


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

UpperCAmelCase__   	=							{
    '''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''',
}
UpperCAmelCase__   	=							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 for attribute in key.split('''.'''						):
  _lowercase							      =getattr(__snake_case     ,						__snake_case						)

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

 logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =[]
 _lowercase							      =fairseq_model.state_dict()

 _lowercase							      =hf_model.unispeech_sat.feature_extractor

 for name, value in fairseq_dict.items():
  _lowercase							      =False
  if "conv_layers" in name:
   load_conv_layer(
       __snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						hf_model.config.feat_extract_norm == '''group'''     ,						)
   _lowercase							      =True
  else:
   for key, mapped_key in MAPPING.items():
    _lowercase							      ='''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
     _lowercase							      =True
     if "*" in mapped_key:
      _lowercase							      =name.split(__snake_case						)[0].split('''.'''						)[-2]
      _lowercase							      =mapped_key.replace('''*'''     ,						__snake_case						)
     if "weight_g" in name:
      _lowercase							      ='''weight_g'''
     elif "weight_v" in name:
      _lowercase							      ='''weight_v'''
     elif "bias" in name:
      _lowercase							      ='''bias'''
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      _lowercase							      ='''weight'''
     else:
      _lowercase							      =None
     set_recursively(__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)
    continue
  if not is_used:
   unused_weights.append(__snake_case						)

 logger.warning(F"Unused weights: {unused_weights}"						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 _lowercase							      =full_name.split('''conv_layers.'''						)[-1]
 _lowercase							      =name.split('''.'''						)
 _lowercase							      =int(items[0]						)
 _lowercase							      =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."						)
   _lowercase							      =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."						)
   _lowercase							      =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."						)
   _lowercase							      =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."						)
   _lowercase							      =value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(__snake_case						)
@torch.no_grad()
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=None     ,						__snake_case=None     ,						__snake_case=True						)     ->			Any:







 """simple docstring"""


 if config_path is not None:
  _lowercase							      =UniSpeechSatConfig.from_pretrained(__snake_case						)
 else:
  _lowercase							      =UniSpeechSatConfig()

 _lowercase							      =''''''

 if is_finetuned:
  _lowercase							      =UniSpeechSatForCTC(__snake_case						)
 else:
  _lowercase							      =UniSpeechSatForPreTraining(__snake_case						)

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

 recursively_load_weights(__snake_case     ,						__snake_case						)

 hf_wavavec.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__   	=							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'''
  )
  UpperCAmelCase__   	=							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 math import isqrt
def 				UpperCAmelCase_					(							__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =[True] * max_number
 for i in range(2     ,						isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2     ,						__snake_case     ,						__snake_case						):
    _lowercase							      =False

 return [i for i in range(2     ,						__snake_case						) if is_prime[i]]
def 				UpperCAmelCase_					(							__snake_case = 10**8						)     ->			int:







 """simple docstring"""


 _lowercase							      =calculate_prime_numbers(max_number // 2						)

 _lowercase							      =0
 _lowercase							      =0
 _lowercase							      =len(__snake_case						) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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

