





'''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


_SCREAMING_SNAKE_CASE   =      {
    '''gwf-440k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/gwf-440k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-small-190k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''jmann-large-580k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt''',
        '''sample_rate''': 4_8_0_0_0,
        '''sample_size''': 1_3_1_0_7_2,
    },
    '''maestro-uncond-150k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''unlocked-uncond-250k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
    '''honk-140k''': {
        '''url''': '''https://model-server.zqevans2.workers.dev/honk-140k.ckpt''',
        '''sample_rate''': 1_6_0_0_0,
        '''sample_size''': 6_5_5_3_6,
    },
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any						,    lowerCamelCase_     :					Dict			):
			return torch.atana(lowerCamelCase_						,    lowerCamelCase_			) / math.pi * 2




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any			):
			__lowercase			  =							torch.sin(t * math.pi / 2			) ** 2
			__lowercase			  =							(1 - sigma**2) ** 0.5
			return alpha_sigma_to_t(lowerCamelCase_						,    lowerCamelCase_			)






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	pass






class 							__lowercase    ( nn.Module  ):







	'''simple docstring'''

	def __init__(self       ,_lowerCamelCase	)							->							Optional[int]:







				'''simple docstring'''





				super().__init__()

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





def 				_lowerCAmelCase  (			lowerCamelCase_     :					Any			):
			__lowercase			  =							MODELS_MAP[model_name]['''url''']
			os.system(f"wget {url} ./"			)

			return f"./{model_name}.ckpt"


_SCREAMING_SNAKE_CASE   =      {
    '''1''': '''resnets.0''',
    '''2''': '''attentions.0''',
    '''3''': '''resnets.1''',
    '''4''': '''attentions.1''',
    '''5''': '''resnets.2''',
    '''6''': '''attentions.2''',
}
_SCREAMING_SNAKE_CASE   =      {
    '''8''': '''resnets.0''',
    '''9''': '''attentions.0''',
    '''10''': '''resnets.1''',
    '''11''': '''attentions.1''',
    '''12''': '''resnets.2''',
    '''13''': '''attentions.2''',
}
_SCREAMING_SNAKE_CASE   =      {
    '''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''',
}
_SCREAMING_SNAKE_CASE   =      {
    '''0''': '''resnets.0''',
    '''1''': '''resnets.1''',
    '''2''': '''resnets.2''',
    '''4''': '''resnets.0''',
    '''5''': '''resnets.1''',
    '''6''': '''resnets.2''',
}

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

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[Any]			):
			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  (			lowerCamelCase_     :					Union[str, Any]			):
			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 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]						,    lowerCamelCase_     :					Dict=1_3			):
			__lowercase			  =							input_string

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

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

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

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

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

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

			__lowercase			  =							string_left[1:]

			if "resnets" in new_layer:
						__lowercase			  =							convert_resconv_naming(lowerCamelCase_			)
			elif "attentions" in new_layer:
						__lowercase			  =							convert_attn_naming(lowerCamelCase_			)
						__lowercase			  =							new_string_left

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




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

						__lowercase			  =							rename(lowerCamelCase_			)

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

			return new_state_dict




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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Optional[int]			):
			__lowercase			  =							torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''			)

			__lowercase			  =							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()}"
						__lowercase			  =							download(lowerCamelCase_			)

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

			__lowercase			  =							Object()
			__lowercase			  =							sample_size
			__lowercase			  =							sample_rate
			__lowercase			  =							0

			__lowercase			  =							UNetaDModel(sample_size=lowerCamelCase_						,    sample_rate=lowerCamelCase_			)
			__lowercase			  =							diffusers_model.state_dict()

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

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

						__lowercase			  =							value

			diffusers_model.load_state_dict(lowerCamelCase_			)

			__lowercase			  =							1_0_0
			__lowercase			  =							3_3

			__lowercase			  =							IPNDMScheduler(num_train_timesteps=lowerCamelCase_			)

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

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

			__lowercase			  =							DanceDiffusionPipeline(unet=lowerCamelCase_						,    scheduler=lowerCamelCase_			)

			__lowercase			  =							torch.manual_seed(3_3			)
			__lowercase			  =							pipe(num_inference_steps=lowerCamelCase_						,    generator=lowerCamelCase_			).audios

			__lowercase			  =							sampling.iplms_sample(lowerCamelCase_						,    lowerCamelCase_						,    lowerCamelCase_						,    {}			)
			__lowercase			  =							generated.clamp(-1						,    1			)

			__lowercase			  =							(generated - audio).abs().sum()
			__lowercase			  =							(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__":
	_SCREAMING_SNAKE_CASE   =      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.''')
	_SCREAMING_SNAKE_CASE   =      parser.parse_args()

	main(args)












'''simple docstring'''



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


_SCREAMING_SNAKE_CASE   =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =      {
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			Union[str, Any]     =    "gpt_neox_japanese"


	def __init__(self       ,_lowerCamelCase=32000       ,_lowerCamelCase=2560       ,_lowerCamelCase=32       ,_lowerCamelCase=32       ,_lowerCamelCase=4       ,_lowerCamelCase="gelu"       ,_lowerCamelCase=1.0_0       ,_lowerCamelCase=10000       ,_lowerCamelCase=2048       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-5       ,_lowerCamelCase=True       ,_lowerCamelCase=31996       ,_lowerCamelCase=31999       ,_lowerCamelCase=0.1       ,_lowerCamelCase=0.0       ,**_lowerCamelCase       ,)							->							Optional[int]:







				'''simple docstring'''





				super().__init__(bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,**_lowerCamelCase	)
				__lowercase			  =							vocab_size
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_attention_heads
				__lowercase			  =							intermediate_multiple_size
				__lowercase			  =							hidden_act
				__lowercase			  =							rotary_pct
				__lowercase			  =							rotary_emb_base
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							use_cache
				__lowercase			  =							attention_dropout
				__lowercase			  =							hidden_dropout





