



"""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__              =							{
    '''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 			snake_case_    ( A_   :						Dict,							A_   :						List[str]		):


       '''simple docstring'''





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






def 			snake_case_    ( A_   :						Optional[int]		):


       '''simple docstring'''





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






class 			__snake_case			(      _a):
  pass






class 			__snake_case			(      nn.Module):




  def __init__(					self	:		Dict		,   __lowerCAmelCase	:		Optional[Any]						):






         """simple docstring"""






         super().__init__()

         _lowerCamelCase		:							Tuple            =					DiffusionAttnUnetaD(__lowerCAmelCase		,   n_attn_layers=4						)
         _lowerCamelCase		:							List[str]            =					deepcopy(self.diffusion						)
         _lowerCamelCase		:							int            =					torch.quasirandom.SobolEngine(1		,   scramble=__lowerCAmelCase						)






def 			snake_case_    ( A_   :						Any		):


       '''simple docstring'''





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

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


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

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

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






def 			snake_case_    ( A_   :						Dict		):


       '''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 			snake_case_    ( A_   :						Optional[int]		):


       '''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 			snake_case_    ( A_   :						List[str],							A_   :						List[str]=13		):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					input_string

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

       _lowerCamelCase		:							str            =					0
       if string.startswith('''net.3.'''		):
              depth += 1
              _lowerCamelCase		:							Union[str, Any]            =					string[6:]
       elif string.startswith('''net.'''		):
              _lowerCamelCase		:							List[Any]            =					string[4:]

       while string.startswith('''main.7.'''		):
              depth += 1
              _lowerCamelCase		:							Union[str, Any]            =					string[7:]

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

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

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

       _lowerCamelCase		:							Any            =					string_left[1:]

       if "resnets" in new_layer:
              _lowerCamelCase		:							List[str]            =					convert_resconv_naming(a__		)
       elif "attentions" in new_layer:
              _lowerCamelCase		:							Dict            =					convert_attn_naming(a__		)
              _lowerCamelCase		:							Optional[int]            =					new_string_left

       if not isinstance(a__,							a__		):
              _lowerCamelCase		:							Dict            =					prefix + '''.''' + new_layer + '''.''' + string_left
       else:
              _lowerCamelCase		:							Optional[int]            =					[prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
       return new_string






def 			snake_case_    ( A_   :						Tuple		):


       '''simple docstring'''





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

              _lowerCamelCase		:							Tuple            =					rename(a__		)

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

       return new_state_dict






def 			snake_case_    ( A_   :						Optional[int],							A_   :						Optional[Any],							A_   :						Tuple		):


       '''simple docstring'''





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






def 			snake_case_    ( A_   :						Union[str, Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							Tuple            =					torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)

       _lowerCamelCase		:							List[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()}'''
              _lowerCamelCase		:							List[Any]            =					download(a__		)

       _lowerCamelCase		:							List[str]            =					MODELS_MAP[model_name]['''sample_rate''']
       _lowerCamelCase		:							List[Any]            =					MODELS_MAP[model_name]['''sample_size''']

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

       _lowerCamelCase		:							int            =					UNetaDModel(sample_size=a__,							sample_rate=a__		)
       _lowerCamelCase		:							Optional[int]            =					diffusers_model.state_dict()

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

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

              _lowerCamelCase		:							int            =					value

       diffusers_model.load_state_dict(a__		)

       _lowerCamelCase		:							Union[str, Any]            =					1_00
       _lowerCamelCase		:							int            =					33

       _lowerCamelCase		:							str            =					IPNDMScheduler(num_train_timesteps=a__		)

       _lowerCamelCase		:							Optional[Any]            =					torch.manual_seed(a__		)
       _lowerCamelCase		:							Dict            =					torch.randn([1, 2, config.sample_size],							generator=a__		).to(a__		)

       _lowerCamelCase		:							Optional[int]            =					torch.linspace(1,							0,							steps + 1,							device=a__		)[:-1]
       _lowerCamelCase		:							Optional[Any]            =					get_crash_schedule(a__		)

       _lowerCamelCase		:							Optional[Any]            =					DanceDiffusionPipeline(unet=a__,							scheduler=a__		)

       _lowerCamelCase		:							Optional[int]            =					torch.manual_seed(33		)
       _lowerCamelCase		:							Any            =					pipe(num_inference_steps=a__,							generator=a__		).audios

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

       _lowerCamelCase		:							Optional[int]            =					(generated - audio).abs().sum()
       _lowerCamelCase		:							Any            =					(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__              =							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__              =							parser.parse_args()

   main(args)









import copy

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


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""encoder-decoder"""
							snake_case  :   Optional[int]      =							True






							def __init__(    self	,			**__lowerCAmelCase	):
														super().__init__(**__lowerCAmelCase	)
														assert (
														    "encoder" in kwargs and "decoder" in kwargs
														), "Config has to be initialized with encoder and decoder config"
														UpperCamelCase__					  =      kwargs.pop("""encoder"""	)
														UpperCamelCase__					  =      encoder_config.pop("""model_type"""	)
														UpperCamelCase__					  =      kwargs.pop("""decoder"""	)
														UpperCamelCase__					  =      decoder_config.pop("""model_type"""	)

														from ..auto.configuration_auto import AutoConfig

														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      True






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""	)
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      True

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







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      copy.deepcopy(self.__dict__	)
														UpperCamelCase__					  =      self.encoder.to_dict()
														UpperCamelCase__					  =      self.decoder.to_dict()
														UpperCamelCase__					  =      self.__class__.model_type
														return output




