




"""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     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			return torch.atana(snake_case__       , snake_case__				) / math.pi * 2



def     a__ (				snake_case__				)						->					Union[str, Any]:
			lowerCamelCase			      =					torch.sin(t * math.pi / 2				) ** 2
			lowerCamelCase			      =					(1 - sigma**2) ** 0.5
			return alpha_sigma_to_t(snake_case__       , snake_case__				)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				pass






class     __magic_name__							(       nn.Module				):






				'''simple docstring'''





				def __init__(       self		, _a ):





							"""simple docstring"""
							super().__init__()

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




def     a__ (				snake_case__				)						->					Union[str, Any]:
			lowerCamelCase			      =					MODELS_MAP[model_name]["""url"""]
			os.system(F'wget {url} ./'				)

			return F'./{model_name}.ckpt'


lowerCAmelCase							:			Union[str, Any]     							=							{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
lowerCAmelCase							:			Optional[Any]     							=							{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
lowerCAmelCase							:			List[Any]     							=							{
    """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							:			List[Any]     							=							{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

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

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



def     a__ (				snake_case__				)						->					str:
			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     a__ (				snake_case__				)						->					Dict:
			for key, value in ATTN_MAP.items():
						if name.startswith(snake_case__				) and not isinstance(snake_case__       , snake_case__				):
									return name.replace(snake_case__       , snake_case__				)
						elif name.startswith(snake_case__				):
									return [name.replace(snake_case__       , snake_case__				) for v in value]
			raise ValueError(F'Attn error with {name}'				)



def     a__ (				snake_case__       , snake_case__=13				)						->					List[Any]:
			lowerCamelCase			      =					input_string

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

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

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

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

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

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

			lowerCamelCase			      =					string_left[1:]

			if "resnets" in new_layer:
						lowerCamelCase			      =					convert_resconv_naming(snake_case__				)
			elif "attentions" in new_layer:
						lowerCamelCase			      =					convert_attn_naming(snake_case__				)
						lowerCamelCase			      =					new_string_left

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



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

						lowerCamelCase			      =					rename(snake_case__				)

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

			return new_state_dict



def     a__ (				snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			if len(snake_case__				) == 1:
						if len(v.shape				) == 3:
									# weight
									lowerCamelCase			      =					v[:, :, 0]
						else:
									# bias
									lowerCamelCase			      =					v
			else:
						# qkv matrices
						lowerCamelCase			      =					v.shape[0]
						lowerCamelCase			      =					trippled_shape // 3
						for i in range(3				):
									if len(v.shape				) == 3:
												lowerCamelCase			      =					v[i * single_shape : (i + 1) * single_shape, :, 0]
									else:
												lowerCamelCase			      =					v[i * single_shape : (i + 1) * single_shape]
			return new_state_dict



def     a__ (				snake_case__				)						->					List[str]:
			lowerCamelCase			      =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)

			lowerCamelCase			      =					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()}'
						lowerCamelCase			      =					download(snake_case__				)

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

			lowerCamelCase			      =					Object()
			lowerCamelCase			      =					sample_size
			lowerCamelCase			      =					sample_rate
			lowerCamelCase			      =					0

			lowerCamelCase			      =					UNetaDModel(sample_size=snake_case__       , sample_rate=snake_case__				)
			lowerCamelCase			      =					diffusers_model.state_dict()

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

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

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

						lowerCamelCase			      =					value

			diffusers_model.load_state_dict(snake_case__				)

			lowerCamelCase			      =					1_00
			lowerCamelCase			      =					33

			lowerCamelCase			      =					IPNDMScheduler(num_train_timesteps=snake_case__				)

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

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

			lowerCamelCase			      =					DanceDiffusionPipeline(unet=snake_case__       , scheduler=snake_case__				)

			lowerCamelCase			      =					torch.manual_seed(33				)
			lowerCamelCase			      =					pipe(num_inference_steps=snake_case__       , generator=snake_case__				).audios

			lowerCamelCase			      =					sampling.iplms_sample(snake_case__       , snake_case__       , snake_case__       , {}				)
			lowerCamelCase			      =					generated.clamp(-1       , 1				)

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

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

			print("""Diff sum"""       , snake_case__				)
			print("""Diff max"""       , 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__":
					lowerCAmelCase							:			Tuple     							=							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							:			List[Any]     							=							parser.parse_args()

					main(args)









"""simple docstring"""




def     a__ (				snake_case__       , snake_case__ = False				)						->					str:

			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected string as input, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)
			if not isinstance(snake_case__       , snake_case__				):
						lowerCamelCase			      =					F'Expected boolean as use_pascal parameter, found {type(snake_case__				)}'
						raise ValueError(snake_case__				)

			lowerCamelCase			      =					input_str.split("""_"""				)

			lowerCamelCase			      =					0 if use_pascal else 1

			lowerCamelCase			      =					words[start_index:]

			lowerCamelCase			      =					[word[0].upper() + word[1:] for word in words_to_capitalize]

			lowerCamelCase			      =					"""""" if use_pascal else words[0]

			return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
					from doctest import testmod

					testmod()



