


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     =			{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 6_5536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8000,
        """sample_size""": 13_1072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6000,
        """sample_size""": 6_5536,
    },
}
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Optional[int]:
   return torch.atana(lowerCAmelCase_  ,						lowerCAmelCase_   ) / math.pi * 2
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[int]:
   UpperCAmelCase							=     torch.sin(t * math.pi / 2   ) ** 2
   UpperCAmelCase							=     (1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(lowerCAmelCase_  ,						lowerCAmelCase_   )






class   __lowercase				(  __snake_case  ):
      pass






class   __lowercase				(  nn.Module  ):





      def __init__(		self						:					int					,       __lowerCamelCase						:					List[Any]       )    ->   int:






         """simple docstring"""







         super().__init__()

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

def   _UpperCamelCase				(						lowerCAmelCase_   )						->List[Any]:
   UpperCAmelCase							=     MODELS_MAP[model_name]["""url"""]
   os.system(F"""wget {url} ./"""   )

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


__a     =			{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
__a     =			{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
__a     =			{
    """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     =			{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

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

__a     =			{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Dict:
   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   _UpperCamelCase				(						lowerCAmelCase_   )						->List[Any]:
   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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_=1_3   )						->List[str]:
   UpperCAmelCase							=     input_string

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

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

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

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

   # mid block
   if string[:2].isdigit():
      UpperCAmelCase							=     string[:2]
      UpperCAmelCase							=     string[2:]
   else:
      UpperCAmelCase							=     string[0]
      UpperCAmelCase							=     string[1:]

   if depth == max_depth:
      UpperCAmelCase							=     MID_NUM_TO_LAYER[layer_num]
      UpperCAmelCase							=     """mid_block"""
   elif depth > 0 and int(lowerCAmelCase_   ) < 7:
      UpperCAmelCase							=     DOWN_NUM_TO_LAYER[layer_num]
      UpperCAmelCase							=     F"""down_blocks.{depth}"""
   elif depth > 0 and int(lowerCAmelCase_   ) > 7:
      UpperCAmelCase							=     UP_NUM_TO_LAYER[layer_num]
      UpperCAmelCase							=     F"""up_blocks.{max_depth - depth - 1}"""
   elif depth == 0:
      UpperCAmelCase							=     DEPTH_0_TO_LAYER[layer_num]
      UpperCAmelCase							=     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}."""   )

   UpperCAmelCase							=     string_left[1:]

   if "resnets" in new_layer:
      UpperCAmelCase							=     convert_resconv_naming(lowerCAmelCase_   )
   elif "attentions" in new_layer:
      UpperCAmelCase							=     convert_attn_naming(lowerCAmelCase_   )
      UpperCAmelCase							=     new_string_left

   if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      UpperCAmelCase							=     prefix + """.""" + new_layer + """.""" + string_left
   else:
      UpperCAmelCase							=     [prefix + """.""" + new_layer + """.""" + s for s in string_left]
   return new_string
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Any:
   UpperCAmelCase							=     {}
   for k, v in state_dict.items():
      if k.endswith("""kernel"""   ):
         # up- and downsample layers, don't have trainable weights
         continue

      UpperCAmelCase							=     rename(lowerCAmelCase_   )

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

   return new_state_dict
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->str:
   if len(lowerCAmelCase_   ) == 1:
      if len(v.shape   ) == 3:
         # weight
         UpperCAmelCase							=     v[:, :, 0]
      else:
         # bias
         UpperCAmelCase							=     v
   else:
      # qkv matrices
      UpperCAmelCase							=     v.shape[0]
      UpperCAmelCase							=     trippled_shape // 3
      for i in range(3   ):
         if len(v.shape   ) == 3:
            UpperCAmelCase							=     v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            UpperCAmelCase							=     v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

   UpperCAmelCase							=     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							=     download(lowerCAmelCase_   )

   UpperCAmelCase							=     MODELS_MAP[model_name]["""sample_rate"""]
   UpperCAmelCase							=     MODELS_MAP[model_name]["""sample_size"""]

   UpperCAmelCase							=     Object()
   UpperCAmelCase							=     sample_size
   UpperCAmelCase							=     sample_rate
   UpperCAmelCase							=     0

   UpperCAmelCase							=     UNetaDModel(sample_size=lowerCAmelCase_  ,						sample_rate=lowerCAmelCase_   )
   UpperCAmelCase							=     diffusers_model.state_dict()

   UpperCAmelCase							=     DiffusionUncond(lowerCAmelCase_   )
   orig_model.load_state_dict(torch.load(args.model_path  ,						map_location=lowerCAmelCase_   )["""state_dict"""]   )
   UpperCAmelCase							=     orig_model.diffusion_ema.eval()
   UpperCAmelCase							=     orig_model.state_dict()
   UpperCAmelCase							=     rename_orig_weights(lowerCAmelCase_   )

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

      UpperCAmelCase							=     value

   diffusers_model.load_state_dict(lowerCAmelCase_   )

   UpperCAmelCase							=     1_0_0
   UpperCAmelCase							=     3_3

   UpperCAmelCase							=     IPNDMScheduler(num_train_timesteps=lowerCAmelCase_   )

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

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

   UpperCAmelCase							=     DanceDiffusionPipeline(unet=lowerCAmelCase_  ,						scheduler=lowerCAmelCase_   )

   UpperCAmelCase							=     torch.manual_seed(3_3   )
   UpperCAmelCase							=     pipe(num_inference_steps=lowerCAmelCase_  ,						generator=lowerCAmelCase_   ).audios

   UpperCAmelCase							=     sampling.iplms_sample(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						{}   )
   UpperCAmelCase							=     generated.clamp(-1  ,						1   )

   UpperCAmelCase							=     (generated - audio).abs().sum()
   UpperCAmelCase							=     (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     =			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     =			parser.parse_args()

 main(args)







import argparse


__a     =			"""docs/source/_static/js/custom.js"""
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   with open(lowerCAmelCase_  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      UpperCAmelCase							=     f.readlines()
   UpperCAmelCase							=     0

   # First let's put the right version
   while not lines[index].startswith("""const stableVersion ="""   ):
      index += 1
   UpperCAmelCase							=     F"""const stableVersion = \"v{version}\"\n"""

   # Then update the dictionary
   while not lines[index].startswith("""const versionMapping = {"""   ):
      index += 1

   # We go until the end
   while not lines[index].startswith("""}"""   ):
      index += 1
   # We add the new version at the end
   lines[index - 1] += F"""    \"v{version}\": \"v{version}\",\n"""

   with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      f.writelines(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 parser.add_argument("""--version""", help="""Release version.""")
 __a     =			parser.parse_args()
 update_custom_js(args.version)



