




'''simple docstring'''


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_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 						SCREAMING_SNAKE_CASE       (      a_	:							int				,					a_	:							Optional[int]			):
  return torch.atana(a_				,					a_			) / math.pi * 2


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]			):
  __a			=					torch.sin(t * math.pi / 2			) ** 2
  __a			=					(1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(a_				,					a_			)





class    __lowercase						(  __magic_name__ ):
   pass





class    __lowercase						(  nn.Module ):







   def __init__(	self      ,							UpperCamelCase      )				->    Optional[Any]:
     super().__init__()

     __a			=					DiffusionAttnUnetaD(UpperCamelCase      ,							n_attn_layers=4      )
     __a			=					deepcopy(self.diffusion      )
     __a			=					torch.quasirandom.SobolEngine(1      ,							scramble=UpperCamelCase      )


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  __a			=					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       (      a_	:							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       (      a_	:							Dict			):
  for key, value in ATTN_MAP.items():
    if name.startswith(a_			) and not isinstance(a_				,					a_			):
      return name.replace(a_				,					a_			)
    elif name.startswith(a_			):
      return [name.replace(a_				,					a_			) for v in value]
  raise ValueError(f"Attn error with {name}"			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]				,					a_	:							int=13			):
  __a			=					input_string

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

  __a			=					0
  if string.startswith('net.3.'			):
    depth += 1
    __a			=					string[6:]
  elif string.startswith('net.'			):
    __a			=					string[4:]

  while string.startswith('main.7.'			):
    depth += 1
    __a			=					string[7:]

  if string.startswith('main.'			):
    __a			=					string[5:]

  # mid block
  if string[:2].isdigit():
    __a			=					string[:2]
    __a			=					string[2:]
  else:
    __a			=					string[0]
    __a			=					string[1:]

  if depth == max_depth:
    __a			=					MID_NUM_TO_LAYER[layer_num]
    __a			=					'mid_block'
  elif depth > 0 and int(a_			) < 7:
    __a			=					DOWN_NUM_TO_LAYER[layer_num]
    __a			=					f"down_blocks.{depth}"
  elif depth > 0 and int(a_			) > 7:
    __a			=					UP_NUM_TO_LAYER[layer_num]
    __a			=					f"up_blocks.{max_depth - depth - 1}"
  elif depth == 0:
    __a			=					DEPTH_0_TO_LAYER[layer_num]
    __a			=					f"up_blocks.{max_depth - 1}" if int(a_			) > 3 else 'down_blocks.0'

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

  __a			=					string_left[1:]

  if "resnets" in new_layer:
    __a			=					convert_resconv_naming(a_			)
  elif "attentions" in new_layer:
    __a			=					convert_attn_naming(a_			)
    __a			=					new_string_left

  if not isinstance(a_				,					a_			):
    __a			=					prefix + '.' + new_layer + '.' + string_left
  else:
    __a			=					[prefix + '.' + new_layer + '.' + s for s in string_left]
  return new_string


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict			):
  __a			=					{}
  for k, v in state_dict.items():
    if k.endswith('kernel'			):
      # up- and downsample layers, don't have trainable weights
      continue

    __a			=					rename(a_			)

    # check if we need to transform from Conv => Linear for attention
    if isinstance(a_				,					a_			):
      __a			=					transform_conv_attns(a_				,					a_				,					a_			)
    else:
      __a			=					v

  return new_state_dict


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


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  __a			=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

  __a			=					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()}"
    __a			=					download(a_			)

  __a			=					MODELS_MAP[model_name]['sample_rate']
  __a			=					MODELS_MAP[model_name]['sample_size']

  __a			=					Object()
  __a			=					sample_size
  __a			=					sample_rate
  __a			=					0

  __a			=					UNetaDModel(sample_size=a_				,					sample_rate=a_			)
  __a			=					diffusers_model.state_dict()

  __a			=					DiffusionUncond(a_			)
  orig_model.load_state_dict(torch.load(args.model_path				,					map_location=a_			)['state_dict']			)
  __a			=					orig_model.diffusion_ema.eval()
  __a			=					orig_model.state_dict()
  __a			=					rename_orig_weights(a_			)

  __a			=					set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
  __a			=					set(diffusers_state_dict.keys()			) - set(renamed_state_dict.keys()			)

  assert len(a_			) == 0, f"Problem with {renamed_minus_diffusers}"
  assert all(k.endswith('kernel'			) for k in list(a_			)			), 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":
      __a			=					value.squeeze()

    __a			=					value

  diffusers_model.load_state_dict(a_			)

  __a			=					100
  __a			=					33

  __a			=					IPNDMScheduler(num_train_timesteps=a_			)

  __a			=					torch.manual_seed(a_			)
  __a			=					torch.randn([1, 2, config.sample_size]				,					generator=a_			).to(a_			)

  __a			=					torch.linspace(1				,					0				,					steps + 1				,					device=a_			)[:-1]
  __a			=					get_crash_schedule(a_			)

  __a			=					DanceDiffusionPipeline(unet=a_				,					scheduler=a_			)

  __a			=					torch.manual_seed(33			)
  __a			=					pipe(num_inference_steps=a_				,					generator=a_			).audios

  __a			=					sampling.iplms_sample(a_				,					a_				,					a_				,					{}			)
  __a			=					generated.clamp(-1				,					1			)

  __a			=					(generated - audio).abs().sum()
  __a			=					(generated - audio).abs().max()

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

  print('Diff sum'				,					a_			)
  print('Diff max'				,					a_			)

  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)









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							int = 10**12			):
  __a			=					1
  __a			=					0

  __a			=					1
  __a			=					1

  while numerator <= 2 * min_total - 1:
    prev_numerator += 2 * numerator
    numerator += 2 * prev_numerator

    prev_denominator += 2 * denominator
    denominator += 2 * prev_denominator

  return (denominator + 1) // 2


if __name__ == "__main__":
  print(f"""{solution() = }""")



