


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, 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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
__lowerCamelCase        =     [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     for attribute in key.split(""".""" ):
          a_    :     Dict										=    getattr(__UpperCamelCase  ,  __UpperCamelCase )

     if weight_type is not None:
          a_    :     Tuple										=    getattr(__UpperCamelCase  ,  __UpperCamelCase ).shape
     else:
          a_    :     Optional[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_    :     Tuple										=    value
     elif weight_type == "weight_g":
          a_    :     int										=    value
     elif weight_type == "weight_v":
          a_    :     Tuple										=    value
     elif weight_type == "bias":
          a_    :     Optional[Any]										=    value
     else:
          a_    :     Union[str, Any]										=    value

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



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     Dict										=    []
     a_    :     int										=    fairseq_model.state_dict()

     a_    :     Tuple										=    hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          a_    :     List[str]										=    False
          if "conv_layers" in name:
               load_conv_layer(
                   __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  hf_model.config.feat_extract_norm == """group"""  ,  )
               a_    :     str										=    True
          else:
               for key, mapped_key in MAPPING.items():
                    a_    :     Optional[int]										=    """unispeech_sat.""" + 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]:
                         if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
                              # special case since naming is very similar
                              continue
                         a_    :     Optional[int]										=    True
                         if "*" in mapped_key:
                              a_    :     int										=    name.split(__UpperCamelCase )[0].split(""".""" )[-2]
                              a_    :     Optional[Any]										=    mapped_key.replace("""*"""  ,  __UpperCamelCase )
                         if "weight_g" in name:
                              a_    :     List[Any]										=    """weight_g"""
                         elif "weight_v" in name:
                              a_    :     List[Any]										=    """weight_v"""
                         elif "bias" in name:
                              a_    :     Dict										=    """bias"""
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              a_    :     str										=    """weight"""
                         else:
                              a_    :     Dict										=    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  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     List[str]										=    full_name.split("""conv_layers.""" )[-1]
     a_    :     Optional[Any]										=    name.split(""".""" )
     a_    :     Union[str, Any]										=    int(items[0] )
     a_    :     Dict										=    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_    :     str										=    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_    :     Optional[int]										=    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[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[layer_id].layer_norm.weight.data.shape} was found.''' )
               a_    :     Tuple										=    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  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase=True ):
     if config_path is not None:
          a_    :     str										=    UniSpeechSatConfig.from_pretrained(__UpperCamelCase )
     else:
          a_    :     Optional[Any]										=    UniSpeechSatConfig()

     a_    :     Dict										=    """"""

     if is_finetuned:
          a_    :     Tuple										=    UniSpeechSatForCTC(__UpperCamelCase )
     else:
          a_    :     Any										=    UniSpeechSatForPreTraining(__UpperCamelCase )

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

     recursively_load_weights(__UpperCamelCase  ,  __UpperCamelCase )

     hf_wavavec.save_pretrained(__UpperCamelCase )


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_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def  _a			(						__UpperCamelCase ):
     a_    :     int										=    int(number**0.5 )
     return number == sq * sq



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     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(__UpperCamelCase  ,  __UpperCamelCase )
     top //= hcf
     bottom //= hcf
     return top, bottom



def  _a			(						__UpperCamelCase = 3_5 ):
     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_    :     int										=    x_num * y_den + x_den * y_num
                         a_    :     Optional[int]										=    x_den * y_den
                         a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Optional[int]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

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

                    # n=-1
                         a_    :     Union[str, Any]										=    x_num * y_num
                         a_    :     Tuple										=    x_den * y_num + x_num * y_den
                         a_    :     List[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Union[str, Any]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

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

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

     return total.denominator + total.numerator


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



