from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block





@dataclass
class      lowerCAmelCase						(					_UpperCamelCase			):
					_SCREAMING_SNAKE_CASE     :     torch.FloatTensor





class      lowerCAmelCase						(					nn.Module			):



					def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=3				,      lowerCAmelCase__=("DownEncoderBlock2D",)				,      lowerCAmelCase__=(64,)				,      lowerCAmelCase__=2				,      lowerCAmelCase__=32				,      lowerCAmelCase__="silu"				,      lowerCAmelCase__=True				,      ):
												super().__init__()
												_A=	layers_per_block

												_A=	torch.nn.Convad(
												    __a				,      block_out_channels[0]				,      kernel_size=3				,      stride=1				,      padding=1				,      )

												_A=	None
												_A=	nn.ModuleList([]       )

												# down
												_A=	block_out_channels[0]
												for i, down_block_type in enumerate(__a       ):
																			_A=	output_channel
																			_A=	block_out_channels[i]
																			_A=	i == len(__a       ) - 1

																			_A=	get_down_block(
																			    __a				,      num_layers=self.layers_per_block				,      in_channels=__a				,      out_channels=__a				,      add_downsample=not is_final_block				,      resnet_eps=1E-6				,      downsample_padding=0				,      resnet_act_fn=__a				,      resnet_groups=__a				,      attention_head_dim=__a				,      temb_channels=__a				,      )
																			self.down_blocks.append(__a       )

												# mid
												_A=	UNetMidBlockaD(
												    in_channels=block_out_channels[-1]				,      resnet_eps=1E-6				,      resnet_act_fn=__a				,      output_scale_factor=1				,      resnet_time_scale_shift='default'				,      attention_head_dim=block_out_channels[-1]				,      resnet_groups=__a				,      temb_channels=__a				,      )

												# out
												_A=	nn.GroupNorm(num_channels=block_out_channels[-1]				,      num_groups=__a				,      eps=1E-6       )
												_A=	nn.SiLU()

												_A=	2 * out_channels if double_z else out_channels
												_A=	nn.Convad(block_out_channels[-1]				,      __a				,      3				,      padding=1       )

												_A=	False





					def 			a__  (					self				,      lowerCAmelCase__       ):
												_A=	x
												_A=	self.conv_in(__a       )

												if self.training and self.gradient_checkpointing:

																			def create_custom_forward(lowerCAmelCase__       ):
																										def custom_forward(*lowerCAmelCase__       ):
																																	return module(*__a       )

																										return custom_forward

																			# down
																			if is_torch_version('>='				,      '1.11.0'       ):
																										for down_block in self.down_blocks:
																																	_A=	torch.utils.checkpoint.checkpoint(
																																	    create_custom_forward(__a       )				,      __a				,      use_reentrant=__a       )
																										# middle
																										_A=	torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(self.mid_block       )				,      __a				,      use_reentrant=__a       )
																			else:
																										for down_block in self.down_blocks:
																																	_A=	torch.utils.checkpoint.checkpoint(create_custom_forward(__a       )				,      __a       )
																										# middle
																										_A=	torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block       )				,      __a       )

												else:
																			# down
																			for down_block in self.down_blocks:
																										_A=	down_block(__a       )

																			# middle
																			_A=	self.mid_block(__a       )

												# post-process
												_A=	self.conv_norm_out(__a       )
												_A=	self.conv_act(__a       )
												_A=	self.conv_out(__a       )

												return sample





class      lowerCAmelCase						(					nn.Module			):



					def __init__(					self				,      lowerCAmelCase__=3				,      lowerCAmelCase__=3				,      lowerCAmelCase__=("UpDecoderBlock2D",)				,      lowerCAmelCase__=(64,)				,      lowerCAmelCase__=2				,      lowerCAmelCase__=32				,      lowerCAmelCase__="silu"				,      lowerCAmelCase__="group"				,      ):
												super().__init__()
												_A=	layers_per_block

												_A=	nn.Convad(
												    __a				,      block_out_channels[-1]				,      kernel_size=3				,      stride=1				,      padding=1				,      )

												_A=	None
												_A=	nn.ModuleList([]       )

												_A=	in_channels if norm_type == "spatial" else None

												# mid
												_A=	UNetMidBlockaD(
												    in_channels=block_out_channels[-1]				,      resnet_eps=1E-6				,      resnet_act_fn=__a				,      output_scale_factor=1				,      resnet_time_scale_shift='default' if norm_type == 'group' else norm_type				,      attention_head_dim=block_out_channels[-1]				,      resnet_groups=__a				,      temb_channels=__a				,      )

												# up
												_A=	list(reversed(__a       )       )
												_A=	reversed_block_out_channels[0]
												for i, up_block_type in enumerate(__a       ):
																			_A=	output_channel
																			_A=	reversed_block_out_channels[i]

																			_A=	i == len(__a       ) - 1

																			_A=	get_up_block(
																			    __a				,      num_layers=self.layers_per_block + 1				,      in_channels=__a				,      out_channels=__a				,      prev_output_channel=__a				,      add_upsample=not is_final_block				,      resnet_eps=1E-6				,      resnet_act_fn=__a				,      resnet_groups=__a				,      attention_head_dim=__a				,      temb_channels=__a				,      resnet_time_scale_shift=__a				,      )
																			self.up_blocks.append(__a       )
																			_A=	output_channel

												# out
												if norm_type == "spatial":
																			_A=	SpatialNorm(block_out_channels[0]				,      __a       )
												else:
																			_A=	nn.GroupNorm(num_channels=block_out_channels[0]				,      num_groups=__a				,      eps=1E-6       )
												_A=	nn.SiLU()
												_A=	nn.Convad(block_out_channels[0]				,      __a				,      3				,      padding=1       )

												_A=	False





					def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=None       ):
												_A=	z
												_A=	self.conv_in(__a       )

												_A=	next(iter(self.up_blocks.parameters()       )       ).dtype
												if self.training and self.gradient_checkpointing:

																			def create_custom_forward(lowerCAmelCase__       ):
																										def custom_forward(*lowerCAmelCase__       ):
																																	return module(*__a       )

																										return custom_forward

																			if is_torch_version('>='				,      '1.11.0'       ):
																										# middle
																										_A=	torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(self.mid_block       )				,      __a				,      __a				,      use_reentrant=__a       )
																										_A=	sample.to(__a       )

																										# up
																										for up_block in self.up_blocks:
																																	_A=	torch.utils.checkpoint.checkpoint(
																																	    create_custom_forward(__a       )				,      __a				,      __a				,      use_reentrant=__a       )
																			else:
																										# middle
																										_A=	torch.utils.checkpoint.checkpoint(
																										    create_custom_forward(self.mid_block       )				,      __a				,      __a       )
																										_A=	sample.to(__a       )

																										# up
																										for up_block in self.up_blocks:
																																	_A=	torch.utils.checkpoint.checkpoint(create_custom_forward(__a       )				,      __a				,      __a       )
												else:
																			# middle
																			_A=	self.mid_block(__a				,      __a       )
																			_A=	sample.to(__a       )

																			# up
																			for up_block in self.up_blocks:
																										_A=	up_block(__a				,      __a       )

        # post-process
												if latent_embeds is None:
																			_A=	self.conv_norm_out(__a       )
												else:
																			_A=	self.conv_norm_out(__a				,      __a       )
												_A=	self.conv_act(__a       )
												_A=	self.conv_out(__a       )

												return sample





class      lowerCAmelCase						(					nn.Module			):



					def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__=None				,      lowerCAmelCase__="random"				,      lowerCAmelCase__=False				,      lowerCAmelCase__=True       ):
												super().__init__()
												_A=	n_e
												_A=	vq_embed_dim
												_A=	beta
												_A=	legacy

												_A=	nn.Embedding(self.n_e				,      self.vq_embed_dim       )
												self.embedding.weight.data.uniform_(-1.0 / self.n_e				,      1.0 / self.n_e       )

												_A=	remap
												if self.remap is not None:
																			self.register_buffer('used'				,      torch.tensor(np.load(self.remap       )       )       )
																			_A=	self.used.shape[0]
																			_A=	unknown_index  # "random" or "extra" or integer
																			if self.unknown_index == "extra":
																										_A=	self.re_embed
																										_A=	self.re_embed + 1
																			print(
																			    f"Remapping {self.n_e} indices to {self.re_embed} indices. "
																			    f"Using {self.unknown_index} for unknown indices."       )
												else:
																			_A=	n_e

												_A=	sane_index_shape



					def 			a__  (					self				,      lowerCAmelCase__       ):
												_A=	inds.shape
												assert len(__a       ) > 1
												_A=	inds.reshape(ishape[0]				,      -1       )
												_A=	self.used.to(__a       )
												_A=	(inds[:, :, None] == used[None, None, ...]).long()
												_A=	match.argmax(-1       )
												_A=	match.sum(2       ) < 1
												if self.unknown_index == "random":
																			_A=	torch.randint(0				,      self.re_embed				,      size=new[unknown].shape       ).to(device=new.device       )
												else:
																			_A=	self.unknown_index
												return new.reshape(__a       )



					def 			a__  (					self				,      lowerCAmelCase__       ):
												_A=	inds.shape
												assert len(__a       ) > 1
												_A=	inds.reshape(ishape[0]				,      -1       )
												_A=	self.used.to(__a       )
												if self.re_embed > self.used.shape[0]:  # extra token
																			_A=	0  # simply set to zero
												_A=	torch.gather(used[None, :][inds.shape[0] * [0], :]				,      1				,      __a       )
												return back.reshape(__a       )



					def 			a__  (					self				,      lowerCAmelCase__       ):
												# reshape z -> (batch, height, width, channel) and flatten
												_A=	z.permute(0				,      2				,      3				,      1       ).contiguous()
												_A=	z.view(-1				,      self.vq_embed_dim       )

												# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
												_A=	torch.argmin(torch.cdist(__a				,      self.embedding.weight       )				,      dim=1       )

												_A=	self.embedding(__a       ).view(z.shape       )
												_A=	None
												_A=	None

												# compute loss for embedding
												if not self.legacy:
																			_A=	self.beta * torch.mean((z_q.detach() - z) ** 2       ) + torch.mean((z_q - z.detach()) ** 2       )
												else:
																			_A=	torch.mean((z_q.detach() - z) ** 2       ) + self.beta * torch.mean((z_q - z.detach()) ** 2       )

												# preserve gradients
												_A=	z + (z_q - z).detach()

												# reshape back to match original input shape
												_A=	z_q.permute(0				,      3				,      1				,      2       ).contiguous()

												if self.remap is not None:
																			_A=	min_encoding_indices.reshape(z.shape[0]				,      -1       )  # add batch axis
																			_A=	self.remap_to_used(__a       )
																			_A=	min_encoding_indices.reshape(-1				,      1       )  # flatten

												if self.sane_index_shape:
																			_A=	min_encoding_indices.reshape(z_q.shape[0]				,      z_q.shape[2]				,      z_q.shape[3]       )

												return z_q, loss, (perplexity, min_encodings, min_encoding_indices)





					def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__       ):
												# shape specifying (batch, height, width, channel)
												if self.remap is not None:
																			_A=	indices.reshape(shape[0]				,      -1       )  # add batch axis
																			_A=	self.unmap_to_all(__a       )
																			_A=	indices.reshape(-1       )  # flatten again

												# get quantized latent vectors
												_A=	self.embedding(__a       )

												if shape is not None:
																			_A=	z_q.view(__a       )
																			# reshape back to match original input shape
																			_A=	z_q.permute(0				,      3				,      1				,      2       ).contiguous()

												return z_q





class      lowerCAmelCase						(					_UpperCamelCase			):



					def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False       ):
												_A=	parameters
												_A=	torch.chunk(__a				,      2				,      dim=1       )
												_A=	torch.clamp(self.logvar				,      -30.0				,      20.0       )
												_A=	deterministic
												_A=	torch.exp(0.5 * self.logvar       )
												_A=	torch.exp(self.logvar       )
												if self.deterministic:
																			_A=	torch.zeros_like(
																			    self.mean				,      device=self.parameters.device				,      dtype=self.parameters.dtype       )



					def 			a__  (					self				,      lowerCAmelCase__ = None       ):
												# make sure sample is on the same device as the parameters and has same dtype
												_A=	randn_tensor(
												    self.mean.shape				,      generator=__a				,      device=self.parameters.device				,      dtype=self.parameters.dtype       )
												_A=	self.mean + self.std * sample
												return x



					def 			a__  (					self				,      lowerCAmelCase__=None       ):
												if self.deterministic:
																			return torch.Tensor([0.0]       )
												else:
																			if other is None:
																										return 0.5 * torch.sum(torch.pow(self.mean				,      2       ) + self.var - 1.0 - self.logvar				,      dim=[1, 2, 3]       )
																			else:
																										return 0.5 * torch.sum(
																										    torch.pow(self.mean - other.mean				,      2       ) / other.var
																										    + self.var / other.var
																										    - 1.0
																										    - self.logvar
																										    + other.logvar				,      dim=[1, 2, 3]				,      )



					def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=[1, 2, 3]       ):
												if self.deterministic:
																			return torch.Tensor([0.0]       )
												_A=	np.log(2.0 * np.pi       )
												return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean				,      2       ) / self.var				,      dim=__a       )





					def 			a__  (					self       ):
												return self.mean
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 DetrImageProcessor





class      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=7				,      lowerCAmelCase__=3				,      lowerCAmelCase__=30				,      lowerCAmelCase__=400				,      lowerCAmelCase__=True				,      lowerCAmelCase__=None				,      lowerCAmelCase__=True				,      lowerCAmelCase__=1 / 255				,      lowerCAmelCase__=True				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      lowerCAmelCase__=[0.5, 0.5, 0.5]				,      lowerCAmelCase__=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': 1333}
            _A=	parent
            _A=	batch_size
            _A=	num_channels
            _A=	min_resolution
            _A=	max_resolution
            _A=	do_resize
            _A=	size
            _A=	do_rescale
            _A=	rescale_factor
            _A=	do_normalize
            _A=	image_mean
            _A=	image_std
            _A=	do_pad



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





     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False       ):
            if not batched:
                   _A=	image_inputs[0]
                   if isinstance(lowerCAmelCase__				,      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(lowerCAmelCase__				,      key=lambda lowerCAmelCase__       : item[0]       )[0]
                   _A=	max(lowerCAmelCase__				,      key=lambda lowerCAmelCase__       : item[1]       )[1]

            return expected_height, expected_width





@require_torch
@require_vision
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     List[str]		=DetrImageProcessor if is_vision_available() else None



     def 			a__  (					self       ):
            _A=	DetrImageProcessingTester(self       )



     @property
     def 			a__  (					self       ):
            return self.image_processor_tester.prepare_image_processor_dict()



     def 			a__  (					self       ):
            _A=	self.image_processing_class(**self.image_processor_dict       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_mean'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'image_std'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_normalize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_rescale'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'rescale_factor'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_resize'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'size'       )       )
            self.assertTrue(hasattr(lowerCAmelCase__				,      'do_pad'       )       )



     def 			a__  (					self       ):
            _A=	self.image_processing_class.from_dict(self.image_processor_dict       )
            self.assertEqual(image_processor.size				,      {'shortest_edge': 18, 'longest_edge': 1333}       )
            self.assertEqual(image_processor.do_pad				,      lowerCAmelCase__       )

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



     def 			a__  (					self       ):
            pass



     def 			a__  (					self       ):
            # 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=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      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(lowerCAmelCase__       )

            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(lowerCAmelCase__				,      batched=lowerCAmelCase__       )

            _A=	image_processing(lowerCAmelCase__				,      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 			a__  (					self       ):
            # 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=lowerCAmelCase__				,      numpify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      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(lowerCAmelCase__       )

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

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

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

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



     def 			a__  (					self       ):
            # 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=lowerCAmelCase__				,      torchify=lowerCAmelCase__       )
            for image in image_inputs:
                   self.assertIsInstance(lowerCAmelCase__				,      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(lowerCAmelCase__       )

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

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

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

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



     @slow
     def 			a__  (					self       ):
            # 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': 39769, 'annotations': target}

            # encode them
            _A=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'       )
            _A=	image_processing(images=lowerCAmelCase__				,      annotations=lowerCAmelCase__				,      return_tensors='pt'       )

            # verify pixel values
            _A=	torch.Size([1, 3, 800, 1066]       )
            self.assertEqual(encoding['pixel_values'].shape				,      lowerCAmelCase__       )

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

            # verify area
            _A=	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,      lowerCAmelCase__       )       )
            # verify boxes
            _A=	torch.Size([6, 4]       )
            self.assertEqual(encoding['labels'][0]['boxes'].shape				,      lowerCAmelCase__       )
            _A=	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,      lowerCAmelCase__				,      atol=1E-3       )       )
            # verify image_id
            _A=	torch.tensor([39769]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,      lowerCAmelCase__       )       )
            # verify is_crowd
            _A=	torch.tensor([0, 0, 0, 0, 0, 0]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,      lowerCAmelCase__       )       )
            # verify class_labels
            _A=	torch.tensor([75, 75, 63, 65, 17, 17]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,      lowerCAmelCase__       )       )
            # verify orig_size
            _A=	torch.tensor([480, 640]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,      lowerCAmelCase__       )       )
            # verify size
            _A=	torch.tensor([800, 1066]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,      lowerCAmelCase__       )       )





     @slow
     def 			a__  (					self       ):
            # 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': 39769, 'segments_info': target}

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

            # encode them
            _A=	DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'       )
            _A=	image_processing(images=lowerCAmelCase__				,      annotations=lowerCAmelCase__				,      masks_path=lowerCAmelCase__				,      return_tensors='pt'       )

            # verify pixel values
            _A=	torch.Size([1, 3, 800, 1066]       )
            self.assertEqual(encoding['pixel_values'].shape				,      lowerCAmelCase__       )

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

            # verify area
            _A=	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['area']				,      lowerCAmelCase__       )       )
            # verify boxes
            _A=	torch.Size([6, 4]       )
            self.assertEqual(encoding['labels'][0]['boxes'].shape				,      lowerCAmelCase__       )
            _A=	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]				,      lowerCAmelCase__				,      atol=1E-3       )       )
            # verify image_id
            _A=	torch.tensor([39769]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']				,      lowerCAmelCase__       )       )
            # verify is_crowd
            _A=	torch.tensor([0, 0, 0, 0, 0, 0]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']				,      lowerCAmelCase__       )       )
            # verify class_labels
            _A=	torch.tensor([17, 17, 63, 75, 75, 93]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']				,      lowerCAmelCase__       )       )
            # verify masks
            _A=	822873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()				,      lowerCAmelCase__       )
            # verify orig_size
            _A=	torch.tensor([480, 640]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']				,      lowerCAmelCase__       )       )
            # verify size
            _A=	torch.tensor([800, 1066]       )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']				,      lowerCAmelCase__       )       )