

'''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


UpperCamelCase_							=					{
    '''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 							_lowerCAmelCase (      __magic_name__	:    Optional[int]      ,   __magic_name__	:    Optional[Any]	)							->     List[Any]:
						return torch.atana(__magic_name__      ,   __magic_name__	) / math.pi * 2






def 							_lowerCAmelCase (      __magic_name__	:    List[str]	)							->     Any:
						lowercase   :       Tuple								=torch.sin(t * math.pi / 2	) ** 2
						lowercase   :       str								=(1 - sigma**2) ** 0.5
						return alpha_sigma_to_t(__magic_name__      ,   __magic_name__	)





class 		__SCREAMING_SNAKE_CASE       ( UpperCamelCase__			):
				pass





class 		__SCREAMING_SNAKE_CASE       ( nn.Module			):







				def __init__(					self    :		List[Any]		,				UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										super().__init__()

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






def 							_lowerCAmelCase (      __magic_name__	:    Any	)							->     Optional[int]:
						lowercase   :       int								=MODELS_MAP[model_name]['''url''']
						os.system(f'''wget {url} ./'''	)

						return f'''./{model_name}.ckpt'''


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

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

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






def 							_lowerCAmelCase (      __magic_name__	:    Optional[Any]	)							->     Tuple:




						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 (      __magic_name__	:    str	)							->     Optional[Any]:




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






def 							_lowerCAmelCase (      __magic_name__	:    List[Any]      ,   __magic_name__	:    Optional[int]=13	)							->     Optional[int]:
						lowercase   :       List[Any]								=input_string

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

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

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

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

						# mid block
						if string[:2].isdigit():
												lowercase   :       Optional[int]								=string[:2]
												lowercase   :       Optional[int]								=string[2:]
						else:
												lowercase   :       str								=string[0]
												lowercase   :       str								=string[1:]

						if depth == max_depth:
												lowercase   :       Optional[int]								=MID_NUM_TO_LAYER[layer_num]
												lowercase   :       Optional[int]								='''mid_block'''
						elif depth > 0 and int(__magic_name__	) < 7:
												lowercase   :       str								=DOWN_NUM_TO_LAYER[layer_num]
												lowercase   :       List[str]								=f'''down_blocks.{depth}'''
						elif depth > 0 and int(__magic_name__	) > 7:
												lowercase   :       List[Any]								=UP_NUM_TO_LAYER[layer_num]
												lowercase   :       Dict								=f'''up_blocks.{max_depth - depth - 1}'''
						elif depth == 0:
												lowercase   :       Tuple								=DEPTH_0_TO_LAYER[layer_num]
												lowercase   :       List[str]								=f'''up_blocks.{max_depth - 1}''' if int(__magic_name__	) > 3 else '''down_blocks.0'''

						if not string_left.startswith('''.'''	):
												raise ValueError(f'''Naming error with {input_string} and string_left: {string_left}.'''	)

						lowercase   :       Tuple								=string_left[1:]

						if "resnets" in new_layer:
												lowercase   :       List[str]								=convert_resconv_naming(__magic_name__	)
						elif "attentions" in new_layer:
												lowercase   :       Union[str, Any]								=convert_attn_naming(__magic_name__	)
												lowercase   :       Optional[int]								=new_string_left

						if not isinstance(__magic_name__      ,   __magic_name__	):
												lowercase   :       Optional[Any]								=prefix + '''.''' + new_layer + '''.''' + string_left
						else:
												lowercase   :       Union[str, Any]								=[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
						return new_string






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

												lowercase   :       Dict								=rename(__magic_name__	)

												# check if we need to transform from Conv => Linear for attention
												if isinstance(__magic_name__      ,   __magic_name__	):
																		lowercase   :       int								=transform_conv_attns(__magic_name__      ,   __magic_name__      ,   __magic_name__	)
												else:
																		lowercase   :       Optional[Any]								=v

						return new_state_dict






def 							_lowerCAmelCase (      __magic_name__	:    Dict      ,   __magic_name__	:    int      ,   __magic_name__	:    int	)							->     List[Any]:




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






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     str:
						lowercase   :       List[str]								=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)

						lowercase   :       str								=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   :       int								=download(__magic_name__	)

						lowercase   :       Optional[Any]								=MODELS_MAP[model_name]['''sample_rate''']
						lowercase   :       List[Any]								=MODELS_MAP[model_name]['''sample_size''']

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

						lowercase   :       List[Any]								=UNetaDModel(sample_size=__magic_name__      ,   sample_rate=__magic_name__	)
						lowercase   :       Optional[Any]								=diffusers_model.state_dict()

						lowercase   :       Tuple								=DiffusionUncond(__magic_name__	)
						orig_model.load_state_dict(torch.load(args.model_path      ,   map_location=__magic_name__	)['''state_dict''']	)
						lowercase   :       Tuple								=orig_model.diffusion_ema.eval()
						lowercase   :       str								=orig_model.state_dict()
						lowercase   :       str								=rename_orig_weights(__magic_name__	)

						lowercase   :       Dict								=set(renamed_state_dict.keys()	) - set(diffusers_state_dict.keys()	)
						lowercase   :       Optional[Any]								=set(diffusers_state_dict.keys()	) - set(renamed_state_dict.keys()	)

						assert len(__magic_name__	) == 0, f'''Problem with {renamed_minus_diffusers}'''
						assert all(k.endswith('''kernel'''	) for k in list(__magic_name__	)	), 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   :       Any								=value.squeeze()

												lowercase   :       Dict								=value

						diffusers_model.load_state_dict(__magic_name__	)

						lowercase   :       Any								=100
						lowercase   :       Optional[Any]								=33

						lowercase   :       List[str]								=IPNDMScheduler(num_train_timesteps=__magic_name__	)

						lowercase   :       Tuple								=torch.manual_seed(__magic_name__	)
						lowercase   :       Tuple								=torch.randn([1, 2, config.sample_size]      ,   generator=__magic_name__	).to(__magic_name__	)

						lowercase   :       Union[str, Any]								=torch.linspace(1      ,   0      ,   steps + 1      ,   device=__magic_name__	)[:-1]
						lowercase   :       int								=get_crash_schedule(__magic_name__	)

						lowercase   :       Any								=DanceDiffusionPipeline(unet=__magic_name__      ,   scheduler=__magic_name__	)

						lowercase   :       Optional[int]								=torch.manual_seed(33	)
						lowercase   :       int								=pipe(num_inference_steps=__magic_name__      ,   generator=__magic_name__	).audios

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

						lowercase   :       List[str]								=(generated - audio).abs().sum()
						lowercase   :       Optional[Any]								=(generated - audio).abs().max()

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

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

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

						print(f'''Conversion for {model_name} successful!'''	)


if __name__ == "__main__":
							UpperCamelCase_							=					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.""")
							UpperCamelCase_							=					parser.parse_args()

							main(args)








'''simple docstring'''





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
							from ... import PreTrainedTokenizerBase, TensorType

UpperCamelCase_							=					logging.get_logger(__name__)





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						'vision-encoder-decoder'
				lowerCamelCase_							=						True







				def __init__(					self    :		Optional[int]		,				**UpperCAmelCase__    :		Tuple      ):
										'''simple docstring'''
										super().__init__(**UpperCAmelCase__      )
										if "encoder" not in kwargs or "decoder" not in kwargs:
																raise ValueError(
																    F'''A configuraton of type {self.model_type} cannot be instantiated because '''
																    F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''      )

										lowercase   :       Optional[Any]								=kwargs.pop('''encoder'''      )
										lowercase   :       List[Any]								=encoder_config.pop('''model_type'''      )
										lowercase   :       List[str]								=kwargs.pop('''decoder'''      )
										lowercase   :       Dict								=decoder_config.pop('''model_type'''      )

										lowercase   :       Union[str, Any]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       List[str]								=AutoConfig.for_model(UpperCAmelCase__		,				**UpperCAmelCase__      )
										lowercase   :       str								=True







				@classmethod
				def    lowerCamelCase_  (					cls    :		List[str]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				**UpperCAmelCase__    :		Optional[int]      ):
										'''simple docstring'''
										logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''      )
										lowercase   :       int								=True
										lowercase   :       Optional[Any]								=True

										return cls(encoder=encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       int								=copy.deepcopy(self.__dict__      )
										lowercase   :       Union[str, Any]								=self.encoder.to_dict()
										lowercase   :       Union[str, Any]								=self.decoder.to_dict()
										lowercase   :       int								=self.__class__.model_type
										return output





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):
				lowerCamelCase_							=						version.parse('1.11'			)







				@property
				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										    ]      )







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return 1E-4







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}      )





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										lowercase   :       List[str]								=OrderedDict()
										lowercase   :       Tuple								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       Optional[int]								={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
										lowercase   :       int								={0: '''batch''', 1: '''encoder_sequence'''}

										return common_inputs







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		"PreTrainedTokenizerBase"		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		int = -1		,				UpperCAmelCase__    :		bool = False		,				UpperCAmelCase__    :		Optional["TensorType"] = None		,				):
										'''simple docstring'''
										import torch

										lowercase   :       Optional[Any]								=OrderedDict()

										lowercase   :       List[Any]								=super().generate_dummy_inputs(
										    UpperCAmelCase__		,				batch_size=UpperCAmelCase__		,				seq_length=UpperCAmelCase__		,				is_pair=UpperCAmelCase__		,				framework=UpperCAmelCase__      )

										lowercase		,  lowercase   :       Optional[int]								=dummy_input['''input_ids'''].shape
										lowercase   :       Union[str, Any]								=(batch, encoder_sequence, self._config.encoder_hidden_size)
										lowercase   :       List[str]								=dummy_input.pop('''input_ids'''      )
										lowercase   :       Tuple								=dummy_input.pop('''attention_mask'''      )
										lowercase   :       Union[str, Any]								=torch.zeros(UpperCAmelCase__      )

										return common_inputs





class 		__SCREAMING_SNAKE_CASE       ( lowercase__			):







				@property
				def    lowerCamelCase_  (					self    :		int      ):
										'''simple docstring'''
										pass







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		PretrainedConfig      ):
										'''simple docstring'''
										return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Optional[int]		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		PretrainedConfig		,				UpperCAmelCase__    :		str = "default"      ):
										'''simple docstring'''
										lowercase   :       List[Any]								=encoder_config.hidden_size
										return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase__		,				UpperCAmelCase__      )





