




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       :       int        =    {
    '''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 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   Dict:
   return torch.atana(lowercase						,lowercase     ) / math.pi * 2




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Optional[Any]:
   snake_case      :				List[Any]				=       torch.sin(t * math.pi / 2     ) ** 2
   snake_case      :				Optional[int]				=       (1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(lowercase						,lowercase     )




class 						__lowercase		(A__							):




      """simple docstring"""





      pass




class 						__lowercase		(nn.Module							):




      """simple docstring"""





      def __init__(      self				,      A	)		->				Optional[Any]:
         super().__init__()

         snake_case      :				int				=       DiffusionAttnUnetaD(_A				,      n_attn_layers=4	)
         snake_case      :				List[Any]				=       deepcopy(self.diffusion	)
         snake_case      :				int				=       torch.quasirandom.SobolEngine(1				,      scramble=_A	)




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   List[Any]:
   snake_case      :				int				=       MODELS_MAP[model_name]["url"]
   os.system(f"""wget {url} ./"""     )

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


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

lowerCamelCase       :       Tuple        =    {
    '''skip''': '''conv_skip''',
    '''main.0''': '''conv_1''',
    '''main.1''': '''group_norm_1''',
    '''main.3''': '''conv_2''',
    '''main.4''': '''group_norm_2''',
}

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




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   List[str]:







   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 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Union[str, Any]:







   for key, value in ATTN_MAP.items():
      if name.startswith(lowercase     ) and not isinstance(lowercase						,lowercase     ):
         return name.replace(lowercase						,lowercase     )
      elif name.startswith(lowercase     ):
         return [name.replace(lowercase						,lowercase     ) for v in value]
   raise ValueError(f"""Attn error with {name}"""     )




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase=13     )  ->   Any:
   snake_case      :				str				=       input_string

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

   snake_case      :				Tuple				=       0
   if string.startswith("""net.3."""     ):
      depth += 1
      snake_case      :				Dict				=       string[6:]
   elif string.startswith("""net."""     ):
      snake_case      :				Optional[Any]				=       string[4:]

   while string.startswith("""main.7."""     ):
      depth += 1
      snake_case      :				Union[str, Any]				=       string[7:]

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

   # mid block
   if string[:2].isdigit():
      snake_case      :				Any				=       string[:2]
      snake_case      :				Optional[int]				=       string[2:]
   else:
      snake_case      :				int				=       string[0]
      snake_case      :				Dict				=       string[1:]

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

   if "resnets" in new_layer:
      snake_case      :				str				=       convert_resconv_naming(lowercase     )
   elif "attentions" in new_layer:
      snake_case      :				str				=       convert_attn_naming(lowercase     )
      snake_case      :				Optional[int]				=       new_string_left

   if not isinstance(lowercase						,lowercase     ):
      snake_case      :				int				=       prefix + "." + new_layer + "." + string_left
   else:
      snake_case      :				List[str]				=       [prefix + "." + new_layer + "." + s for s in string_left]
   return new_string




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Tuple:
   snake_case      :				int				=       {}
   for k, v in state_dict.items():
      if k.endswith("""kernel"""     ):
         # up- and downsample layers, don't have trainable weights
         continue

      snake_case      :				List[Any]				=       rename(lowercase     )

      # check if we need to transform from Conv => Linear for attention
      if isinstance(lowercase						,lowercase     ):
         snake_case      :				List[str]				=       transform_conv_attns(lowercase						,lowercase						,lowercase     )
      else:
         snake_case      :				Dict				=       v

   return new_state_dict




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase     )  ->   List[str]:







   if len(lowercase     ) == 1:
      if len(v.shape     ) == 3:
         # weight
         snake_case      :				str				=       v[:, :, 0]
      else:
         # bias
         snake_case      :				Optional[int]				=       v
   else:
      # qkv matrices
      snake_case      :				List[Any]				=       v.shape[0]
      snake_case      :				Optional[Any]				=       trippled_shape // 3
      for i in range(3     ):
         if len(v.shape     ) == 3:
            snake_case      :				Union[str, Any]				=       v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            snake_case      :				int				=       v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Tuple:
   snake_case      :				Any				=       torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )

   snake_case      :				List[str]				=       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      :				str				=       download(lowercase     )

   snake_case      :				Dict				=       MODELS_MAP[model_name]["sample_rate"]
   snake_case      :				List[Any]				=       MODELS_MAP[model_name]["sample_size"]

   snake_case      :				str				=       Object()
   snake_case      :				Any				=       sample_size
   snake_case      :				Dict				=       sample_rate
   snake_case      :				Any				=       0

   snake_case      :				Any				=       UNetaDModel(sample_size=lowercase						,sample_rate=lowercase     )
   snake_case      :				Union[str, Any]				=       diffusers_model.state_dict()

   snake_case      :				Optional[Any]				=       DiffusionUncond(lowercase     )
   orig_model.load_state_dict(torch.load(args.model_path						,map_location=lowercase     )["""state_dict"""]     )
   snake_case      :				Optional[Any]				=       orig_model.diffusion_ema.eval()
   snake_case      :				Union[str, Any]				=       orig_model.state_dict()
   snake_case      :				int				=       rename_orig_weights(lowercase     )

   snake_case      :				List[str]				=       set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
   snake_case      :				Optional[int]				=       set(diffusers_state_dict.keys()     ) - set(renamed_state_dict.keys()     )

   assert len(lowercase     ) == 0, f"""Problem with {renamed_minus_diffusers}"""
   assert all(k.endswith("""kernel"""     ) for k in list(lowercase     )     ), 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      :				int				=       value.squeeze()

      snake_case      :				Optional[int]				=       value

   diffusers_model.load_state_dict(lowercase     )

   snake_case      :				int				=       100
   snake_case      :				List[Any]				=       33

   snake_case      :				Union[str, Any]				=       IPNDMScheduler(num_train_timesteps=lowercase     )

   snake_case      :				Optional[Any]				=       torch.manual_seed(lowercase     )
   snake_case      :				Dict				=       torch.randn([1, 2, config.sample_size]						,generator=lowercase     ).to(lowercase     )

   snake_case      :				Optional[int]				=       torch.linspace(1						,0						,steps + 1						,device=lowercase     )[:-1]
   snake_case      :				Dict				=       get_crash_schedule(lowercase     )

   snake_case      :				int				=       DanceDiffusionPipeline(unet=lowercase						,scheduler=lowercase     )

   snake_case      :				Tuple				=       torch.manual_seed(33     )
   snake_case      :				Optional[Any]				=       pipe(num_inference_steps=lowercase						,generator=lowercase     ).audios

   snake_case      :				Optional[int]				=       sampling.iplms_sample(lowercase						,lowercase						,lowercase						,{}     )
   snake_case      :				List[str]				=       generated.clamp(-1						,1     )

   snake_case      :				str				=       (generated - audio).abs().sum()
   snake_case      :				Dict				=       (generated - audio).abs().max()

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

   print("""Diff sum"""						,lowercase     )
   print("""Diff max"""						,lowercase     )

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

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


if __name__ == "__main__":
       lowerCamelCase       :       Optional[Any]        =    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       :       Union[str, Any]        =    parser.parse_args()

       main(args)






def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    int(lowerCAmelCase	)
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    divmod(lowerCAmelCase   , 2	)
		return binary_recursive(lowerCAmelCase	) + str(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		str				      =    str(lowerCAmelCase	).strip()
		if not number:
				raise ValueError("No input value was provided"	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    "-" if number.startswith("-"	) else ""
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    number.lstrip("-"	)
		if not number.isnumeric():
				raise ValueError("Input value is not an integer"	)
		return f'{negative}0b{binary_recursive(int(lowerCAmelCase	)	)}'


if __name__ == "__main__":
		from doctest import testmod

		testmod()
