


"""simple docstring"""




from __future__ import annotations


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int] ,			SCREAMING_SNAKE_CASE_		:				list[int] ,			SCREAMING_SNAKE_CASE_		:				list[int] ,			SCREAMING_SNAKE_CASE_		:				list[list[str]] ,			SCREAMING_SNAKE_CASE_		:				int ,			)   ->						None:
						SCREAMING_SNAKE_CASE													=					len(SCREAMING_SNAKE_CASE_  )

						# If row is equal to the size of the board it means there are a queen in each row in
						# the current board (possible_board)
						if row == n:
												# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
												# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
												boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board]  )
												return

						# We iterate each column in the row to find all possible results in each row
						for col in range(SCREAMING_SNAKE_CASE_  ):
												# We apply that we learned previously. First we check that in the current board
												# (possible_board) there are not other same value because if there is it means
												# that there are a collision in vertical. Then we apply the two formulas we
												# learned before:
												#
												# 45º: y - x = b or 45: row - col = b
												# 135º: y + x = b or row + col = b.
												#
												# And we verify if the results of this two formulas not exist in their variables
												# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
												#
												# If any or these are True it means there is a collision so we continue to the
												# next value in the for loop.
												if (
												    col in possible_board
												    or row - col in diagonal_right_collisions
												    or row + col in diagonal_left_collisions
												):
																		continue

												# If it is False we call dfs function again and we update the inputs
												depth_first_search(
												    [*possible_board, col] ,			[*diagonal_right_collisions, row - col] ,			[*diagonal_left_collisions, row + col] ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						None:
						SCREAMING_SNAKE_CASE													=					[]
						depth_first_search([] ,			[] ,			[] ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						# Print all the boards
						for board in boards:
												for column in board:
																		print(SCREAMING_SNAKE_CASE_  )
												print(''  )

						print(len(SCREAMING_SNAKE_CASE_  ) ,			'solutions were found.'  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)






"""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


__UpperCamelCase						=      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 65536,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 48000,
        '''sample_size''': 131072,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 16000,
        '''sample_size''': 65536,
    },
}


def     lowercase			(SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						Optional[Any]:
						return torch.atana(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) / math.pi * 2


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[int]  )   ->						int:
						SCREAMING_SNAKE_CASE													=					torch.sin(t * math.pi / 2  ) ** 2
						SCREAMING_SNAKE_CASE													=					(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
class 					lowerCAmelCase	(					lowerCamelCase_    ):




		'''simple docstring'''







		pass





class 					lowerCAmelCase	(					nn.Module    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__				)     ->							int:
								super().__init__()

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





def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					MODELS_MAP[model_name]['url']
						os.system(F'wget {url} ./'  )

						return F'./{model_name}.ckpt'


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

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

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						List[Any]:
						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     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						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     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple=13  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					input_string

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

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

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

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

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

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

						SCREAMING_SNAKE_CASE													=					string_left[1:]

						if "resnets" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_resconv_naming(SCREAMING_SNAKE_CASE_  )
						elif "attentions" in new_layer:
												SCREAMING_SNAKE_CASE													=					convert_attn_naming(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					new_string_left

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


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any]  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					{}
						for k, v in state_dict.items():
												if k.endswith('kernel'  ):
																		# up- and downsample layers, don't have trainable weights
																		continue

												SCREAMING_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_  ):
																		SCREAMING_SNAKE_CASE													=					transform_conv_attns(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												else:
																		SCREAMING_SNAKE_CASE													=					v

						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						str:
						if len(SCREAMING_SNAKE_CASE_  ) == 1:
												if len(v.shape  ) == 3:
																		# weight
																		SCREAMING_SNAKE_CASE													=					v[:, :, 0]
												else:
																		# bias
																		SCREAMING_SNAKE_CASE													=					v
						else:
												# qkv matrices
												SCREAMING_SNAKE_CASE													=					v.shape[0]
												SCREAMING_SNAKE_CASE													=					trippled_shape // 3
												for i in range(3  ):
																		if len(v.shape  ) == 3:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape, :, 0]
																		else:
																								SCREAMING_SNAKE_CASE													=					v[i * single_shape : (i + 1) * single_shape]
						return new_state_dict


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						List[Any]:
						SCREAMING_SNAKE_CASE													=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )

						SCREAMING_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()}'
												SCREAMING_SNAKE_CASE													=					download(SCREAMING_SNAKE_CASE_  )

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

						SCREAMING_SNAKE_CASE													=					Object()
						SCREAMING_SNAKE_CASE													=					sample_size
						SCREAMING_SNAKE_CASE													=					sample_rate
						SCREAMING_SNAKE_CASE													=					0

						SCREAMING_SNAKE_CASE													=					UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ ,			sample_rate=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					diffusers_model.state_dict()

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

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

												SCREAMING_SNAKE_CASE													=					value

						diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					1_00
						SCREAMING_SNAKE_CASE													=					33

						SCREAMING_SNAKE_CASE													=					IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_  )

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

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

						SCREAMING_SNAKE_CASE													=					DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ ,			scheduler=SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.manual_seed(33  )
						SCREAMING_SNAKE_CASE													=					pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ ,			generator=SCREAMING_SNAKE_CASE_  ).audios

						SCREAMING_SNAKE_CASE													=					sampling.iplms_sample(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			{}  )
						SCREAMING_SNAKE_CASE													=					generated.clamp(-1 ,			1  )

						SCREAMING_SNAKE_CASE													=					(generated - audio).abs().sum()
						SCREAMING_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						=      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						=      parser.parse_args()

	main(args)


