





import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


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


_UpperCAmelCase             =							['model.decoder.embed_positions.weights']






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   )      ->   Optional[int]:
 if "emb" in name:
  __lowerCAmelCase					:	Tuple      =       name.replace("""emb"""   ,   """model.decoder.embed_tokens"""   )
 if "transformer" in name:
  __lowerCAmelCase					:	Any      =       name.replace("""transformer"""   ,   """model.decoder"""   )
 if "cross_attention" in name:
  __lowerCAmelCase					:	Tuple      =       name.replace("""cross_attention"""   ,   """encoder_attn"""   )
 if "linear1" in name:
  __lowerCAmelCase					:	int      =       name.replace("""linear1"""   ,   """fc1"""   )
 if "linear2" in name:
  __lowerCAmelCase					:	Optional[int]      =       name.replace("""linear2"""   ,   """fc2"""   )
 if "norm1" in name:
  __lowerCAmelCase					:	Optional[int]      =       name.replace("""norm1"""   ,   """self_attn_layer_norm"""   )
 if "norm_cross" in name:
  __lowerCAmelCase					:	int      =       name.replace("""norm_cross"""   ,   """encoder_attn_layer_norm"""   )
 if "norm2" in name:
  __lowerCAmelCase					:	int      =       name.replace("""norm2"""   ,   """final_layer_norm"""   )
 if "out_norm" in name:
  __lowerCAmelCase					:	str      =       name.replace("""out_norm"""   ,   """model.decoder.layer_norm"""   )
 if "linears" in name:
  __lowerCAmelCase					:	Any      =       name.replace("""linears"""   ,   """lm_heads"""   )
 if "condition_provider.conditioners.description.output_proj" in name:
  __lowerCAmelCase					:	Any      =       name.replace("""condition_provider.conditioners.description.output_proj"""   ,   """enc_to_dec_proj"""   )
 return name






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:OrderedDict   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Tuple[Dict, Dict]:
 __lowerCAmelCase					:	int      =       list(state_dict.keys()   )
 __lowerCAmelCase					:	Dict      =       {}
 for key in keys:
  __lowerCAmelCase					:	str      =       state_dict.pop(SCREAMING_SNAKE_CASE   )
  __lowerCAmelCase					:	Dict      =       rename_keys(SCREAMING_SNAKE_CASE   )
  if "in_proj_weight" in key:
   # split fused qkv proj
   __lowerCAmelCase					:	Optional[Any]      =       val[:hidden_size, :]
   __lowerCAmelCase					:	List[str]      =       val[hidden_size : 2 * hidden_size, :]
   __lowerCAmelCase					:	str      =       val[-hidden_size:, :]
  elif "enc_to_dec_proj" in key:
   __lowerCAmelCase					:	Any      =       val
  else:
   __lowerCAmelCase					:	int      =       val
 return state_dict, enc_dec_proj_state_dict






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   )      ->   MusicgenDecoderConfig:
 if checkpoint == "small":
  # default config values
  __lowerCAmelCase					:	Optional[Any]      =       1_024
  __lowerCAmelCase					:	Dict      =       24
  __lowerCAmelCase					:	Union[str, Any]      =       16
 elif checkpoint == "medium":
  __lowerCAmelCase					:	Tuple      =       1_536
  __lowerCAmelCase					:	Tuple      =       48
  __lowerCAmelCase					:	List[str]      =       24
 elif checkpoint == "large":
  __lowerCAmelCase					:	List[Any]      =       2_048
  __lowerCAmelCase					:	Optional[int]      =       48
  __lowerCAmelCase					:	List[str]      =       32
 else:
  raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''   )
 __lowerCAmelCase					:	List[Any]      =       MusicgenDecoderConfig(
     hidden_size=SCREAMING_SNAKE_CASE   ,   ffn_dim=hidden_size * 4   ,   num_hidden_layers=SCREAMING_SNAKE_CASE   ,   num_attention_heads=SCREAMING_SNAKE_CASE   ,   )
 return config






@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   ,   SCREAMING_SNAKE_CASE		:str=None   ,   SCREAMING_SNAKE_CASE		:List[Any]=None   ,   SCREAMING_SNAKE_CASE		:List[Any]="cpu"   )      ->   Optional[int]:
 __lowerCAmelCase					:	Tuple      =       MusicGen.get_pretrained(SCREAMING_SNAKE_CASE   ,   device=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[Any]      =       decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Dict      =       fairseq_model.lm.state_dict()
 __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[int]      =       rename_state_dict(
     SCREAMING_SNAKE_CASE   ,   hidden_size=decoder_config.hidden_size   )

 __lowerCAmelCase					:	Optional[Any]      =       TaEncoderModel.from_pretrained("""t5-base"""   )
 __lowerCAmelCase					:	List[str]      =       EncodecModel.from_pretrained("""facebook/encodec_32khz"""   )
 __lowerCAmelCase					:	Optional[int]      =       MusicgenForCausalLM(SCREAMING_SNAKE_CASE   ).eval()

 # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
 __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       decoder.load_state_dict(SCREAMING_SNAKE_CASE   ,   strict=SCREAMING_SNAKE_CASE   )

 for key in missing_keys.copy():
  if key.startswith(("""text_encoder""", """audio_encoder""")   ) or key in EXPECTED_MISSING_KEYS:
   missing_keys.remove(SCREAMING_SNAKE_CASE   )

 if len(SCREAMING_SNAKE_CASE   ) > 0:
  raise ValueError(F'''Missing key(s) in state_dict: {missing_keys}'''   )

 if len(SCREAMING_SNAKE_CASE   ) > 0:
  raise ValueError(F'''Unexpected key(s) in state_dict: {unexpected_keys}'''   )

 # init the composite model
 __lowerCAmelCase					:	Optional[int]      =       MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE   ,   audio_encoder=SCREAMING_SNAKE_CASE   ,   decoder=SCREAMING_SNAKE_CASE   )

 # load the pre-trained enc-dec projection (from the decoder state dict)
 model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE   )

 # check we can do a forward pass
 __lowerCAmelCase					:	List[Any]      =       torch.arange(0   ,   8   ,   dtype=torch.long   ).reshape(2   ,   -1   )
 __lowerCAmelCase					:	Union[str, Any]      =       input_ids.reshape(2 * 4   ,   -1   )

 with torch.no_grad():
  __lowerCAmelCase					:	List[Any]      =       model(input_ids=SCREAMING_SNAKE_CASE   ,   decoder_input_ids=SCREAMING_SNAKE_CASE   ).logits

 if logits.shape != (8, 1, 2_048):
  raise ValueError("""Incorrect shape for logits"""   )

 # now construct the processor
 __lowerCAmelCase					:	Optional[Any]      =       AutoTokenizer.from_pretrained("""t5-base"""   )
 __lowerCAmelCase					:	Union[str, Any]      =       AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""   ,   padding_side="""left"""   )

 __lowerCAmelCase					:	Tuple      =       MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE   ,   tokenizer=SCREAMING_SNAKE_CASE   )

 # set the appropriate bos/pad token ids
 __lowerCAmelCase					:	int      =       2_048
 __lowerCAmelCase					:	str      =       2_048

 # set other default generation config params
 __lowerCAmelCase					:	List[str]      =       int(30 * audio_encoder.config.frame_rate   )
 __lowerCAmelCase					:	List[str]      =       True
 __lowerCAmelCase					:	Dict      =       3.0

 if pytorch_dump_folder is not None:
  Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
  logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}'''   )
  model.save_pretrained(SCREAMING_SNAKE_CASE   )
  processor.save_pretrained(SCREAMING_SNAKE_CASE   )

 if repo_id:
  logger.info(F'''Pushing model {checkpoint} to {repo_id}'''   )
  model.push_to_hub(SCREAMING_SNAKE_CASE   )
  processor.push_to_hub(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      _UpperCAmelCase             =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--checkpoint',
          default='small',
          type=str,
          help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
      )
      parser.add_argument(
          '--pytorch_dump_folder',
          required=True,
          default=None,
          type=str,
          help='Path to the output PyTorch model directory.',
      )
      parser.add_argument(
          '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
      )
      parser.add_argument(
          '--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
      )

      _UpperCAmelCase             =							parser.parse_args()
      convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






from math import isqrt






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   list[int]:
 __lowerCAmelCase					:	Tuple      =       [True] * max_number
 for i in range(2   ,   isqrt(max_number - 1   ) + 1   ):
  if is_prime[i]:
   for j in range(i**2   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
    __lowerCAmelCase					:	Tuple      =       False

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






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int = 10**8   )      ->   int:
 __lowerCAmelCase					:	int      =       calculate_prime_numbers(max_number // 2   )

 __lowerCAmelCase					:	List[Any]      =       0
 __lowerCAmelCase					:	List[str]      =       0
 __lowerCAmelCase					:	List[Any]      =       len(SCREAMING_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() = }''')