


'''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


__lowercase     :						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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					return torch.atana(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ) / math.pi * 2






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					__a      :	int    =  torch.sin(t * math.pi / 2       ) ** 2
					__a      :	Optional[int]    =  (1 - sigma**2) ** 0.5
					return alpha_sigma_to_t(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	pass





class 							__UpperCamelCase    (		nn.Module ):




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__()

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Any       ):
					__a      :	Tuple    =  MODELS_MAP[model_name]['url']
					os.system(F"""wget {url} ./"""       )

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


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

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

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[int]       ):
					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							(_SCREAMING_SNAKE_CASE				:       List[str]       ):
					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							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[int]=13       ):
					__a      :	Optional[int]    =  input_string

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

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

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

					if string.startswith('main.'       ):
										__a      :	Optional[Any]    =  string[5:]

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

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

					__a      :	Any    =  string_left[1:]

					if "resnets" in new_layer:
										__a      :	Dict    =  convert_resconv_naming(_SCREAMING_SNAKE_CASE       )
					elif "attentions" in new_layer:
										__a      :	str    =  convert_attn_naming(_SCREAMING_SNAKE_CASE       )
										__a      :	Tuple    =  new_string_left

					if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	List[str]    =  prefix + '.' + new_layer + '.' + string_left
					else:
										__a      :	List[str]    =  [prefix + '.' + new_layer + '.' + s for s in string_left]
					return new_string






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

										__a      :	Tuple    =  rename(_SCREAMING_SNAKE_CASE       )

										# check if we need to transform from Conv => Linear for attention
										if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															__a      :	Union[str, Any]    =  transform_conv_attns(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	List[Any]    =  v

					return new_state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       Dict						,				_SCREAMING_SNAKE_CASE				:       List[Any]       ):
					if len(_SCREAMING_SNAKE_CASE       ) == 1:
										if len(v.shape       ) == 3:
															# weight
															__a      :	List[Any]    =  v[:, :, 0]
										else:
															# bias
															__a      :	Optional[int]    =  v
					else:
										# qkv matrices
										__a      :	Optional[int]    =  v.shape[0]
										__a      :	List[str]    =  trippled_shape // 3
										for i in range(3       ):
															if len(v.shape       ) == 3:
																				__a      :	Dict    =  v[i * single_shape : (i + 1) * single_shape, :, 0]
															else:
																				__a      :	Optional[Any]    =  v[i * single_shape : (i + 1) * single_shape]
					return new_state_dict






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					__a      :	str    =  torch.device('cuda' if torch.cuda.is_available() else 'cpu'       )

					__a      :	Union[str, 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()}"""
										__a      :	List[Any]    =  download(_SCREAMING_SNAKE_CASE       )

					__a      :	Any    =  MODELS_MAP[model_name]['sample_rate']
					__a      :	Any    =  MODELS_MAP[model_name]['sample_size']

					__a      :	Tuple    =  Object()
					__a      :	List[Any]    =  sample_size
					__a      :	List[Any]    =  sample_rate
					__a      :	Optional[int]    =  0

					__a      :	Dict    =  UNetaDModel(sample_size=_SCREAMING_SNAKE_CASE						,				sample_rate=_SCREAMING_SNAKE_CASE       )
					__a      :	Any    =  diffusers_model.state_dict()

					__a      :	Union[str, Any]    =  DiffusionUncond(_SCREAMING_SNAKE_CASE       )
					orig_model.load_state_dict(torch.load(args.model_path						,				map_location=_SCREAMING_SNAKE_CASE       )['state_dict']       )
					__a      :	int    =  orig_model.diffusion_ema.eval()
					__a      :	Any    =  orig_model.state_dict()
					__a      :	Optional[int]    =  rename_orig_weights(_SCREAMING_SNAKE_CASE       )

					__a      :	List[str]    =  set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
					__a      :	List[Any]    =  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":
															__a      :	Dict    =  value.squeeze()

										__a      :	int    =  value

					diffusers_model.load_state_dict(_SCREAMING_SNAKE_CASE       )

					__a      :	str    =  100
					__a      :	Optional[int]    =  33

					__a      :	str    =  IPNDMScheduler(num_train_timesteps=_SCREAMING_SNAKE_CASE       )

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

					__a      :	List[str]    =  torch.linspace(1						,				0						,				steps + 1						,				device=_SCREAMING_SNAKE_CASE       )[:-1]
					__a      :	Optional[Any]    =  get_crash_schedule(_SCREAMING_SNAKE_CASE       )

					__a      :	Optional[int]    =  DanceDiffusionPipeline(unet=_SCREAMING_SNAKE_CASE						,				scheduler=_SCREAMING_SNAKE_CASE       )

					__a      :	Union[str, Any]    =  torch.manual_seed(33       )
					__a      :	Any    =  pipe(num_inference_steps=_SCREAMING_SNAKE_CASE						,				generator=_SCREAMING_SNAKE_CASE       ).audios

					__a      :	Optional[int]    =  sampling.iplms_sample(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				{}       )
					__a      :	List[Any]    =  generated.clamp(-1						,				1       )

					__a      :	List[Any]    =  (generated - audio).abs().sum()
					__a      :	Union[str, Any]    =  (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__":
	__lowercase     :						Optional[int]	=     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.')
	__lowercase     :						List[Any]	=     parser.parse_args()

	main(args)







'''simple docstring'''



import sys

__lowercase     :						Union[str, Any]	=     (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					__a      :	List[str]    =  1
					for digit in s:
										product *= int(_SCREAMING_SNAKE_CASE       )
					return product






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str = N       ):
					__a      :	Optional[int]    =  -sys.maxsize - 1
					__a      :	Optional[Any]    =  n[:13]
					__a      :	int    =  13
					while cur_index < len(_SCREAMING_SNAKE_CASE       ) - 13:
										if int(n[cur_index]       ) >= int(substr[0]       ):
															__a      :	List[Any]    =  substr[1:] + n[cur_index]
															cur_index += 1
										else:
															__a      :	Dict    =  max(_SCREAMING_SNAKE_CASE						,				str_eval(_SCREAMING_SNAKE_CASE       )       )
															__a      :	Optional[Any]    =  n[cur_index : cur_index + 13]
															cur_index += 13
					return largest_product


if __name__ == "__main__":
	print(f'''{solution() = }''')



