

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


_UpperCAmelCase		:   Optional[int]										=       {
    """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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






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







class  lowercase				(       __snake_case			):
				pass







class  lowercase				(       nn.Module			):




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

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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    MODELS_MAP[model_name]["url"]
							os.system(F'''wget {url} ./'''      )

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


_UpperCAmelCase		:   Tuple										=       {
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
_UpperCAmelCase		:   Dict										=       {
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
_UpperCAmelCase		:   str										=       {
    """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""",
}
_UpperCAmelCase		:   Any										=       {
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

_UpperCAmelCase		:   List[Any]										=       {
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''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 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=13      ):







							'''simple docstring'''






							snake_case_							=    input_string

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

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

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

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

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

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

							snake_case_							=    string_left[1:]

							if "resnets" in new_layer:
														snake_case_							=    convert_resconv_naming(__SCREAMING_SNAKE_CASE      )
							elif "attentions" in new_layer:
														snake_case_							=    convert_attn_naming(__SCREAMING_SNAKE_CASE      )
														snake_case_							=    new_string_left

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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






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

														snake_case_							=    rename(__SCREAMING_SNAKE_CASE      )

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

							return new_state_dict





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )

							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()}'''
														snake_case_							=    download(__SCREAMING_SNAKE_CASE      )

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

							snake_case_							=    Object()
							snake_case_							=    sample_size
							snake_case_							=    sample_rate
							snake_case_							=    0

							snake_case_							=    UNetaDModel(sample_size=__SCREAMING_SNAKE_CASE   ,							sample_rate=__SCREAMING_SNAKE_CASE      )
							snake_case_							=    diffusers_model.state_dict()

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

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

														snake_case_							=    value

							diffusers_model.load_state_dict(__SCREAMING_SNAKE_CASE      )

							snake_case_							=    100
							snake_case_							=    33

							snake_case_							=    IPNDMScheduler(num_train_timesteps=__SCREAMING_SNAKE_CASE      )

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

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

							snake_case_							=    DanceDiffusionPipeline(unet=__SCREAMING_SNAKE_CASE   ,							scheduler=__SCREAMING_SNAKE_CASE      )

							snake_case_							=    torch.manual_seed(33      )
							snake_case_							=    pipe(num_inference_steps=__SCREAMING_SNAKE_CASE   ,							generator=__SCREAMING_SNAKE_CASE      ).audios

							snake_case_							=    sampling.iplms_sample(__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							{}      )
							snake_case_							=    generated.clamp(-1   ,							1      )

							snake_case_							=    (generated - audio).abs().sum()
							snake_case_							=    (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__":
					_UpperCAmelCase		:   List[Any]										=       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.""")
					_UpperCAmelCase		:   Union[str, Any]										=       parser.parse_args()

					main(args)







"""simple docstring"""


from numpy import exp, pi, sqrt






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE = 0.0					, __SCREAMING_SNAKE_CASE = 1.0 )	->	int:
		return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )


if __name__ == "__main__":
			import doctest

			doctest.testmod()


