
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_						     =					{
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 6_55_36,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_80_00,
        '''sample_size''': 13_10_72,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_60_00,
        '''sample_size''': 6_55_36,
    },
}






def 						__magic_name__			(   __a      :	int	,		__a      :	Tuple						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	int						):



     '''simple docstring'''




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




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  pass






class      __A(    nn.Module			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_						):
       super().__init__()

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






def 						__magic_name__			(   __a      :	Dict						):



     '''simple docstring'''




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

     return f"./{model_name}.ckpt"


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

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

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






def 						__magic_name__			(   __a      :	Tuple						):



     '''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 						__magic_name__			(   __a      :	List[str]						):



     '''simple docstring'''




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






def 						__magic_name__			(   __a      :	Any	,		__a      :	Union[str, Any]=13						):



     '''simple docstring'''




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

     if not isinstance(__a	,		__a						):
          UpperCamelCase__				=    prefix + """.""" + new_layer + """.""" + string_left
     else:
          UpperCamelCase__				=    [prefix + """.""" + new_layer + """.""" + s for s in string_left]
     return new_string






def 						__magic_name__			(   __a      :	int						):



     '''simple docstring'''




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

          UpperCamelCase__				=    rename(__a						)

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

     return new_state_dict






def 						__magic_name__			(   __a      :	Union[str, Any]	,		__a      :	Optional[Any]	,		__a      :	List[str]						):



     '''simple docstring'''




     if len(__a						) == 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 						__magic_name__			(   __a      :	List[str]						):



     '''simple docstring'''




     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(__a						)

     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=__a	,		sample_rate=__a						)
     UpperCamelCase__				=    diffusers_model.state_dict()

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

     UpperCamelCase__				=    set(renamed_state_dict.keys()						) - set(diffusers_state_dict.keys()						)
     UpperCamelCase__				=    set(diffusers_state_dict.keys()						) - set(renamed_state_dict.keys()						)

     assert len(__a						) == 0, f"Problem with {renamed_minus_diffusers}"
     assert all(k.endswith("""kernel"""						) for k in list(__a						)						), 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(__a						)

     UpperCamelCase__				=    100
     UpperCamelCase__				=    33

     UpperCamelCase__				=    IPNDMScheduler(num_train_timesteps=__a						)

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

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

     UpperCamelCase__				=    DanceDiffusionPipeline(unet=__a	,		scheduler=__a						)

     UpperCamelCase__				=    torch.manual_seed(33						)
     UpperCamelCase__				=    pipe(num_inference_steps=__a	,		generator=__a						).audios

     UpperCamelCase__				=    sampling.iplms_sample(__a	,		__a	,		__a	,		{}						)
     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"""	,		__a						)
     print("""Diff max"""	,		__a						)

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

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


if __name__ == "__main__":
    lowerCamelCase_						     =					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_						     =					parser.parse_args()

    main(args)







def 						__magic_name__			(   __a      :	int	,		__a      :	int						):



     '''simple docstring'''




     while a != 0:
          UpperCamelCase__		,	UpperCamelCase__				=    b % a, a
     return b






def 						__magic_name__			(   __a      :	int	,		__a      :	int						):



     '''simple docstring'''




     if gcd(__a	,		__a						) != 1:
          UpperCamelCase__				=    f"mod inverse of {a!r} and {m!r} does not exist"
          raise ValueError(__a						)
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    1, 0, a
     UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    0, 1, m
     while va != 0:
          UpperCamelCase__				=    ua // va
          UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
     return ua % m





