'''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    :				List[str]							     =	{
    '''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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Optional[Any]			)							->      str:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      Optional[Any]:







  """simple docstring"""





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







class __snake_case       (		lowerCamelCase_	):
   pass







class __snake_case       (		nn.Module	):




   def __init__(					self    :       Dict ,			_lowercase    :       str   ):



     """simple docstring"""






     super().__init__()

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





def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   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    :				Any							     =	{
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
__lowerCamelCase    :				Tuple							     =	{
    '''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[Any]							     =	{
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

__lowerCamelCase    :				Optional[Any]							     =	{
    '''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						(      __UpperCamelCase					: Dict			)							->      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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[int]			)							->      Union[str, Any]:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Optional[Any]				,  __UpperCamelCase					: Optional[int]=13			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   input_string

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

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

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

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

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

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

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

  SCREAMING_SNAKE_CASE__    							=   string_left[1:]

  if "resnets" in new_layer:
    SCREAMING_SNAKE_CASE__    							=   convert_resconv_naming(__UpperCamelCase			)
  elif "attentions" in new_layer:
    SCREAMING_SNAKE_CASE__    							=   convert_attn_naming(__UpperCamelCase			)
    SCREAMING_SNAKE_CASE__    							=   new_string_left

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Any			)							->      Optional[Any]:







  """simple docstring"""





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

    SCREAMING_SNAKE_CASE__    							=   rename(__UpperCamelCase			)

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

  return new_state_dict



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Tuple				,  __UpperCamelCase					: Union[str, Any]			)							->      Tuple:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""			)

  SCREAMING_SNAKE_CASE__    							=   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()}"""
    SCREAMING_SNAKE_CASE__    							=   download(__UpperCamelCase			)

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

  SCREAMING_SNAKE_CASE__    							=   Object()
  SCREAMING_SNAKE_CASE__    							=   sample_size
  SCREAMING_SNAKE_CASE__    							=   sample_rate
  SCREAMING_SNAKE_CASE__    							=   0

  SCREAMING_SNAKE_CASE__    							=   UNetaDModel(sample_size=__UpperCamelCase				,  sample_rate=__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   diffusers_model.state_dict()

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

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

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

    SCREAMING_SNAKE_CASE__    							=   value

  diffusers_model.load_state_dict(__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   1_00
  SCREAMING_SNAKE_CASE__    							=   33

  SCREAMING_SNAKE_CASE__    							=   IPNDMScheduler(num_train_timesteps=__UpperCamelCase			)

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

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

  SCREAMING_SNAKE_CASE__    							=   DanceDiffusionPipeline(unet=__UpperCamelCase				,  scheduler=__UpperCamelCase			)

  SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(33			)
  SCREAMING_SNAKE_CASE__    							=   pipe(num_inference_steps=__UpperCamelCase				,  generator=__UpperCamelCase			).audios

  SCREAMING_SNAKE_CASE__    							=   sampling.iplms_sample(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  {}			)
  SCREAMING_SNAKE_CASE__    							=   generated.clamp(-1				,  1			)

  SCREAMING_SNAKE_CASE__    							=   (generated - audio).abs().sum()
  SCREAMING_SNAKE_CASE__    							=   (generated - audio).abs().max()

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

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

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

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


if __name__ == "__main__":
   __lowerCamelCase    :				int							     =	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    :				Optional[Any]							     =	parser.parse_args()

   main(args)

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu







@require_cpu
class __snake_case       (		unittest.TestCase	):




   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   torch.nn.Linear(10 ,			10   )
     SCREAMING_SNAKE_CASE__    							=   torch.optim.SGD(model.parameters() ,			0.1   )
     SCREAMING_SNAKE_CASE__    							=   Accelerator()
     SCREAMING_SNAKE_CASE__    							=   accelerator.prepare(_lowercase   )
     try:
       pickle.loads(pickle.dumps(_lowercase   )   )
     except Exception as e:
       self.fail(f"""Accelerated optimizer pickling failed with {e}"""   )
     AcceleratorState._reset_state()
