





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

lowerCAmelCase_ : int	  =       {
    "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[Any]	  =       {
    "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_ : List[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_ : List[str]	  =       {
    "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_ : Dict	  =       {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
lowerCAmelCase_ : Dict	  =       {
    "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_ : Dict	  =       {
    "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_ : Optional[int]	  =       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_ : List[str]	  =       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowerCAmelCase_ : int	  =       []
lowerCAmelCase_ : Union[str, Any]	  =       [
    "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_ : Dict	  =       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_ : Optional[int]	  =       IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]







def  _lowerCamelCase	(__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  str       ,	__lowerCamelCase	:  Optional[Any]       ,	__lowerCamelCase	:  Optional[int]       ,	__lowerCamelCase	:  int  ) ->     Any:
			for attribute in key.split("."  ):
						a__	 =			getattr(_UpperCamelCase       ,	_UpperCamelCase  )

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

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







def  _lowerCamelCase	(__lowerCamelCase	:  Tuple       ,	__lowerCamelCase	:  List[str]  ) ->     Dict:
			for key in ignore_keys:
						if key.endswith(".*"  ):
									if name.startswith(key[:-1]  ):
												return True
						elif ".*." in key:
									a__ ,					a__	 =			key.split(".*."  )
									if prefix in name and suffix in name:
												return True
						elif key in name:
									return True
			return False







def  _lowerCamelCase	(__lowerCamelCase	:  int       ,	__lowerCamelCase	:  Dict       ,	__lowerCamelCase	:  List[Any]  ) ->     Tuple:
			a__	 =			[]

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

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

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

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

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

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

			a__	 =			SpeechTaFeatureExtractor()
			a__	 =			SpeechTaProcessor(tokenizer=_UpperCamelCase       ,	feature_extractor=_UpperCamelCase  )
			processor.save_pretrained(_UpperCamelCase  )

			a__	 =			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_ : 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_ : Union[str, 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,
		)








from __future__ import annotations





def 		a__      (		_UpperCamelCase      :			list[float]				):
   if len(_UpperCamelCase				) < 2:
      raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''				)
   if any(i <= 0 for i in nums				):
      raise ValueError('''All values must be greater than 0'''				)
   __lowerCamelCase    				=     nums.copy()
   copy_nums.sort()
   return copy_nums[-1] < sum(copy_nums[:-1]				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()






