





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


lowercase_          =		{
    """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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       int:
  return torch.atana(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		) / math.pi * 2




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       str:
  lowercase__           =  torch.sin(t * math.pi / 2		) ** 2
  lowercase__           =  (1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   pass






class     SCREAMING_SNAKE_CASE       (nn.Module				):


   def __init__(   self      :    int      ,					a      :    int	)->       Union[str, Any]:





     """simple docstring"""







     super().__init__()

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






def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Dict:
  lowercase__           =  MODELS_MAP[model_name]['url']
  os.system(F"""wget {url} ./"""		)

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


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

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

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




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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=13		)    ->       List[str]:
  lowercase__           =  input_string

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

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

  while string.startswith('main.7.'		):
    depth += 1
    lowercase__           =  string[7:]

  if string.startswith('main.'		):
    lowercase__           =  string[5:]

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

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

  lowercase__           =  string_left[1:]

  if "resnets" in new_layer:
    lowercase__           =  convert_resconv_naming(_SCREAMING_SNAKE_CASE		)
  elif "attentions" in new_layer:
    lowercase__           =  convert_attn_naming(_SCREAMING_SNAKE_CASE		)
    lowercase__           =  new_string_left

  if not isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  prefix + '.' + new_layer + '.' + string_left
  else:
    lowercase__           =  [prefix + '.' + new_layer + '.' + s for s in string_left]
  return new_string




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  lowercase__           =  {}
  for k, v in state_dict.items():
    if k.endswith('kernel'		):
      # up- and downsample layers, don't have trainable weights
      continue

    lowercase__           =  rename(_SCREAMING_SNAKE_CASE		)

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

  return new_state_dict




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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  lowercase__           =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)

  lowercase__           =  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()}"""
    lowercase__           =  download(_SCREAMING_SNAKE_CASE		)

  lowercase__           =  MODELS_MAP[model_name]['sample_rate']
  lowercase__           =  MODELS_MAP[model_name]['sample_size']

  lowercase__           =  Object()
  lowercase__           =  sample_size
  lowercase__           =  sample_rate
  lowercase__           =  0

  lowercase__           =  UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE		,						sample_rate=_SCREAMING_SNAKE_CASE		)
  lowercase__           =  diffusers_model.state_dict()

  lowercase__           =  DiffusionUncond(_SCREAMING_SNAKE_CASE		)
  orig_model.load_state_dict(torch.load(args.model_path		,						map_location=_SCREAMING_SNAKE_CASE		)['state_dict']		)
  lowercase__           =  orig_model.diffusion_ema.eval()
  lowercase__           =  orig_model.state_dict()
  lowercase__           =  rename_orig_weights(_SCREAMING_SNAKE_CASE		)

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

  assert len(_SCREAMING_SNAKE_CASE		) == 0, F"""Problem with {renamed_minus_diffusers}"""
  assert all(k.endswith('kernel'		) for k in list(_SCREAMING_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":
      lowercase__           =  value.squeeze()

    lowercase__           =  value

  diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE		)

  lowercase__           =  100
  lowercase__           =  33

  lowercase__           =  IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE		)

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

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

  lowercase__           =  DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE		,						scheduler=_SCREAMING_SNAKE_CASE		)

  lowercase__           =  torch.manual_seed(33		)
  lowercase__           =  pipe(num_inference_steps=_SCREAMING_SNAKE_CASE		,						generator=_SCREAMING_SNAKE_CASE		).audios

  lowercase__           =  sampling.iplms_sample(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						{}		)
  lowercase__           =  generated.clamp(-1		,						1		)

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

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

  print('Diff sum'		,						_SCREAMING_SNAKE_CASE		)
  print('Diff max'		,						_SCREAMING_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__":
      lowercase_          =		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.""")
      lowercase_          =		parser.parse_args()

      main(args)









from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

lowercase_          =		TypeVar("""KEY""")
lowercase_          =		TypeVar("""VAL""")
@dataclass(frozen=UpperCAmelCase      ,     slots=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (Generic[KEY, VAL]				):
   _UpperCamelCase   :		KEY
   _UpperCamelCase   :		VAL






class     SCREAMING_SNAKE_CASE       (_Item				):


   def __init__(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     super().__init__(a      ,					a	)


   def __bool__(   self      :    str	)->       bool:





     """simple docstring"""







     return False






lowercase_          =		_DeletedItem()
class     SCREAMING_SNAKE_CASE       (MutableMapping[KEY, VAL]				):


   def __init__(   self      :    Tuple      ,					a      :    int = 8      ,					a      :    float = 0.75	)->       None:





     """simple docstring"""







     lowercase__           =  initial_block_size
     lowercase__           =  [None] * initial_block_size
     assert 0.0 < capacity_factor < 1.0
     lowercase__           =  capacity_factor
     lowercase__           =  0


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       int:





     """simple docstring"""







     return hash(a	) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[Any]      ,					a      :    int	)->       int:





     """simple docstring"""







     return (ind + 1) % len(self._buckets	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    int      ,					a      :    KEY      ,					a      :    VAL	)->       bool:





     """simple docstring"""







     lowercase__           =  self._buckets[ind]
     if not stored:
       lowercase__           =  _Item(a      ,					a	)
       self._len += 1
       return True
     elif stored.key == key:
       lowercase__           =  _Item(a      ,					a	)
       return True
     else:
       return False


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       bool:





     """simple docstring"""







     lowercase__           =  len(self._buckets	) * self._capacity_factor
     return len(self	) >= int(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       bool:





     """simple docstring"""







     if len(self._buckets	) <= self._initial_block_size:
       return False
     lowercase__           =  len(self._buckets	) * self._capacity_factor / 2
     return len(self	) < limit


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]      ,					a      :    int	)->       None:





     """simple docstring"""







     lowercase__           =  self._buckets
     lowercase__           =  [None] * new_size
     lowercase__           =  0
     for item in old_buckets:
       if item:
         self._add_item(item.key      ,					item.val	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) * 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Optional[int]	)->       None:





     """simple docstring"""







     self._resize(len(self._buckets	) // 2	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any      ,					a      :    KEY	)->       Iterator[int]:





     """simple docstring"""







     lowercase__           =  self._get_bucket_index(a	)
     for _ in range(len(self._buckets	)	):
       yield ind
       lowercase__           =  self._get_next_ind(a	)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       if self._try_set(a      ,					a      ,					a	):
         break


   def __setitem__(   self      :    List[Any]      ,					a      :    KEY      ,					a      :    VAL	)->       None:





     """simple docstring"""







     if self._is_full():
       self._size_up()

     self._add_item(a      ,					a	)


   def __delitem__(   self      :    str      ,					a      :    KEY	)->       None:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         raise KeyError(a	)
       if item is _deleted:
         continue
       if item.key == key:
         lowercase__           =  _deleted
         self._len -= 1
         break
     if self._is_sparse():
       self._size_down()


   def __getitem__(   self      :    List[str]      ,					a      :    KEY	)->       VAL:





     """simple docstring"""







     for ind in self._iterate_buckets(a	):
       lowercase__           =  self._buckets[ind]
       if item is None:
         break
       if item is _deleted:
         continue
       if item.key == key:
         return item.val
     raise KeyError(a	)


   def __len__(   self      :    Tuple	)->       int:





     """simple docstring"""







     return self._len


   def __iter__(   self      :    int	)->       Iterator[KEY]:





     """simple docstring"""







     yield from (item.key for item in self._buckets if item)


   def __repr__(   self      :    Union[str, Any]	)->       str:





     """simple docstring"""







     lowercase__           =  ' ,'.join(
         f"""{item.key}: {item.val}""" for item in self._buckets if item	)
     return f"""HashMap({val_string})"""


