
"""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


lowerCamelCase__ :       List[str]						     =			{
    "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 __A						(   a_ :							List[str]		,		a_ :							Any     )->							int:




		'''simple docstring'''






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





def __A						(   a_ :							List[str]     )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= torch.sin(t * math.pi / 2     ) ** 2
		SCREAMING_SNAKE_CASE       :     Tuple				= (1 - sigma**2) ** 0.5
		return alpha_sigma_to_t(a_		,		a_     )




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	pass





class 				lowercase__(				nn.Module   ):
	'''simple docstring'''

	def __init__(						self    :Tuple		, lowerCamelCase_    :Tuple					)							->			Optional[Any]:







			'''simple docstring'''




			super().__init__()

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





def __A						(   a_ :							Tuple     )->							Optional[int]:




		'''simple docstring'''






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

		return F"./{model_name}.ckpt"


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

lowerCamelCase__ :       Dict						     =			{
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

lowerCamelCase__ :       List[Any]						     =			{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}





def __A						(   a_ :							Dict     )->							Union[str, 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 __A						(   a_ :							str     )->							Optional[Any]:




		'''simple docstring'''






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





def __A						(   a_ :							Dict		,		a_ :							Optional[Any]=13     )->							Tuple:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= input_string

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

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

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

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

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

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

		if "resnets" in new_layer:
				SCREAMING_SNAKE_CASE       :     List[str]				= convert_resconv_naming(a_     )
		elif "attentions" in new_layer:
				SCREAMING_SNAKE_CASE       :     Tuple				= convert_attn_naming(a_     )
				SCREAMING_SNAKE_CASE       :     str				= new_string_left

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





def __A						(   a_ :							Tuple     )->							Optional[Any]:




		'''simple docstring'''






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

				SCREAMING_SNAKE_CASE       :     Optional[int]				= rename(a_     )

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

		return new_state_dict





def __A						(   a_ :							Any		,		a_ :							Tuple		,		a_ :							List[Any]     )->							Union[str, Any]:




		'''simple docstring'''






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





def __A						(   a_ :							Dict     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''     )

		SCREAMING_SNAKE_CASE       :     List[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()}"
				SCREAMING_SNAKE_CASE       :     Tuple				= download(a_     )

		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= MODELS_MAP[model_name]['''sample_rate''']
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= MODELS_MAP[model_name]['''sample_size''']

		SCREAMING_SNAKE_CASE       :     int				= Object()
		SCREAMING_SNAKE_CASE       :     int				= sample_size
		SCREAMING_SNAKE_CASE       :     Optional[int]				= sample_rate
		SCREAMING_SNAKE_CASE       :     Dict				= 0

		SCREAMING_SNAKE_CASE       :     Dict				= UNetaDModel(sample_size=a_		,		sample_rate=a_     )
		SCREAMING_SNAKE_CASE       :     Optional[int]				= diffusers_model.state_dict()

		SCREAMING_SNAKE_CASE       :     int				= DiffusionUncond(a_     )
		orig_model.load_state_dict(torch.load(args.model_path		,		map_location=a_     )['''state_dict''']     )
		SCREAMING_SNAKE_CASE       :     Tuple				= orig_model.diffusion_ema.eval()
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= orig_model.state_dict()
		SCREAMING_SNAKE_CASE       :     List[str]				= rename_orig_weights(a_     )

		SCREAMING_SNAKE_CASE       :     List[str]				= set(renamed_state_dict.keys()     ) - set(diffusers_state_dict.keys()     )
		SCREAMING_SNAKE_CASE       :     str				= set(diffusers_state_dict.keys()     ) - set(renamed_state_dict.keys()     )

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

				SCREAMING_SNAKE_CASE       :     Dict				= value

		diffusers_model.load_state_dict(a_     )

		SCREAMING_SNAKE_CASE       :     Tuple				= 1_00
		SCREAMING_SNAKE_CASE       :     int				= 33

		SCREAMING_SNAKE_CASE       :     Optional[int]				= IPNDMScheduler(num_train_timesteps=a_     )

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

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

		SCREAMING_SNAKE_CASE       :     Optional[Any]				= DanceDiffusionPipeline(unet=a_		,		scheduler=a_     )

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

		SCREAMING_SNAKE_CASE       :     str				= sampling.iplms_sample(a_		,		a_		,		a_		,		{}     )
		SCREAMING_SNAKE_CASE       :     int				= generated.clamp(-1		,		1     )

		SCREAMING_SNAKE_CASE       :     Dict				= (generated - audio).abs().sum()
		SCREAMING_SNAKE_CASE       :     Dict				= (generated - audio).abs().max()

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

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

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

		print(F"Conversion for {model_name} successful!"     )


if __name__ == "__main__":
					lowerCamelCase__ :       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.")
					lowerCamelCase__ :       Dict						     =			parser.parse_args()

					main(args)







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """luke"""





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :Union[str, Any]=5_02_67		, lowerCamelCase_    :int=50_00_00		, lowerCamelCase_    :Tuple=7_68		, lowerCamelCase_    :List[str]=2_56		, lowerCamelCase_    :Dict=12		, lowerCamelCase_    :Optional[int]=12		, lowerCamelCase_    :Optional[Any]=30_72		, lowerCamelCase_    :List[Any]="gelu"		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :List[str]=5_12		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Tuple=0.0_2		, lowerCamelCase_    :Optional[int]=1E-12		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Any=0		, lowerCamelCase_    :str=2		, **lowerCamelCase_    :List[Any]		, )							->			Optional[int]:







			'''simple docstring'''




			super().__init__(pad_token_id=lowerCamelCase_		, bos_token_id=lowerCamelCase_		, eos_token_id=lowerCamelCase_		, **lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Dict				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= entity_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= entity_emb_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Tuple				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_entity_aware_attention
			SCREAMING_SNAKE_CASE       :     List[str]				= classifier_dropout





