

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase           =					{
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
lowerCamelCase           =					[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : Any	,      _A      : Any	,      _A      : Dict	,      _A      : Dict	,      _A      : Any							):





  '''simple docstring'''







  for attribute in key.split('''.'''							):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        a__  ='lm_head'

    a__  =getattr(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE							)

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

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




def 			UpperCAmelCase__				(			_A      : int	,      _A      : int	,      _A      : List[str]							):





  '''simple docstring'''







  a__  =[]
  a__  =fairseq_model.state_dict()

  a__  =hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    a__  =False
    if "conv_layers" in name:
      load_conv_layer(
          _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      hf_model.config.feat_extract_norm == '''group'''	,      )
      a__  =True
    else:
      for key, mapped_key in MAPPING.items():
        a__  ='unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
        if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
          a__  =True
          if "*" in mapped_key:
            a__  =name.split(_SCREAMING_SNAKE_CASE							)[0].split('''.'''							)[-2]
            a__  =mapped_key.replace('''*'''	,      _SCREAMING_SNAKE_CASE							)
          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:
            # TODO: don't match quantizer.weight_proj
            a__  ='weight'
          else:
            a__  =None
          set_recursively(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE							)
        continue
    if not is_used:
      unused_weights.append(_SCREAMING_SNAKE_CASE							)

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




def 			UpperCAmelCase__				(			_A      : Tuple	,      _A      : Any	,      _A      : List[Any]	,      _A      : Any	,      _A      : List[Any]							):





  '''simple docstring'''







  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:
      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."""
      )
      a__  =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."""
      )
      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:
      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."
      )
      a__  =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."""
      )
      a__  =value
      logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
  else:
    unused_weights.append(_SCREAMING_SNAKE_CASE							)




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : Union[str, Any]	,      _A      : List[str]	,      _A      : Optional[Any]=None	,      _A      : List[Any]=None	,      _A      : List[str]=True							):





  '''simple docstring'''







  if config_path is not None:
    a__  =UniSpeechConfig.from_pretrained(_SCREAMING_SNAKE_CASE							)
  else:
    a__  =UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      a__  =Dictionary.load_from_json(_SCREAMING_SNAKE_CASE							)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      a__  =target_dict.pad_index
      a__  =target_dict.bos_index
      a__  =target_dict.eos_index
      a__  =len(target_dict.symbols							)
      a__  =os.path.join(_SCREAMING_SNAKE_CASE	,      '''vocab.json'''							)
      if not os.path.isdir(_SCREAMING_SNAKE_CASE							):
        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_SCREAMING_SNAKE_CASE							)							)
        return
      os.makedirs(_SCREAMING_SNAKE_CASE	,      exist_ok=_SCREAMING_SNAKE_CASE							)
      a__  =target_dict.indices

      # fairseq has the <pad> and <s> switched
      a__  =42
      a__  =43
      with open(_SCREAMING_SNAKE_CASE	,      '''w'''	,      encoding='''utf-8'''							) as vocab_handle:
        json.dump(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE							)
      a__  =WavaVecaPhonemeCTCTokenizer(
          _SCREAMING_SNAKE_CASE	,      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=_SCREAMING_SNAKE_CASE	,      )
      a__  =True if config.feat_extract_norm == 'layer' else False
      a__  =WavaVecaFeatureExtractor(
          feature_size=1	,      sampling_rate=1_60_00	,      padding_value=0	,      do_normalize=_SCREAMING_SNAKE_CASE	,      return_attention_mask=_SCREAMING_SNAKE_CASE	,      )
      a__  =WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE	,      tokenizer=_SCREAMING_SNAKE_CASE							)
      processor.save_pretrained(_SCREAMING_SNAKE_CASE							)

    a__  =UniSpeechForCTC(_SCREAMING_SNAKE_CASE							)
  else:
    a__  =UniSpeechForPreTraining(_SCREAMING_SNAKE_CASE							)

  if is_finetuned:
    a__  =fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							), '''w2v_path''': checkpoint_path}							)
  else:
    a__  =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

  a__  =model[0].eval()

  recursively_load_weights(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE							)

  hf_unispeech.save_pretrained(_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
       lowerCamelCase           =					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(
           '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
       )
       lowerCamelCase           =					parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )




'''simple docstring'''



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  int(number**0.5   )
					return number == sq * sq






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       int					    =  x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
					__a			:       int					    =  x_den * y_den * z_den
					__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
					top //= hcf
					bottom //= hcf
					return top, bottom






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int = 35   ):
					__a			:       set					    =  set()
					__a			:       int
					__a			:       Fraction					    =  Fraction(0   )
					__a			:       tuple[int, int]

					for x_num in range(1						,      order + 1   ):
										for x_den in range(x_num + 1						,      order + 1   ):
															for y_num in range(1						,      order + 1   ):
																				for y_den in range(y_num + 1						,      order + 1   ):
																									# n=1
																									__a			:       Union[str, Any]					    =  x_num * y_den + x_den * y_num
																									__a			:       Optional[Any]					    =  x_den * y_den
																									__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														__a			:       Any					    =  add_three(
																														    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														unique_s.add(_SCREAMING_SNAKE_CASE   )

																									# n=2
																									__a			:       Optional[int]					    =  (
																									    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																									)
																									__a			:       Union[str, Any]					    =  x_den * x_den * y_den * y_den
																									if is_sq(_SCREAMING_SNAKE_CASE   ) and is_sq(_SCREAMING_SNAKE_CASE   ):
																														__a			:       List[Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Any					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Optional[int]					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			__a			:       List[Any]					    =  add_three(
																																			    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																																			unique_s.add(_SCREAMING_SNAKE_CASE   )

                    # n=-1
																									__a			:       int					    =  x_num * y_num
																									__a			:       Optional[Any]					    =  x_den * y_num + x_num * y_den
																									__a			:       Tuple					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														__a			:       Any					    =  add_three(
																														    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														unique_s.add(_SCREAMING_SNAKE_CASE   )

																									# n=2
																									__a			:       List[Any]					    =  x_num * x_num * y_num * y_num
																									__a			:       List[Any]					    =  (
																									    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																									)
																									if is_sq(_SCREAMING_SNAKE_CASE   ) and is_sq(_SCREAMING_SNAKE_CASE   ):
																														__a			:       Optional[Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       Union[str, Any]					    =  int(sqrt(_SCREAMING_SNAKE_CASE   )   )
																														__a			:       int					    =  gcd(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			__a			:       List[str]					    =  add_three(
																																			    _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																																			unique_s.add(_SCREAMING_SNAKE_CASE   )

					for num, den in unique_s:
										total += Fraction(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

					return total.denominator + total.numerator


if __name__ == "__main__":
	print(f'''{solution() = }''')



