





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


lowerCAmelCase_						   =    {
    """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     lowerCamelCase_	(      lowerCAmelCase:	List[str]    ,							lowerCAmelCase:	List[str]				)->		int:
  return torch.atana(lowerCAmelCase    ,							lowerCAmelCase				) / math.pi * 2


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]				)->		Optional[Any]:
  _snake_case   :				List[Any]														=	torch.sin(t * math.pi / 2				) ** 2
  _snake_case   :				Dict														=	(1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(lowerCAmelCase    ,							lowerCAmelCase				)



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  pass




class        _lowerCAmelCase    (			nn.Module							):


  '''simple docstring'''







  def __init__(      self      :					Dict      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





    super().__init__()

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




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

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


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

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

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


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


def     lowerCamelCase_	(      lowerCAmelCase:	Optional[Any]    ,							lowerCAmelCase:	str=13				)->		Any:
  _snake_case   :				Optional[Any]														=	input_string

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

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

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

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

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

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

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

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


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

    _snake_case   :				int														=	rename(lowerCAmelCase				)

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

  return new_state_dict


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


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		List[str]:
  _snake_case   :				Tuple														=	torch.device('cuda' if torch.cuda.is_available() else 'cpu'				)

  _snake_case   :				int														=	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   :				Union[str, Any]														=	download(lowerCAmelCase				)

  _snake_case   :				Union[str, Any]														=	MODELS_MAP[model_name]['sample_rate']
  _snake_case   :				str														=	MODELS_MAP[model_name]['sample_size']

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

  _snake_case   :				Any														=	UNetaDModel(sample_size=lowerCAmelCase    ,							sample_rate=lowerCAmelCase				)
  _snake_case   :				Tuple														=	diffusers_model.state_dict()

  _snake_case   :				Optional[int]														=	DiffusionUncond(lowerCAmelCase				)
  orig_model.load_state_dict(torch.load(args.model_path    ,							map_location=lowerCAmelCase				)['state_dict']				)
  _snake_case   :				Any														=	orig_model.diffusion_ema.eval()
  _snake_case   :				Tuple														=	orig_model.state_dict()
  _snake_case   :				Tuple														=	rename_orig_weights(lowerCAmelCase				)

  _snake_case   :				List[str]														=	set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
  _snake_case   :				List[str]														=	set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

  assert len(lowerCAmelCase				) == 0, F"""Problem with {renamed_minus_diffusers}"""
  assert all(k.endswith('kernel'				) for k in list(lowerCAmelCase				)				), 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   :				Optional[int]														=	value.squeeze()

    _snake_case   :				str														=	value

  diffusers_model.load_state_dict(lowerCAmelCase				)

  _snake_case   :				Dict														=	1_00
  _snake_case   :				Optional[Any]														=	33

  _snake_case   :				Optional[Any]														=	IPNDMScheduler(num_train_timesteps=lowerCAmelCase				)

  _snake_case   :				Dict														=	torch.manual_seed(lowerCAmelCase				)
  _snake_case   :				Tuple														=	torch.randn([1, 2, config.sample_size]    ,							generator=lowerCAmelCase				).to(lowerCAmelCase				)

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

  _snake_case   :				List[Any]														=	DanceDiffusionPipeline(unet=lowerCAmelCase    ,							scheduler=lowerCAmelCase				)

  _snake_case   :				int														=	torch.manual_seed(33				)
  _snake_case   :				Tuple														=	pipe(num_inference_steps=lowerCAmelCase    ,							generator=lowerCAmelCase				).audios

  _snake_case   :				str														=	sampling.iplms_sample(lowerCAmelCase    ,							lowerCAmelCase    ,							lowerCAmelCase    ,							{}				)
  _snake_case   :				Dict														=	generated.clamp(-1    ,							1				)

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

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

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

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

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


if __name__ == "__main__":
      lowerCAmelCase_						   =    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.""")
      lowerCAmelCase_						   =    parser.parse_args()

      main(args)











def     lowerCamelCase_	(      lowerCAmelCase:	bytes				)->		str:
  return "".join([hex(lowerCAmelCase				)[2:].zfill(2				).upper() for byte in list(lowerCAmelCase				)]				)


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		bytes:
  # Check data validity, following RFC3548
  # https://www.ietf.org/rfc/rfc3548.txt
  if (len(lowerCAmelCase				) % 2) != 0:
    raise ValueError(
        'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'				)
  # Check the character set - the standard base16 alphabet
  # is uppercase according to RFC3548 section 6
  if not set(lowerCAmelCase				) <= set('0123456789ABCDEF'				):
    raise ValueError(
        'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'				)
  # For every two hexadecimal digits (= a byte), turn it into an integer.
  # Then, string the result together into bytes, and return it.
  return bytes(int(data[i] + data[i + 1]    ,							16				) for i in range(0    ,							len(lowerCAmelCase				)    ,							2				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




