




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

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder







@dataclass
class 						lowercase							(   UpperCamelCase__		):
     _a							 =						42







class 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						True

     @register_to_config
     def __init__(  self		,     _a = 3		,     _a = 3		,     _a = ("DownEncoderBlock2D",)		,     _a = ("UpDecoderBlock2D",)		,     _a = (64,)		,     _a = 1		,     _a = "silu"		,     _a = 4		,     _a = 32		,     _a = 32		,     _a = 0.18215		,     )						->							Any:
           super().__init__()

           # pass init params to Encoder
           _A							:     Tuple			=						Encoder(
               in_channels=_a		,     out_channels=_a		,     down_block_types=_a		,     block_out_channels=_a		,     layers_per_block=_a		,     act_fn=_a		,     norm_num_groups=_a		,     double_z=_a		,     )

           # pass init params to Decoder
           _A							:     Union[str, Any]			=						Decoder(
               in_channels=_a		,     out_channels=_a		,     up_block_types=_a		,     block_out_channels=_a		,     layers_per_block=_a		,     norm_num_groups=_a		,     act_fn=_a		,     )

           _A							:     Any			=						nn.Convad(2 * latent_channels		,     2 * latent_channels		,     1				)
           _A							:     List[str]			=						nn.Convad(_a		,     _a		,     1				)

           _A							:     List[Any]			=						False
           _A							:     Any			=						False

           # only relevant if vae tiling is enabled
           _A							:     Any			=						self.config.sample_size
           _A							:     Union[str, Any]			=						(
               self.config.sample_size[0]
               if isinstance(self.config.sample_size		,     (list, tuple)				)
               else self.config.sample_size
           )
           _A							:     List[str]			=						int(sample_size / (2 ** (len(self.config.block_out_channels				) - 1))				)
           _A							:     int			=						0.25

     def 					a__	(  self		,     _a		,     _a=False				)						->							str:
           if isinstance(_a		,     (Encoder, Decoder)				):
                 _A							:     Dict			=						value

     def 					a__	(  self		,     _a = True				)						->							int:
           _A							:     int			=						use_tiling

     def 					a__	(  self				)						->							Dict:
           self.enable_tiling(_a				)

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     Tuple			=						True

     def 					a__	(  self				)						->							List[str]:
           _A							:     List[Any]			=						False

     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def 					a__	(  self				)						->							Dict[str, AttentionProcessor]:
           _A							:     Dict			=						{}

           def fn_recursive_add_processors(_a		,     _a		,     _a				):
                 if hasattr(_a		,     """set_processor"""				):
                       _A							:     Optional[int]			=						module.processor

                 for sub_name, child in module.named_children():
                       fn_recursive_add_processors(F'''{name}.{sub_name}'''		,     _a		,     _a				)

                 return processors

           for name, module in self.named_children():
                 fn_recursive_add_processors(_a		,     _a		,     _a				)

           return processors

     def 					a__	(  self		,     _a				)						->							str:
           _A							:     int			=						len(self.attn_processors.keys()				)

           if isinstance(_a		,     _a				) and len(_a				) != count:
                 raise ValueError(
                     F'''A dict of processors was passed, but the number of processors {len(_a				)} does not match the'''
                     F''' number of attention layers: {count}. Please make sure to pass {count} processor classes.'''				)

           def fn_recursive_attn_processor(_a		,     _a		,     _a				):
                 if hasattr(_a		,     """set_processor"""				):
                       if not isinstance(_a		,     _a				):
                             module.set_processor(_a				)
                       else:
                             module.set_processor(processor.pop(F'''{name}.processor'''				)				)

                 for sub_name, child in module.named_children():
                       fn_recursive_attn_processor(F'''{name}.{sub_name}'''		,     _a		,     _a				)

           for name, module in self.named_children():
                 fn_recursive_attn_processor(_a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Dict:
           self.set_attn_processor(AttnProcessor()				)

     @apply_forward_hook
     def 					a__	(  self		,     _a		,     _a = True				)						->							AutoencoderKLOutput:
           if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
                 return self.tiled_encode(_a		,     return_dict=_a				)

           if self.use_slicing and x.shape[0] > 1:
                 _A							:     List[Any]			=						[self.encoder(_a				) for x_slice in x.split(1				)]
                 _A							:     Dict			=						torch.cat(_a				)
           else:
                 _A							:     Any			=						self.encoder(_a				)

           _A							:     Tuple			=						self.quant_conv(_a				)
           _A							:     Any			=						DiagonalGaussianDistribution(_a				)

           if not return_dict:
                 return (posterior,)

           return AutoencoderKLOutput(latent_dist=_a				)

     def 					a__	(  self		,     _a		,     _a = True				)						->							Union[DecoderOutput, torch.FloatTensor]:
           if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
                 return self.tiled_decode(_a		,     return_dict=_a				)

           _A							:     Union[str, Any]			=						self.post_quant_conv(_a				)
           _A							:     int			=						self.decoder(_a				)

           if not return_dict:
                 return (dec,)

           return DecoderOutput(sample=_a				)

     @apply_forward_hook
     def 					a__	(  self		,     _a		,     _a = True				)						->							Union[DecoderOutput, torch.FloatTensor]:
           if self.use_slicing and z.shape[0] > 1:
                 _A							:     Dict			=						[self._decode(_a				).sample for z_slice in z.split(1				)]
                 _A							:     str			=						torch.cat(_a				)
           else:
                 _A							:     Optional[int]			=						self._decode(_a				).sample

           if not return_dict:
                 return (decoded,)

           return DecoderOutput(sample=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							Any:
           _A							:     int			=						min(a.shape[2]		,     b.shape[2]		,     _a				)
           for y in range(_a				):
                 _A							:     List[Any]			=						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
           return b

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							Tuple:
           _A							:     Union[str, Any]			=						min(a.shape[3]		,     b.shape[3]		,     _a				)
           for x in range(_a				):
                 _A							:     int			=						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
           return b

     def 					a__	(  self		,     _a		,     _a = True				)						->							AutoencoderKLOutput:
           _A							:     Tuple			=						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)				)
           _A							:     List[Any]			=						int(self.tile_latent_min_size * self.tile_overlap_factor				)
           _A							:     Union[str, Any]			=						self.tile_latent_min_size - blend_extent

           # Split the image into 512x512 tiles and encode them separately.
           _A							:     str			=						[]
           for i in range(0		,     x.shape[2]		,     _a				):
                 _A							:     Dict			=						[]
                 for j in range(0		,     x.shape[3]		,     _a				):
                       _A							:     Optional[int]			=						x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
                       _A							:     Any			=						self.encoder(_a				)
                       _A							:     List[str]			=						self.quant_conv(_a				)
                       row.append(_a				)
                 rows.append(_a				)
           _A							:     List[str]			=						[]
           for i, row in enumerate(_a				):
                 _A							:     Any			=						[]
                 for j, tile in enumerate(_a				):
                       # blend the above tile and the left tile
                       # to the current tile and add the current tile to the result row
                       if i > 0:
                             _A							:     str			=						self.blend_v(rows[i - 1][j]		,     _a		,     _a				)
                       if j > 0:
                             _A							:     Tuple			=						self.blend_h(row[j - 1]		,     _a		,     _a				)
                       result_row.append(tile[:, :, :row_limit, :row_limit]				)
                 result_rows.append(torch.cat(_a		,     dim=3				)				)

           _A							:     Tuple			=						torch.cat(_a		,     dim=2				)
           _A							:     Union[str, Any]			=						DiagonalGaussianDistribution(_a				)

           if not return_dict:
                 return (posterior,)

           return AutoencoderKLOutput(latent_dist=_a				)

     def 					a__	(  self		,     _a		,     _a = True				)						->							Union[DecoderOutput, torch.FloatTensor]:
           _A							:     List[str]			=						int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)				)
           _A							:     List[str]			=						int(self.tile_sample_min_size * self.tile_overlap_factor				)
           _A							:     Dict			=						self.tile_sample_min_size - blend_extent

           # Split z into overlapping 64x64 tiles and decode them separately.
           # The tiles have an overlap to avoid seams between tiles.
           _A							:     Optional[Any]			=						[]
           for i in range(0		,     z.shape[2]		,     _a				):
                 _A							:     List[str]			=						[]
                 for j in range(0		,     z.shape[3]		,     _a				):
                       _A							:     Any			=						z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
                       _A							:     List[str]			=						self.post_quant_conv(_a				)
                       _A							:     List[Any]			=						self.decoder(_a				)
                       row.append(_a				)
                 rows.append(_a				)
           _A							:     Dict			=						[]
           for i, row in enumerate(_a				):
                 _A							:     str			=						[]
                 for j, tile in enumerate(_a				):
                       # blend the above tile and the left tile
                       # to the current tile and add the current tile to the result row
                       if i > 0:
                             _A							:     Optional[Any]			=						self.blend_v(rows[i - 1][j]		,     _a		,     _a				)
                       if j > 0:
                             _A							:     Tuple			=						self.blend_h(row[j - 1]		,     _a		,     _a				)
                       result_row.append(tile[:, :, :row_limit, :row_limit]				)
                 result_rows.append(torch.cat(_a		,     dim=3				)				)

           _A							:     Optional[Any]			=						torch.cat(_a		,     dim=2				)
           if not return_dict:
                 return (dec,)

           return DecoderOutput(sample=_a				)



     def 					a__	(  self		,     _a		,     _a = False		,     _a = True		,     _a = None		,     )						->							Union[DecoderOutput, torch.FloatTensor]:
           _A							:     Tuple			=						sample
           _A							:     Union[str, Any]			=						self.encode(_a				).latent_dist
           if sample_posterior:
                 _A							:     str			=						posterior.sample(generator=_a				)
           else:
                 _A							:     Any			=						posterior.mode()
           _A							:     str			=						self.decode(_a				).sample

           if not return_dict:
                 return (dec,)

           return DecoderOutput(sample=_a				)







from __future__ import annotations

import numpy as np







def 				lowerCAmelCase_	(     snake_case_     ):
      return np.maximum(0,snake_case_     )


if __name__ == "__main__":
  print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]

