





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''': 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_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


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



class  __lowerCAmelCase	(      _a					):
			pass




class  __lowerCAmelCase	(      nn.Module					):






			def __init__(self ,							__magic_name__   )					->       List[str]:


							'''simple docstring'''



							super().__init__()

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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   Dict           =  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 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""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 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase=13			)		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  input_string

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

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

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

				if string.startswith('''main.'''			):
								snake_case_						:   Union[str, Any]           =  string[5:]

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

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

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

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

				if "resnets" in new_layer:
								snake_case_						:   Optional[Any]           =  convert_resconv_naming(_UpperCamelCase			)
				elif "attentions" in new_layer:
								snake_case_						:   List[str]           =  convert_attn_naming(_UpperCamelCase			)
								snake_case_						:   List[Any]           =  new_string_left

				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								snake_case_						:   List[str]           =  prefix + '''.''' + new_layer + '''.''' + string_left
				else:
								snake_case_						:   str           =  [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
				return new_string






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


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

								snake_case_						:   Any           =  rename(_UpperCamelCase			)

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

				return new_state_dict






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


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






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


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

				snake_case_						:   Tuple           =  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()}'''
								snake_case_						:   Union[str, Any]           =  download(_UpperCamelCase			)

				snake_case_						:   Dict           =  MODELS_MAP[model_name]['''sample_rate''']
				snake_case_						:   Union[str, Any]           =  MODELS_MAP[model_name]['''sample_size''']

				snake_case_						:   Optional[Any]           =  Object()
				snake_case_						:   List[str]           =  sample_size
				snake_case_						:   Any           =  sample_rate
				snake_case_						:   str           =  0

				snake_case_						:   Any           =  UNetaDModel(sample_size=_UpperCamelCase			,    sample_rate=_UpperCamelCase			)
				snake_case_						:   Any           =  diffusers_model.state_dict()

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

				snake_case_						:   Dict           =  set(renamed_state_dict.keys()			) - set(diffusers_state_dict.keys()			)
				snake_case_						:   Any           =  set(diffusers_state_dict.keys()			) - set(renamed_state_dict.keys()			)

				assert len(_UpperCamelCase			) == 0, f'''Problem with {renamed_minus_diffusers}'''
				assert all(k.endswith('''kernel'''			) for k in list(_UpperCamelCase			)			), 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":
												snake_case_						:   Optional[Any]           =  value.squeeze()

								snake_case_						:   Optional[Any]           =  value

				diffusers_model.load_state_dict(_UpperCamelCase			)

				snake_case_						:   Union[str, Any]           =  100
				snake_case_						:   Optional[int]           =  33

				snake_case_						:   Union[str, Any]           =  IPNDMScheduler(num_train_timesteps=_UpperCamelCase			)

				snake_case_						:   Optional[int]           =  torch.manual_seed(_UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  torch.randn([1, 2, config.sample_size]			,    generator=_UpperCamelCase			).to(_UpperCamelCase			)

				snake_case_						:   Optional[Any]           =  torch.linspace(1			,    0			,    steps + 1			,    device=_UpperCamelCase			)[:-1]
				snake_case_						:   Dict           =  get_crash_schedule(_UpperCamelCase			)

				snake_case_						:   Dict           =  DanceDiffusionPipeline(unet=_UpperCamelCase			,    scheduler=_UpperCamelCase			)

				snake_case_						:   Dict           =  torch.manual_seed(33			)
				snake_case_						:   str           =  pipe(num_inference_steps=_UpperCamelCase			,    generator=_UpperCamelCase			).audios

				snake_case_						:   Optional[Any]           =  sampling.iplms_sample(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    {}			)
				snake_case_						:   Tuple           =  generated.clamp(-1			,    1			)

				snake_case_						:   Optional[Any]           =  (generated - audio).abs().sum()
				snake_case_						:   Dict           =  (generated - audio).abs().max()

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

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

				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 flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD



class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   Tuple           =  []
							snake_case_						:   List[str]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   Union[str, Any]           =  resnets
							snake_case_						:   Union[str, Any]           =  attentions

							if self.add_downsample:
											snake_case_						:   List[str]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   str           =  ()

							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   List[str]           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   Union[str, Any]           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if i == 0 else self.out_channels

											snake_case_						:   Tuple           =  FlaxResnetBlockaD(
											    in_channels=__magic_name__ ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )
							snake_case_						:   Dict           =  resnets

							if self.add_downsample:
											snake_case_						:   Optional[int]           =  FlaxDownsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  ()

							for resnet in self.resnets:
											snake_case_						:   List[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											output_states += (hidden_states,)

							if self.add_downsample:
											snake_case_						:   str           =  self.downsamplers_a(__magic_name__   )
											output_states += (hidden_states,)

							return hidden_states, output_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  []
							snake_case_						:   Optional[Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   List[Any]           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[int]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

											snake_case_						:   List[str]           =  FlaxTransformeraDModel(
											    in_channels=self.out_channels ,							n_heads=self.num_attention_heads ,							d_head=self.out_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							only_cross_attention=self.only_cross_attention ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

							snake_case_						:   List[Any]           =  resnets
							snake_case_						:   Tuple           =  attentions

							if self.add_upsample:
											snake_case_						:   List[Any]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Union[str, Any]:


							'''simple docstring'''



							for resnet, attn in zip(self.resnets ,							self.attentions   ):
											# pop res hidden states
											snake_case_						:   Dict           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Optional[int]           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Tuple           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[Any]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				True
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  []

							for i in range(self.num_layers   ):
											snake_case_						:   Tuple           =  self.in_channels if (i == self.num_layers - 1) else self.out_channels
											snake_case_						:   Optional[Any]           =  self.prev_output_channel if i == 0 else self.out_channels

											snake_case_						:   int           =  FlaxResnetBlockaD(
											    in_channels=resnet_in_channels + res_skip_channels ,							out_channels=self.out_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Tuple           =  resnets

							if self.add_upsample:
											snake_case_						:   List[str]           =  FlaxUpsampleaD(self.out_channels ,							dtype=self.dtype   )






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       List[Any]:


							'''simple docstring'''



							for resnet in self.resnets:
											# pop res hidden states
											snake_case_						:   Tuple           =  res_hidden_states_tuple[-1]
											snake_case_						:   List[Any]           =  res_hidden_states_tuple[:-1]
											snake_case_						:   Dict           =  jnp.concatenate((hidden_states, res_hidden_states) ,							axis=-1   )

											snake_case_						:   Optional[Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							if self.add_upsample:
											snake_case_						:   Optional[int]           =  self.upsamplers_a(__magic_name__   )

							return hidden_states




class  __lowerCAmelCase	(      nn.Module					):
			lowerCamelCase_					:					int
			lowerCamelCase_					:					float	      =				0.0
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					int	      =				1
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					bool	      =				False
			lowerCamelCase_					:					jnp.dtype	      =				jnp.floataa






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Dict           =  [
							    FlaxResnetBlockaD(
							        in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
							]

							snake_case_						:   int           =  []

							for _ in range(self.num_layers   ):
											snake_case_						:   str           =  FlaxTransformeraDModel(
											    in_channels=self.in_channels ,							n_heads=self.num_attention_heads ,							d_head=self.in_channels // self.num_attention_heads ,							depth=1 ,							use_linear_projection=self.use_linear_projection ,							use_memory_efficient_attention=self.use_memory_efficient_attention ,							dtype=self.dtype ,							)
											attentions.append(__magic_name__   )

											snake_case_						:   Dict           =  FlaxResnetBlockaD(
											    in_channels=self.in_channels ,							out_channels=self.in_channels ,							dropout_prob=self.dropout ,							dtype=self.dtype ,							)
											resnets.append(__magic_name__   )

							snake_case_						:   Optional[Any]           =  resnets
							snake_case_						:   Optional[int]           =  attentions






			def __call__(self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=True   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.resnets[0](__magic_name__ ,							__magic_name__   )
							for attn, resnet in zip(self.attentions ,							self.resnets[1:]   ):
											snake_case_						:   Tuple           =  attn(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )
											snake_case_						:   Union[str, Any]           =  resnet(__magic_name__ ,							__magic_name__ ,							deterministic=__magic_name__   )

							return hidden_states
