






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 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





     """simple docstring"""






     a_			=	4_2



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     UpperCamelCase_		):





     """simple docstring"""






     a_			=	True



     @register_to_config
     def __init__(      self  :						int							,	__A  :						int = 3							,	__A  :						int = 3							,	__A  :						Tuple[str] = ("DownEncoderBlock2D",)							,	__A  :						Tuple[str] = ("UpDecoderBlock2D",)							,	__A  :						Tuple[int] = (6_4,)							,	__A  :						int = 1							,	__A  :						str = "silu"							,	__A  :						int = 4							,	__A  :						int = 3_2							,	__A  :						int = 3_2							,	__A  :						float = 0.1_8_2_1_5							,	):
      super().__init__()

      # pass init params to Encoder
      snake_case__				:   List[str]     =						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
      snake_case__				:   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							,	)

      snake_case__				:   List[Any]     =						nn.Convad(2 * latent_channels							,	2 * latent_channels							,	1 )
      snake_case__				:   Any     =						nn.Convad(__A							,	__A							,	1 )

      snake_case__				:   str     =						False
      snake_case__				:   Dict     =						False

      # only relevant if vae tiling is enabled
      snake_case__				:   List[str]     =						self.config.sample_size
      snake_case__				:   List[Any]     =						(
          self.config.sample_size[0]
          if isinstance(self.config.sample_size							,	(list, tuple) )
          else self.config.sample_size
      )
      snake_case__				:   Tuple     =						int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
      snake_case__				:   List[str]     =						0.2_5



     def  _lowercase  (      self  :						int							,	__A  :						int							,	__A  :						int=False ):
      if isinstance(__A							,	(Encoder, Decoder) ):
       snake_case__				:   List[Any]     =						value



     def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						bool = True ):
      snake_case__				:   int     =						use_tiling



     def  _lowercase  (      self  :						Tuple ):
      self.enable_tiling(__A )



     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   str     =						True



     def  _lowercase  (      self  :						Tuple ):
      snake_case__				:   Optional[int]     =						False



     @property
     # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
     def  _lowercase  (      self  :						Optional[Any] ):
      snake_case__				:   Optional[Any]     =						{}

      def fn_recursive_add_processors(__A  :						str							,	__A  :						torch.nn.Module							,	__A  :						Dict[str, AttentionProcessor] ):
       if hasattr(__A							,	"set_processor" ):
        snake_case__				:   List[Any]     =						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  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
      snake_case__				:   str     =						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  :						str							,	__A  :						torch.nn.Module							,	__A  :						List[str] ):
       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  _lowercase  (      self  :						Any ):
      self.set_attn_processor(AttnProcessor() )



     @apply_forward_hook
     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						torch.FloatTensor							,	__A  :						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(__A							,	return_dict=__A )

      if self.use_slicing and x.shape[0] > 1:
       snake_case__				:   int     =						[self.encoder(__A ) for x_slice in x.split(1 )]
       snake_case__				:   str     =						torch.cat(__A )
      else:
       snake_case__				:   Dict     =						self.encoder(__A )

      snake_case__				:   Optional[int]     =						self.quant_conv(__A )
      snake_case__				:   int     =						DiagonalGaussianDistribution(__A )

      if not return_dict:
       return (posterior,)

      return AutoencoderKLOutput(latent_dist=__A )



     def  _lowercase  (      self  :						Optional[Any]							,	__A  :						torch.FloatTensor							,	__A  :						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(__A							,	return_dict=__A )

      snake_case__				:   Optional[int]     =						self.post_quant_conv(__A )
      snake_case__				:   List[str]     =						self.decoder(__A )

      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=__A )



     @apply_forward_hook
     def  _lowercase  (      self  :						str							,	__A  :						torch.FloatTensor							,	__A  :						bool = True ):
      if self.use_slicing and z.shape[0] > 1:
       snake_case__				:   int     =						[self._decode(__A ).sample for z_slice in z.split(1 )]
       snake_case__				:   Union[str, Any]     =						torch.cat(__A )
      else:
       snake_case__				:   Optional[Any]     =						self._decode(__A ).sample

      if not return_dict:
       return (decoded,)

      return DecoderOutput(sample=__A )



     def  _lowercase  (      self  :						List[Any]							,	__A  :						Union[str, Any]							,	__A  :						Dict							,	__A  :						Union[str, Any] ):
      snake_case__				:   List[str]     =						min(a.shape[2]							,	b.shape[2]							,	__A )
      for y in range(__A ):
       snake_case__				:   Optional[Any]     =						a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
      return b



     def  _lowercase  (      self  :						str							,	__A  :						int							,	__A  :						Union[str, Any]							,	__A  :						str ):
      snake_case__				:   Optional[Any]     =						min(a.shape[3]							,	b.shape[3]							,	__A )
      for x in range(__A ):
       snake_case__				:   Tuple     =						a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
      return b



     def  _lowercase  (      self  :						List[str]							,	__A  :						torch.FloatTensor							,	__A  :						bool = True ):
      snake_case__				:   List[Any]     =						int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
      snake_case__				:   Union[str, Any]     =						int(self.tile_latent_min_size * self.tile_overlap_factor )
      snake_case__				:   Union[str, Any]     =						self.tile_latent_min_size - blend_extent

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

      snake_case__				:   Any     =						torch.cat(__A							,	dim=2 )
      snake_case__				:   Optional[Any]     =						DiagonalGaussianDistribution(__A )

      if not return_dict:
       return (posterior,)

      return AutoencoderKLOutput(latent_dist=__A )



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

      snake_case__				:   Any     =						torch.cat(__A							,	dim=2 )
      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=__A )






     def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						torch.FloatTensor							,	__A  :						bool = False							,	__A  :						bool = True							,	__A  :						Optional[torch.Generator] = None							,	):
      snake_case__				:   List[Any]     =						sample
      snake_case__				:   int     =						self.encode(__A ).latent_dist
      if sample_posterior:
       snake_case__				:   Union[str, Any]     =						posterior.sample(generator=__A )
      else:
       snake_case__				:   int     =						posterior.mode()
      snake_case__				:   int     =						self.decode(__A ).sample

      if not return_dict:
       return (dec,)

      return DecoderOutput(sample=__A )











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 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					def __init__(      self  :						List[Any]							,	__A  :						Dict							,	__A  :						int=7							,	__A  :						Optional[Any]=3							,	__A  :						List[str]=3_0							,	__A  :						List[Any]=4_0_0							,	__A  :						Union[str, Any]=True							,	__A  :						List[Any]=None							,	__A  :						Optional[Any]=True							,	__A  :						Tuple=[0.5, 0.5, 0.5]							,	__A  :						Union[str, Any]=[0.5, 0.5, 0.5]							,	__A  :						List[str]=True							,	__A  :						Any=1 / 2_5_5							,	__A  :						Optional[int]=True							,	):
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						snake_case__				:   List[str]     =						size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
						snake_case__				:   Dict     =						parent
						snake_case__				:   Optional[int]     =						batch_size
						snake_case__				:   Union[str, Any]     =						num_channels
						snake_case__				:   str     =						min_resolution
						snake_case__				:   Tuple     =						max_resolution
						snake_case__				:   List[Any]     =						do_resize
						snake_case__				:   Dict     =						size
						snake_case__				:   List[str]     =						do_normalize
						snake_case__				:   Optional[int]     =						image_mean
						snake_case__				:   Optional[int]     =						image_std
						snake_case__				:   Any     =						do_rescale
						snake_case__				:   Optional[int]     =						rescale_factor
						snake_case__				:   int     =						do_pad



					def  _lowercase  (      self  :						Dict ):
						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  _lowercase  (      self  :						Optional[int]							,	__A  :						Dict							,	__A  :						List[Any]=False ):
						if not batched:
							snake_case__				:   List[str]     =						image_inputs[0]
							if isinstance(__A							,	Image.Image ):
								snake_case__,		snake_case__				:   Tuple     =						image.size
							else:
								snake_case__,		snake_case__				:   List[str]     =						image.shape[1], image.shape[2]
							if w < h:
								snake_case__				:   Dict     =						int(self.size["shortest_edge"] * h / w )
								snake_case__				:   Optional[int]     =						self.size["shortest_edge"]
							elif w > h:
								snake_case__				:   List[Any]     =						self.size["shortest_edge"]
								snake_case__				:   Union[str, Any]     =						int(self.size["shortest_edge"] * w / h )
							else:
								snake_case__				:   Dict     =						self.size["shortest_edge"]
								snake_case__				:   Dict     =						self.size["shortest_edge"]

						else:
							snake_case__				:   str     =						[]
							for image in image_inputs:
								snake_case__,		snake_case__				:   str     =						self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
							snake_case__				:   Dict     =						max(__A							,	key=lambda __A : item[0] )[0]
							snake_case__				:   Tuple     =						max(__A							,	key=lambda __A : item[1] )[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_				,     unittest.TestCase		):





					"""simple docstring"""






					a_			=	ConditionalDetrImageProcessor if is_vision_available() else None



					def  _lowercase  (      self  :						int ):
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessingTester(self )



					@property
					def  _lowercase  (      self  :						Any ):
						return self.image_processor_tester.prepare_image_processor_dict()



					def  _lowercase  (      self  :						Any ):
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__A							,	"image_mean" ) )
						self.assertTrue(hasattr(__A							,	"image_std" ) )
						self.assertTrue(hasattr(__A							,	"do_normalize" ) )
						self.assertTrue(hasattr(__A							,	"do_resize" ) )
						self.assertTrue(hasattr(__A							,	"size" ) )



					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Optional[Any]     =						self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 1_8, "longest_edge": 1_3_3_3} )
						self.assertEqual(image_processor.do_pad							,	__A )

						snake_case__				:   Any     =						self.image_processing_class.from_dict(
						    self.image_processor_dict							,	size=4_2							,	max_size=8_4							,	pad_and_return_pixel_mask=__A )
						self.assertEqual(image_processor.size							,	{"shortest_edge": 4_2, "longest_edge": 8_4} )
						self.assertEqual(image_processor.do_pad							,	__A )



					def  _lowercase  (      self  :						Union[str, Any] ):
						pass



					def  _lowercase  (      self  :						List[str] ):
						# Initialize image_processing
						snake_case__				:   Dict     =						self.image_processing_class(**self.image_processor_dict )
						# create random PIL images
						snake_case__				:   Dict     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	Image.Image )

						# Test not batched input
						snake_case__				:   Union[str, Any]     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Union[str, Any]     =						self.image_processor_tester.get_expected_values(__A )

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

						# Test batched
						snake_case__,		snake_case__				:   Tuple     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

						snake_case__				:   int     =						image_processing(__A							,	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  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   List[str]     =						self.image_processing_class(**self.image_processor_dict )
						# create random numpy tensors
						snake_case__				:   List[str]     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	numpify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	np.ndarray )

						# Test not batched input
						snake_case__				:   int     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Dict     =						self.image_processor_tester.get_expected_values(__A )

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

						# Test batched
						snake_case__				:   Optional[Any]     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   str     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

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



					def  _lowercase  (      self  :						Tuple ):
						# Initialize image_processing
						snake_case__				:   Optional[Any]     =						self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						snake_case__				:   str     =						prepare_image_inputs(self.image_processor_tester							,	equal_resolution=__A							,	torchify=__A )
						for image in image_inputs:
							self.assertIsInstance(__A							,	torch.Tensor )

						# Test not batched input
						snake_case__				:   Tuple     =						image_processing(image_inputs[0]							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   Optional[int]     =						self.image_processor_tester.get_expected_values(__A )

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

						# Test batched
						snake_case__				:   Dict     =						image_processing(__A							,	return_tensors="pt" ).pixel_values

						snake_case__,		snake_case__				:   int     =						self.image_processor_tester.get_expected_values(__A							,	batched=__A )

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



					@slow
					def  _lowercase  (      self  :						List[Any] ):
						# prepare image and target
						snake_case__				:   Union[str, Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,	"r" ) as f:
							snake_case__				:   Union[str, Any]     =						json.loads(f.read() )

						snake_case__				:   Optional[Any]     =						{"image_id": 3_9_7_6_9, "annotations": target}

						# encode them
						snake_case__				:   Tuple     =						ConditionalDetrImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50" )
						snake_case__				:   int     =						image_processing(images=__A							,	annotations=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   str     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   Tuple     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Optional[int]     =						torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Tuple     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   List[Any]     =						torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   List[Any]     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[int]     =						torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify orig_size
						snake_case__				:   Dict     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[str]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )






					@slow
					def  _lowercase  (      self  :						str ):
						# prepare image, target and masks_path
						snake_case__				:   Optional[Any]     =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,	"r" ) as f:
							snake_case__				:   int     =						json.loads(f.read() )

						snake_case__				:   Optional[int]     =						{"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

						snake_case__				:   Optional[Any]     =						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )

						# encode them
						snake_case__				:   Optional[int]     =						ConditionalDetrImageProcessor(format="coco_panoptic" )
						snake_case__				:   Tuple     =						image_processing(images=__A							,	annotations=__A							,	masks_path=__A							,	return_tensors="pt" )

						# verify pixel values
						snake_case__				:   Optional[Any]     =						torch.Size([1, 3, 8_0_0, 1_0_6_6] )
						self.assertEqual(encoding["pixel_values"].shape							,	__A )

						snake_case__				:   List[str]     =						torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
						self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,	__A							,	atol=1e-4 ) )

						# verify area
						snake_case__				:   Tuple     =						torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,	__A ) )
						# verify boxes
						snake_case__				:   Dict     =						torch.Size([6, 4] )
						self.assertEqual(encoding["labels"][0]["boxes"].shape							,	__A )
						snake_case__				:   int     =						torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,	__A							,	atol=1e-3 ) )
						# verify image_id
						snake_case__				:   str     =						torch.tensor([3_9_7_6_9] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,	__A ) )
						# verify is_crowd
						snake_case__				:   Tuple     =						torch.tensor([0, 0, 0, 0, 0, 0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,	__A ) )
						# verify class_labels
						snake_case__				:   Optional[Any]     =						torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,	__A ) )
						# verify masks
						snake_case__				:   str     =						8_2_2_8_7_3
						self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,	__A )
						# verify orig_size
						snake_case__				:   int     =						torch.tensor([4_8_0, 6_4_0] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,	__A ) )
						# verify size
						snake_case__				:   List[Any]     =						torch.tensor([8_0_0, 1_0_6_6] )
						self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,	__A ) )



