

'''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					:			Optional[Any]       =	{
    'gwf-440k': {
        'url': 'https://model-server.zqevans2.workers.dev/gwf-440k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_8_0_0_0,
        'sample_size': 1_3_1_0_7_2,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_6_0_0_0,
        'sample_size': 6_5_5_3_6,
    },
}







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     return torch.atana(a__ ,       a__		) / math.pi * 2







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = torch.sin(t * math.pi / 2		) ** 2
     __SCREAMING_SNAKE_CASE      = (1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(a__ ,       a__		)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 pass







class 		lowerCAmelCase__  ( nn.Module   ):



 """simple docstring"""



 def __init__(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					List[Any]:


      """simple docstring"""




      super().__init__()

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







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = MODELS_MAP[model_name]["""url"""]
     os.system(F'wget {url} ./'		)

     return F'./{model_name}.ckpt'


UpperCAmelCase					:			Tuple       =	{
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
UpperCAmelCase					:			Optional[int]       =	{
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
UpperCAmelCase					:			Optional[Any]       =	{
    '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					:			Optional[Any]       =	{
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

UpperCAmelCase					:			Optional[int]       =	{
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

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







def   a__			(    a__		):




     """simple docstring"""


     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__			(    a__		):




     """simple docstring"""


     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   a__			(    a__ ,       a__=13		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = input_string

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

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

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

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

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

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

     __SCREAMING_SNAKE_CASE      = string_left[1:]

     if "resnets" in new_layer:
          __SCREAMING_SNAKE_CASE      = convert_resconv_naming(a__		)
     elif "attentions" in new_layer:
          __SCREAMING_SNAKE_CASE      = convert_attn_naming(a__		)
          __SCREAMING_SNAKE_CASE      = new_string_left

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







def   a__			(    a__		):




     """simple docstring"""


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

          __SCREAMING_SNAKE_CASE      = rename(a__		)

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

     return new_state_dict







def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     if len(a__		) == 1:
          if len(v.shape		) == 3:
               # weight
               __SCREAMING_SNAKE_CASE      = v[:, :, 0]
          else:
               # bias
               __SCREAMING_SNAKE_CASE      = v
     else:
          # qkv matrices
          __SCREAMING_SNAKE_CASE      = v.shape[0]
          __SCREAMING_SNAKE_CASE      = trippled_shape // 3
          for i in range(3		):
               if len(v.shape		) == 3:
                    __SCREAMING_SNAKE_CASE      = v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    __SCREAMING_SNAKE_CASE      = v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""		)

     __SCREAMING_SNAKE_CASE      = 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()}'
          __SCREAMING_SNAKE_CASE      = download(a__		)

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

     __SCREAMING_SNAKE_CASE      = Object()
     __SCREAMING_SNAKE_CASE      = sample_size
     __SCREAMING_SNAKE_CASE      = sample_rate
     __SCREAMING_SNAKE_CASE      = 0

     __SCREAMING_SNAKE_CASE      = UNetaDModel(sample_size=a__ ,       sample_rate=a__		)
     __SCREAMING_SNAKE_CASE      = diffusers_model.state_dict()

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

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

          __SCREAMING_SNAKE_CASE      = value

     diffusers_model.load_state_dict(a__		)

     __SCREAMING_SNAKE_CASE      = 1_00
     __SCREAMING_SNAKE_CASE      = 33

     __SCREAMING_SNAKE_CASE      = IPNDMScheduler(num_train_timesteps=a__		)

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

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

     __SCREAMING_SNAKE_CASE      = DanceDiffusionPipeline(unet=a__ ,       scheduler=a__		)

     __SCREAMING_SNAKE_CASE      = torch.manual_seed(33		)
     __SCREAMING_SNAKE_CASE      = pipe(num_inference_steps=a__ ,       generator=a__		).audios

     __SCREAMING_SNAKE_CASE      = sampling.iplms_sample(a__ ,       a__ ,       a__ ,       {}		)
     __SCREAMING_SNAKE_CASE      = generated.clamp(-1 ,       1		)

     __SCREAMING_SNAKE_CASE      = (generated - audio).abs().sum()
     __SCREAMING_SNAKE_CASE      = (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					:			int       =	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					:			Any       =	parser.parse_args()

  main(args)









'''simple docstring'''



import math







def   a__			(    a__		):




     """simple docstring"""


     return math.sqrt(a__		) * math.sqrt(a__		) == num







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = 0
     __SCREAMING_SNAKE_CASE      = n
     while left <= right:
          __SCREAMING_SNAKE_CASE      = (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               __SCREAMING_SNAKE_CASE      = mid - 1
          else:
               __SCREAMING_SNAKE_CASE      = mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()






