
"""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_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-small-190k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 6_55_36,
    },
    'jmann-large-580k': {
        'url': 'https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt',
        'sample_rate': 4_80_00,
        'sample_size': 13_10_72,
    },
    'maestro-uncond-150k': {
        'url': 'https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'unlocked-uncond-250k': {
        'url': 'https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
    'honk-140k': {
        'url': 'https://model-server.zqevans2.workers.dev/honk-140k.ckpt',
        'sample_rate': 1_60_00,
        'sample_size': 6_55_36,
    },
}



def     __magic_name__		(					UpperCamelCase     :		Optional[Any]							,		UpperCamelCase     :		Optional[Any]     )     ->						Optional[Any]:
    return torch.atana(__UpperCAmelCase							,		__UpperCAmelCase     ) / math.pi * 2



def     __magic_name__		(					UpperCamelCase     :		int     )     ->						Optional[Any]:
    a__		=			torch.sin(t * math.pi / 2     ) ** 2
    a__		=			(1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(__UpperCAmelCase							,		__UpperCAmelCase     )







class     lowercase(_lowerCamelCase       ):
     pass







class     lowercase(nn.Module       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         super().__init__()

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




def     __magic_name__		(					UpperCamelCase     :		str     )     ->						int:
    a__		=			MODELS_MAP[model_name]['url']
    os.system(f'wget {url} ./'     )

    return f'./{model_name}.ckpt'


a		:       Any     						=       {
    '1': 'resnets.0',
    '2': 'attentions.0',
    '3': 'resnets.1',
    '4': 'attentions.1',
    '5': 'resnets.2',
    '6': 'attentions.2',
}
a		:       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		:       Optional[int]     						=       {
    '0': 'resnets.0',
    '1': 'resnets.1',
    '2': 'resnets.2',
    '4': 'resnets.0',
    '5': 'resnets.1',
    '6': 'resnets.2',
}

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

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



def     __magic_name__		(					UpperCamelCase     :		Any     )     ->						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     __magic_name__		(					UpperCamelCase     :		Union[str, Any]     )     ->						Tuple:

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



def     __magic_name__		(					UpperCamelCase     :		Dict							,		UpperCamelCase     :		Any=13     )     ->						Union[str, Any]:
    a__		=			input_string

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

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

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

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

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

    if depth == max_depth:
        a__		=			MID_NUM_TO_LAYER[layer_num]
        a__		=			'mid_block'
    elif depth > 0 and int(__UpperCAmelCase     ) < 7:
        a__		=			DOWN_NUM_TO_LAYER[layer_num]
        a__		=			f'down_blocks.{depth}'
    elif depth > 0 and int(__UpperCAmelCase     ) > 7:
        a__		=			UP_NUM_TO_LAYER[layer_num]
        a__		=			f'up_blocks.{max_depth - depth - 1}'
    elif depth == 0:
        a__		=			DEPTH_0_TO_LAYER[layer_num]
        a__		=			f'up_blocks.{max_depth - 1}' if int(__UpperCAmelCase     ) > 3 else 'down_blocks.0'

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

    a__		=			string_left[1:]

    if "resnets" in new_layer:
        a__		=			convert_resconv_naming(__UpperCAmelCase     )
    elif "attentions" in new_layer:
        a__		=			convert_attn_naming(__UpperCAmelCase     )
        a__		=			new_string_left

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



def     __magic_name__		(					UpperCamelCase     :		Dict     )     ->						List[str]:
    a__		=			{}
    for k, v in state_dict.items():
        if k.endswith('kernel'     ):
            # up- and downsample layers, don't have trainable weights
            continue

        a__		=			rename(__UpperCAmelCase     )

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

    return new_state_dict



def     __magic_name__		(					UpperCamelCase     :		Any							,		UpperCamelCase     :		List[Any]							,		UpperCamelCase     :		List[Any]     )     ->						List[str]:

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



def     __magic_name__		(					UpperCamelCase     :		Dict     )     ->						Optional[Any]:
    a__		=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'     )

    a__		=			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()}'
        a__		=			download(__UpperCAmelCase     )

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

    a__		=			Object()
    a__		=			sample_size
    a__		=			sample_rate
    a__		=			0

    a__		=			UNetaDModel(sample_size=__UpperCAmelCase							,		sample_rate=__UpperCAmelCase     )
    a__		=			diffusers_model.state_dict()

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

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

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

        a__		=			value

    diffusers_model.load_state_dict(__UpperCAmelCase     )

    a__		=			100
    a__		=			33

    a__		=			IPNDMScheduler(num_train_timesteps=__UpperCAmelCase     )

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

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

    a__		=			DanceDiffusionPipeline(unet=__UpperCAmelCase							,		scheduler=__UpperCAmelCase     )

    a__		=			torch.manual_seed(33     )
    a__		=			pipe(num_inference_steps=__UpperCAmelCase							,		generator=__UpperCAmelCase     ).audios

    a__		=			sampling.iplms_sample(__UpperCAmelCase							,		__UpperCAmelCase							,		__UpperCAmelCase							,		{}     )
    a__		=			generated.clamp(-1							,		1     )

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

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

    print('Diff sum'							,		__UpperCAmelCase     )
    print('Diff max'							,		__UpperCAmelCase     )

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

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


if __name__ == "__main__":
    a		:       List[str]     						=       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		:       List[Any]     						=       parser.parse_args()

    main(args)








'''simple docstring'''

import heapq
import sys

import numpy as np

a    :							Dict										=    tuple[int, int]






class 		a							:


				def __init__(						self       :   Dict   ):
									snake_case_		     =							[]
									snake_case_		     =							set()


				def     A_					(						self       :   int   ):
									if not self.empty():
														return self.elements[0][0]
									else:
														return float('''inf'''   )


				def     A_					(						self       :   List[str]   ):
									return len(self.elements   ) == 0


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   str			,							lowercase_       :   List[str]   ):
									if item not in self.set:
														heapq.heappush(self.elements			,							(priority, item)   )
														self.set.add(lowercase_   )
									else:
														# update
														# print("update", item)
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pri, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														temp.append((priority, item)   )
														for pro, xxx in temp:
																			heapq.heappush(self.elements			,							(pro, xxx)   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   List[Any]   ):
									if item in self.set:
														self.set.remove(lowercase_   )
														snake_case_		     =							[]
														((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														while x != item:
																			temp.append((pro, x)   )
																			((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
														for prito, yyy in temp:
																			heapq.heappush(self.elements			,							(prito, yyy)   )


				def     A_					(						self       :   Optional[int]   ):
									return self.elements[0][1]


				def     A_					(						self       :   Tuple   ):
									((snake_case_)     ,(snake_case_))		     =							heapq.heappop(self.elements   )
									self.set.remove(lowercase_   )
									return (priority, item)







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							np.array(__UpperCAmelCase      )
					snake_case_		     =							np.array(__UpperCAmelCase      )
					return np.linalg.norm(a - b      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return consistent_heuristic(__UpperCAmelCase, __UpperCAmelCase      ) // t


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					return abs(p[0] - goal[0]      ) + abs(p[1] - goal[1]      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[int]:

					'''simple docstring'''

					snake_case_		     =							g_function[start] + Wa * heuristics[i](__UpperCAmelCase, __UpperCAmelCase      )
					return ans


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   Optional[Any]:

					'''simple docstring'''

					snake_case_		     =							np.chararray((n, n)      )
					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															snake_case_		     =							'''*'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (j, (n - 1) - i) in blocks:
																				snake_case_		     =							'''#'''

					snake_case_		     =							'''-'''
					snake_case_		     =							back_pointer[goal]
					while x != start:
										((snake_case_)     ,(snake_case_))		     =							x
										# print(x)
										snake_case_		     =							'''-'''
										snake_case_		     =							back_pointer[x]
					snake_case_		     =							'''-'''

					for i in range(__UpperCAmelCase      ):
										for j in range(__UpperCAmelCase      ):
															if (i, j) == (0, n - 1):
																				print(grid[i][j], end=''' '''      )
																				print('''<-- End position''', end=''' '''      )
															else:
																				print(grid[i][j], end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )
					print('''PATH TAKEN BY THE ALGORITHM IS:-'''      )
					snake_case_		     =							back_pointer[goal]
					while x != start:
										print(__UpperCAmelCase, end=''' '''      )
										snake_case_		     =							back_pointer[x]
					print(__UpperCAmelCase      )
					sys.exit()


def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					if p[0] < 0 or p[0] > n - 1:
										return False
					if p[1] < 0 or p[1] > n - 1:
										return False
					return True


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )						->   str:

					'''simple docstring'''

					for itera in range(__UpperCAmelCase      ):
										open_list[itera].remove_element(__UpperCAmelCase      )
					# print("s", s)
					# print("j", j)
					((snake_case_)     ,(snake_case_))		     =							s
					snake_case_		     =							(x - 1, y)
					snake_case_		     =							(x + 1, y)
					snake_case_		     =							(x, y + 1)
					snake_case_		     =							(x, y - 1)

					for neighbours in [left, right, up, down]:
										if neighbours not in blocks:
															if valid(__UpperCAmelCase      ) and neighbours not in visited:
																				# print("neighbour", neighbours)
																				visited.add(__UpperCAmelCase      )
																				snake_case_		     =							-1
																				snake_case_		     =							float('''inf'''      )

															if valid(__UpperCAmelCase      ) and g_function[neighbours] > g_function[s] + 1:
																				snake_case_		     =							g_function[s] + 1
																				snake_case_		     =							s
																				if neighbours not in close_list_anchor:
																									open_list[0].put(__UpperCAmelCase, key(__UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      )      )
																									if neighbours not in close_list_inad:
																														for var in range(1, __UpperCAmelCase      ):
																																			if key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      ) <= Wa * key(
																																			    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase      ):
																																								open_list[j].put(
																																								    __UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )


def 						__magic_name__       (      )						->   str:

					'''simple docstring'''

					snake_case_		     =							[]
					for x in range(1, 5      ):
										for y in range(1, 6      ):
															some_list.append((x, y)      )

					for x in range(15, 20      ):
										some_list.append((x, 17)      )

					for x in range(10, 19      ):
										for y in range(1, 15      ):
															some_list.append((x, y)      )

    # L block
					for x in range(1, 4      ):
										for y in range(12, 19      ):
															some_list.append((x, y)      )
					for x in range(3, 13      ):
										for y in range(16, 19      ):
															some_list.append((x, y)      )
					return some_list


a    :							Tuple										=    {0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

a    :							Dict										=    [
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
a    :							List[str]										=    make_common_ground()


a    :							Optional[int]										=    blocks_blk
# hyper parameters
a    :							Union[str, Any]										=    1
a    :							str										=    1
a    :							int										=    20
a    :							Optional[int]										=    3  # one consistent and two other inconsistent

# start and end destination
a    :							List[str]										=    (0, 0)
a    :							str										=    (n - 1, n - 1)

a    :							str										=    1


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )						->   List[str]:

					'''simple docstring'''

					snake_case_		     =							{start: 0, goal: float('''inf'''      )}
					snake_case_		     =							{start: -1, goal: -1}
					snake_case_		     =							[]
					snake_case_		     =							set()

					for i in range(__UpperCAmelCase      ):
										open_list.append(PriorityQueue()      )
										open_list[i].put(__UpperCAmelCase, key(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )      )

					snake_case_		     =							[]
					snake_case_		     =							[]
					while open_list[0].minkey() < float('''inf'''      ):
										for i in range(1, __UpperCAmelCase      ):
															# print(open_list[0].minkey(), open_list[i].minkey())
															if open_list[i].minkey() <= Wa * open_list[0].minkey():
																				global t
																				t += 1
																				if g_function[goal] <= open_list[i].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_     ,snake_case_		     =							open_list[i].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_inad.append(__UpperCAmelCase      )
															else:
																				if g_function[goal] <= open_list[0].minkey():
																									if g_function[goal] < float('''inf'''      ):
																														do_something(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )
																				else:
																									snake_case_		     =							open_list[0].top_show()
																									visited.add(__UpperCAmelCase      )
																									expand_state(
																									    __UpperCAmelCase, 0, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, )
																									close_list_anchor.append(__UpperCAmelCase      )
					print('''No path found to goal'''      )
					print()
					for i in range(n - 1, -1, -1      ):
										for j in range(__UpperCAmelCase      ):
															if (j, i) in blocks:
																				print('''#''', end=''' '''      )
															elif (j, i) in back_pointer:
																				if (j, i) == (n - 1, n - 1):
																									print('''*''', end=''' '''      )
																				else:
																									print('''-''', end=''' '''      )
															else:
																				print('''*''', end=''' '''      )
															if (j, i) == (n - 1, n - 1):
																				print('''<-- End position''', end=''' '''      )
										print()
					print('''^'''      )
					print('''Start position'''      )
					print()
					print('''# is an obstacle'''      )
					print('''- is the path taken by algorithm'''      )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)

