




"""simple docstring"""
import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize

class __lowercase:




					'''simple docstring'''


					def __init__(     self    ,						__a    ,						__a=sys.maxsize  ):
								__lowerCamelCase       :						List[Any]													=					'bilinear'
								__lowerCamelCase       :						Optional[Any]													=					max_size
								__lowerCamelCase       :						List[str]													=					short_edge_length





					def __call__(     self    ,						__a  ):
								__lowerCamelCase       :						str													=					[]
								for img in imgs:
											__lowerCamelCase							,   __lowerCamelCase       :						Any													=					img.shape[:2]
											# later: provide list and randomly choose index for resize
											__lowerCamelCase       :						Any													=					np.random.randint(self.short_edge_length[0]    ,						self.short_edge_length[1] + 1  )
											if size == 0:
														return img
											__lowerCamelCase       :						Any													=					size * 1.0 / min(__lowerCAmelCase    ,						__lowerCAmelCase  )
											if h < w:
														__lowerCamelCase							,   __lowerCamelCase       :						Dict													=					size, scale * w
											else:
														__lowerCamelCase							,   __lowerCamelCase       :						Tuple													=					scale * h, size
											if max(__lowerCAmelCase    ,						__lowerCAmelCase  ) > self.max_size:
														__lowerCamelCase       :						Any													=					self.max_size * 1.0 / max(__lowerCAmelCase    ,						__lowerCAmelCase  )
														__lowerCamelCase       :						Any													=					newh * scale
														__lowerCamelCase       :						Dict													=					neww * scale
											__lowerCamelCase       :						Dict													=					int(neww + 0.5  )
											__lowerCamelCase       :						Dict													=					int(newh + 0.5  )

											if img.dtype == np.uinta:
														__lowerCamelCase       :						Optional[int]													=					Image.fromarray(__lowerCAmelCase  )
														__lowerCamelCase       :						int													=					pil_image.resize((neww, newh)    ,						PILImageResampling.BILINEAR  )
														__lowerCamelCase       :						int													=					np.asarray(__lowerCAmelCase  )
											else:
														__lowerCamelCase       :						Tuple													=					img.permute(2    ,						0    ,						1  ).unsqueeze(0  )  # 3, 0, 1)  # hw(c) -> nchw
														__lowerCamelCase       :						int													=					nn.functional.interpolate(
														    __lowerCAmelCase    ,						(newh, neww)    ,						mode=self.interp_method    ,						align_corners=__lowerCAmelCase  ).squeeze(0  )
											img_augs.append(__lowerCAmelCase  )

								return img_augs



class __lowercase:




					'''simple docstring'''


					def __init__(     self    ,						__a  ):
								__lowerCamelCase       :						Any													=					ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,						cfg.INPUT.MAX_SIZE_TEST  )
								__lowerCamelCase       :						int													=					cfg.INPUT.FORMAT
								__lowerCamelCase       :						Tuple													=					cfg.SIZE_DIVISIBILITY
								__lowerCamelCase       :						Optional[Any]													=					cfg.PAD_VALUE
								__lowerCamelCase       :						List[Any]													=					cfg.INPUT.MAX_SIZE_TEST
								__lowerCamelCase       :						str													=					cfg.MODEL.DEVICE
								__lowerCamelCase       :						Optional[int]													=					torch.tensor(cfg.MODEL.PIXEL_STD  ).to(self.device  ).view(len(cfg.MODEL.PIXEL_STD  )    ,						1    ,						1  )
								__lowerCamelCase       :						List[str]													=					torch.tensor(cfg.MODEL.PIXEL_MEAN  ).to(self.device  ).view(len(cfg.MODEL.PIXEL_STD  )    ,						1    ,						1  )
								__lowerCamelCase       :						str													=					lambda __a  : (x - self.pixel_mean) / self.pixel_std
					def      snake_case_					(     self    ,						__a  ):
								__lowerCamelCase       :						int													=					tuple(max(__lowerCAmelCase  ) for s in zip(*[img.shape for img in images]  )  )
								__lowerCamelCase       :						Dict													=					[im.shape[-2:] for im in images]
								__lowerCamelCase       :						Union[str, Any]													=					[
								    nn.functional.pad(
								        __lowerCAmelCase    ,						[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,						value=self.pad_value    ,						)
								    for size, im in zip(__lowerCAmelCase    ,						__lowerCAmelCase  )
								]

								return torch.stack(__lowerCAmelCase  ), torch.tensor(__lowerCAmelCase  )





					def __call__(     self    ,						__a    ,						__a=False  ):


								with torch.no_grad():
											if not isinstance(__lowerCAmelCase    ,						__lowerCAmelCase  ):
														__lowerCamelCase       :						Any													=					[images]
											if single_image:
														assert len(__lowerCAmelCase  ) == 1
											for i in range(len(__lowerCAmelCase  )  ):
														if isinstance(images[i]    ,						torch.Tensor  ):
																	images.insert(__lowerCAmelCase    ,						images.pop(__lowerCAmelCase  ).to(self.device  ).float()  )
														elif not isinstance(images[i]    ,						torch.Tensor  ):
																	images.insert(
																	    __lowerCAmelCase    ,						torch.as_tensor(img_tensorize(images.pop(__lowerCAmelCase  )    ,						input_format=self.input_format  )  )
																	    .to(self.device  )
																	    .float()    ,						)
            # resize smallest edge
											__lowerCamelCase       :						str													=					torch.tensor([im.shape[:2] for im in images]  )
											__lowerCamelCase       :						List[str]													=					self.aug(__lowerCAmelCase  )
											# transpose images and convert to torch tensors
											# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
											# now normalize before pad to avoid useless arithmetic
											__lowerCamelCase       :						Optional[Any]													=					[self.normalizer(__lowerCAmelCase  ) for x in images]
											# now pad them to do the following operations
											__lowerCamelCase							,   __lowerCamelCase       :						Any													=					self.pad(__lowerCAmelCase  )
											# Normalize

											if self.size_divisibility > 0:
														raise NotImplementedError()
											# pad
											__lowerCamelCase       :						Dict													=					torch.true_divide(__lowerCAmelCase    ,						__lowerCAmelCase  )
											if single_image:
														return images[0], sizes[0], scales_yx[0]
											else:
														return images, sizes, scales_yx





def     UpperCAmelCase					(      A__:     List[Any]				,					A__:     Union[str, Any]  )	->  str:
			boxes[:, 0::2] *= scale_yx[:, 1]
			boxes[:, 1::2] *= scale_yx[:, 0]
			return boxes






def     UpperCAmelCase					(      A__:     Optional[Any]				,					A__:     Tuple[int, int]  )	->  Any:
			assert torch.isfinite(_A  ).all(), "Box tensor contains infinite or NaN!"
			__lowerCamelCase							,   __lowerCamelCase       :						Optional[Any]													=					box_size
			tensor[:, 0].clamp_(min=0				,					max=_A  )
			tensor[:, 1].clamp_(min=0				,					max=_A  )
			tensor[:, 2].clamp_(min=0				,					max=_A  )
			tensor[:, 3].clamp_(min=0				,					max=_A  )




'''simple docstring'''




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__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	"DiagonalGaussianDistribution"







class 							a__(   lowerCAmelCase__					,		lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			True



			@register_to_config
			def __init__(  self			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = 3			,       __lowerCAmelCase = ("DownEncoderBlock2D",)			,       __lowerCAmelCase = ("UpDecoderBlock2D",)			,       __lowerCAmelCase = (64,)			,       __lowerCAmelCase = 1			,       __lowerCAmelCase = "silu"			,       __lowerCAmelCase = 4			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 32			,       __lowerCAmelCase = 0.18215			,       ):




					"""simple docstring"""


					super().__init__()

					# pass init params to Encoder
					lowerCAmelCase     =    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
					lowerCAmelCase     =    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			,       )

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

					lowerCAmelCase     =    False
					lowerCAmelCase     =    False

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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=False):




					"""simple docstring"""


					if isinstance(__lowerCAmelCase			,       (Encoder, Decoder)):
							lowerCAmelCase     =    value



			def 				a_			(  self			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    use_tiling



			def 				a_			(  self):




					"""simple docstring"""


					self.enable_tiling(__lowerCAmelCase)



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    True



			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    False



			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    {}

					def fn_recursive_add_processors(__lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):
							if hasattr(__lowerCAmelCase			,       """set_processor"""):
									lowerCAmelCase     =    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):




					"""simple docstring"""


					lowerCAmelCase     =    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):




					"""simple docstring"""


					self.set_attn_processor(AttnProcessor())



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					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:
							lowerCAmelCase     =    [self.encoder(__lowerCAmelCase) for x_slice in x.split(1)]
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    self.encoder(__lowerCAmelCase)

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

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					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)

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

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)



			@apply_forward_hook
			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					if self.use_slicing and z.shape[0] > 1:
							lowerCAmelCase     =    [self._decode(__lowerCAmelCase).sample for z_slice in z.split(1)]
							lowerCAmelCase     =    torch.cat(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    self._decode(__lowerCAmelCase).sample

					if not return_dict:
							return (decoded,)

					return DecoderOutput(sample=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


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



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
					lowerCAmelCase     =    int(self.tile_latent_min_size * self.tile_overlap_factor)
					lowerCAmelCase     =    self.tile_latent_min_size - blend_extent

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

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

					if not return_dict:
							return (posterior,)

					return AutoencoderKLOutput(latent_dist=__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = True):




					"""simple docstring"""


					lowerCAmelCase     =    int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
					lowerCAmelCase     =    int(self.tile_sample_min_size * self.tile_overlap_factor)
					lowerCAmelCase     =    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.
					lowerCAmelCase     =    []
					for i in range(0			,       z.shape[2]			,       __lowerCAmelCase):
							lowerCAmelCase     =    []
							for j in range(0			,       z.shape[3]			,       __lowerCAmelCase):
									lowerCAmelCase     =    z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
									lowerCAmelCase     =    self.post_quant_conv(__lowerCAmelCase)
									lowerCAmelCase     =    self.decoder(__lowerCAmelCase)
									row.append(__lowerCAmelCase)
							rows.append(__lowerCAmelCase)
					lowerCAmelCase     =    []
					for i, row in enumerate(__lowerCAmelCase):
							lowerCAmelCase     =    []
							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:
											lowerCAmelCase     =    self.blend_v(rows[i - 1][j]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									if j > 0:
											lowerCAmelCase     =    self.blend_h(row[j - 1]			,       __lowerCAmelCase			,       __lowerCAmelCase)
									result_row.append(tile[:, :, :row_limit, :row_limit])
							result_rows.append(torch.cat(__lowerCAmelCase			,       dim=3))

					lowerCAmelCase     =    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			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    sample
					lowerCAmelCase     =    self.encode(__lowerCAmelCase).latent_dist
					if sample_posterior:
							lowerCAmelCase     =    posterior.sample(generator=__lowerCAmelCase)
					else:
							lowerCAmelCase     =    posterior.mode()
					lowerCAmelCase     =    self.decode(__lowerCAmelCase).sample

					if not return_dict:
							return (dec,)

					return DecoderOutput(sample=__lowerCAmelCase)




