= SpeechTaForSpeechToText(SCREAMING_SNAKE_CASE      )
	elif task == "t2s":
		UpperCAmelCase__:	Dict									= 1_8_7_6
		UpperCAmelCase__:	Dict									= 6_0_0
		UpperCAmelCase__:	str									= config.max_speech_positions
		UpperCAmelCase__:	int									= SpeechTaForTextToSpeech(SCREAMING_SNAKE_CASE      )
	elif task == "s2s":
		UpperCAmelCase__:	Any									= 1_8_7_6
		UpperCAmelCase__:	List[str]									= config.max_speech_positions
		UpperCAmelCase__:	Union[str, Any]									= SpeechTaForSpeechToSpeech(SCREAMING_SNAKE_CASE      )
	else:
		raise ValueError(f"Unknown task name: {task}"      )

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

	UpperCAmelCase__:	int									= SpeechTaFeatureExtractor()
	UpperCAmelCase__:	Optional[Any]									= SpeechTaProcessor(tokenizer=SCREAMING_SNAKE_CASE	,feature_extractor=SCREAMING_SNAKE_CASE      )
	processor.save_pretrained(SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	int									= 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  :      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[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,
					)