




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

					'''simple docstring'''

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


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

					'''simple docstring'''

					snake_case_		     =							torch.sin(t * math.pi / 2      ) ** 2
					snake_case_		     =							(1 - sigma**2) ** 0.5
					return alpha_sigma_to_t(__UpperCAmelCase, __UpperCAmelCase      )






class 		a							(  _lowerCamelCase	):
				pass







class 		a							(  nn.Module	):


				def __init__(						self       :   str			,							lowercase_       :   Optional[Any]   ):
									super().__init__()

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







def 						__magic_name__       (      __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					snake_case_		     =							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      )						->   str:

					'''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 						__magic_name__       (      __UpperCAmelCase      )						->   Tuple:

					'''simple docstring'''

					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, __UpperCAmelCase=13      )						->   Union[str, Any]:

					'''simple docstring'''

					snake_case_		     =							input_string

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

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

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

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

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

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

					snake_case_		     =							string_left[1:]

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

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


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

					'''simple docstring'''

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

										snake_case_		     =							rename(__UpperCAmelCase      )

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

					return new_state_dict


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

					'''simple docstring'''

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


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

					'''simple docstring'''

					snake_case_		     =							torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''      )

					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()}"
										snake_case_		     =							download(__UpperCAmelCase      )

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

					snake_case_		     =							Object()
					snake_case_		     =							sample_size
					snake_case_		     =							sample_rate
					snake_case_		     =							0

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

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

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

										snake_case_		     =							value

					diffusers_model.load_state_dict(__UpperCAmelCase      )

					snake_case_		     =							100
					snake_case_		     =							33

					snake_case_		     =							IPNDMScheduler(num_train_timesteps=__UpperCAmelCase      )

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

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

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

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

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

					snake_case_		     =							(generated - audio).abs().sum()
					snake_case_		     =							(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'''

from collections.abc import Sequence
from queue import Queue






class 		a							:


				def __init__(						self       :   List[str]			,							lowercase_       :   str			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int=None			,							lowercase_       :   List[Any]=None   ):
									snake_case_		     =							start
									snake_case_		     =							end
									snake_case_		     =							val
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							left
									snake_case_		     =							right


				def __repr__(						self       :   Any   ):
									return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"







class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   Sequence			,							lowercase_       :   List[str]   ):
									snake_case_		     =							collection
									snake_case_		     =							function
									if self.collection:
														snake_case_		     =							self._build_tree(0			,							len(lowercase_   ) - 1   )


				def     A_					(						self       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									self._update_tree(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]   ):
									return self._query_range(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   int			,							lowercase_       :   int   ):
									if start == end:
														return SegmentTreeNode(lowercase_			,							lowercase_			,							self.collection[start]   )
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							self._build_tree(lowercase_			,							lowercase_   )
									snake_case_		     =							self._build_tree(mid + 1			,							lowercase_   )
									return SegmentTreeNode(lowercase_			,							lowercase_			,							self.fn(left.val			,							right.val   )			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]   ):
									if node.start == i and node.end == i:
														snake_case_		     =							val
														return
									if i <= node.mid:
														self._update_tree(node.left			,							lowercase_			,							lowercase_   )
									else:
														self._update_tree(node.right			,							lowercase_			,							lowercase_   )
									snake_case_		     =							self.fn(node.left.val			,							node.right.val   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									if node.start == i and node.end == j:
														return node.val

									if i <= node.mid:
														if j <= node.mid:
																			# range in left child tree
																			return self._query_range(node.left			,							lowercase_			,							lowercase_   )
														else:
																			# range in left child tree and right child tree
																			return self.fn(
																			    self._query_range(node.left			,							lowercase_			,							node.mid   )			,							self._query_range(node.right			,							node.mid + 1			,							lowercase_   )			,							)
									else:
														# range in right child tree
														return self._query_range(node.right			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any   ):
									if self.root is not None:
														snake_case_		     =							Queue()
														queue.put(self.root   )
														while not queue.empty():
																			snake_case_		     =							queue.get()
																			yield node

																			if node.left is not None:
																								queue.put(node.left   )

																			if node.right is not None:
																								queue.put(node.right   )


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print('*' * 50)
														a    :							List[Any]										=    SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()

