

'''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__    							= logging.get_logger("transformers.models.speecht5")

lowerCAmelCase__    							= {
    "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__    							= {
    "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__    							= {
    "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__    							= {
    "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__    							= {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
lowerCAmelCase__    							= {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
lowerCAmelCase__    							= {
    "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__    							= {
    "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__    							= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowerCAmelCase__    							= {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__    							= {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase__    							= []
lowerCAmelCase__    							= [
    "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__    							= IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
lowerCAmelCase__    							= IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
lowerCAmelCase__    							= IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	for attribute in key.split('.'  ):
		UpperCAmelCase_		:      Optional[Any]       	=					getattr(UpperCamelCase   ,UpperCamelCase  )

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

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







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

	if task == "s2t":
		UpperCAmelCase_		:      int       	=					hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase_		:      str       	=					MAPPING_S2T
		UpperCAmelCase_		:      Optional[int]       	=					IGNORE_KEYS_S2T
	elif task == "t2s":
		UpperCAmelCase_		:      List[str]       	=					None
		UpperCAmelCase_		:      Tuple       	=					MAPPING_T2S
		UpperCAmelCase_		:      Any       	=					IGNORE_KEYS_T2S
	elif task == "s2s":
		UpperCAmelCase_		:      Any       	=					hf_model.speechta.encoder.prenet.feature_encoder
		UpperCAmelCase_		:      Optional[int]       	=					MAPPING_S2S
		UpperCAmelCase_		:      Any       	=					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_		:      Optional[int]       	=					False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,hf_model.config.feat_extract_norm == 'group'   ,)
			UpperCAmelCase_		:      Union[str, 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_		:      int       	=					key.split('.*.'  )
					if prefix in name and suffix in name:
						UpperCAmelCase_		:      Union[str, Any]       	=					suffix

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

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

	if vocab_path:
		UpperCAmelCase_		:      int       	=					SpeechTaTokenizer(UpperCamelCase   ,model_max_length=config.max_text_positions  )

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

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

	UpperCAmelCase_		:      int       	=					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__    							= 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__    							= 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 __future__ import annotations

from typing import Any



class 					lowercase						(  a_ ):
							pass



class 					lowercase						:


							def __init__(			self    ,			_snake_case)							->				None:
								UpperCAmelCase_		:      Any       	=					data
								UpperCAmelCase_		:      Node | None       	=					None


							def __iter__(			self)							->				Optional[int]:
								UpperCAmelCase_		:      int       	=					self
								UpperCAmelCase_		:      List[str]       	=					[]
								while node:
									if node in visited:
										raise ContainsLoopError
									visited.append(_snake_case)
									yield node.data
									UpperCAmelCase_		:      Tuple       	=					node.next_node





							@property
							def 						_snake_case							(			self)							->				bool:
								try:
									list(self)
									return False
								except ContainsLoopError:
									return True


if __name__ == "__main__":
						lowerCAmelCase__										= Node(1)
						lowerCAmelCase__										= Node(2)
						lowerCAmelCase__										= Node(3)
						lowerCAmelCase__										= Node(4)
						print(root_node.has_loop)  # False
						lowerCAmelCase__										= root_node.next_node
						print(root_node.has_loop)  # True

						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						lowerCAmelCase__										= Node(5)
						lowerCAmelCase__										= Node(6)
						print(root_node.has_loop)  # False

						lowerCAmelCase__										= Node(1)
						print(root_node.has_loop)  # False






