


"""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''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}
def 					A_      (      _lowerCAmelCase   :    List[str],   _lowerCAmelCase   :    Tuple				):






     """simple docstring"""

     return torch.atana(UpperCamelCase__,   UpperCamelCase__				) / math.pi * 2
def 					A_      (      _lowerCAmelCase   :    List[Any]				):






     """simple docstring"""

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


class 			__lowerCamelCase     (    snake_case_ ):

     '''simple docstring'''
     pass







class 			__lowerCamelCase     (    nn.Module ):

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase							)				->							Union[str, Any]:
          super().__init__()

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







def 					A_      (      _lowerCAmelCase   :    Any				):






     """simple docstring"""

     _a					= 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_      (      _lowerCAmelCase   :    Union[str, Any]				):






     """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_      (      _lowerCAmelCase   :    Dict				):






     """simple docstring"""

     for key, value in ATTN_MAP.items():
          if name.startswith(UpperCamelCase__				) and not isinstance(UpperCamelCase__,   UpperCamelCase__				):
               return name.replace(UpperCamelCase__,   UpperCamelCase__				)
          elif name.startswith(UpperCamelCase__				):
               return [name.replace(UpperCamelCase__,   UpperCamelCase__				) for v in value]
     raise ValueError(f'Attn error with {name}'				)
def 					A_      (      _lowerCAmelCase   :    Optional[int],   _lowerCAmelCase   :    Optional[int]=13				):






     """simple docstring"""

     _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(UpperCamelCase__				) < 7:
          _a					= DOWN_NUM_TO_LAYER[layer_num]
          _a					= f'down_blocks.{depth}'
     elif depth > 0 and int(UpperCamelCase__				) > 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(UpperCamelCase__				) > 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(UpperCamelCase__				)
     elif "attentions" in new_layer:
          _a					= convert_attn_naming(UpperCamelCase__				)
          _a					= new_string_left

     if not isinstance(UpperCamelCase__,   UpperCamelCase__				):
          _a					= prefix + """.""" + new_layer + """.""" + string_left
     else:
          _a					= [prefix + """.""" + new_layer + """.""" + s for s in string_left]
     return new_string
def 					A_      (      _lowerCAmelCase   :    str				):






     """simple docstring"""

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

          _a					= rename(UpperCamelCase__				)

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

     return new_state_dict
def 					A_      (      _lowerCAmelCase   :    Union[str, Any],   _lowerCAmelCase   :    Dict,   _lowerCAmelCase   :    Optional[Any]				):






     """simple docstring"""

     if len(UpperCamelCase__				) == 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 					A_      (      _lowerCAmelCase   :    int				):






     """simple docstring"""

     _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(UpperCamelCase__				)

     _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=UpperCamelCase__,   sample_rate=UpperCamelCase__				)
     _a					= diffusers_model.state_dict()

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

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

     assert len(UpperCamelCase__				) == 0, f'Problem with {renamed_minus_diffusers}'
     assert all(k.endswith('''kernel'''				) for k in list(UpperCamelCase__				)				), 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(UpperCamelCase__				)

     _a					= 1_00
     _a					= 33

     _a					= IPNDMScheduler(num_train_timesteps=UpperCamelCase__				)

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

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

     _a					= DanceDiffusionPipeline(unet=UpperCamelCase__,   scheduler=UpperCamelCase__				)

     _a					= torch.manual_seed(33				)
     _a					= pipe(num_inference_steps=UpperCamelCase__,   generator=UpperCamelCase__				).audios

     _a					= sampling.iplms_sample(UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   {}				)
     _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''',   UpperCamelCase__				)
     print('''Diff max''',   UpperCamelCase__				)

     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"""




def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''

  # Check if the input is valid
  if not len(UpperCamelCase__				) == len(UpperCamelCase__				) == 3:
    raise ValueError("""Please enter a valid equation."""				)
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError("""Both a & b of two equations can't be zero."""				)

  # Extract the coefficients
  _a      ,  _a      ,  _a  :       Any             =	equationa
  _a      ,  _a      ,  _a  :       Tuple             =	equationa

  # Calculate the determinants of the matrices
  _a  :       int             =	aa * ba - aa * ba
  _a  :       str             =	ca * ba - ca * ba
  _a  :       str             =	aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError("""Infinite solutions. (Consistent system)"""				)
    else:
      raise ValueError("""No solution. (Inconsistent system)"""				)
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      _a  :       Dict             =	determinant_x / determinant
      _a  :       str             =	determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)

