


'''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


a_					:    List[str]          =    {
    '''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				(lowerCAmelCase__  :Optional[int]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Tuple:




     '''simple docstring'''



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






def 	_A				(lowerCAmelCase__  :List[str]		)   ->  Any:




     '''simple docstring'''



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




class  a     (     UpperCamelCase__       ):
       pass





class  a     (     nn.Module       ):
       def __init__( self      ,				__magic_name__						)					->							Tuple:
            super().__init__()

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






def 	_A				(lowerCAmelCase__  :Any		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							MODELS_MAP[model_name]['url']
     os.system(f'wget {url} ./'		)

     return f'./{model_name}.ckpt'


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

a_					:    List[Any]          =    {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

a_					:    Tuple          =    {
    '''norm''': '''group_norm''',
    '''qkv_proj''': ['''query''', '''key''', '''value'''],
    '''out_proj''': ['''proj_attn'''],
}






def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  str:




     '''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__  :str		)   ->  Optional[Any]:




     '''simple docstring'''



     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 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Optional[int]=13		)   ->  int:




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

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






def 	_A				(lowerCAmelCase__  :List[Any]		)   ->  Tuple:




     '''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(lowerCAmelCase__		)

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

     return new_state_dict






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :int				,						lowerCAmelCase__  :int		)   ->  Dict:




     '''simple docstring'''



     if len(lowerCAmelCase__		) == 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__  :Dict		)   ->  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(lowerCAmelCase__		)

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

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

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

          _a			      =							value

     diffusers_model.load_state_dict(lowerCAmelCase__		)

     _a			      =							1_00
     _a			      =							33

     _a			      =							IPNDMScheduler(num_train_timesteps=lowerCAmelCase__		)

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

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

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

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

     _a			      =							sampling.iplms_sample(lowerCAmelCase__				,						lowerCAmelCase__				,						lowerCAmelCase__				,						{}		)
     _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'				,						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__":
 a_					:    List[str]          =    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.")
 a_					:    Any          =    parser.parse_args()

 main(args)





'''simple docstring'''






class  a     :
 def __init__( self						)					->							List[Any]:
      _a			      =							''
      _a			      =							''
      _a			      =							[]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if m == -1:
           return n + 1
      elif n == -1:
           return m + 1
      elif self.dp[m][n] > -1:
           return self.dp[m][n]
      else:
           if self.worda[m] == self.worda[n]:
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
           else:
                _a			      =							self.__min_dist_top_down_dp(__magic_name__      ,				n - 1						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				__magic_name__						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
                _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           return self.dp[m][n]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							[[-1 for _ in range(len(__magic_name__						)						)] for _ in range(len(__magic_name__						)						)]

      return self.__min_dist_top_down_dp(len(__magic_name__						) - 1      ,				len(__magic_name__						) - 1						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							len(__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							[[0 for _ in range(n + 1						)] for _ in range(m + 1						)]

      for i in range(m + 1						):
           for j in range(n + 1						):
                if i == 0:  # first string is empty
                     _a			      =							j
                elif j == 0:  # second string is empty
                     _a			      =							i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                     _a			      =							self.dp[i - 1][j - 1]
                else:
                     _a			      =							self.dp[i][j - 1]
                     _a			      =							self.dp[i - 1][j]
                     _a			      =							self.dp[i - 1][j - 1]
                     _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return self.dp[m][n]


if __name__ == "__main__":
 a_					:    Dict          =    EditDistance()

 print("****************** Testing Edit Distance DP Algorithm ******************")
 print()

 a_					:    Tuple          =    input("Enter the first string: ").strip()
 a_					:    Dict          =    input("Enter the second string: ").strip()

 print()
 print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
 print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
 print()
 print("*************** End of Testing Edit Distance DP Algorithm ***************")

