




"""simple docstring"""

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConditionalDetrImageProcessor





class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Optional[int]=7   ,							_UpperCAmelCase	:	Union[str, Any]=3   ,							_UpperCAmelCase	:	List[str]=30   ,							_UpperCAmelCase	:	Optional[Any]=400   ,							_UpperCAmelCase	:	Optional[Any]=True   ,							_UpperCAmelCase	:	Dict=None   ,							_UpperCAmelCase	:	List[Any]=True   ,							_UpperCAmelCase	:	Optional[int]=[0.5, 0.5, 0.5]   ,							_UpperCAmelCase	:	Optional[int]=[0.5, 0.5, 0.5]   ,							_UpperCAmelCase	:	Dict=True   ,							_UpperCAmelCase	:	List[Any]=1 / 255   ,							_UpperCAmelCase	:	Any=True   ,							):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _A        =       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
       _A        =       parent
       _A        =       batch_size
       _A        =       num_channels
       _A        =       min_resolution
       _A        =       max_resolution
       _A        =       do_resize
       _A        =       size
       _A        =       do_normalize
       _A        =       image_mean
       _A        =       image_std
       _A        =       do_rescale
       _A        =       rescale_factor
       _A        =       do_pad

 def       lowerCAmelCase_							(			self	:	int		):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }



 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	Tuple=False		):
       if not batched:
             _A        =       image_inputs[0]
             if isinstance(_UpperCAmelCase   ,							Image.Image		):
                   _A       ,   _A        =       image.size
             else:
                   _A       ,   _A        =       image.shape[1], image.shape[2]
             if w < h:
                   _A        =       int(self.size['shortest_edge'] * h / w		)
                   _A        =       self.size['shortest_edge']
             elif w > h:
                   _A        =       self.size['shortest_edge']
                   _A        =       int(self.size['shortest_edge'] * w / h		)
             else:
                   _A        =       self.size['shortest_edge']
                   _A        =       self.size['shortest_edge']

       else:
             _A        =       []
             for image in image_inputs:
                   _A       ,   _A        =       self.get_expected_values([image]		)
                   expected_values.append((expected_height, expected_width)		)
             _A        =       max(_UpperCAmelCase   ,							key=lambda _UpperCAmelCase		: item[0]		)[0]
             _A        =       max(_UpperCAmelCase   ,							key=lambda _UpperCAmelCase		: item[1]		)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     ConditionalDetrImageProcessor if is_vision_available() else None

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       ConditionalDetrImageProcessingTester(self		)

 @property
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'image_mean'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'image_std'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_normalize'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'do_resize'		)		)
       self.assertTrue(hasattr(_UpperCAmelCase   ,							'size'		)		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size   ,							{'shortest_edge': 18, 'longest_edge': 1_333}		)
       self.assertEqual(image_processor.do_pad   ,							_UpperCAmelCase		)

       _A        =       self.image_processing_class.from_dict(
           self.image_processor_dict   ,							size=42   ,							max_size=84   ,							pad_and_return_pixel_mask=_UpperCAmelCase		)
       self.assertEqual(image_processor.size   ,							{'shortest_edge': 42, 'longest_edge': 84}		)
       self.assertEqual(image_processor.do_pad   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       pass

 def       lowerCAmelCase_							(			self	:	int		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _A        =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							Image.Image		)

       # Test not batched input
       _A        =       image_processing(image_inputs[0]   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       self.image_processor_tester.get_expected_values(_UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

       # Test batched
       _A       ,   _A        =       self.image_processor_tester.get_expected_values(_UpperCAmelCase   ,							batched=_UpperCAmelCase		)

       _A        =       image_processing(_UpperCAmelCase   ,							return_tensors='pt'		).pixel_values
       self.assertEqual(
           encoded_images.shape   ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,							)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       _A        =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_UpperCAmelCase   ,							numpify=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							np.ndarray		)

       # Test not batched input
       _A        =       image_processing(image_inputs[0]   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       self.image_processor_tester.get_expected_values(_UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

       # Test batched
       _A        =       image_processing(_UpperCAmelCase   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       self.image_processor_tester.get_expected_values(_UpperCAmelCase   ,							batched=_UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape   ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,							)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       _A        =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_UpperCAmelCase   ,							torchify=_UpperCAmelCase		)
       for image in image_inputs:
             self.assertIsInstance(_UpperCAmelCase   ,							torch.Tensor		)

       # Test not batched input
       _A        =       image_processing(image_inputs[0]   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       self.image_processor_tester.get_expected_values(_UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

       # Test batched
       _A        =       image_processing(_UpperCAmelCase   ,							return_tensors='pt'		).pixel_values

       _A       ,   _A        =       self.image_processor_tester.get_expected_values(_UpperCAmelCase   ,							batched=_UpperCAmelCase		)

       self.assertEqual(
           encoded_images.shape   ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,							)

 @slow
 def       lowerCAmelCase_							(			self	:	Tuple		):
       # prepare image and target
       _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
       with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,							'r'		) as f:
             _A        =       json.loads(f.read()		)

       _A        =       {'image_id': 39_769, 'annotations': target}

       # encode them
       _A        =       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'		)
       _A        =       image_processing(images=_UpperCAmelCase   ,							annotations=_UpperCAmelCase   ,							return_tensors='pt'		)

       # verify pixel values
       _A        =       torch.Size([1, 3, 800, 1_066]		)
       self.assertEqual(encoding['pixel_values'].shape   ,							_UpperCAmelCase		)

       _A        =       torch.tensor([0.2796, 0.3138, 0.3481]		)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,							_UpperCAmelCase   ,							atol=1E-4		)		)

       # verify area
       _A        =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,							_UpperCAmelCase		)		)
       # verify boxes
       _A        =       torch.Size([6, 4]		)
       self.assertEqual(encoding['labels'][0]['boxes'].shape   ,							_UpperCAmelCase		)
       _A        =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,							_UpperCAmelCase   ,							atol=1E-3		)		)
       # verify image_id
       _A        =       torch.tensor([39_769]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,							_UpperCAmelCase		)		)
       # verify is_crowd
       _A        =       torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,							_UpperCAmelCase		)		)
       # verify class_labels
       _A        =       torch.tensor([75, 75, 63, 65, 17, 17]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,							_UpperCAmelCase		)		)
       # verify orig_size
       _A        =       torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,							_UpperCAmelCase		)		)
       # verify size
       _A        =       torch.tensor([800, 1_066]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,							_UpperCAmelCase		)		)



 @slow
 def       lowerCAmelCase_							(			self	:	Any		):
       # prepare image, target and masks_path
       _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
       with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,							'r'		) as f:
             _A        =       json.loads(f.read()		)

       _A        =       {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

       _A        =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

       # encode them
       _A        =       ConditionalDetrImageProcessor(format='coco_panoptic'		)
       _A        =       image_processing(images=_UpperCAmelCase   ,							annotations=_UpperCAmelCase   ,							masks_path=_UpperCAmelCase   ,							return_tensors='pt'		)

       # verify pixel values
       _A        =       torch.Size([1, 3, 800, 1_066]		)
       self.assertEqual(encoding['pixel_values'].shape   ,							_UpperCAmelCase		)

       _A        =       torch.tensor([0.2796, 0.3138, 0.3481]		)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,							_UpperCAmelCase   ,							atol=1E-4		)		)

       # verify area
       _A        =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,							_UpperCAmelCase		)		)
       # verify boxes
       _A        =       torch.Size([6, 4]		)
       self.assertEqual(encoding['labels'][0]['boxes'].shape   ,							_UpperCAmelCase		)
       _A        =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,							_UpperCAmelCase   ,							atol=1E-3		)		)
       # verify image_id
       _A        =       torch.tensor([39_769]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,							_UpperCAmelCase		)		)
       # verify is_crowd
       _A        =       torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,							_UpperCAmelCase		)		)
       # verify class_labels
       _A        =       torch.tensor([17, 17, 63, 75, 75, 93]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,							_UpperCAmelCase		)		)
       # verify masks
       _A        =       822_873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,							_UpperCAmelCase		)
       # verify orig_size
       _A        =       torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,							_UpperCAmelCase		)		)
       # verify size
       _A        =       torch.tensor([800, 1_066]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,							_UpperCAmelCase		)		)







"""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     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					"DiagonalGaussianDistribution"





class     lowercase_	(					__lowerCAmelCase      ,     __lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[Any]				  =     True

 @register_to_config
 def __init__(			self	:	List[str]   ,							_UpperCAmelCase	:	int = 3   ,							_UpperCAmelCase	:	int = 3   ,							_UpperCAmelCase	:	Tuple[str] = ("DownEncoderBlock2D",)   ,							_UpperCAmelCase	:	Tuple[str] = ("UpDecoderBlock2D",)   ,							_UpperCAmelCase	:	Tuple[int] = (64,)   ,							_UpperCAmelCase	:	int = 1   ,							_UpperCAmelCase	:	str = "silu"   ,							_UpperCAmelCase	:	int = 4   ,							_UpperCAmelCase	:	int = 32   ,							_UpperCAmelCase	:	int = 32   ,							_UpperCAmelCase	:	float = 0.1_8215   ,							):
       super().__init__()

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

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

       _A        =       nn.Convad(2 * latent_channels   ,							2 * latent_channels   ,							1		)
       _A        =       nn.Convad(_UpperCAmelCase   ,							_UpperCAmelCase   ,							1		)

       _A        =       False
       _A        =       False

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

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Tuple=False		):
       if isinstance(_UpperCAmelCase   ,							(Encoder, Decoder)		):
             _A        =       value

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       use_tiling

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       self.enable_tiling(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       True

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       False

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

       def fn_recursive_add_processors(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	torch.nn.Module   ,							_UpperCAmelCase	:	Dict[str, AttentionProcessor]		):
             if hasattr(_UpperCAmelCase   ,							'set_processor'		):
                   _A        =       module.processor

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

             return processors

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

       return processors

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Union[AttentionProcessor, Dict[str, AttentionProcessor]]		):
       _A        =       len(self.attn_processors.keys()		)

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

       def fn_recursive_attn_processor(_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	torch.nn.Module   ,							_UpperCAmelCase	:	int		):
             if hasattr(_UpperCAmelCase   ,							'set_processor'		):
                   if not isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                         module.set_processor(_UpperCAmelCase		)
                   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}'''   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.set_attn_processor(AttnProcessor()		)

 @apply_forward_hook
 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = 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(_UpperCAmelCase   ,							return_dict=_UpperCAmelCase		)

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

       _A        =       self.quant_conv(_UpperCAmelCase		)
       _A        =       DiagonalGaussianDistribution(_UpperCAmelCase		)

       if not return_dict:
             return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = 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(_UpperCAmelCase   ,							return_dict=_UpperCAmelCase		)

       _A        =       self.post_quant_conv(_UpperCAmelCase		)
       _A        =       self.decoder(_UpperCAmelCase		)

       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)

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

       if not return_dict:
             return (decoded,)

       return DecoderOutput(sample=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Any		):
       _A        =       min(a.shape[2]   ,							b.shape[2]   ,							_UpperCAmelCase		)
       for y in range(_UpperCAmelCase		):
             _A        =       a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
       return b

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]		):
       _A        =       min(a.shape[3]   ,							b.shape[3]   ,							_UpperCAmelCase		)
       for x in range(_UpperCAmelCase		):
             _A        =       a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
       return b

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = True		):
       _A        =       int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)		)
       _A        =       int(self.tile_latent_min_size * self.tile_overlap_factor		)
       _A        =       self.tile_latent_min_size - blend_extent

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

       _A        =       torch.cat(_UpperCAmelCase   ,							dim=2		)
       _A        =       DiagonalGaussianDistribution(_UpperCAmelCase		)

       if not return_dict:
             return (posterior,)

       return AutoencoderKLOutput(latent_dist=_UpperCAmelCase		)

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

       _A        =       torch.cat(_UpperCAmelCase   ,							dim=2		)
       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	torch.FloatTensor   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	Optional[torch.Generator] = None   ,							):
       _A        =       sample
       _A        =       self.encode(_UpperCAmelCase		).latent_dist
       if sample_posterior:
             _A        =       posterior.sample(generator=_UpperCAmelCase		)
       else:
             _A        =       posterior.mode()
       _A        =       self.decode(_UpperCAmelCase		).sample

       if not return_dict:
             return (dec,)

       return DecoderOutput(sample=_UpperCAmelCase		)

