


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       A    (   A_ ):
							UpperCamelCase_   :							"DiagonalGaussianDistribution"





class       A    (   A_					,				A_ ):
							UpperCamelCase_   :							List[str]       =True


							@register_to_config
							def __init__(self      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = 3      ,				lowerCAmelCase = ("DownEncoderBlock2D",)      ,				lowerCAmelCase = ("UpDecoderBlock2D",)      ,				lowerCAmelCase = (6_4,)      ,				lowerCAmelCase = 1      ,				lowerCAmelCase = "silu"      ,				lowerCAmelCase = 4      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 3_2      ,				lowerCAmelCase = 0.1_82_15      ,				):
									super().__init__()

									# pass init params to Encoder
									__lowercase=			Encoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				down_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				double_z=lowerCAmelCase      ,				)

									# pass init params to Decoder
									__lowercase=			Decoder(
									    in_channels=lowerCAmelCase      ,				out_channels=lowerCAmelCase      ,				up_block_types=lowerCAmelCase      ,				block_out_channels=lowerCAmelCase      ,				layers_per_block=lowerCAmelCase      ,				norm_num_groups=lowerCAmelCase      ,				act_fn=lowerCAmelCase      ,				)

									__lowercase=			nn.Convad(2 * latent_channels      ,				2 * latent_channels      ,				1					)
									__lowercase=			nn.Convad(lowerCAmelCase      ,				lowerCAmelCase      ,				1					)

									__lowercase=			False
									__lowercase=			False

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


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=False					):
									if isinstance(lowerCAmelCase      ,				(Encoder, Decoder)					):
											__lowercase=			value


							def 			_A       (self      ,				lowerCAmelCase = True					):
									__lowercase=			use_tiling


							def 			_A       (self					):
									self.enable_tiling(lowerCAmelCase					)


							def 			_A       (self					):
									__lowercase=			True


							def 			_A       (self					):
									__lowercase=			False


							@property
							# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
							def 			_A       (self					):
									__lowercase=			{}

									def fn_recursive_add_processors(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
											if hasattr(lowerCAmelCase      ,				'set_processor'					):
													__lowercase=			module.processor

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

											return processors

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

									return processors


							def 			_A       (self      ,				lowerCAmelCase					):
									__lowercase=			len(self.attn_processors.keys()					)

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

									def fn_recursive_attn_processor(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
											if hasattr(lowerCAmelCase      ,				'set_processor'					):
													if not isinstance(lowerCAmelCase      ,				lowerCAmelCase					):
															module.set_processor(lowerCAmelCase					)
													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}'      ,				lowerCAmelCase      ,				lowerCAmelCase					)

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


							def 			_A       (self					):
									self.set_attn_processor(AttnProcessor()					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									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(lowerCAmelCase      ,				return_dict=lowerCAmelCase					)

									if self.use_slicing and x.shape[0] > 1:
											__lowercase=			[self.encoder(lowerCAmelCase					) for x_slice in x.split(1					)]
											__lowercase=			torch.cat(lowerCAmelCase					)
									else:
											__lowercase=			self.encoder(lowerCAmelCase					)

									__lowercase=			self.quant_conv(lowerCAmelCase					)
									__lowercase=			DiagonalGaussianDistribution(lowerCAmelCase					)

									if not return_dict:
											return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									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(lowerCAmelCase      ,				return_dict=lowerCAmelCase					)

									__lowercase=			self.post_quant_conv(lowerCAmelCase					)
									__lowercase=			self.decoder(lowerCAmelCase					)

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)


							@apply_forward_hook
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									if self.use_slicing and z.shape[0] > 1:
											__lowercase=			[self._decode(lowerCAmelCase					).sample for z_slice in z.split(1					)]
											__lowercase=			torch.cat(lowerCAmelCase					)
									else:
											__lowercase=			self._decode(lowerCAmelCase					).sample

									if not return_dict:
											return (decoded,)

									return DecoderOutput(sample=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			min(a.shape[2]      ,				b.shape[2]      ,				lowerCAmelCase					)
									for y in range(lowerCAmelCase					):
											__lowercase=			a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
									return b


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			min(a.shape[3]      ,				b.shape[3]      ,				lowerCAmelCase					)
									for x in range(lowerCAmelCase					):
											__lowercase=			a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
									return b


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)					)
									__lowercase=			int(self.tile_latent_min_size * self.tile_overlap_factor					)
									__lowercase=			self.tile_latent_min_size - blend_extent

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

									__lowercase=			torch.cat(lowerCAmelCase      ,				dim=2					)
									__lowercase=			DiagonalGaussianDistribution(lowerCAmelCase					)

									if not return_dict:
											return (posterior,)

									return AutoencoderKLOutput(latent_dist=lowerCAmelCase					)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = True					):
									__lowercase=			int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)					)
									__lowercase=			int(self.tile_sample_min_size * self.tile_overlap_factor					)
									__lowercase=			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.
									__lowercase=			[]
									for i in range(0      ,				z.shape[2]      ,				lowerCAmelCase					):
											__lowercase=			[]
											for j in range(0      ,				z.shape[3]      ,				lowerCAmelCase					):
													__lowercase=			z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
													__lowercase=			self.post_quant_conv(lowerCAmelCase					)
													__lowercase=			self.decoder(lowerCAmelCase					)
													row.append(lowerCAmelCase					)
											rows.append(lowerCAmelCase					)
									__lowercase=			[]
									for i, row in enumerate(lowerCAmelCase					):
											__lowercase=			[]
											for j, tile in enumerate(lowerCAmelCase					):
													# blend the above tile and the left tile
													# to the current tile and add the current tile to the result row
													if i > 0:
															__lowercase=			self.blend_v(rows[i - 1][j]      ,				lowerCAmelCase      ,				lowerCAmelCase					)
													if j > 0:
															__lowercase=			self.blend_h(row[j - 1]      ,				lowerCAmelCase      ,				lowerCAmelCase					)
													result_row.append(tile[:, :, :row_limit, :row_limit]					)
											result_rows.append(torch.cat(lowerCAmelCase      ,				dim=3					)					)

									__lowercase=			torch.cat(lowerCAmelCase      ,				dim=2					)
									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = False      ,				lowerCAmelCase = True      ,				lowerCAmelCase = None      ,				):
									__lowercase=			sample
									__lowercase=			self.encode(lowerCAmelCase					).latent_dist
									if sample_posterior:
											__lowercase=			posterior.sample(generator=lowerCAmelCase					)
									else:
											__lowercase=			posterior.mode()
									__lowercase=			self.decode(lowerCAmelCase					).sample

									if not return_dict:
											return (dec,)

									return DecoderOutput(sample=lowerCAmelCase					)










from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	bool:

		'''simple docstring'''







		__lowercase=			get_failure_array(lowercase__	)

		# 2) Step through text searching for pattern
		__lowercase,							__lowercase=			0, 0  # index into text, pattern
		while i < len(lowercase__	):
				if pattern[j] == text[i]:
						if j == (len(lowercase__	) - 1):
								return True
						j += 1

				# if this is a prefix in our pattern
				# just go back far enough to continue
				elif j > 0:
						__lowercase=			failure[j - 1]
						continue
				i += 1
		return False







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			[0]
		__lowercase=			0
		__lowercase=			1
		while j < len(lowercase__	):
				if pattern[i] == pattern[j]:
						i += 1
				elif i > 0:
						__lowercase=			failure[i - 1]
						continue
				j += 1
				failure.append(lowercase__	)
		return failure


if __name__ == "__main__":
	# Test 1)
	lowerCAmelCase           =		'''abc1abc12'''
	lowerCAmelCase           =		'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
	lowerCAmelCase           =		'''alskfjaldsk23adsfabcabc'''
	assert kmp(pattern, texta) and not kmp(pattern, texta)

	# Test 2)
	lowerCAmelCase           =		'''ABABX'''
	lowerCAmelCase           =		'''ABABZABABYABABX'''
	assert kmp(pattern, text)

	# Test 3)
	lowerCAmelCase           =		'''AAAB'''
	lowerCAmelCase           =		'''ABAAAAAB'''
	assert kmp(pattern, text)

	# Test 4)
	lowerCAmelCase           =		'''abcdabcy'''
	lowerCAmelCase           =		'''abcxabcdabxabcdabcdabcy'''
	assert kmp(pattern, text)

	# Test 5)
	lowerCAmelCase           =		'''aabaabaaa'''
	assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]






