





from math import isqrt



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
			'''simple docstring'''


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

			return [i for i in range(2  ,UpperCAmelCase__     ) if is_prime[i]]



def     _lowerCAmelCase		(  _lowerCAmelCase = 1_0**8     ):
			'''simple docstring'''


			A_     :				Dict			      =	calculate_prime_numbers(max_number // 2     )

			A_     :				Optional[Any]			      =	0
			A_     :				Dict			      =	0
			A_     :				Optional[Any]			      =	len(UpperCAmelCase__     ) - 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() = }''')













import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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""",
    """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""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
_lowerCAmelCase 				=     [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   for attribute in key.split("""."""     ):
      if is_finetuned:
         if attribute in ["quantizer", "project_q", "project_hid"]:
            # those layers are only relevant for pretraining and should be dropped
            return

         if attribute == "ctc_proj":
            # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
            A_     :				Tuple			      =	"""lm_head"""

      A_     :				int			      =	getattr(_lowerCAmelCase  ,_lowerCAmelCase     )

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

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	fairseq_model.state_dict()

   A_     :				Any			      =	hf_model.unispeech.feature_extractor

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

   logger.warning(f"""Unused weights: {unused_weights}"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	full_name.split("""conv_layers."""     )[-1]
   A_     :				Union[str, Any]			      =	name.split("""."""     )
   A_     :				List[str]			      =	int(items[0]     )
   A_     :				Optional[Any]			      =	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."""
         )
         A_     :				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."""
         )
         A_     :				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:
         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."
         )
         A_     :				List[str]			      =	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."""
         )
         A_     :				Tuple			      =	value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
   else:
      unused_weights.append(_lowerCAmelCase     )



@torch.no_grad()
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None  ,_lowerCAmelCase=None  ,_lowerCAmelCase=True     ):
   '''simple docstring'''


   if config_path is not None:
      A_     :				Optional[int]			      =	UniSpeechConfig.from_pretrained(_lowerCAmelCase     )
   else:
      A_     :				List[str]			      =	UniSpeechConfig()

   if is_finetuned:
      if dict_path:
         A_     :				str			      =	Dictionary.load_from_json(_lowerCAmelCase     )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         A_     :				List[Any]			      =	target_dict.pad_index
         A_     :				str			      =	target_dict.bos_index
         A_     :				Optional[Any]			      =	target_dict.eos_index
         A_     :				Optional[int]			      =	len(target_dict.symbols     )
         A_     :				str			      =	os.path.join(_lowerCAmelCase  ,"""vocab.json"""     )
         if not os.path.isdir(_lowerCAmelCase     ):
            logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_lowerCAmelCase     )     )
            return
         os.makedirs(_lowerCAmelCase  ,exist_ok=_lowerCAmelCase     )
         A_     :				Optional[int]			      =	target_dict.indices

         # fairseq has the <pad> and <s> switched
         A_     :				Dict			      =	4_2
         A_     :				Optional[Any]			      =	4_3
         with open(_lowerCAmelCase  ,"""w"""  ,encoding="""utf-8"""     ) as vocab_handle:
            json.dump(_lowerCAmelCase  ,_lowerCAmelCase     )
         A_     :				Tuple			      =	WavaVecaPhonemeCTCTokenizer(
             _lowerCAmelCase  ,unk_token=target_dict.unk_word  ,pad_token=target_dict.pad_word  ,bos_token=target_dict.bos_word  ,eos_token=target_dict.eos_word  ,word_delimiter_token="""|"""  ,do_lower_case=_lowerCAmelCase  ,)
         A_     :				Any			      =	True if config.feat_extract_norm == """layer""" else False
         A_     :				List[str]			      =	WavaVecaFeatureExtractor(
             feature_size=1  ,sampling_rate=1_6_0_0_0  ,padding_value=0  ,do_normalize=_lowerCAmelCase  ,return_attention_mask=_lowerCAmelCase  ,)
         A_     :				Optional[int]			      =	WavaVecaProcessor(feature_extractor=_lowerCAmelCase  ,tokenizer=_lowerCAmelCase     )
         processor.save_pretrained(_lowerCAmelCase     )

      A_     :				int			      =	UniSpeechForCTC(_lowerCAmelCase     )
   else:
      A_     :				str			      =	UniSpeechForPreTraining(_lowerCAmelCase     )

   if is_finetuned:
      A_      ,				A_      ,				A_     :				Union[str, Any]			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]  ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""     )[:-1]     ), """w2v_path""": checkpoint_path}     )
   else:
      A_      ,				A_      ,				A_     :				Any			      =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     )

   A_     :				List[str]			      =	model[0].eval()

   recursively_load_weights(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   hf_unispeech.save_pretrained(_lowerCAmelCase     )


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






