

from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		int(number**0.5   )
      return number == sq * sq







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
      A_							       =		x_den * y_den * z_den
      A_							       =		gcd(_UpperCamelCase		,				_UpperCamelCase   )
      top //= hcf
      bottom //= hcf
      return top, bottom







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 35   ):
      '''simple docstring'''







      A_							       =		set()
      A_							       =		42
      A_							       =		Fraction(0   )
      A_							       =		42

      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_							       =		x_num * y_den + x_den * y_num
                              A_							       =		x_den * y_den
                              A_							       =		gcd(_UpperCamelCase		,				_UpperCamelCase   )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                    A_							       =		add_three(
                                        _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase   )
                                    unique_s.add(_UpperCamelCase   )

                              # n=2
                              A_							       =		(
                                  x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                              )
                              A_							       =		x_den * x_den * y_den * y_den
                              if is_sq(_UpperCamelCase   ) and is_sq(_UpperCamelCase   ):
                                    A_							       =		int(sqrt(_UpperCamelCase   )   )
                                    A_							       =		int(sqrt(_UpperCamelCase   )   )
                                    A_							       =		gcd(_UpperCamelCase		,				_UpperCamelCase   )
                                    z_num //= hcf
                                    z_den //= hcf
                                    if 0 < z_num < z_den <= order:
                                          A_							       =		add_three(
                                              _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase   )
                                          unique_s.add(_UpperCamelCase   )

                                                       # n=-1
                              A_							       =		x_num * y_num
                              A_							       =		x_den * y_num + x_num * y_den
                              A_							       =		gcd(_UpperCamelCase		,				_UpperCamelCase   )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                    A_							       =		add_three(
                                        _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase   )
                                    unique_s.add(_UpperCamelCase   )

                              # n=2
                              A_							       =		x_num * x_num * y_num * y_num
                              A_							       =		(
                                  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_							       =		int(sqrt(_UpperCamelCase   )   )
                                    A_							       =		int(sqrt(_UpperCamelCase   )   )
                                    A_							       =		gcd(_UpperCamelCase		,				_UpperCamelCase   )
                                    z_num //= hcf
                                    z_den //= hcf
                                    if 0 < z_num < z_den <= order:
                                          A_							       =		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() = }')








import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
a_     :    Dict     		=   logging.get_logger(__name__)

a_     :    List[str]     		=   {
    "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_     :    Union[str, Any]     		=   [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Optional[Any]						)		->				Optional[int]:
 '''simple docstring'''


 for attribute in key.split('.'						):
  SCREAMING_SNAKE_CASE		    =      getattr(_UpperCamelCase		,				_UpperCamelCase						)

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

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      []
 SCREAMING_SNAKE_CASE		    =      fairseq_model.state_dict()

 SCREAMING_SNAKE_CASE		    =      hf_model.feature_extractor
 SCREAMING_SNAKE_CASE		    =      hf_model.adapter

 for name, value in fairseq_dict.items():
  SCREAMING_SNAKE_CASE		    =      False
  if "conv_layers" in name:
   load_conv_layer(
       _UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				hf_model.config.feat_extract_norm == 'group'		,				)
   SCREAMING_SNAKE_CASE		    =      True
  elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']						):
   load_adapter(_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase		,				_UpperCamelCase						)
   SCREAMING_SNAKE_CASE		    =      True
  else:
   for key, mapped_key in MAPPING.items():
    if key in name or key.split('w2v_model.'						)[-1] == name.split('.'						)[0]:
     SCREAMING_SNAKE_CASE		    =      True
     if "*" in mapped_key:
      SCREAMING_SNAKE_CASE		    =      name.split(_UpperCamelCase						)[0].split('.'						)[-2]
      SCREAMING_SNAKE_CASE		    =      mapped_key.replace('*'		,				_UpperCamelCase						)
     if "weight_g" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_g'
     elif "weight_v" in name:
      SCREAMING_SNAKE_CASE		    =      'weight_v'
     elif "bias" in name:
      SCREAMING_SNAKE_CASE		    =      'bias'
     elif "weight" in name:
      SCREAMING_SNAKE_CASE		    =      'weight'
     else:
      SCREAMING_SNAKE_CASE		    =      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 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	int		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	List[Any]						)		->				Optional[Any]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('conv_layers.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)
 SCREAMING_SNAKE_CASE		    =      int(items[0]						)
 SCREAMING_SNAKE_CASE		    =      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		    =      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		    =      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		    =      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		    =      value
   logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Union[str, Any]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      full_name.split('adaptor.'						)[-1]
 SCREAMING_SNAKE_CASE		    =      name.split('.'						)

 if items[1].isdigit():
  SCREAMING_SNAKE_CASE		    =      int(items[1]						)
 else:
  SCREAMING_SNAKE_CASE		    =      None

 if "adaptor" not in full_name:
  if "proj_ln" in full_name:
   # has to be layer norm
   if "bias" in name:
    assert (
        value.shape == adapter.proj_layer_norm.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj_layer_norm.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
  else:
   # has to be projection layer
   if "bias" in name:
    assert (
        value.shape == adapter.proj.bias.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""						)
   if "weight" in name:
    assert (
        value.shape == adapter.proj.weight.data.shape
    ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
    SCREAMING_SNAKE_CASE		    =      value
    logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""						)
 elif isinstance(_UpperCamelCase		,				_UpperCamelCase						):
  if "bias" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.bias.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
  elif "weight" in name:
   assert (
       value.shape == adapter.layers[layer_id].conv.weight.data.shape
   ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
   SCREAMING_SNAKE_CASE		    =      value
   logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
 else:
  unused_weights.append(_UpperCamelCase						)


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      emb.weight.shape
 SCREAMING_SNAKE_CASE		    =      nn.Linear(_UpperCamelCase		,				_UpperCamelCase		,				bias=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      emb.weight.data
 return lin_layer


@torch.no_grad()
def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Any		,				_UpperCamelCase		:	Optional[int]		,				_UpperCamelCase		:	Tuple		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	List[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Optional[Any]		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Tuple		,				)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      WavaVecaConfig.from_pretrained(
     _UpperCamelCase		,				add_adapter=_UpperCamelCase		,				adapter_stride=_UpperCamelCase		,				adapter_kernel_size=_UpperCamelCase		,				use_auth_token=_UpperCamelCase		,				output_hidden_size=_UpperCamelCase		,				)
 SCREAMING_SNAKE_CASE		    =      MBartConfig.from_pretrained(_UpperCamelCase						)

 # load model
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
     [checkpoint_path]		,				arg_overrides={
         'config_yaml': config_yaml_path,
         'data': '/'.join(dict_path.split('/'						)[:-1]						),
         'w2v_path': checkpoint_path,
         'load_pretrained_decoder_from': None,
     }		,				)
 SCREAMING_SNAKE_CASE		    =      model[0].eval()

 # load feature extractor
 SCREAMING_SNAKE_CASE		    =      WavaVecaFeatureExtractor.from_pretrained(_UpperCamelCase		,				use_auth_token=_UpperCamelCase						)

 # set weights for wav2vec2 encoder
 SCREAMING_SNAKE_CASE		    =      WavaVecaModel(_UpperCamelCase						)

 recursively_load_weights_wavaveca(model.encoder		,				_UpperCamelCase						)

 # load decoder weights
 SCREAMING_SNAKE_CASE		    =      MBartForCausalLM(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()		,				strict=_UpperCamelCase						)
 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}"""						)

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderModel(encoder=_UpperCamelCase		,				decoder=_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      False

 SCREAMING_SNAKE_CASE		    =      MBartaaTokenizer(_UpperCamelCase						)
 tokenizer.save_pretrained(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      hf_wavavec.config.to_dict()
 SCREAMING_SNAKE_CASE		    =      tokenizer.pad_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.bos_token_id
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      'mbart50'
 SCREAMING_SNAKE_CASE		    =      'wav2vec2'

 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id
 SCREAMING_SNAKE_CASE		    =      25_00_04
 SCREAMING_SNAKE_CASE		    =      tokenizer.eos_token_id

 SCREAMING_SNAKE_CASE		    =      SpeechEncoderDecoderConfig.from_dict(_UpperCamelCase						)

 hf_wavavec.save_pretrained(_UpperCamelCase						)
 feature_extractor.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
      a_     :    List[str]     		=   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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
      parser.add_argument(
          "--encoder_config_path",
          default="facebook/wav2vec2-xls-r-1b",
          type=str,
          help="Path to hf encoder wav2vec2 checkpoint config",
      )
      parser.add_argument(
          "--decoder_config_path",
          default="facebook/mbart-large-50-one-to-many-mmt",
          type=str,
          help="Path to hf decoder checkpoint config",
      )
      parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
      parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
      parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
      parser.add_argument("--encoder_output_dim", default=1024, type=int, help="encoder output dim")
      parser.add_argument("--start_token_id", default=25_0004, type=int, help="`decoder_start_token_id` of model config")

      a_     :    Dict     		=   parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.dict_path,
          args.config_yaml_path,
          encoder_config_path=args.encoder_config_path,
          decoder_config_path=args.decoder_config_path,
          add_adapter=args.add_adapter,
          adapter_kernel_size=args.adapter_kernel_size,
          adapter_stride=args.adapter_stride,
          decoder_start_token_id=args.start_token_id,
          encoder_output_dim=args.encoder_output_dim,
      )

