
'''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


SCREAMING_SNAKE_CASE     =       {
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 4_8_0_0_0,
        """sample_size""": 1_3_1_0_7_2,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 1_6_0_0_0,
        """sample_size""": 6_5_5_3_6,
    },
}







def snake_case_     (					lowercase__   ,       lowercase__		):
  return torch.atana(lowercase__   ,       lowercase__		) / math.pi * 2







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Tuple				       =							torch.sin(t * math.pi / 2		) ** 2
  UpperCAmelCase__      :					Any				       =							(1 - sigma**2) ** 0.5
  return alpha_sigma_to_t(lowercase__   ,       lowercase__		)






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 pass






class 							UpperCAmelCase_							(    nn.Module				):

 '''simple docstring'''







 def __init__(					self		:  Dict			,				snake_case__		:  Optional[int] ):



   '''simple docstring'''




   super().__init__()

   UpperCAmelCase__      :					Optional[int]				       =							DiffusionAttnUnetaD(snake_case__			,				n_attn_layers=4 )
   UpperCAmelCase__      :					Optional[Any]				       =							deepcopy(self.diffusion )
   UpperCAmelCase__      :					Optional[Any]				       =							torch.quasirandom.SobolEngine(1			,				scramble=snake_case__ )







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					List[Any]				       =							MODELS_MAP[model_name]["url"]
  os.system(F"""wget {url} ./"""		)

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


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

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

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







def snake_case_     (					lowercase__		):
  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 snake_case_     (					lowercase__		):
  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 snake_case_     (					lowercase__   ,       lowercase__=1_3		):
  UpperCAmelCase__      :					Optional[Any]				       =							input_string

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

  UpperCAmelCase__      :					List[str]				       =							0
  if string.startswith("net.3."		):
    depth += 1
    UpperCAmelCase__      :					Optional[Any]				       =							string[6:]
  elif string.startswith("net."		):
    UpperCAmelCase__      :					Union[str, Any]				       =							string[4:]

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

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

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

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

  UpperCAmelCase__      :					List[Any]				       =							string_left[1:]

  if "resnets" in new_layer:
    UpperCAmelCase__      :					Optional[int]				       =							convert_resconv_naming(lowercase__		)
  elif "attentions" in new_layer:
    UpperCAmelCase__      :					List[Any]				       =							convert_attn_naming(lowercase__		)
    UpperCAmelCase__      :					Optional[Any]				       =							new_string_left

  if not isinstance(lowercase__   ,       lowercase__		):
    UpperCAmelCase__      :					Tuple				       =							prefix + "." + new_layer + "." + string_left
  else:
    UpperCAmelCase__      :					Optional[int]				       =							[prefix + "." + new_layer + "." + s for s in string_left]
  return new_string







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

    UpperCAmelCase__      :					Optional[int]				       =							rename(lowercase__		)

    # check if we need to transform from Conv => Linear for attention
    if isinstance(lowercase__   ,       lowercase__		):
      UpperCAmelCase__      :					Dict				       =							transform_conv_attns(lowercase__   ,       lowercase__   ,       lowercase__		)
    else:
      UpperCAmelCase__      :					List[Any]				       =							v

  return new_state_dict







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__		):
  if len(lowercase__		) == 1:
    if len(v.shape		) == 3:
      # weight
      UpperCAmelCase__      :					str				       =							v[:, :, 0]
    else:
      # bias
      UpperCAmelCase__      :					str				       =							v
  else:
    # qkv matrices
    UpperCAmelCase__      :					Optional[Any]				       =							v.shape[0]
    UpperCAmelCase__      :					Any				       =							trippled_shape // 3
    for i in range(3		):
      if len(v.shape		) == 3:
        UpperCAmelCase__      :					str				       =							v[i * single_shape : (i + 1) * single_shape, :, 0]
      else:
        UpperCAmelCase__      :					Dict				       =							v[i * single_shape : (i + 1) * single_shape]
  return new_state_dict







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Union[str, Any]				       =							torch.device("cuda" if torch.cuda.is_available() else "cpu"		)

  UpperCAmelCase__      :					Union[str, Any]				       =							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__      :					int				       =							download(lowercase__		)

  UpperCAmelCase__      :					Dict				       =							MODELS_MAP[model_name]["sample_rate"]
  UpperCAmelCase__      :					Union[str, Any]				       =							MODELS_MAP[model_name]["sample_size"]

  UpperCAmelCase__      :					List[str]				       =							Object()
  UpperCAmelCase__      :					Tuple				       =							sample_size
  UpperCAmelCase__      :					List[str]				       =							sample_rate
  UpperCAmelCase__      :					List[str]				       =							0

  UpperCAmelCase__      :					Dict				       =							UNetaDModel(sample_size=lowercase__   ,       sample_rate=lowercase__		)
  UpperCAmelCase__      :					Optional[Any]				       =							diffusers_model.state_dict()

  UpperCAmelCase__      :					List[str]				       =							DiffusionUncond(lowercase__		)
  orig_model.load_state_dict(torch.load(args.model_path   ,       map_location=lowercase__		)["state_dict"]		)
  UpperCAmelCase__      :					Any				       =							orig_model.diffusion_ema.eval()
  UpperCAmelCase__      :					Any				       =							orig_model.state_dict()
  UpperCAmelCase__      :					List[str]				       =							rename_orig_weights(lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							set(renamed_state_dict.keys()		) - set(diffusers_state_dict.keys()		)
  UpperCAmelCase__      :					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":
      UpperCAmelCase__      :					Union[str, Any]				       =							value.squeeze()

    UpperCAmelCase__      :					Optional[Any]				       =							value

  diffusers_model.load_state_dict(lowercase__		)

  UpperCAmelCase__      :					List[str]				       =							1_0_0
  UpperCAmelCase__      :					List[str]				       =							3_3

  UpperCAmelCase__      :					Tuple				       =							IPNDMScheduler(num_train_timesteps=lowercase__		)

  UpperCAmelCase__      :					int				       =							torch.manual_seed(lowercase__		)
  UpperCAmelCase__      :					List[Any]				       =							torch.randn([1, 2, config.sample_size]   ,       generator=lowercase__		).to(lowercase__		)

  UpperCAmelCase__      :					int				       =							torch.linspace(1   ,       0   ,       steps + 1   ,       device=lowercase__		)[:-1]
  UpperCAmelCase__      :					List[str]				       =							get_crash_schedule(lowercase__		)

  UpperCAmelCase__      :					int				       =							DanceDiffusionPipeline(unet=lowercase__   ,       scheduler=lowercase__		)

  UpperCAmelCase__      :					List[Any]				       =							torch.manual_seed(3_3		)
  UpperCAmelCase__      :					str				       =							pipe(num_inference_steps=lowercase__   ,       generator=lowercase__		).audios

  UpperCAmelCase__      :					Any				       =							sampling.iplms_sample(lowercase__   ,       lowercase__   ,       lowercase__   ,       {}		)
  UpperCAmelCase__      :					List[str]				       =							generated.clamp(-1   ,       1		)

  UpperCAmelCase__      :					Optional[Any]				       =							(generated - audio).abs().sum()
  UpperCAmelCase__      :					List[Any]				       =							(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__":
      SCREAMING_SNAKE_CASE     =       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.""")
      SCREAMING_SNAKE_CASE     =       parser.parse_args()

      main(args)





'''simple docstring'''





def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					Dict				       =							set()

  # Replace all the whitespace in our sentence
  UpperCAmelCase__      :					str				       =							input_str.replace(" "   ,       ""		)
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()		)
  return len(lowercase__		) == 2_6







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					str				       =							[False] * 2_6
  for char in input_str:
    if char.islower():
      UpperCAmelCase__      :					List[Any]				       =							True
    elif char.isupper():
      UpperCAmelCase__      :					List[Any]				       =							True
  return all(lowercase__		)







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  return len({char for char in input_str.lower() if char.isalpha()}		) == 2_6







def snake_case_     (					):
  from timeit import timeit

  UpperCAmelCase__      :					Union[str, Any]				       =							"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_faster()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_fastest()"   ,       setup=lowercase__		)		)
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



