



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


a__    =	{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 6_55_36,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 6_55_36,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_80_00,
        """sample_size""": 13_10_72,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_60_00,
        """sample_size""": 6_55_36,
    },
}



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	int				)  ->     Any:
 return torch.atana(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) / math.pi * 2



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Any:
 _snake_case    :  Any					=					torch.sin(t * math.pi / 2				) ** 2
 _snake_case    :  str					=					(1 - sigma**2) ** 0.5
 return alpha_sigma_to_t(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   pass







class 		snake_case						(	nn.Module  ):



   '''simple docstring'''






   def __init__(					self							:			str				,		lowerCAmelCase							:			Dict)				->				Any:

    """simple docstring"""







    super().__init__()

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     List[Any]:
 _snake_case    :  str					=					MODELS_MAP[model_name]["""url"""]
 os.system(F'''wget {url} ./'''				)

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


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

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

a__    =	{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     List[Any]:
 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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[Any]				)  ->     str:
 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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=13				)  ->     List[str]:
 _snake_case    :  Dict					=					input_string

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

 _snake_case    :  List[str]					=					0
 if string.startswith("""net.3."""				):
  depth += 1
  _snake_case    :  Optional[int]					=					string[6:]
 elif string.startswith("""net."""				):
  _snake_case    :  Dict					=					string[4:]

 while string.startswith("""main.7."""				):
  depth += 1
  _snake_case    :  List[Any]					=					string[7:]

 if string.startswith("""main."""				):
  _snake_case    :  List[str]					=					string[5:]

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

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

 _snake_case    :  Optional[Any]					=					string_left[1:]

 if "resnets" in new_layer:
  _snake_case    :  str					=					convert_resconv_naming(SCREAMING_SNAKE_CASE__				)
 elif "attentions" in new_layer:
  _snake_case    :  Dict					=					convert_attn_naming(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[str]					=					new_string_left

 if not isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
  _snake_case    :  List[str]					=					prefix + """.""" + new_layer + """.""" + string_left
 else:
  _snake_case    :  Union[str, Any]					=					[prefix + """.""" + new_layer + """.""" + s for s in string_left]
 return new_string



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

  _snake_case    :  Any					=					rename(SCREAMING_SNAKE_CASE__				)

  # check if we need to transform from Conv => Linear for attention
  if isinstance(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				):
   _snake_case    :  str					=					transform_conv_attns(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  else:
   _snake_case    :  Tuple					=					v

 return new_state_dict



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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     List[str]:
 _snake_case    :  Optional[int]					=					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)

 _snake_case    :  Optional[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()}'''
  _snake_case    :  str					=					download(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					MODELS_MAP[model_name]["""sample_rate"""]
 _snake_case    :  List[str]					=					MODELS_MAP[model_name]["""sample_size"""]

 _snake_case    :  Union[str, Any]					=					Object()
 _snake_case    :  Tuple					=					sample_size
 _snake_case    :  List[Any]					=					sample_rate
 _snake_case    :  Optional[Any]					=					0

 _snake_case    :  Any					=					UNetaDModel(sample_size=SCREAMING_SNAKE_CASE__				,       sample_rate=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[Any]					=					diffusers_model.state_dict()

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

 _snake_case    :  Tuple					=					set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
 _snake_case    :  List[Any]					=					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":
   _snake_case    :  str					=					value.squeeze()

  _snake_case    :  Any					=					value

 diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					100
 _snake_case    :  Dict					=					33

 _snake_case    :  Union[str, Any]					=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					torch.manual_seed(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Optional[int]					=					torch.randn([1, 2, config.sample_size]				,       generator=SCREAMING_SNAKE_CASE__				).to(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Union[str, Any]					=					torch.linspace(1				,       0				,       steps + 1				,       device=SCREAMING_SNAKE_CASE__				)[:-1]
 _snake_case    :  Dict					=					get_crash_schedule(SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Dict					=					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE__				,       scheduler=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  int					=					torch.manual_seed(33				)
 _snake_case    :  str					=					pipe(num_inference_steps=SCREAMING_SNAKE_CASE__				,       generator=SCREAMING_SNAKE_CASE__				).audios

 _snake_case    :  Optional[Any]					=					sampling.iplms_sample(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       {}				)
 _snake_case    :  List[Any]					=					generated.clamp(-1				,       1				)

 _snake_case    :  Optional[Any]					=					(generated - audio).abs().sum()
 _snake_case    :  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__":
  a__    =	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.""")
  a__    =	parser.parse_args()

  main(args)





def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int = 1_000				)  ->     int:
 _snake_case	,    _snake_case    :  str					=					1, 1
 _snake_case    :  List[Any]					=					2
 while True:
  _snake_case    :  Union[str, Any]					=					0
  _snake_case    :  int					=					fa + fa
  _snake_case	,    _snake_case    :  Union[str, Any]					=					fa, f
  index += 1
  for _ in str(SCREAMING_SNAKE_CASE__				):
   i += 1
  if i == n:
   break
 return index


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))
