


"""simple docstring"""



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 _lowerCAmelCase				(      snake_case_	):
   __UpperCAmelCase :       torch.FloatTensor







class _lowerCAmelCase				(      snake_case_    ,							snake_case_	):







   @register_to_config
   def __init__(	self						,	UpperCamelCase__ = 6_5536						,	UpperCamelCase__ = None						,	UpperCamelCase__ = 2						,	UpperCamelCase__ = 2						,	UpperCamelCase__ = 0						,	UpperCamelCase__ = "fourier"						,	UpperCamelCase__ = True						,	UpperCamelCase__ = False						,	UpperCamelCase__ = 0.0						,	UpperCamelCase__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")						,	UpperCamelCase__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")						,	UpperCamelCase__ = "UNetMidBlock1D"						,	UpperCamelCase__ = None						,	UpperCamelCase__ = (32, 32, 64)						,	UpperCamelCase__ = None						,	UpperCamelCase__ = 8						,	UpperCamelCase__ = 1						,	UpperCamelCase__ = False						,	)	->		int:

       '''simple docstring'''




       super().__init__()
       snake_case    :      List[Any]      					=    sample_size

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

       if use_timestep_embedding:
           snake_case    :      List[str]      					=    block_out_channels[0] * 4
           snake_case    :      List[str]      					=    TimestepEmbedding(
               in_channels=UpperCamelCase__						,	time_embed_dim=UpperCamelCase__						,	act_fn=UpperCamelCase__						,	out_dim=block_out_channels[0]						,	)

       snake_case    :      Optional[int]      					=    nn.ModuleList([]						)
       snake_case    :      Dict      					=    None
       snake_case    :      Tuple      					=    nn.ModuleList([]						)
       snake_case    :      int      					=    None

       # down
       snake_case    :      Any      					=    in_channels
       for i, down_block_type in enumerate(UpperCamelCase__						):
           snake_case    :      Dict      					=    output_channel
           snake_case    :      int      					=    block_out_channels[i]

           if i == 0:
               input_channel += extra_in_channels

           snake_case    :      Any      					=    i == len(UpperCamelCase__						) - 1

           snake_case    :      Tuple      					=    get_down_block(
               UpperCamelCase__						,	num_layers=UpperCamelCase__						,	in_channels=UpperCamelCase__						,	out_channels=UpperCamelCase__						,	temb_channels=block_out_channels[0]						,	add_downsample=not is_final_block or downsample_each_block						,	)
           self.down_blocks.append(UpperCamelCase__						)

       # mid
       snake_case    :      str      					=    get_mid_block(
           UpperCamelCase__						,	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=UpperCamelCase__						,	add_downsample=UpperCamelCase__						,	)

       # up
       snake_case    :      Optional[int]      					=    list(reversed(UpperCamelCase__						)						)
       snake_case    :      int      					=    reversed_block_out_channels[0]
       if out_block_type is None:
           snake_case    :      List[Any]      					=    out_channels
       else:
           snake_case    :      int      					=    block_out_channels[0]

       for i, up_block_type in enumerate(UpperCamelCase__						):
           snake_case    :      Optional[int]      					=    output_channel
           snake_case    :      List[Any]      					=    (
               reversed_block_out_channels[i + 1] if i < len(UpperCamelCase__						) - 1 else final_upsample_channels
           )

           snake_case    :      List[str]      					=    i == len(UpperCamelCase__						) - 1

           snake_case    :      Union[str, Any]      					=    get_up_block(
               UpperCamelCase__						,	num_layers=UpperCamelCase__						,	in_channels=UpperCamelCase__						,	out_channels=UpperCamelCase__						,	temb_channels=block_out_channels[0]						,	add_upsample=not is_final_block						,	)
           self.up_blocks.append(UpperCamelCase__						)
           snake_case    :      Any      					=    output_channel

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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ = True						,	)	->		Union[UNetaDOutput, Tuple]:

       '''simple docstring'''




       snake_case    :      Tuple      					=    timestep
       if not torch.is_tensor(UpperCamelCase__						):
           snake_case    :      Tuple      					=    torch.tensor([timesteps]						,	dtype=torch.long						,	device=sample.device						)
       elif torch.is_tensor(UpperCamelCase__						) and len(timesteps.shape						) == 0:
           snake_case    :      List[str]      					=    timesteps[None].to(sample.device						)

       snake_case    :      str      					=    self.time_proj(UpperCamelCase__						)
       if self.config.use_timestep_embedding:
           snake_case    :      Tuple      					=    self.time_mlp(UpperCamelCase__						)
       else:
           snake_case    :      int      					=    timestep_embed[..., None]
           snake_case    :      Optional[Any]      					=    timestep_embed.repeat([1, 1, sample.shape[2]]						).to(sample.dtype						)
           snake_case    :      Optional[int]      					=    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					,snake_case    :      Dict      					=    downsample_block(hidden_states=UpperCamelCase__						,	temb=UpperCamelCase__						)
           down_block_res_samples += res_samples

       # 3. mid
       if self.mid_block:
           snake_case    :      int      					=    self.mid_block(UpperCamelCase__						,	UpperCamelCase__						)

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

       # 5. post-process
       if self.out_block:
           snake_case    :      Any      					=    self.out_block(UpperCamelCase__						,	UpperCamelCase__						)

       if not return_dict:
           return (sample,)

       return UNetaDOutput(sample=UpperCamelCase__						)









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Union[str, Any]						,							lowercase  :  List[str]						,							lowercase  :  Tuple						,							lowercase  :  List[Any]						,							lowercase  :  int )	->						List[Any]:


    """simple docstring"""





    if index == r:
        for j in range(lowercase ):
            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(lowercase						,							lowercase						,							lowercase						,							index + 1						,							lowercase						,							i + 1 )
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							i + 1 )
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()







def   __lowerCAmelCase						(						lowercase  :  Any						,							lowercase  :  Union[str, Any]						,							lowercase  :  Optional[int] )	->						List[Any]:


    """simple docstring"""





    snake_case    :      Any      					=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(lowercase						,							lowercase						,							lowercase						,							0						,							lowercase						,							0 )


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





