
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  :					Any													=       {
    '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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			List[Any])					->    str:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[Any])					->    Dict:






							'''simple docstring'''





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




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	pass




class  lowerCamelCase__							(				nn.Module):




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]     ,   a		:int						)		-> List[str]:
								super().__init__()

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




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






							'''simple docstring'''





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

							return F'./{model_name}.ckpt'


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

lowercase  :					Optional[int]													=       {
    '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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int])					->    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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any)					->    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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			Any=13)					->    Tuple:






							'''simple docstring'''





							__UpperCamelCase	:   Dict			   =					input_string

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

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

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

							if string.startswith("main."):
														__UpperCamelCase	:   int			   =					string[5:]

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

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

							__UpperCamelCase	:   Optional[int]			   =					string_left[1:]

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

							if not isinstance(_lowerCamelCase						,  _lowerCamelCase):
														__UpperCamelCase	:   List[str]			   =					prefix + "." + new_layer + "." + string_left
							else:
														__UpperCamelCase	:   str			   =					[prefix + "." + new_layer + "." + s for s in string_left]
							return new_string




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str)					->    Any:






							'''simple docstring'''





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

														__UpperCamelCase	:   Dict			   =					rename(_lowerCamelCase)

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

							return new_state_dict




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int]						,  _lowerCamelCase :			int						,  _lowerCamelCase :			int)					->    Tuple:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Optional[int])					->    int:






							'''simple docstring'''





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

							__UpperCamelCase	:   int			   =					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()}'
														__UpperCamelCase	:   str			   =					download(_lowerCamelCase)

							__UpperCamelCase	:   Optional[Any]			   =					MODELS_MAP[model_name]["sample_rate"]
							__UpperCamelCase	:   Tuple			   =					MODELS_MAP[model_name]["sample_size"]

							__UpperCamelCase	:   Union[str, Any]			   =					Object()
							__UpperCamelCase	:   Optional[int]			   =					sample_size
							__UpperCamelCase	:   str			   =					sample_rate
							__UpperCamelCase	:   Optional[Any]			   =					0

							__UpperCamelCase	:   List[Any]			   =					UNetaDModel(sample_size=_lowerCamelCase						,  sample_rate=_lowerCamelCase)
							__UpperCamelCase	:   List[str]			   =					diffusers_model.state_dict()

							__UpperCamelCase	:   List[Any]			   =					DiffusionUncond(_lowerCamelCase)
							orig_model.load_state_dict(torch.load(args.model_path						,  map_location=_lowerCamelCase)["state_dict"])
							__UpperCamelCase	:   Any			   =					orig_model.diffusion_ema.eval()
							__UpperCamelCase	:   Dict			   =					orig_model.state_dict()
							__UpperCamelCase	:   int			   =					rename_orig_weights(_lowerCamelCase)

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

														__UpperCamelCase	:   Optional[int]			   =					value

							diffusers_model.load_state_dict(_lowerCamelCase)

							__UpperCamelCase	:   Union[str, Any]			   =					100
							__UpperCamelCase	:   List[str]			   =					33

							__UpperCamelCase	:   List[str]			   =					IPNDMScheduler(num_train_timesteps=_lowerCamelCase)

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

							__UpperCamelCase	:   str			   =					torch.linspace(1						,  0						,  steps + 1						,  device=_lowerCamelCase)[:-1]
							__UpperCamelCase	:   List[str]			   =					get_crash_schedule(_lowerCamelCase)

							__UpperCamelCase	:   Union[str, Any]			   =					DanceDiffusionPipeline(unet=_lowerCamelCase						,  scheduler=_lowerCamelCase)

							__UpperCamelCase	:   Union[str, Any]			   =					torch.manual_seed(33)
							__UpperCamelCase	:   int			   =					pipe(num_inference_steps=_lowerCamelCase						,  generator=_lowerCamelCase).audios

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

							__UpperCamelCase	:   str			   =					(generated - audio).abs().sum()
							__UpperCamelCase	:   List[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__":
		lowercase  :					Tuple													=       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  :					str													=       parser.parse_args()

		main(args)

class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:Union[str, Any]						)		-> None:
								__UpperCamelCase	:   dict[str, TrieNode]			   =					{}  # Mapping from char to TrieNode
								__UpperCamelCase	:   List[str]			   =					False







	def    _lowerCamelCase				(					self		:Any     ,   a		:list[str]						)		-> None:
								for word in words:
															self.insert(a						)







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:str						)		-> None:
								__UpperCamelCase	:   Dict			   =					self
								for char in word:
															if char not in curr.nodes:
																						__UpperCamelCase	:   List[Any]			   =					TrieNode()
															__UpperCamelCase	:   List[Any]			   =					curr.nodes[char]
								__UpperCamelCase	:   Union[str, Any]			   =					True







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> bool:
								__UpperCamelCase	:   Union[str, Any]			   =					self
								for char in word:
															if char not in curr.nodes:
																						return False
															__UpperCamelCase	:   Union[str, Any]			   =					curr.nodes[char]
								return curr.is_leaf







	def    _lowerCamelCase				(					self		:Any     ,   a		:str						)		-> None:

								def _delete(a		:TrieNode     ,   a		:str     ,   a		:int						) -> bool:
															if index == len(a						):
																						# If word does not exist
																						if not curr.is_leaf:
																													return False
																						__UpperCamelCase	:   str			   =					False
																						return len(curr.nodes						) == 0
															__UpperCamelCase	:   List[Any]			   =					word[index]
															__UpperCamelCase	:   Optional[int]			   =					curr.nodes.get(a						)
															# If char not in current trie node
															if not char_node:
																						return False
															# Flag to check if node can be deleted
															__UpperCamelCase	:   int			   =					_delete(a     ,   a     ,   index + 1						)
															if delete_curr:
																						del curr.nodes[char]
																						return len(curr.nodes						) == 0
															return delete_curr

								_delete(self     ,   a     ,   0						)




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			TrieNode						,  _lowerCamelCase :			str)					->    None:






							'''simple docstring'''





							if node.is_leaf:
														print(_lowerCamelCase						,  end=" ")

							for key, value in node.nodes.items():
														print_words(_lowerCamelCase						,  word + key)




def 	_SCREAMING_SNAKE_CASE					(		)					->    bool:






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					"banana bananas bandana band apple all beast".split()
							__UpperCamelCase	:   Union[str, Any]			   =					TrieNode()
							root.insert_many(_lowerCamelCase)
							# print_words(root, "")
							assert all(root.find(_lowerCamelCase) for word in words)
							assert root.find("banana")
							assert not root.find("bandanas")
							assert not root.find("apps")
							assert root.find("apple")
							assert root.find("all")
							root.delete("all")
							assert not root.find("all")
							root.delete("banana")
							assert not root.find("banana")
							assert root.find("bananas")
							return True




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			str						,  _lowerCamelCase :			bool)					->    None:






							'''simple docstring'''





							print(str(_lowerCamelCase)						,  "works!" if passes else "doesn't work :(")




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							assert test_trie()




def 	_SCREAMING_SNAKE_CASE					(		)					->    None:






							'''simple docstring'''





							print_results("Testing trie functionality"						,  test_trie())


if __name__ == "__main__":
		main()