
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()
lowercase__          =logging.get_logger('transformers.models.speecht5')

lowercase__          ={
    "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",
}
lowercase__          ={
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
lowercase__          ={
    "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",
}
lowercase__          ={
    "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",
}
lowercase__          ={
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
lowercase__          ={
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
lowercase__          ={
    "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",
}
lowercase__          ={
    "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",
}
lowercase__          ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase__          ={
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase__          ={
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase__          =[]
lowercase__          =[
    "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",
]
lowercase__          =IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
lowercase__          =IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
lowercase__          =IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    List[Any]  ):
	for attribute in key.split('''.'''  ):
		__a			:  Optional[int]		    =  getattr(_UpperCAmelCase  ,       _UpperCAmelCase  )

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

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




def        __UpperCamelCase					(    lowerCAmelCase__							:    Union[str, Any]  ,       lowerCAmelCase__							:    List[str]  ):
	for key in ignore_keys:
		if key.endswith('''.*'''  ):
			if name.startswith(key[:-1]  ):
				return True
		elif ".*." in key:
			__a			:  Optional[int]		    =  key.split('''.*.'''  )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False




def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    Optional[Any]  ,       lowerCAmelCase__							:    Dict  ):
	__a			:  List[Any]		    =  []

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

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

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

	if vocab_path:
		__a			:  Dict		    =  SpeechTaTokenizer(_UpperCAmelCase  ,       model_max_length=config.max_text_positions  )

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

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

	__a			:  Union[str, Any]		    =  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__":
						lowercase__          =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.'
						)

						lowercase__          =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

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase__          =10




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  if array[i] == target:
   return i
 return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 __a			:  List[Any]		    =  0
 __a			:  Union[str, Any]		    =  len(lowerCAmelCase__  )
 while left <= right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

  __a			:  str		    =  (left + right) // 3 + 1
  __a			:  List[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   __a			:  List[str]		    =  one_third - 1
  elif array[two_third] < target:
   __a			:  List[str]		    =  two_third + 1

  else:
   __a			:  Dict		    =  one_third + 1
   __a			:  Any		    =  two_third - 1
 else:
  return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 if left < right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Union[str, Any]		    =  (left + right) // 3 + 1
  __a			:  Optional[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(lowerCAmelCase__  ,       one_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  else:
   return rec_ternary_search(one_third + 1  ,       two_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase__          =input('Enter numbers separated by comma:\n').strip()
      lowercase__          =[int(item.strip()) for item in user_input.split(',')]
      assert collection == sorted(collection), F"List must be ordered.\n{collection}."
      lowercase__          =int(input('Enter the number to be found in the list:\n').strip())
      lowercase__          =ite_ternary_search(collection, target)
      lowercase__          =rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(F"""Iterative search: {target} found at positions: {resulta}""")
            print(F"""Recursive search: {target} found at positions: {resulta}""")
      else:
            print('Not found')
