





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()
_snake_case  :    Optional[int]						    =  logging.get_logger(__name__)

_snake_case  :    Tuple						    =  {
    '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',
}
_snake_case  :    Optional[int]						    =  [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			Any	):
   for attribute in key.split('.'	):
      __lowerCAmelCase								=	getattr(lowerCAmelCase_,	lowerCAmelCase_	)

   if weight_type is not None:
      __lowerCAmelCase								=	getattr(lowerCAmelCase_,	lowerCAmelCase_	).shape
   else:
      __lowerCAmelCase								=	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":
      __lowerCAmelCase								=	value
   elif weight_type == "weight_g":
      __lowerCAmelCase								=	value
   elif weight_type == "weight_v":
      __lowerCAmelCase								=	value
   elif weight_type == "bias":
      __lowerCAmelCase								=	value
   else:
      __lowerCAmelCase								=	value

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

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			List[str]	):
   __lowerCAmelCase								=	[]
   __lowerCAmelCase								=	fairseq_model.state_dict()

   __lowerCAmelCase								=	hf_model.feature_extractor

   # if encoder has different dim to decoder -> use proj_weight
   __lowerCAmelCase								=	None

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

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

   return proj_weight

def   a_    ( lowerCAmelCase_      :			List[Any],	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			List[Any]	):
   __lowerCAmelCase								=	full_name.split('conv_layers.'	)[-1]
   __lowerCAmelCase								=	name.split('.'	)
   __lowerCAmelCase								=	int(items[0]	)
   __lowerCAmelCase								=	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."""
         )
         __lowerCAmelCase								=	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."""
         )
         __lowerCAmelCase								=	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."
         )
         __lowerCAmelCase								=	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."""
         )
         __lowerCAmelCase								=	value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
   else:
      unused_weights.append(lowerCAmelCase_	)

def   a_    ( lowerCAmelCase_      :			List[str]	):
   __lowerCAmelCase	,	__lowerCAmelCase								=	emb.weight.shape
   __lowerCAmelCase								=	nn.Linear(lowerCAmelCase_,	lowerCAmelCase_,	bias=lowerCAmelCase_	)
   __lowerCAmelCase								=	emb.weight.data
   return lin_layer

def   a_    ( lowerCAmelCase_      :			Optional[Any]	):
   with open(lowerCAmelCase_,	'r',	encoding='utf-8'	) as f:
      __lowerCAmelCase								=	f.readlines()
      __lowerCAmelCase								=	[line.split(' '	)[0] for line in lines]

   __lowerCAmelCase								=	len(lowerCAmelCase_	)

   __lowerCAmelCase								=	{
       '<s>': 0,
       '<pad>': 1,
       '</s>': 2,
       '<unk>': 3,
   }

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

@torch.no_grad()
def   a_    ( lowerCAmelCase_      :			Any,	lowerCAmelCase_      :			Tuple,	lowerCAmelCase_      :			Union[str, Any],	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			Optional[int],	lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			Optional[int],	):
   __lowerCAmelCase								=	WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SpeechaTextaConfig.from_pretrained(
       lowerCAmelCase_,	vocab_size=lowerCAmelCase_,	decoder_layers=lowerCAmelCase_,	do_stable_layer_norm=lowerCAmelCase_	)

   __lowerCAmelCase								=	WavaVecaFeatureExtractor(
       feature_size=1,	sampling_rate=1_6000,	padding_value=0,	do_normalize=lowerCAmelCase_,	return_attention_mask=lowerCAmelCase_,	)

   __lowerCAmelCase	,	__lowerCAmelCase	,	__lowerCAmelCase								=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path],	arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
   __lowerCAmelCase								=	model[0].eval()

   # set weights for wav2vec2 encoder
   __lowerCAmelCase								=	WavaVecaModel(lowerCAmelCase_	)
   __lowerCAmelCase								=	recursively_load_weights_wavaveca(model.encoder,	lowerCAmelCase_	)

   __lowerCAmelCase								=	SpeechaTextaForCausalLM(lowerCAmelCase_	)
   __lowerCAmelCase	,	__lowerCAmelCase								=	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),	strict=lowerCAmelCase_	)

   # set output linear layer
   unexpected_keys.remove('embed_out'	)
   __lowerCAmelCase								=	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}"""	)

   __lowerCAmelCase								=	SpeechEncoderDecoderModel(encoder=lowerCAmelCase_,	decoder=lowerCAmelCase_	)
   __lowerCAmelCase								=	False

   # add projection layer
   __lowerCAmelCase								=	nn.Parameter(projection_layer.weight	)
   __lowerCAmelCase								=	nn.Parameter(projection_layer.bias	)

   __lowerCAmelCase								=	create_vocab_dict(lowerCAmelCase_	)

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

   __lowerCAmelCase								=	SpeechaTextaTokenizer(os.path.join(lowerCAmelCase_,	'vocab.json'	)	)
   tokenizer.save_pretrained(lowerCAmelCase_	)

   __lowerCAmelCase								=	hf_wavavec.config.to_dict()
   __lowerCAmelCase								=	tokenizer.pad_token_id
   __lowerCAmelCase								=	tokenizer.bos_token_id
   __lowerCAmelCase								=	tokenizer.eos_token_id
   __lowerCAmelCase								=	'speech_to_text_2'
   __lowerCAmelCase								=	'wav2vec2'

   __lowerCAmelCase								=	SpeechEncoderDecoderConfig.from_dict(lowerCAmelCase_	)

   hf_wavavec.save_pretrained(lowerCAmelCase_	)
   feature_extractor.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
     _snake_case  :    Tuple						    =  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=10224, type=int, help='Vocab size of decoder')
     parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

     _snake_case  :    Tuple						    =  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 timeit import timeit

def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('the value of input must not be negative'	)
   __lowerCAmelCase								=	0
   while number:
      number &= number - 1
      result += 1
   return result

def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('the value of input must not be negative'	)
   __lowerCAmelCase								=	0
   while number:
      if number % 2 == 1:
         result += 1
      number >>= 1
   return result

def   a_    ( ):

   def do_benchmark(lowerCAmelCase_      :			int	) -> None:
      __lowerCAmelCase								=	'import __main__ as z'
      print(F"""Benchmark when {number = }:"""	)
      print(F"""{get_set_bits_count_using_modulo_operator(lowerCAmelCase_	) = }"""	)
      __lowerCAmelCase								=	timeit('z.get_set_bits_count_using_modulo_operator(25)',	setup=lowerCAmelCase_	)
      print(F"""timeit() runs in {timing} seconds"""	)
      print(F"""{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase_	) = }"""	)
      __lowerCAmelCase								=	timeit(
          'z.get_set_bits_count_using_brian_kernighans_algorithm(25)',	setup=lowerCAmelCase_,	)
      print(F"""timeit() runs in {timing} seconds"""	)

   for number in (25, 37, 58, 0):
      do_benchmark(lowerCAmelCase_	)
      print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

