




"""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


__A   :  str					=      {
    '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__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase				)			->Any:






		"""simple docstring"""


		__lowercase				:		Union[str, Any]												=				torch.sin(t * math.pi / 2				) ** 2
		__lowercase				:		Optional[Any]												=				(1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(_lowerCamelCase,  _lowerCamelCase				)

class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				pass





class       lowerCAmelCase__       (			nn.Module    ):


				"""simple docstring"""





				def __init__(     self					:	Dict    ,   lowercase__					:	Union[str, Any]						):
						super().__init__()

						__lowercase				:		str												=				DiffusionAttnUnetaD(lowercase__    ,   n_attn_layers=4						)
						__lowercase				:		List[Any]												=				deepcopy(self.diffusion						)
						__lowercase				:		Any												=				torch.quasirandom.SobolEngine(1    ,   scramble=lowercase__						)





def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


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

		return F'./{model_name}.ckpt'


__A   :  int					=      {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
__A   :  Optional[int]					=      {
    '8': 'resnets.0',
    '9': 'attentions.0',
    '10': 'resnets.1',
    '11': 'attentions.1',
    '12': 'resnets.2',
    '13': 'attentions.2',
}
__A   :  Optional[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',
}
__A   :  Tuple					=      {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

__A   :  List[str]					=      {
    'skip': 'conv_skip',
    'main.0': 'conv_1',
    'main.1': 'group_norm_1',
    'main.3': 'conv_2',
    'main.4': 'group_norm_2',
}

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

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""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 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


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

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase=13				)			->Union[str, Any]:






		"""simple docstring"""


		__lowercase				:		Dict												=				input_string

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

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

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

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

		# mid block
		if string[:2].isdigit():
				__lowercase				:		Optional[Any]												=				string[:2]
				__lowercase				:		Optional[int]												=				string[2:]
		else:
				__lowercase				:		Dict												=				string[0]
				__lowercase				:		int												=				string[1:]

		if depth == max_depth:
				__lowercase				:		List[Any]												=				MID_NUM_TO_LAYER[layer_num]
				__lowercase				:		Tuple												=				"mid_block"
		elif depth > 0 and int(_lowerCamelCase				) < 7:
				__lowercase				:		Union[str, Any]												=				DOWN_NUM_TO_LAYER[layer_num]
				__lowercase				:		Dict												=				F'down_blocks.{depth}'
		elif depth > 0 and int(_lowerCamelCase				) > 7:
				__lowercase				:		Any												=				UP_NUM_TO_LAYER[layer_num]
				__lowercase				:		Tuple												=				F'up_blocks.{max_depth - depth - 1}'
		elif depth == 0:
				__lowercase				:		List[Any]												=				DEPTH_0_TO_LAYER[layer_num]
				__lowercase				:		str												=				F'up_blocks.{max_depth - 1}' if int(_lowerCamelCase				) > 3 else "down_blocks.0"

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

		__lowercase				:		Any												=				string_left[1:]

		if "resnets" in new_layer:
				__lowercase				:		List[str]												=				convert_resconv_naming(_lowerCamelCase				)
		elif "attentions" in new_layer:
				__lowercase				:		List[Any]												=				convert_attn_naming(_lowerCamelCase				)
				__lowercase				:		Tuple												=				new_string_left

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

def 						snake_case__       (					_lowerCamelCase				)			->List[str]:






		"""simple docstring"""


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

				__lowercase				:		str												=				rename(_lowerCamelCase				)

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

		return new_state_dict

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		if len(_lowerCamelCase				) == 1:
				if len(v.shape				) == 3:
						# weight
						__lowercase				:		Any												=				v[:, :, 0]
				else:
						# bias
						__lowercase				:		List[str]												=				v
		else:
				# qkv matrices
				__lowercase				:		Tuple												=				v.shape[0]
				__lowercase				:		Union[str, Any]												=				trippled_shape // 3
				for i in range(3				):
						if len(v.shape				) == 3:
								__lowercase				:		Union[str, Any]												=				v[i * single_shape : (i + 1) * single_shape, :, 0]
						else:
								__lowercase				:		str												=				v[i * single_shape : (i + 1) * single_shape]
		return new_state_dict

def 						snake_case__       (					_lowerCamelCase				)			->Optional[Any]:






		"""simple docstring"""


		__lowercase				:		List[Any]												=				torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

		__lowercase				:		Optional[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()}'
				__lowercase				:		List[Any]												=				download(_lowerCamelCase				)

		__lowercase				:		List[str]												=				MODELS_MAP[model_name]["sample_rate"]
		__lowercase				:		Dict												=				MODELS_MAP[model_name]["sample_size"]

		__lowercase				:		Union[str, Any]												=				Object()
		__lowercase				:		List[str]												=				sample_size
		__lowercase				:		Tuple												=				sample_rate
		__lowercase				:		Dict												=				0

		__lowercase				:		Dict												=				UNetaDModel(sample_size=_lowerCamelCase,  sample_rate=_lowerCamelCase				)
		__lowercase				:		Any												=				diffusers_model.state_dict()

		__lowercase				:		Optional[Any]												=				DiffusionUncond(_lowerCamelCase				)
		orig_model.load_state_dict(torch.load(args.model_path,  map_location=_lowerCamelCase				)["state_dict"]				)
		__lowercase				:		Optional[Any]												=				orig_model.diffusion_ema.eval()
		__lowercase				:		str												=				orig_model.state_dict()
		__lowercase				:		str												=				rename_orig_weights(_lowerCamelCase				)

		__lowercase				:		int												=				set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
		__lowercase				:		int												=				set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

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

				__lowercase				:		List[Any]												=				value

		diffusers_model.load_state_dict(_lowerCamelCase				)

		__lowercase				:		str												=				1_00
		__lowercase				:		List[str]												=				33

		__lowercase				:		Any												=				IPNDMScheduler(num_train_timesteps=_lowerCamelCase				)

		__lowercase				:		Optional[Any]												=				torch.manual_seed(_lowerCamelCase				)
		__lowercase				:		List[str]												=				torch.randn([1, 2, config.sample_size],  generator=_lowerCamelCase				).to(_lowerCamelCase				)

		__lowercase				:		Tuple												=				torch.linspace(1,  0,  steps + 1,  device=_lowerCamelCase				)[:-1]
		__lowercase				:		Dict												=				get_crash_schedule(_lowerCamelCase				)

		__lowercase				:		Any												=				DanceDiffusionPipeline(unet=_lowerCamelCase,  scheduler=_lowerCamelCase				)

		__lowercase				:		Optional[Any]												=				torch.manual_seed(33				)
		__lowercase				:		Optional[int]												=				pipe(num_inference_steps=_lowerCamelCase,  generator=_lowerCamelCase				).audios

		__lowercase				:		List[str]												=				sampling.iplms_sample(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  {}				)
		__lowercase				:		List[str]												=				generated.clamp(-1,  1				)

		__lowercase				:		List[str]												=				(generated - audio).abs().sum()
		__lowercase				:		Any												=				(generated - audio).abs().max()

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

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

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

		print(F'Conversion for {model_name} successful!'				)


if __name__ == "__main__":
						__A   :  Optional[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.')
						__A   :  str					=      parser.parse_args()

						main(args)






"""simple docstring"""







from __future__ import annotations

import math

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if len(_lowerCamelCase				) != 2 or len(a[0]				) != 2 or len(_lowerCamelCase				) != 2 or len(b[0]				) != 2:
				raise Exception("Matrices are not 2x2"				)
		__lowercase				:		Tuple												=				[
		    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
		    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
		]
		return new_matrix

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Dict:






		"""simple docstring"""


		return [
		    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]				)				)]
		    for row in range(len(_lowerCamelCase				)				)
		]

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		return [
		    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]				)				)]
		    for row in range(len(_lowerCamelCase				)				)
		]

def 						snake_case__       (					_lowerCamelCase				)			->tuple[list, list, list, list]:






		"""simple docstring"""


		if len(_lowerCamelCase				) % 2 != 0 or len(a[0]				) % 2 != 0:
				raise Exception("Odd matrices are not supported!"				)

		__lowercase				:		Tuple												=				len(_lowerCamelCase				)
		__lowercase				:		Any												=				matrix_length // 2

		__lowercase				:		Dict												=				[[a[i][j] for j in range(_lowerCamelCase,  _lowerCamelCase				)] for i in range(_lowerCamelCase				)]
		__lowercase				:		List[str]												=				[
		    [a[i][j] for j in range(_lowerCamelCase,  _lowerCamelCase				)] for i in range(_lowerCamelCase,  _lowerCamelCase				)
		]

		__lowercase				:		Union[str, Any]												=				[[a[i][j] for j in range(_lowerCamelCase				)] for i in range(_lowerCamelCase				)]
		__lowercase				:		List[str]												=				[[a[i][j] for j in range(_lowerCamelCase				)] for i in range(_lowerCamelCase,  _lowerCamelCase				)]

		return top_left, top_right, bot_left, bot_right

def 						snake_case__       (					_lowerCamelCase				)			->tuple[int, int]:






		"""simple docstring"""


		return len(_lowerCamelCase				), len(matrix[0]				)

def 						snake_case__       (					_lowerCamelCase				)			->None:






		"""simple docstring"""


		print("\n".join(str(_lowerCamelCase				) for line in matrix				)				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if matrix_dimensions(_lowerCamelCase				) == (2, 2):
				return default_matrix_multiplication(_lowerCamelCase,  _lowerCamelCase				)

		__lowercase						,__lowercase						,__lowercase						,__lowercase				:		Optional[Any]												=				split_matrix(_lowerCamelCase				)
		__lowercase						,__lowercase						,__lowercase						,__lowercase				:		Any												=				split_matrix(_lowerCamelCase				)

		__lowercase				:		str												=				actual_strassen(_lowerCamelCase,  matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Dict												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Any												=				actual_strassen(_lowerCamelCase,  matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		List[Any]												=				actual_strassen(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Optional[int]												=				actual_strassen(matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)
		__lowercase				:		Optional[Any]												=				actual_strassen(matrix_subtraction(_lowerCamelCase,  _lowerCamelCase				),  matrix_addition(_lowerCamelCase,  _lowerCamelCase				)				)

		__lowercase				:		str												=				matrix_addition(matrix_subtraction(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				),  _lowerCamelCase				)
		__lowercase				:		Dict												=				matrix_addition(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		int												=				matrix_addition(_lowerCamelCase,  _lowerCamelCase				)
		__lowercase				:		Union[str, Any]												=				matrix_subtraction(matrix_subtraction(matrix_addition(_lowerCamelCase,  _lowerCamelCase				),  _lowerCamelCase				),  _lowerCamelCase				)

		# construct the new matrix from our 4 quadrants
		__lowercase				:		Optional[int]												=				[]
		for i in range(len(_lowerCamelCase				)				):
				new_matrix.append(top_left[i] + top_right[i]				)
		for i in range(len(_lowerCamelCase				)				):
				new_matrix.append(bot_left[i] + bot_right[i]				)
		return new_matrix

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->list:






		"""simple docstring"""


		if matrix_dimensions(_lowerCamelCase				)[1] != matrix_dimensions(_lowerCamelCase				)[0]:
				__lowercase				:		int												=				(
				    "Unable to multiply these matrices, please check the dimensions.\n"
				    F'Matrix A: {matrixa}\n'
				    F'Matrix B: {matrixa}'
				)
				raise Exception(_lowerCamelCase				)
		__lowercase				:		List[Any]												=				matrix_dimensions(_lowerCamelCase				)
		__lowercase				:		Tuple												=				matrix_dimensions(_lowerCamelCase				)

		if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
				return [matrixa, matrixa]

		__lowercase				:		int												=				max(*_lowerCamelCase,  *_lowerCamelCase				)
		__lowercase				:		Any												=				int(math.pow(2,  math.ceil(math.loga(_lowerCamelCase				)				)				)				)
		__lowercase				:		Dict												=				matrixa
		__lowercase				:		str												=				matrixa

		# Adding zeros to the matrices so that the arrays dimensions are the same and also
		# power of 2
		for i in range(0,  _lowerCamelCase				):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								new_matrixa[i].append(0				)
				else:
						new_matrixa.append([0] * maxim				)
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								new_matrixa[i].append(0				)
				else:
						new_matrixa.append([0] * maxim				)

		__lowercase				:		Dict												=				actual_strassen(_lowerCamelCase,  _lowerCamelCase				)

		# Removing the additional zeros
		for i in range(0,  _lowerCamelCase				):
				if i < dimensiona[0]:
						for _ in range(dimensiona[1],  _lowerCamelCase				):
								final_matrix[i].pop()
				else:
						final_matrix.pop()
		return final_matrix


if __name__ == "__main__":
						__A   :  int					=      [
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 4, 3, 1],
						    [2, 3, 6, 7],
						    [3, 1, 2, 4],
						    [2, 3, 4, 5],
						    [6, 2, 3, 1],
						]
						__A   :  Union[str, Any]					=      [[0, 2, 1, 1], [1_6, 2, 3, 3], [2, 2, 7, 7], [1_3, 1_1, 2_2, 4]]
						print(strassen(matrixa, matrixa))
