
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase_ 				=			{
    '''post_extract_proj''': '''feature_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.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""






  for attribute in key.split('''.'''							):
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		getattr(lowerCAmelCase_       ,			lowerCAmelCase_							)

  if weight_type is not None:
    __SCREAMING_SNAKE_CASE      :	Any					=		getattr(lowerCAmelCase_       ,			lowerCAmelCase_							).shape
  else:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		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":
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		value
  elif weight_type == "weight_g":
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		value
  elif weight_type == "weight_v":
    __SCREAMING_SNAKE_CASE      :	str					=		value
  elif weight_type == "bias":
    __SCREAMING_SNAKE_CASE      :	Any					=		value
  else:
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		value

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


def 							a__					(							snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Tuple					=		[]
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		fairseq_model.state_dict()

  __SCREAMING_SNAKE_CASE      :	Any					=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			hf_model.config.feat_extract_norm == '''group'''       ,			)
      __SCREAMING_SNAKE_CASE      :	str					=		True
    else:
      for key, mapped_key in MAPPING.items():
        __SCREAMING_SNAKE_CASE      :	Any					=		'''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

        if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
          __SCREAMING_SNAKE_CASE      :	Dict					=		True
          if "*" in mapped_key:
            __SCREAMING_SNAKE_CASE      :	str					=		name.split(lowerCAmelCase_							)[0].split('''.'''							)[-2]
            __SCREAMING_SNAKE_CASE      :	Tuple					=		mapped_key.replace('''*'''       ,			lowerCAmelCase_							)
          if "weight_g" in name:
            __SCREAMING_SNAKE_CASE      :	Optional[int]					=		'''weight_g'''
          elif "weight_v" in name:
            __SCREAMING_SNAKE_CASE      :	List[Any]					=		'''weight_v'''
          elif "weight" in name:
            __SCREAMING_SNAKE_CASE      :	Tuple					=		'''weight'''
          elif "bias" in name:
            __SCREAMING_SNAKE_CASE      :	int					=		'''bias'''
          else:
            __SCREAMING_SNAKE_CASE      :	str					=		None
          set_recursively(lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_							)
        continue
    if not is_used:
      unused_weights.append(lowerCAmelCase_							)

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


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[str]					=		full_name.split('''conv_layers.'''							)[-1]
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		name.split('''.'''							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		int(items[0]							)
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		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.'''
      )
      __SCREAMING_SNAKE_CASE      :	int					=		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.'''
      )
      __SCREAMING_SNAKE_CASE      :	Union[str, 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."
      )
      __SCREAMING_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.'''
      )
      __SCREAMING_SNAKE_CASE      :	Tuple					=		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__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		SEWConfig()
  if is_finetuned:
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		model.wav_encoder.wav_model.cfg
  else:
    __SCREAMING_SNAKE_CASE      :	Any					=		model.cfg

  __SCREAMING_SNAKE_CASE      :	int					=		fs_config.conv_bias
  __SCREAMING_SNAKE_CASE      :	Dict					=		eval(fs_config.conv_feature_layers							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		[x[0] for x in conv_layers]
  __SCREAMING_SNAKE_CASE      :	str					=		[x[1] for x in conv_layers]
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		[x[2] for x in conv_layers]
  __SCREAMING_SNAKE_CASE      :	List[str]					=		'''gelu'''
  __SCREAMING_SNAKE_CASE      :	Tuple					=		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
  __SCREAMING_SNAKE_CASE      :	Tuple					=		0.0
  __SCREAMING_SNAKE_CASE      :	Any					=		fs_config.activation_fn.name
  __SCREAMING_SNAKE_CASE      :	str					=		fs_config.encoder_embed_dim
  __SCREAMING_SNAKE_CASE      :	Dict					=		0.02
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		fs_config.encoder_ffn_embed_dim
  __SCREAMING_SNAKE_CASE      :	Dict					=		1E-5
  __SCREAMING_SNAKE_CASE      :	str					=		fs_config.encoder_layerdrop
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		fs_config.encoder_attention_heads
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		fs_config.conv_pos_groups
  __SCREAMING_SNAKE_CASE      :	Tuple					=		fs_config.conv_pos
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		len(lowerCAmelCase_							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		fs_config.encoder_layers
  __SCREAMING_SNAKE_CASE      :	List[str]					=		fs_config.squeeze_factor

  # take care of any params that are overridden by the Wav2VecCtc model
  if is_finetuned:
    __SCREAMING_SNAKE_CASE      :	int					=		model.cfg
    __SCREAMING_SNAKE_CASE      :	Tuple					=		fs_config.final_dropout
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		fs_config.layerdrop
  __SCREAMING_SNAKE_CASE      :	List[str]					=		fs_config.activation_dropout
  __SCREAMING_SNAKE_CASE      :	str					=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		fs_config.attention_dropout
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		fs_config.dropout_input
  __SCREAMING_SNAKE_CASE      :	Dict					=		fs_config.dropout
  __SCREAMING_SNAKE_CASE      :	Tuple					=		fs_config.mask_channel_length
  __SCREAMING_SNAKE_CASE      :	Tuple					=		fs_config.mask_channel_prob
  __SCREAMING_SNAKE_CASE      :	str					=		fs_config.mask_length
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		fs_config.mask_prob

  __SCREAMING_SNAKE_CASE      :	List[Any]					=		'''Wav2Vec2FeatureExtractor'''
  __SCREAMING_SNAKE_CASE      :	List[Any]					=		'''Wav2Vec2CTCTokenizer'''

  return config


@torch.no_grad()
def 							a__					(							snake_case       ,			snake_case       ,			snake_case=None       ,			snake_case=None       ,			snake_case=True							):





  """simple docstring"""






  if is_finetuned:
    __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	int					=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]       ,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
  else:
    __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE      :	List[Any]					=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

  if config_path is not None:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		SEWConfig.from_pretrained(lowerCAmelCase_							)
  else:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		convert_config(model[0]       ,			lowerCAmelCase_							)
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		model[0].eval()

  __SCREAMING_SNAKE_CASE      :	str					=		True if config.feat_extract_norm == '''layer''' else False
  __SCREAMING_SNAKE_CASE      :	Dict					=		WavaVecaFeatureExtractor(
      feature_size=1       ,			sampling_rate=16_000       ,			padding_value=0       ,			do_normalize=lowerCAmelCase_       ,			return_attention_mask=lowerCAmelCase_       ,			)

  if is_finetuned:
    if dict_path:
      __SCREAMING_SNAKE_CASE      :	Any					=		Dictionary.load(lowerCAmelCase_							)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      __SCREAMING_SNAKE_CASE      :	Dict					=		target_dict.pad_index
      __SCREAMING_SNAKE_CASE      :	int					=		target_dict.bos_index
      __SCREAMING_SNAKE_CASE      :	Dict					=		target_dict.pad_index
      __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		target_dict.bos_index
      __SCREAMING_SNAKE_CASE      :	Dict					=		target_dict.eos_index
      __SCREAMING_SNAKE_CASE      :	str					=		len(target_dict.symbols							)
      __SCREAMING_SNAKE_CASE      :	int					=		os.path.join(lowerCAmelCase_       ,			'''vocab.json'''							)
      if not os.path.isdir(lowerCAmelCase_							):
        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCAmelCase_							)							)
        return
      os.makedirs(lowerCAmelCase_       ,			exist_ok=lowerCAmelCase_							)
      with open(lowerCAmelCase_       ,			'''w'''       ,			encoding='''utf-8'''							) as vocab_handle:
        json.dump(target_dict.indices       ,			lowerCAmelCase_							)
      __SCREAMING_SNAKE_CASE      :	Tuple					=		WavaVecaCTCTokenizer(
          lowerCAmelCase_       ,			unk_token=target_dict.unk_word       ,			pad_token=target_dict.pad_word       ,			bos_token=target_dict.bos_word       ,			eos_token=target_dict.eos_word       ,			word_delimiter_token='''|'''       ,			do_lower_case=lowerCAmelCase_       ,			)
      __SCREAMING_SNAKE_CASE      :	Any					=		WavaVecaProcessor(feature_extractor=lowerCAmelCase_       ,			tokenizer=lowerCAmelCase_							)
      processor.save_pretrained(lowerCAmelCase_							)

    __SCREAMING_SNAKE_CASE      :	int					=		SEWForCTC(lowerCAmelCase_							)
  else:
    __SCREAMING_SNAKE_CASE      :	Tuple					=		SEWModel(lowerCAmelCase_							)
    feature_extractor.save_pretrained(lowerCAmelCase_							)

  recursively_load_weights(lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_							)

  hf_model.save_pretrained(lowerCAmelCase_							)


if __name__ == "__main__":
   lowercase_ 				=			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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
   )
   lowercase_ 				=			parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )




from __future__ import annotations

from math import gcd



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int = 2		,    lowerCAmelCase_				:int = 1		,    lowerCAmelCase_				:int = 3		,    )->int | None:
   '''simple docstring'''






   if num < 2:
      raise ValueError("The input value cannot be less than 2"				)

   # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
   # algorithm struggles to find factors that are divisible by two.
   # As a workaround, we specifically check for two and even inputs.
   #   See: https://math.stackexchange.com/a/2856214/165820
   if num > 2 and num % 2 == 0:
      return 2

   # Pollard's Rho algorithm requires a function that returns pseudorandom
   # values between 0 <= X < ``num``.  It doesn't need to be random in the
   # sense that the output value is cryptographically secure or difficult
   # to calculate, it only needs to be random in the sense that all output
   # values should be equally likely to appear.
   # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
   # However, the success of Pollard's algorithm isn't guaranteed and is
   # determined in part by the initial seed and the chosen random function.
   # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
   # where ``C`` is a value that we can modify between each attempt.
   def rand_fn(lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				) -> int:
      return (pow(lowerCAmelCase_		,    2				) + step) % modulus

   for _ in range(lowerCAmelCase_				):
      # These track the position within the cycle detection logic.
      snake_case_            =       seed
      snake_case_            =       seed

      while True:
         # At each iteration, the tortoise moves one step and the hare moves two.
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)
         snake_case_            =       rand_fn(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_				)

         # At some point both the tortoise and the hare will enter a cycle whose
         # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
         # the tortoise and hare will end up on the same value modulo ``p``.
         # We can detect when this happens because the position difference between
         # the tortoise and the hare will share a common divisor with ``num``.
         snake_case_            =       gcd(hare - tortoise		,    lowerCAmelCase_				)

         if divisor == 1:
            # No common divisor yet, just keep searching.
            continue
         else:
            # We found a common divisor!
            if divisor == num:
               # Unfortunately, the divisor is ``num`` itself and is useless.
               break
            else:
               # The divisor is a nontrivial factor of ``num``!
               return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
      snake_case_            =       hare

      # The new step value for the random function can just be incremented.
      # At first the results will be similar to what the old function would
      # have produced, but the value will quickly diverge after a bit.
      step += 1

   # We haven't found a divisor within the requested number of attempts.
   # We were unlucky or ``num`` itself is actually prime.
   return None


if __name__ == "__main__":
   import argparse

   SCREAMING_SNAKE_CASE						:Union[str, Any]			=  argparse.ArgumentParser()
   parser.add_argument(
       '''num''',
       type=int,
       help='''The value to find a divisor of''',
   )
   parser.add_argument(
       '''--attempts''',
       type=int,
       default=3,
       help='''The number of attempts before giving up''',
   )
   SCREAMING_SNAKE_CASE						:List[str]			=  parser.parse_args()

   SCREAMING_SNAKE_CASE						:List[Any]			=  pollard_rho(args.num, attempts=args.attempts)
   if divisor is None:
      print(F'''{args.num} is probably prime''')
   else:
      SCREAMING_SNAKE_CASE						:List[str]			=  args.num // divisor
      print(F'''{args.num} = {divisor} * {quotient}''')




