



'''simple docstring'''





import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowercase_	 =						{
    "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",
}
lowercase_	 =						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     for attribute in key.split('''.'''):
          _a											=						getattr(__A						,			__A)

     if weight_type is not None:
          _a											=						getattr(__A						,			__A).shape
     else:
          _a											=						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											=						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 lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						[]
     _a											=						fairseq_model.state_dict()

     _a											=						hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          _a											=						False
          if "conv_layers" in name:
               load_conv_layer(
                   __A						,			__A						,			__A						,			__A						,			hf_model.config.feat_extract_norm == '''group'''						,			)
               _a											=						True
          else:
               for key, mapped_key in MAPPING.items():
                    _a											=						'''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											=						True
                         if "*" in mapped_key:
                              _a											=						name.split(__A)[0].split('''.''')[-2]
                              _a											=						mapped_key.replace('''*'''						,			__A)
                         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(__A						,			__A						,			__A						,			__A						,			__A)
                    continue
          if not is_used:
               unused_weights.append(__A)

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




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A):



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




@torch.no_grad()
def lowerCAmelCase						(__A						,			__A						,			__A=None						,			__A=None						,			__A=True):



     """simple docstring"""



     if config_path is not None:
          _a											=						UniSpeechSatConfig.from_pretrained(__A)
     else:
          _a											=						UniSpeechSatConfig()

     _a											=						''''''

     if is_finetuned:
          _a											=						UniSpeechSatForCTC(__A)
     else:
          _a											=						UniSpeechSatForPreTraining(__A)

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

     recursively_load_weights(__A						,			__A)

     hf_wavavec.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_	 =						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"
  )
  lowercase_	 =						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
  )








'''simple docstring'''





from __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


if __name__ == "__main__":
  print(F"""{len(find_circular_primes()) = }""")



