


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							torch.FloatTensor

class     a		(_lowerCAmelCase					,    _lowerCAmelCase				):




							"""simple docstring"""


							@register_to_config
							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  int = 65536   ,     lowerCamelCase	:  Optional[int] = None   ,     lowerCamelCase	:  int = 2   ,     lowerCamelCase	:  int = 2   ,     lowerCamelCase	:  int = 0   ,     lowerCamelCase	:  str = "fourier"   ,     lowerCamelCase	:  bool = True   ,     lowerCamelCase	:  bool = False   ,     lowerCamelCase	:  float = 0.0   ,     lowerCamelCase	:  Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")   ,     lowerCamelCase	:  Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")   ,     lowerCamelCase	:  Tuple[str] = "UNetMidBlock1D"   ,     lowerCamelCase	:  str = None   ,     lowerCamelCase	:  Tuple[int] = (32, 32, 64)   ,     lowerCamelCase	:  str = None   ,     lowerCamelCase	:  int = 8   ,     lowerCamelCase	:  int = 1   ,     lowerCamelCase	:  bool = False   ,     )     ->   str:
														super().__init__()
														__snake_case		:      int										=       sample_size

														# time
														if time_embedding_type == "fourier":
																					__snake_case		:      str										=       GaussianFourierProjection(
																					    embedding_size=8   ,     set_W_to_weight=lowerCamelCase   ,     log=lowerCamelCase   ,     flip_sin_to_cos=lowerCamelCase    )
																					__snake_case		:      List[str]										=       2 * block_out_channels[0]
														elif time_embedding_type == "positional":
																					__snake_case		:      str										=       Timesteps(
																					    block_out_channels[0]   ,     flip_sin_to_cos=lowerCamelCase   ,     downscale_freq_shift=lowerCamelCase    )
																					__snake_case		:      Union[str, Any]										=       block_out_channels[0]

														if use_timestep_embedding:
																					__snake_case		:      Optional[int]										=       block_out_channels[0] * 4
																					__snake_case		:      Union[str, Any]										=       TimestepEmbedding(
																					    in_channels=lowerCamelCase   ,     time_embed_dim=lowerCamelCase   ,     act_fn=lowerCamelCase   ,     out_dim=block_out_channels[0]   ,     )

														__snake_case		:      List[Any]										=       nn.ModuleList([]    )
														__snake_case		:      Tuple										=       None
														__snake_case		:      Optional[Any]										=       nn.ModuleList([]    )
														__snake_case		:      Union[str, Any]										=       None

														# down
														__snake_case		:      List[Any]										=       in_channels
														for i, down_block_type in enumerate(lowerCamelCase    ):
																					__snake_case		:      str										=       output_channel
																					__snake_case		:      Dict										=       block_out_channels[i]

																					if i == 0:
																												input_channel += extra_in_channels

																					__snake_case		:      Any										=       i == len(lowerCamelCase    ) - 1

																					__snake_case		:      List[str]										=       get_down_block(
																					    lowerCamelCase   ,     num_layers=lowerCamelCase   ,     in_channels=lowerCamelCase   ,     out_channels=lowerCamelCase   ,     temb_channels=block_out_channels[0]   ,     add_downsample=not is_final_block or downsample_each_block   ,     )
																					self.down_blocks.append(lowerCamelCase    )

														# mid
														__snake_case		:      str										=       get_mid_block(
														    lowerCamelCase   ,     in_channels=block_out_channels[-1]   ,     mid_channels=block_out_channels[-1]   ,     out_channels=block_out_channels[-1]   ,     embed_dim=block_out_channels[0]   ,     num_layers=lowerCamelCase   ,     add_downsample=lowerCamelCase   ,     )

														# up
														__snake_case		:      List[str]										=       list(reversed(lowerCamelCase    )    )
														__snake_case		:      str										=       reversed_block_out_channels[0]
														if out_block_type is None:
																					__snake_case		:      Tuple										=       out_channels
														else:
																					__snake_case		:      List[Any]										=       block_out_channels[0]

														for i, up_block_type in enumerate(lowerCamelCase    ):
																					__snake_case		:      Any										=       output_channel
																					__snake_case		:      Dict										=       (
																					    reversed_block_out_channels[i + 1] if i < len(lowerCamelCase    ) - 1 else final_upsample_channels
																					)

																					__snake_case		:      List[str]										=       i == len(lowerCamelCase    ) - 1

																					__snake_case		:      str										=       get_up_block(
																					    lowerCamelCase   ,     num_layers=lowerCamelCase   ,     in_channels=lowerCamelCase   ,     out_channels=lowerCamelCase   ,     temb_channels=block_out_channels[0]   ,     add_upsample=not is_final_block   ,     )
																					self.up_blocks.append(lowerCamelCase    )
																					__snake_case		:      List[str]										=       output_channel

														# out
														__snake_case		:      Any										=       norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4   ,     32    )
														__snake_case		:      Any										=       get_out_block(
														    out_block_type=lowerCamelCase   ,     num_groups_out=lowerCamelCase   ,     embed_dim=block_out_channels[0]   ,     out_channels=lowerCamelCase   ,     act_fn=lowerCamelCase   ,     fc_dim=block_out_channels[-1] // 4   ,     )






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  torch.FloatTensor   ,     lowerCamelCase	:  Union[torch.Tensor, float, int]   ,     lowerCamelCase	:  bool = True   ,     )     ->   Union[UNetaDOutput, Tuple]:
														__snake_case		:      List[Any]										=       timestep
														if not torch.is_tensor(lowerCamelCase    ):
																					__snake_case		:      Dict										=       torch.tensor([timesteps]   ,     dtype=torch.long   ,     device=sample.device    )
														elif torch.is_tensor(lowerCamelCase    ) and len(timesteps.shape    ) == 0:
																					__snake_case		:      List[str]										=       timesteps[None].to(sample.device    )

														__snake_case		:      List[Any]										=       self.time_proj(lowerCamelCase    )
														if self.config.use_timestep_embedding:
																					__snake_case		:      str										=       self.time_mlp(lowerCamelCase    )
														else:
																					__snake_case		:      Any										=       timestep_embed[..., None]
																					__snake_case		:      Optional[Any]										=       timestep_embed.repeat([1, 1, sample.shape[2]]    ).to(sample.dtype    )
																					__snake_case		:      List[Any]										=       timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])    )

														# 2. down
														__snake_case		:      List[str]										=       ()
														for downsample_block in self.down_blocks:
																					__snake_case		:      str										=       downsample_block(hidden_states=lowerCamelCase   ,     temb=lowerCamelCase    )
																					down_block_res_samples += res_samples

														# 3. mid
														if self.mid_block:
																					__snake_case		:      List[str]										=       self.mid_block(lowerCamelCase   ,     lowerCamelCase    )

														# 4. up
														for i, upsample_block in enumerate(self.up_blocks    ):
																					__snake_case		:      Optional[int]										=       down_block_res_samples[-1:]
																					__snake_case		:      Optional[int]										=       down_block_res_samples[:-1]
																					__snake_case		:      Any										=       upsample_block(lowerCamelCase   ,     res_hidden_states_tuple=lowerCamelCase   ,     temb=lowerCamelCase    )

														# 5. post-process
														if self.out_block:
																					__snake_case		:      int										=       self.out_block(lowerCamelCase   ,     lowerCamelCase    )

														if not return_dict:
																					return (sample,)

														return UNetaDOutput(sample=lowerCamelCase    )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if index == r:
														for j in range(__lowerCamelCase     ):
																					print(data[j]							,     end=" "     )
														print(" "     )
														return
							#  When no more elements are there to put in data[]
							if i >= n:
														return
							# current is included, put next at next location
							__snake_case		:      Union[str, Any]										=       arr[i]
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     index + 1							,     __lowerCamelCase							,     i + 1     )
							# current is excluded, replace it with
							# next (Note that i+1 is passed, but
							# index is not changed)
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     i + 1     )
							# The main function that prints all combinations
							# of size r in arr[] of size n. This function
							# mainly uses combinationUtil()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# A temporary array to store all combination one by one
							__snake_case		:      Union[str, Any]										=       [0] * r
							# Print all combination using temporary array 'data[]'
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     0							,     __lowerCamelCase							,     0     )


if __name__ == "__main__":
			# Driver code to check the function above
			_snake_case					:		List[str]      =    [10, 20, 30, 40, 50]
			print_combination(arr, len(arr), 3)
			# This code is contributed by Ambuj sahu

