






'''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""": 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     __lowercase  (_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     return torch.atana(_lowercase,						_lowercase     ) / math.pi * 2
def     __lowercase  (_lowercase     )   ->     Union[str, Any]:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				torch.sin(t * math.pi / 2     ) ** 2
     __lowerCamelCase						:       int				   =				(1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(_lowercase,						_lowercase     )







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       pass







class 	SCREAMING_SNAKE_CASE	(      nn.Module  ):




       def __init__(					self      : str	,    A__      : List[str]					):


            """simple docstring"""







            super().__init__()

            __lowerCamelCase						:       Optional[int]				   =				DiffusionAttnUnetaD(A__	,    n_attn_layers=4					)
            __lowerCamelCase						:       List[str]				   =				deepcopy(self.diffusion					)
            __lowerCamelCase						:       Tuple				   =				torch.quasirandom.SobolEngine(1	,    scramble=A__					)

def     __lowercase  (_lowercase     )   ->     List[str]:


     """simple docstring"""
     __lowerCamelCase						:       Tuple				   =				MODELS_MAP[model_name]["""url"""]
     os.system(f"wget {url} ./"     )

     return f"./{model_name}.ckpt"


UpperCAmelCase__						:Optional[Any]          =			{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
UpperCAmelCase__						:List[str]          =			{
    """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__						:Any          =			{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

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

UpperCAmelCase__						:str          =			{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}
def     __lowercase  (_lowercase     )   ->     Optional[int]:


     """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     __lowercase  (_lowercase     )   ->     List[Any]:


     """simple docstring"""
     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     __lowercase  (_lowercase,						_lowercase=13     )   ->     Dict:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				input_string

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

     __lowerCamelCase						:       Optional[int]				   =				0
     if string.startswith("""net.3."""     ):
          depth += 1
          __lowerCamelCase						:       Tuple				   =				string[6:]
     elif string.startswith("""net."""     ):
          __lowerCamelCase						:       str				   =				string[4:]

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

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

     # mid block
     if string[:2].isdigit():
          __lowerCamelCase						:       int				   =				string[:2]
          __lowerCamelCase						:       int				   =				string[2:]
     else:
          __lowerCamelCase						:       Union[str, Any]				   =				string[0]
          __lowerCamelCase						:       Union[str, Any]				   =				string[1:]

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

     __lowerCamelCase						:       Any				   =				string_left[1:]

     if "resnets" in new_layer:
          __lowerCamelCase						:       Dict				   =				convert_resconv_naming(_lowercase     )
     elif "attentions" in new_layer:
          __lowerCamelCase						:       Tuple				   =				convert_attn_naming(_lowercase     )
          __lowerCamelCase						:       List[Any]				   =				new_string_left

     if not isinstance(_lowercase,						_lowercase     ):
          __lowerCamelCase						:       List[str]				   =				prefix + """.""" + new_layer + """.""" + string_left
     else:
          __lowerCamelCase						:       Dict				   =				[prefix + """.""" + new_layer + """.""" + s for s in string_left]
     return new_string
def     __lowercase  (_lowercase     )   ->     str:


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

          __lowerCamelCase						:       int				   =				rename(_lowercase     )

          # check if we need to transform from Conv => Linear for attention
          if isinstance(_lowercase,						_lowercase     ):
               __lowerCamelCase						:       Optional[int]				   =				transform_conv_attns(_lowercase,						_lowercase,						_lowercase     )
          else:
               __lowerCamelCase						:       int				   =				v

     return new_state_dict
def     __lowercase  (_lowercase,						_lowercase,						_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     if len(_lowercase     ) == 1:
          if len(v.shape     ) == 3:
               # weight
               __lowerCamelCase						:       Any				   =				v[:, :, 0]
          else:
               # bias
               __lowerCamelCase						:       Any				   =				v
     else:
          # qkv matrices
          __lowerCamelCase						:       Tuple				   =				v.shape[0]
          __lowerCamelCase						:       List[Any]				   =				trippled_shape // 3
          for i in range(3     ):
               if len(v.shape     ) == 3:
                    __lowerCamelCase						:       Union[str, Any]				   =				v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    __lowerCamelCase						:       str				   =				v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict
def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     __lowerCamelCase						:       List[Any]				   =				torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""     )

     __lowerCamelCase						:       Tuple				   =				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()}"
          __lowerCamelCase						:       Any				   =				download(_lowercase     )

     __lowerCamelCase						:       Optional[int]				   =				MODELS_MAP[model_name]["""sample_rate"""]
     __lowerCamelCase						:       Optional[Any]				   =				MODELS_MAP[model_name]["""sample_size"""]

     __lowerCamelCase						:       List[str]				   =				Object()
     __lowerCamelCase						:       List[Any]				   =				sample_size
     __lowerCamelCase						:       Any				   =				sample_rate
     __lowerCamelCase						:       Dict				   =				0

     __lowerCamelCase						:       List[Any]				   =				UNetaDModel(sample_size=_lowercase,						sample_rate=_lowercase     )
     __lowerCamelCase						:       List[Any]				   =				diffusers_model.state_dict()

     __lowerCamelCase						:       Optional[int]				   =				DiffusionUncond(_lowercase     )
     orig_model.load_state_dict(torch.load(args.model_path,						map_location=_lowercase     )["""state_dict"""]     )
     __lowerCamelCase						:       Optional[Any]				   =				orig_model.diffusion_ema.eval()
     __lowerCamelCase						:       Dict				   =				orig_model.state_dict()
     __lowerCamelCase						:       Optional[Any]				   =				rename_orig_weights(_lowercase     )

     __lowerCamelCase						:       Dict				   =				set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
     __lowerCamelCase						:       List[Any]				   =				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":
               __lowerCamelCase						:       Dict				   =				value.squeeze()

          __lowerCamelCase						:       Tuple				   =				value

     diffusers_model.load_state_dict(_lowercase     )

     __lowerCamelCase						:       Any				   =				100
     __lowerCamelCase						:       Tuple				   =				33

     __lowerCamelCase						:       Union[str, Any]				   =				IPNDMScheduler(num_train_timesteps=_lowercase     )

     __lowerCamelCase						:       str				   =				torch.manual_seed(_lowercase     )
     __lowerCamelCase						:       Optional[Any]				   =				torch.randn([1, 2, config.sample_size],						generator=_lowercase     ).to(_lowercase     )

     __lowerCamelCase						:       int				   =				torch.linspace(1,						0,						steps + 1,						device=_lowercase     )[:-1]
     __lowerCamelCase						:       List[Any]				   =				get_crash_schedule(_lowercase     )

     __lowerCamelCase						:       Tuple				   =				DanceDiffusionPipeline(unet=_lowercase,						scheduler=_lowercase     )

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

     __lowerCamelCase						:       Tuple				   =				sampling.iplms_sample(_lowercase,						_lowercase,						_lowercase,						{}     )
     __lowerCamelCase						:       Optional[int]				   =				generated.clamp(-1,						1     )

     __lowerCamelCase						:       List[str]				   =				(generated - audio).abs().sum()
     __lowerCamelCase						:       Tuple				   =				(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__":
   UpperCAmelCase__						:Tuple          =			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__						:Tuple          =			parser.parse_args()

   main(args)











'''simple docstring'''

from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCAmelCase__						:List[str]          =			typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCAmelCase__						:Optional[int]          =			typing.Union[np.floataa, int, float]  # noqa: UP007
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return np.sqrt(np.sum((np.asarray(_lowercase     ) - np.asarray(_lowercase     )) ** 2     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return sum((va - va) ** 2 for va, va in zip(_lowercase,						_lowercase     )     ) ** (1 / 2)


if __name__ == "__main__":
   def     __lowercase  ()   ->     None:


        """simple docstring"""
        from timeit import timeit

        print("""Without Numpy"""     )
        print(
            timeit(
                """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
        print("""With Numpy"""     )
        print(
            timeit(
                """euclidean_distance([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
   benchmark()



