




"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)

A_						=				{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
A_						=				[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					Dict		,   snake_case__							:					Any		,   snake_case__							:					str		,   snake_case__							:					str    ):
  """simple docstring"""

  for attribute in key.split("""."""    ):
    _snake_case							:		Optional[Any]								=  getattr(snake_case__		,   snake_case__    )

  if weight_type is not None:
    _snake_case							:		Optional[Any]								=  getattr(snake_case__		,   snake_case__    ).shape
  else:
    _snake_case							:		Optional[Any]								=  hf_pointer.shape

  assert hf_shape == value.shape, (
      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							:		int								=  value
  elif weight_type == "weight_g":
    _snake_case							:		str								=  value
  elif weight_type == "weight_v":
    _snake_case							:		Tuple								=  value
  elif weight_type == "bias":
    _snake_case							:		List[str]								=  value
  else:
    _snake_case							:		int								=  value

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


def 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  []
  _snake_case							:		Optional[Any]								=  fairseq_model.state_dict()

  _snake_case							:		str								=  hf_model.feature_extractor

  # if encoder has different dim to decoder -> use proj_weight
  _snake_case							:		Optional[Any]								=  None

  for name, value in fairseq_dict.items():
    _snake_case							:		Optional[Any]								=  False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   hf_model.config.feat_extract_norm == """group"""		,   )
      _snake_case							:		Dict								=  True
    elif name.split("""."""    )[0] == "proj":
      _snake_case							:		Dict								=  fairseq_model.proj
      _snake_case							:		Optional[int]								=  True
    else:
      for key, mapped_key in MAPPING.items():
        if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
          _snake_case							:		Dict								=  True
          if "*" in mapped_key:
            _snake_case							:		Optional[int]								=  name.split(snake_case__    )[0].split("""."""    )[-2]
            _snake_case							:		Union[str, Any]								=  mapped_key.replace("""*"""		,   snake_case__    )
          if "weight_g" in name:
            _snake_case							:		str								=  """weight_g"""
          elif "weight_v" in name:
            _snake_case							:		Optional[Any]								=  """weight_v"""
          elif "bias" in name:
            _snake_case							:		Union[str, Any]								=  """bias"""
          elif "weight" in name:
            _snake_case							:		int								=  """weight"""
          else:
            _snake_case							:		Optional[int]								=  None
          set_recursively(snake_case__		,   snake_case__		,   snake_case__		,   snake_case__		,   snake_case__    )
        continue
    if not is_used:
      unused_weights.append(snake_case__    )

  logger.warning(F"Unused weights: {unused_weights}"    )

  return proj_weight


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Dict		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					Union[str, Any]		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Any								=  full_name.split("""conv_layers."""    )[-1]
  _snake_case							:		Optional[int]								=  name.split("""."""    )
  _snake_case							:		List[str]								=  int(items[0]    )
  _snake_case							:		Dict								=  int(items[1]    )

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          F"{full_name} has size {value.shape}, but"
          F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
      )
      _snake_case							:		Tuple								=  value
      logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          F"{full_name} has size {value.shape}, but"
          F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
      )
      _snake_case							:		List[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:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
          " found."
      )
      _snake_case							:		int								=  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          F"{full_name} has size {value.shape}, but"
          F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
      )
      _snake_case							:		List[str]								=  value
      logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
  else:
    unused_weights.append(snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  _snake_case		,					_snake_case							:		Optional[Any]								=  emb.weight.shape
  _snake_case							:		Optional[int]								=  nn.Linear(snake_case__		,   snake_case__		,   bias=snake_case__    )
  _snake_case							:		Union[str, Any]								=  emb.weight.data
  return lin_layer


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  with open(snake_case__		,   """r"""		,   encoding="""utf-8"""    ) as f:
    _snake_case							:		Any								=  f.readlines()
    _snake_case							:		Optional[Any]								=  [line.split(""" """    )[0] for line in lines]

  _snake_case							:		str								=  len(snake_case__    )

  _snake_case							:		Tuple								=  {
      """<s>""": 0,
      """<pad>""": 1,
      """</s>""": 2,
      """<unk>""": 3,
  }

  vocab_dict.update(dict(zip(snake_case__		,   range(4		,   num_words + 4    )    )    )    )
  return vocab_dict


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					int		,   snake_case__							:					List[str]		,   snake_case__							:					int		,   snake_case__							:					Dict		,   snake_case__							:					List[Any]		,   snake_case__							:					str		,   snake_case__							:					Union[str, Any]		,   ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  WavaVecaConfig.from_pretrained(snake_case__    )
  _snake_case							:		List[str]								=  SpeechaTextaConfig.from_pretrained(
      snake_case__		,   vocab_size=snake_case__		,   decoder_layers=snake_case__		,   do_stable_layer_norm=snake_case__    )

  _snake_case							:		Dict								=  WavaVecaFeatureExtractor(
      feature_size=1		,   sampling_rate=1_60_00		,   padding_value=0		,   do_normalize=snake_case__		,   return_attention_mask=snake_case__		,   )

  _snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]		,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
  _snake_case							:		Optional[Any]								=  model[0].eval()

  # set weights for wav2vec2 encoder
  _snake_case							:		Any								=  WavaVecaModel(snake_case__    )
  _snake_case							:		Optional[Any]								=  recursively_load_weights_wavaveca(model.encoder		,   snake_case__    )

  _snake_case							:		Optional[Any]								=  SpeechaTextaForCausalLM(snake_case__    )
  _snake_case		,					_snake_case							:		List[str]								=  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,   strict=snake_case__    )

  # set output linear layer
  unexpected_keys.remove("""embed_out"""    )
  _snake_case							:		Any								=  nn.Parameter(model.decoder.embed_out.detach()    )

  # layer norm is init to identity matrix so leaving it is fine
  logger.warning(F"The following keys are missing when loading the decoder weights: {missing_keys}"    )
  logger.warning(F"The following keys are unexpected when loading the decoder weights: {unexpected_keys}"    )

  _snake_case							:		Any								=  SpeechEncoderDecoderModel(encoder=snake_case__		,   decoder=snake_case__    )
  _snake_case							:		Any								=  False

  # add projection layer
  _snake_case							:		int								=  nn.Parameter(projection_layer.weight    )
  _snake_case							:		Any								=  nn.Parameter(projection_layer.bias    )

  _snake_case							:		Any								=  create_vocab_dict(snake_case__    )

  with open(os.path.join(snake_case__		,   """vocab.json"""    )		,   """w"""    ) as fp:
    json.dump(snake_case__		,   snake_case__    )

  _snake_case							:		Dict								=  SpeechaTextaTokenizer(os.path.join(snake_case__		,   """vocab.json"""    )    )
  tokenizer.save_pretrained(snake_case__    )

  _snake_case							:		str								=  hf_wavavec.config.to_dict()
  _snake_case							:		List[str]								=  tokenizer.pad_token_id
  _snake_case							:		Union[str, Any]								=  tokenizer.bos_token_id
  _snake_case							:		Union[str, Any]								=  tokenizer.eos_token_id
  _snake_case							:		Optional[Any]								=  """speech_to_text_2"""
  _snake_case							:		Optional[int]								=  """wav2vec2"""

  _snake_case							:		Tuple								=  SpeechEncoderDecoderConfig.from_dict(snake_case__    )

  hf_wavavec.save_pretrained(snake_case__    )
  feature_extractor.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
     parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
     parser.add_argument(
         '''--encoder_config_path''',
         default='''facebook/wav2vec2-large-lv60''',
         type=str,
         help='''Path to hf encoder wav2vec2 checkpoint config''',
     )
     parser.add_argument(
         '''--decoder_config_path''',
         default='''facebook/s2t-small-mustc-en-fr-st''',
         type=str,
         help='''Path to hf decoder s2t checkpoint config''',
     )
     parser.add_argument('''--vocab_size''', default=1_02_24, type=int, help='''Vocab size of decoder''')
     parser.add_argument('''--num_decoder_layers''', default=7, type=int, help='''Number of decoder layers''')

     A_						=				parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.dict_path,
         encoder_config_path=args.encoder_config_path,
         decoder_config_path=args.decoder_config_path,
         vocab_size=args.vocab_size,
         num_decoder_layers=args.num_decoder_layers,
     )









from __future__ import annotations

from random import random






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		value
				lowerCamelCase__								=		random()
				lowerCamelCase__								=		None
				lowerCamelCase__								=		None





			def __repr__(    self							):



				'''simple docstring'''







				from pprint import pformat

				if self.left is None and self.right is None:
					return F'\'{self.value}: {self.prior:.5}\''
				else:
					return pformat(
					    {F'{self.value}: {self.prior:.5}': (self.left, self.right)}       ,  indent=1							)





			def __str__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		str(self.value							) + ''' '''
				lowerCamelCase__								=		str(self.left or ''''''							)
				lowerCamelCase__								=		str(self.right or ''''''							)
				return value + left + right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      tuple[Node | None, Node | None]:







	'''simple docstring'''







	if root is None:  # None tree is split into 2 Nones
		return None, None
	elif root.value is None:
		return None, None
	else:
		if value < root.value:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.left       ,__snake_case							)
			return left, root
		else:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.right       ,__snake_case							)
			return root, right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		lowerCamelCase__								=		merge(left.right       ,__snake_case							)
		return left
	else:
		lowerCamelCase__								=		merge(__snake_case       ,right.left							)
		return right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__								=		Node(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(merge(__snake_case       ,__snake_case							)       ,__snake_case							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,value - 1							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(__snake_case       ,__snake_case							)





def lowerCAmelCase__(__snake_case							)				->      None:







	'''simple docstring'''







	if not root:  # None
		return
	else:
		inorder(root.left							)
		print(root.value       ,end=''','''							)
		inorder(root.right							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	for arg in args.split():
		if arg[0] == "+":
			lowerCamelCase__								=		insert(__snake_case       ,int(arg[1:]							)							)

		elif arg[0] == "-":
			lowerCamelCase__								=		erase(__snake_case       ,int(arg[1:]							)							)

		else:
			print('''Unknown command'''							)

	return root





def lowerCAmelCase__()				->      None:







	'''simple docstring'''







	lowerCamelCase__								=		None
	print(
	    '''enter numbers to create a tree, + value to add value into treap, '''
	    '''- value to erase all nodes with value. \'q\' to quit. '''							)

	lowerCamelCase__								=		input()
	while args != "q":
		lowerCamelCase__								=		interact_treap(__snake_case       ,__snake_case							)
		print(__snake_case							)
		lowerCamelCase__								=		input()

	print('''good by!'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



