'''simple docstring'''
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_A		:      List[Any]						   ={
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}







def 				__UpperCamelCase (					__UpperCAmelCase  :					str ,   __UpperCAmelCase  :					Dict ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					List[str] ,   __UpperCAmelCase  :					Tuple			)							->	Tuple:
  for attribute in key.split('''.'''			):
    lowercase				:							int									=   getattr(__UpperCAmelCase ,   __UpperCAmelCase			)

  if weight_type is not None:
    lowercase				:							int									=   getattr(__UpperCAmelCase ,   __UpperCAmelCase			).shape
  else:
    lowercase				:							Dict									=   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":
    lowercase				:							Union[str, Any]									=   value
  elif weight_type == "weight_g":
    lowercase				:							Tuple									=   value
  elif weight_type == "weight_v":
    lowercase				:							str									=   value
  elif weight_type == "bias":
    lowercase				:							Any									=   value
  else:
    lowercase				:							Dict									=   value

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







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any] ,   __UpperCAmelCase  :					Optional[int]			)							->	Dict:
  lowercase				:							Tuple									=   []
  lowercase				:							Tuple									=   fairseq_model.state_dict()

  lowercase				:							str									=   hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    lowercase				:							int									=   False
    if "conv_layers" in name:
      load_conv_layer(
          __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase ,   hf_model.config.feat_extract_norm == '''group''' ,   )
      lowercase				:							List[Any]									=   True
    else:
      for key, mapped_key in MAPPING.items():
        lowercase				:							Tuple									=   mapped_key

        if key in name:
          lowercase				:							Optional[Any]									=   True
          if "*" in mapped_key:
            lowercase				:							Union[str, Any]									=   name.split(__UpperCAmelCase			)[0].split('''.'''			)[-2]
            lowercase				:							List[Any]									=   mapped_key.replace('''*''' ,   __UpperCAmelCase			)
          if "weight_g" in name:
            lowercase				:							int									=   '''weight_g'''
          elif "weight_v" in name:
            lowercase				:							Dict									=   '''weight_v'''
          elif "weight" in name:
            lowercase				:							Optional[int]									=   '''weight'''
          elif "bias" in name:
            lowercase				:							Union[str, Any]									=   '''bias'''
          else:
            lowercase				:							Any									=   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 				__UpperCamelCase (					__UpperCAmelCase  :					Optional[Any] ,   __UpperCAmelCase  :					Union[str, Any] ,   __UpperCAmelCase  :					Optional[Any] ,   __UpperCAmelCase  :					List[str] ,   __UpperCAmelCase  :					List[str]			)							->	Optional[int]:
  lowercase				:							Dict									=   full_name.split('''conv_layers.'''			)[-1]
  lowercase				:							str									=   name.split('''.'''			)
  lowercase				:							int									=   int(items[0]			)
  lowercase				:							Dict									=   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."""
      )
      lowercase				:							List[Any]									=   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."""
      )
      lowercase				:							List[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."
      )
      lowercase				:							Union[str, Any]									=   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."""
      )
      lowercase				:							int									=   value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""			)
  else:
    unused_weights.append(__UpperCAmelCase			)







def 				__UpperCamelCase (					__UpperCAmelCase  :					Dict			)							->	str:
  lowercase				:							Optional[int]									=   HubertConfig()
  lowercase				:							Dict									=   model.config

  lowercase				:							Union[str, Any]									=   fs_config.activation_dropout
  lowercase				:							Dict									=   False
  lowercase				:							Any									=   fs_config.attention_dropout
  lowercase				:							List[Any]									=   False
  lowercase				:							int									=   eval(fs_config.extractor_conv_feature_layers			)
  lowercase				:							Optional[Any]									=   [x[0] for x in conv_layers]
  lowercase				:							str									=   [x[1] for x in conv_layers]
  lowercase				:							Dict									=   [x[2] for x in conv_layers]
  lowercase				:							List[Any]									=   '''gelu'''
  lowercase				:							int									=   '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
  lowercase				:							str									=   False
  lowercase				:							Tuple									=   0.0
  lowercase				:							int									=   0.0
  lowercase				:							Dict									=   fs_config.activation_fn
  lowercase				:							Union[str, Any]									=   fs_config.dropout
  lowercase				:							Any									=   fs_config.encoder_embed_dim
  lowercase				:							Optional[Any]									=   0.02
  lowercase				:							Tuple									=   fs_config.encoder_ffn_embed_dim
  lowercase				:							str									=   1e-5
  lowercase				:							Optional[int]									=   0.0
  lowercase				:							Any									=   fs_config.encoder_attention_heads
  lowercase				:							Optional[Any]									=   fs_config.conv_pos_groups
  lowercase				:							Optional[int]									=   fs_config.conv_pos
  lowercase				:							Optional[int]									=   len(__UpperCAmelCase			)
  lowercase				:							Tuple									=   fs_config.encoder_layers

  return config







@torch.no_grad()
def 				__UpperCamelCase (					__UpperCAmelCase  :					str ,   __UpperCAmelCase  :					Dict=None			)							->	Dict:
  lowercase				:							Any									=   distilhubert().model.model

  if config_path is not None:
    lowercase				:							Dict									=   HubertConfig.from_pretrained(__UpperCAmelCase			)
  else:
    lowercase				:							Optional[Any]									=   convert_config(__UpperCAmelCase			)
  lowercase				:							int									=   model.eval()

  lowercase				:							Union[str, Any]									=   WavaVecaFeatureExtractor(
      feature_size=1 ,   sampling_rate=16_000 ,   padding_value=0 ,   do_normalize=__UpperCAmelCase ,   return_attention_mask=__UpperCAmelCase ,   )
  lowercase				:							Optional[Any]									=   HubertModel(__UpperCAmelCase			)

  recursively_load_weights(__UpperCAmelCase ,   __UpperCAmelCase			)

  feature_extractor.save_pretrained(__UpperCAmelCase			)
  hf_model.save_pretrained(__UpperCAmelCase			)


if __name__ == "__main__":
 _A		:      Tuple						   =argparse.ArgumentParser()
 parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
 _A		:      Union[str, Any]						   =parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
'''simple docstring'''
from __future__ import annotations

import math
import random







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[int]:
  lowercase				:							Union[str, Any]									=   d * e - 1
  lowercase				:							List[str]									=   0
  lowercase				:							Tuple									=   0
  while p == 0:
    lowercase				:							Optional[Any]									=   random.randint(2 ,   n - 1			)
    lowercase				:							Dict									=   k
    while True:
      if t % 2 == 0:
        lowercase				:							List[Any]									=   t // 2
        lowercase				:							Union[str, Any]									=   (g**t) % n
        lowercase				:							Optional[Any]									=   math.gcd(x - 1 ,   __UpperCAmelCase			)
        if x > 1 and y > 1:
          lowercase				:							Dict									=   y
          lowercase				:							Any									=   n // y
          break  # find the correct factors
      else:
        break  # t is not divisible by 2, break and choose another g
  return sorted([p, q]			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()