





import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


_UpperCAmelCase             =							{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 6_5536,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8000,
        'sample_size': 13_1072,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6000,
        'sample_size': 6_5536,
    },
}






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:int   )      ->   List[str]:
 return torch.atana(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ) / math.pi * 2






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   )      ->   int:
 __lowerCAmelCase					:	Any      =       torch.sin(t * math.pi / 2   ) ** 2
 __lowerCAmelCase					:	str      =       (1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






class        snake_case_    (		__lowercase						):
  pass






class        snake_case_    (		nn.Module						):






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					Optional[int]      )->Any:






   '''simple docstring'''

   super().__init__()

   __lowerCAmelCase					:	Dict      =       DiffusionAttnUnetaD(_snake_case       ,  n_attn_layers=4      )
   __lowerCAmelCase					:	Union[str, Any]      =       deepcopy(self.diffusion      )
   __lowerCAmelCase					:	int      =       torch.quasirandom.SobolEngine(1       ,  scramble=_snake_case      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   List[str]:
 __lowerCAmelCase					:	Optional[Any]      =       MODELS_MAP[model_name]["""url"""]
 os.system(F'''wget {url} ./'''   )

 return F'''./{model_name}.ckpt'''


_UpperCAmelCase             =							{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
_UpperCAmelCase             =							{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
_UpperCAmelCase             =							{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
    '8': 'resnets.3',
    '9': 'attentions.3',
    '10': 'resnets.4',
    '11': 'attentions.4',
    '12': 'resnets.5',
    '13': 'attentions.5',
}
_UpperCAmelCase             =							{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

_UpperCAmelCase             =							{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

_UpperCAmelCase             =							{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   )      ->   Dict:
 if name.startswith("""skip"""   ):
  return name.replace("""skip"""   ,   RES_CONV_MAP["""skip"""]   )

 # name has to be of format main.{digit}
 if not name.startswith("""main."""   ):
  raise ValueError(F'''ResConvBlock error with {name}'''   )

 return name.replace(name[:6]   ,   RES_CONV_MAP[name[:6]]   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Any   )      ->   Optional[int]:
 for key, value in ATTN_MAP.items():
  if name.startswith(SCREAMING_SNAKE_CASE   ) and not isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
   return name.replace(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  elif name.startswith(SCREAMING_SNAKE_CASE   ):
   return [name.replace(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ) for v in value]
 raise ValueError(F'''Attn error with {name}'''   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Tuple   ,   SCREAMING_SNAKE_CASE		:Optional[int]=13   )      ->   Dict:
 __lowerCAmelCase					:	List[Any]      =       input_string

 if string.split("""."""   )[0] == "timestep_embed":
  return string.replace("""timestep_embed"""   ,   """time_proj"""   )

 __lowerCAmelCase					:	Tuple      =       0
 if string.startswith("""net.3."""   ):
  depth += 1
  __lowerCAmelCase					:	Dict      =       string[6:]
 elif string.startswith("""net."""   ):
  __lowerCAmelCase					:	int      =       string[4:]

 while string.startswith("""main.7."""   ):
  depth += 1
  __lowerCAmelCase					:	Tuple      =       string[7:]

 if string.startswith("""main."""   ):
  __lowerCAmelCase					:	List[Any]      =       string[5:]

 # mid block
 if string[:2].isdigit():
  __lowerCAmelCase					:	Optional[Any]      =       string[:2]
  __lowerCAmelCase					:	Union[str, Any]      =       string[2:]
 else:
  __lowerCAmelCase					:	List[str]      =       string[0]
  __lowerCAmelCase					:	Optional[int]      =       string[1:]

 if depth == max_depth:
  __lowerCAmelCase					:	Optional[Any]      =       MID_NUM_TO_LAYER[layer_num]
  __lowerCAmelCase					:	Any      =       """mid_block"""
 elif depth > 0 and int(SCREAMING_SNAKE_CASE   ) < 7:
  __lowerCAmelCase					:	str      =       DOWN_NUM_TO_LAYER[layer_num]
  __lowerCAmelCase					:	Tuple      =       F'''down_blocks.{depth}'''
 elif depth > 0 and int(SCREAMING_SNAKE_CASE   ) > 7:
  __lowerCAmelCase					:	Tuple      =       UP_NUM_TO_LAYER[layer_num]
  __lowerCAmelCase					:	Tuple      =       F'''up_blocks.{max_depth - depth - 1}'''
 elif depth == 0:
  __lowerCAmelCase					:	Optional[Any]      =       DEPTH_0_TO_LAYER[layer_num]
  __lowerCAmelCase					:	List[Any]      =       F'''up_blocks.{max_depth - 1}''' if int(SCREAMING_SNAKE_CASE   ) > 3 else """down_blocks.0"""

 if not string_left.startswith("""."""   ):
  raise ValueError(F'''Naming error with {input_string} and string_left: {string_left}.'''   )

 __lowerCAmelCase					:	List[Any]      =       string_left[1:]

 if "resnets" in new_layer:
  __lowerCAmelCase					:	Optional[int]      =       convert_resconv_naming(SCREAMING_SNAKE_CASE   )
 elif "attentions" in new_layer:
  __lowerCAmelCase					:	str      =       convert_attn_naming(SCREAMING_SNAKE_CASE   )
  __lowerCAmelCase					:	List[str]      =       new_string_left

 if not isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase					:	List[Any]      =       prefix + """.""" + new_layer + """.""" + string_left
 else:
  __lowerCAmelCase					:	Optional[int]      =       [prefix + """.""" + new_layer + """.""" + s for s in string_left]
 return new_string






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   Optional[Any]:
 __lowerCAmelCase					:	Optional[int]      =       {}
 for k, v in state_dict.items():
  if k.endswith("""kernel"""   ):
   # up- and downsample layers, don't have trainable weights
   continue

  __lowerCAmelCase					:	str      =       rename(SCREAMING_SNAKE_CASE   )

  # check if we need to transform from Conv => Linear for attention
  if isinstance(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
   __lowerCAmelCase					:	Optional[int]      =       transform_conv_attns(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  else:
   __lowerCAmelCase					:	List[str]      =       v

 return new_state_dict






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:Any   )      ->   str:
 if len(SCREAMING_SNAKE_CASE   ) == 1:
  if len(v.shape   ) == 3:
   # weight
   __lowerCAmelCase					:	Tuple      =       v[:, :, 0]
  else:
   # bias
   __lowerCAmelCase					:	List[str]      =       v
 else:
  # qkv matrices
  __lowerCAmelCase					:	Tuple      =       v.shape[0]
  __lowerCAmelCase					:	Any      =       trippled_shape // 3
  for i in range(3   ):
   if len(v.shape   ) == 3:
    __lowerCAmelCase					:	str      =       v[i * single_shape : (i + 1) * single_shape, :, 0]
   else:
    __lowerCAmelCase					:	Any      =       v[i * single_shape : (i + 1) * single_shape]
 return new_state_dict






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   )      ->   List[Any]:
 __lowerCAmelCase					:	int      =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

 __lowerCAmelCase					:	Any      =       args.model_path.split("""/"""   )[-1].split("""."""   )[0]
 if not os.path.isfile(args.model_path   ):
  assert (
      model_name == args.model_path
  ), F'''Make sure to provide one of the official model names {MODELS_MAP.keys()}'''
  __lowerCAmelCase					:	Union[str, Any]      =       download(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	int      =       MODELS_MAP[model_name]["""sample_rate"""]
 __lowerCAmelCase					:	Optional[Any]      =       MODELS_MAP[model_name]["""sample_size"""]

 __lowerCAmelCase					:	Dict      =       Object()
 __lowerCAmelCase					:	int      =       sample_size
 __lowerCAmelCase					:	Tuple      =       sample_rate
 __lowerCAmelCase					:	int      =       0

 __lowerCAmelCase					:	Tuple      =       UNetaDModel(sample_size=SCREAMING_SNAKE_CASE   ,   sample_rate=SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Tuple      =       diffusers_model.state_dict()

 __lowerCAmelCase					:	Tuple      =       DiffusionUncond(SCREAMING_SNAKE_CASE   )
 orig_model.load_state_dict(torch.load(args.model_path   ,   map_location=SCREAMING_SNAKE_CASE   )["""state_dict"""]   )
 __lowerCAmelCase					:	List[Any]      =       orig_model.diffusion_ema.eval()
 __lowerCAmelCase					:	Optional[Any]      =       orig_model.state_dict()
 __lowerCAmelCase					:	Union[str, Any]      =       rename_orig_weights(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Tuple      =       set(renamed_state_dict.keys()   ) - set(diffusers_state_dict.keys()   )
 __lowerCAmelCase					:	int      =       set(diffusers_state_dict.keys()   ) - set(renamed_state_dict.keys()   )

 assert len(SCREAMING_SNAKE_CASE   ) == 0, F'''Problem with {renamed_minus_diffusers}'''
 assert all(k.endswith("""kernel"""   ) for k in list(SCREAMING_SNAKE_CASE   )   ), F'''Problem with {diffusers_minus_renamed}'''

 for key, value in renamed_state_dict.items():
  assert (
      diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
  ), F'''Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}'''
  if key == "time_proj.weight":
   __lowerCAmelCase					:	List[Any]      =       value.squeeze()

  __lowerCAmelCase					:	Optional[int]      =       value

 diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	str      =       100
 __lowerCAmelCase					:	int      =       33

 __lowerCAmelCase					:	Tuple      =       IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       torch.randn([1, 2, config.sample_size]   ,   generator=SCREAMING_SNAKE_CASE   ).to(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Any      =       torch.linspace(1   ,   0   ,   steps + 1   ,   device=SCREAMING_SNAKE_CASE   )[:-1]
 __lowerCAmelCase					:	List[Any]      =       get_crash_schedule(SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE   ,   scheduler=SCREAMING_SNAKE_CASE   )

 __lowerCAmelCase					:	Union[str, Any]      =       torch.manual_seed(33   )
 __lowerCAmelCase					:	Any      =       pipe(num_inference_steps=SCREAMING_SNAKE_CASE   ,   generator=SCREAMING_SNAKE_CASE   ).audios

 __lowerCAmelCase					:	Union[str, Any]      =       sampling.iplms_sample(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   {}   )
 __lowerCAmelCase					:	Union[str, Any]      =       generated.clamp(-1   ,   1   )

 __lowerCAmelCase					:	List[Any]      =       (generated - audio).abs().sum()
 __lowerCAmelCase					:	Optional[int]      =       (generated - audio).abs().max()

 if args.save:
  pipe.save_pretrained(args.checkpoint_path   )

 print("""Diff sum"""   ,   SCREAMING_SNAKE_CASE   )
 print("""Diff max"""   ,   SCREAMING_SNAKE_CASE   )

 assert diff_max < 1e-3, F'''Diff max: {diff_max} is too much :-/'''

 print(F'''Conversion for {model_name} successful!'''   )


if __name__ == "__main__":
      _UpperCAmelCase             =							argparse.ArgumentParser()

      parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
      parser.add_argument(
          '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
      )
      parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
      _UpperCAmelCase             =							parser.parse_args()

      main(args)






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() = }''')