





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							(						__lowercase    ):
      _snake_case				  =42

class   A							(						__lowercase		,   __lowercase    ):



      @register_to_config
      def __init__(						self:    Optional[Any]		,	_lowerCAmelCase:    int = 6_5536		,	_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		,	)    ->     int:
         '''simple docstring'''

         super().__init__()
         UpperCAmelCase_           =sample_size

         # time
         if time_embedding_type == "fourier":
            UpperCAmelCase_           =GaussianFourierProjection(
                embedding_size=8		,	set_W_to_weight=_lowerCAmelCase		,	log=_lowerCAmelCase		,	flip_sin_to_cos=_lowerCAmelCase							)
            UpperCAmelCase_           =2 * block_out_channels[0]
         elif time_embedding_type == "positional":
            UpperCAmelCase_           =Timesteps(
                block_out_channels[0]		,	flip_sin_to_cos=_lowerCAmelCase		,	downscale_freq_shift=_lowerCAmelCase							)
            UpperCAmelCase_           =block_out_channels[0]

         if use_timestep_embedding:
            UpperCAmelCase_           =block_out_channels[0] * 4
            UpperCAmelCase_           =TimestepEmbedding(
                in_channels=_lowerCAmelCase		,	time_embed_dim=_lowerCAmelCase		,	act_fn=_lowerCAmelCase		,	out_dim=block_out_channels[0]		,	)

         UpperCAmelCase_           =nn.ModuleList([]							)
         UpperCAmelCase_           =None
         UpperCAmelCase_           =nn.ModuleList([]							)
         UpperCAmelCase_           =None

         # down
         UpperCAmelCase_           =in_channels
         for i, down_block_type in enumerate(_lowerCAmelCase							):
            UpperCAmelCase_           =output_channel
            UpperCAmelCase_           =block_out_channels[i]

            if i == 0:
               input_channel += extra_in_channels

            UpperCAmelCase_           =i == len(_lowerCAmelCase							) - 1

            UpperCAmelCase_           =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
         UpperCAmelCase_           =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
         UpperCAmelCase_           =list(reversed(_lowerCAmelCase							)							)
         UpperCAmelCase_           =reversed_block_out_channels[0]
         if out_block_type is None:
            UpperCAmelCase_           =out_channels
         else:
            UpperCAmelCase_           =block_out_channels[0]

         for i, up_block_type in enumerate(_lowerCAmelCase							):
            UpperCAmelCase_           =output_channel
            UpperCAmelCase_           =(
                reversed_block_out_channels[i + 1] if i < len(_lowerCAmelCase							) - 1 else final_upsample_channels
            )

            UpperCAmelCase_           =i == len(_lowerCAmelCase							) - 1

            UpperCAmelCase_           =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							)
            UpperCAmelCase_           =output_channel

         # out
         UpperCAmelCase_           =norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4		,	32							)
         UpperCAmelCase_           =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 						lowerCAmelCase__     (						self:    Tuple		,	_lowerCAmelCase:    torch.FloatTensor		,	_lowerCAmelCase:    Union[torch.Tensor, float, int]		,	_lowerCAmelCase:    bool = True		,	)    ->     Union[UNetaDOutput, Tuple]:
         '''simple docstring'''

         UpperCAmelCase_           =timestep
         if not torch.is_tensor(_lowerCAmelCase							):
            UpperCAmelCase_           =torch.tensor([timesteps]		,	dtype=torch.long		,	device=sample.device							)
         elif torch.is_tensor(_lowerCAmelCase							) and len(timesteps.shape							) == 0:
            UpperCAmelCase_           =timesteps[None].to(sample.device							)

         UpperCAmelCase_           =self.time_proj(_lowerCAmelCase							)
         if self.config.use_timestep_embedding:
            UpperCAmelCase_           =self.time_mlp(_lowerCAmelCase							)
         else:
            UpperCAmelCase_           =timestep_embed[..., None]
            UpperCAmelCase_           =timestep_embed.repeat([1, 1, sample.shape[2]]							).to(sample.dtype							)
            UpperCAmelCase_           =timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])							)

         # 2. down
         UpperCAmelCase_           =()
         for downsample_block in self.down_blocks:
            UpperCAmelCase_    ,							UpperCAmelCase_           =downsample_block(hidden_states=_lowerCAmelCase		,	temb=_lowerCAmelCase							)
            down_block_res_samples += res_samples

         # 3. mid
         if self.mid_block:
            UpperCAmelCase_           =self.mid_block(_lowerCAmelCase		,	_lowerCAmelCase							)

         # 4. up
         for i, upsample_block in enumerate(self.up_blocks							):
            UpperCAmelCase_           =down_block_res_samples[-1:]
            UpperCAmelCase_           =down_block_res_samples[:-1]
            UpperCAmelCase_           =upsample_block(_lowerCAmelCase		,	res_hidden_states_tuple=_lowerCAmelCase		,	temb=_lowerCAmelCase							)

         # 5. post-process
         if self.out_block:
            UpperCAmelCase_           =self.out_block(_lowerCAmelCase		,	_lowerCAmelCase							)

         if not return_dict:
            return (sample,)

         return UNetaDOutput(sample=_lowerCAmelCase							)








"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       warnings.warn(
           "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use SegformerImageProcessor instead."		,      UpperCamelCase__		,      )
       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)





