
'''simple docstring'''



def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						if b == 0:
												return 1
						if (b % 2) == 0:
												return actual_power(__lowerCamelCase					,      int(b / 2						)						) * actual_power(__lowerCamelCase					,      int(b / 2						)						)
						else:
												return a * actual_power(__lowerCamelCase					,      int(b / 2						)						) * actual_power(__lowerCamelCase					,      int(b / 2						)						)


def A (__lowerCamelCase :int					,      __lowerCamelCase :int						):
						if b < 0:
												return 1 / actual_power(__lowerCamelCase					,      __lowerCamelCase						)
						return actual_power(__lowerCamelCase					,      __lowerCamelCase						)


if __name__ == "__main__":
						print(power(-2, -3))








"""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


_lowerCAmelCase	:	Tuple				=      {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 65_536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48_000,
        """sample_size""": 131_072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16_000,
        """sample_size""": 65_536,
    },
}


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : List[str]				)->		Optional[int]:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]				)->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							torch.sin(t * math.pi / 2				) ** 2
     UpperCAmelCase__						:    Optional[int]							=							(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(snake_case			,					snake_case				)




class 			lowerCAmelCase__			(    __magic_name__   ):
   pass




class 			lowerCAmelCase__			(    nn.Module   ):
   def __init__(		self      :	Any							,			snake_case__      :	str							):


        '''simple docstring'''
        super().__init__()

        UpperCAmelCase__						:    Any							=							DiffusionAttnUnetaD(snake_case__							,			n_attn_layers=4							)
        UpperCAmelCase__						:    Any							=							deepcopy(self.diffusion							)
        UpperCAmelCase__						:    Optional[Any]							=							torch.quasirandom.SobolEngine(1							,			scramble=snake_case__							)




def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[Any]				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							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	:	Optional[Any]				=      {
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_lowerCAmelCase	:	List[str]				=      {
    """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	:	List[str]				=      {
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

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

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Tuple				)->		Dict:






     '''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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		int:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[Any]			,					snake_case       : Optional[Any]=13				)->		str:






     '''simple docstring'''



     UpperCAmelCase__						:    Any							=							input_string

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

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

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

     if string.startswith("main."				):
          UpperCAmelCase__						:    Union[str, Any]							=							string[5:]

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

     if depth == max_depth:
          UpperCAmelCase__						:    Any							=							MID_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							"mid_block"
     elif depth > 0 and int(snake_case				) < 7:
          UpperCAmelCase__						:    Optional[Any]							=							DOWN_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    int							=							f'down_blocks.{depth}'
     elif depth > 0 and int(snake_case				) > 7:
          UpperCAmelCase__						:    Optional[int]							=							UP_NUM_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Tuple							=							f'up_blocks.{max_depth - depth - 1}'
     elif depth == 0:
          UpperCAmelCase__						:    Tuple							=							DEPTH_0_TO_LAYER[layer_num]
          UpperCAmelCase__						:    Optional[Any]							=							f'up_blocks.{max_depth - 1}' if int(snake_case				) > 3 else "down_blocks.0"

     if not string_left.startswith("."				):
          raise ValueError(f'Naming error with {input_string} and string_left: {string_left}.'				)

     UpperCAmelCase__						:    List[Any]							=							string_left[1:]

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

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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : List[str]				)->		Tuple:






     '''simple docstring'''



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

          UpperCAmelCase__						:    Optional[int]							=							rename(snake_case				)

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

     return new_state_dict


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : List[Any]			,					snake_case       : int				)->		Union[str, Any]:






     '''simple docstring'''



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


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		Tuple:






     '''simple docstring'''



     UpperCAmelCase__						:    Tuple							=							torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

     UpperCAmelCase__						:    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()}'
          UpperCAmelCase__						:    Optional[int]							=							download(snake_case				)

     UpperCAmelCase__						:    List[str]							=							MODELS_MAP[model_name]["sample_rate"]
     UpperCAmelCase__						:    Dict							=							MODELS_MAP[model_name]["sample_size"]

     UpperCAmelCase__						:    Optional[int]							=							Object()
     UpperCAmelCase__						:    int							=							sample_size
     UpperCAmelCase__						:    List[Any]							=							sample_rate
     UpperCAmelCase__						:    int							=							0

     UpperCAmelCase__						:    int							=							UNetaDModel(sample_size=snake_case			,					sample_rate=snake_case				)
     UpperCAmelCase__						:    int							=							diffusers_model.state_dict()

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

     UpperCAmelCase__						:    List[Any]							=							set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
     UpperCAmelCase__						:    Optional[int]							=							set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

     assert len(snake_case				) == 0, f'Problem with {renamed_minus_diffusers}'
     assert all(k.endswith("kernel"				) for k in list(snake_case				)				), 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":
               UpperCAmelCase__						:    Union[str, Any]							=							value.squeeze()

          UpperCAmelCase__						:    List[str]							=							value

     diffusers_model.load_state_dict(snake_case				)

     UpperCAmelCase__						:    Any							=							100
     UpperCAmelCase__						:    str							=							33

     UpperCAmelCase__						:    Optional[int]							=							IPNDMScheduler(num_train_timesteps=snake_case				)

     UpperCAmelCase__						:    Any							=							torch.manual_seed(snake_case				)
     UpperCAmelCase__						:    List[str]							=							torch.randn([1, 2, config.sample_size]			,					generator=snake_case				).to(snake_case				)

     UpperCAmelCase__						:    Dict							=							torch.linspace(1			,					0			,					steps + 1			,					device=snake_case				)[:-1]
     UpperCAmelCase__						:    List[str]							=							get_crash_schedule(snake_case				)

     UpperCAmelCase__						:    List[str]							=							DanceDiffusionPipeline(unet=snake_case			,					scheduler=snake_case				)

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

     UpperCAmelCase__						:    Optional[Any]							=							sampling.iplms_sample(snake_case			,					snake_case			,					snake_case			,					{}				)
     UpperCAmelCase__						:    Tuple							=							generated.clamp(-1			,					1				)

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

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

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

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

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


if __name__ == "__main__":
 _lowerCAmelCase	:	Dict				=      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	:	Tuple				=      parser.parse_args()

 main(args)


