'''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


__snake_case						  =		{
    """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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  return torch.atana(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						) / math.pi * 2

def   A_      (       SCREAMING_SNAKE_CASE_						)			->str:
  lowercase_     =       torch.sin(t * math.pi / 2						) ** 2
  lowercase_     =       (1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)





class       _a    (		__a      ):


  """simple docstring"""

  pass






class       _a    (		nn.Module      ):


  """simple docstring"""

  def __init__(		self      :       Dict   ,				lowercase_      :       Tuple      ):







    '''simple docstring'''


    super().__init__()

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






def   A_      (       SCREAMING_SNAKE_CASE_						)			->Tuple:
  lowercase_     =       MODELS_MAP[model_name]["""url"""]
  os.system(f"""wget {url} ./"""						)

  return f"""./{model_name}.ckpt"""


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

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

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

def   A_      (       SCREAMING_SNAKE_CASE_						)			->Optional[int]:
  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   A_      (       SCREAMING_SNAKE_CASE_						)			->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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=13						)			->List[str]:
  lowercase_     =       input_string

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

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

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

  if string.startswith("""main."""						):
    lowercase_     =       string[5:]

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

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

  lowercase_     =       string_left[1:]

  if "resnets" in new_layer:
    lowercase_     =       convert_resconv_naming(SCREAMING_SNAKE_CASE_						)
  elif "attentions" in new_layer:
    lowercase_     =       convert_attn_naming(SCREAMING_SNAKE_CASE_						)
    lowercase_     =       new_string_left

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       prefix + """.""" + new_layer + """.""" + string_left
  else:
    lowercase_     =       [prefix + """.""" + new_layer + """.""" + s for s in string_left]
  return new_string

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

    lowercase_     =       rename(SCREAMING_SNAKE_CASE_						)

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

  return new_state_dict

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

def   A_      (       SCREAMING_SNAKE_CASE_						)			->str:
  lowercase_     =       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""						)

  lowercase_     =       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()}"""
    lowercase_     =       download(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       MODELS_MAP[model_name]["""sample_rate"""]
  lowercase_     =       MODELS_MAP[model_name]["""sample_size"""]

  lowercase_     =       Object()
  lowercase_     =       sample_size
  lowercase_     =       sample_rate
  lowercase_     =       0

  lowercase_     =       UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ ,	sample_rate=SCREAMING_SNAKE_CASE_						)
  lowercase_     =       diffusers_model.state_dict()

  lowercase_     =       DiffusionUncond(SCREAMING_SNAKE_CASE_						)
  orig_model.load_state_dict(torch.load(args.model_path ,	map_location=SCREAMING_SNAKE_CASE_						)["""state_dict"""]						)
  lowercase_     =       orig_model.diffusion_ema.eval()
  lowercase_     =       orig_model.state_dict()
  lowercase_     =       rename_orig_weights(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
  lowercase_     =       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":
      lowercase_     =       value.squeeze()

    lowercase_     =       value

  diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_						)

  lowercase_     =       1_00
  lowercase_     =       33

  lowercase_     =       IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_						)

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

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

  lowercase_     =       DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ ,	scheduler=SCREAMING_SNAKE_CASE_						)

  lowercase_     =       torch.manual_seed(33						)
  lowercase_     =       pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ ,	generator=SCREAMING_SNAKE_CASE_						).audios

  lowercase_     =       sampling.iplms_sample(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	{}						)
  lowercase_     =       generated.clamp(-1 ,	1						)

  lowercase_     =       (generated - audio).abs().sum()
  lowercase_     =       (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__":
       __snake_case						  =		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.""")
       __snake_case						  =		parser.parse_args()

       main(args)


'''simple docstring'''


import math
import sys

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  if number != int(SCREAMING_SNAKE_CASE_						):
    raise ValueError("""the value of input must be a natural number"""						)
  if number < 0:
    raise ValueError("""the value of input must not be a negative number"""						)
  if number == 0:
    return 1
  lowercase_     =       [-1] * (number + 1)
  lowercase_     =       0
  for i in range(1 ,	number + 1						):
    lowercase_     =       sys.maxsize
    lowercase_     =       int(math.sqrt(SCREAMING_SNAKE_CASE_						)						)
    for j in range(1 ,	root + 1						):
      lowercase_     =       1 + answers[i - (j**2)]
      lowercase_     =       min(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
    lowercase_     =       answer
  return answers[number]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

