from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																	    F" to this method that includes {self.model_input_names[0]}, but you provided"
																	    F" {list(processed_features.keys()      )}"      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]
												SCREAMING_SNAKE_CASE    			=       (
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												SCREAMING_SNAKE_CASE    			=       required_input[0]
												if isinstance(_UpperCamelCase	,							(list, tuple)      ):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	SCREAMING_SNAKE_CASE    			=       0
																	while len(required_input[index]      ) == 0:
																						index += 1
																	if index < len(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       required_input[index][0]

												if return_tensors is None:
																	if is_tf_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "tf"
																	elif is_torch_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "pt"
																	elif isinstance(_UpperCamelCase	,							(int, float, list, tuple, np.ndarray)      ):
																						SCREAMING_SNAKE_CASE    			=       "np"
																	else:
																						raise ValueError(
																						    F"type of {first_element} unknown: {type(_UpperCamelCase      )}. "
																						    "Should be one of a python, numpy, pytorch or tensorflow object."      )

												for key, value in processed_features.items():
																	if isinstance(value[0]	,							(int, float)      ):
																						SCREAMING_SNAKE_CASE    			=       to_numpy(_UpperCamelCase      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       [to_numpy(_UpperCamelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )
												if not all(len(_UpperCamelCase      ) == batch_size for v in processed_features.values()      ):
																	raise ValueError("Some items in the output dictionary have a different batch size than others."      )

												SCREAMING_SNAKE_CASE    			=       []
												for i in range(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	SCREAMING_SNAKE_CASE    			=       self._truncate(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							truncation=_UpperCamelCase	,							)
																	truncated_inputs.append(_UpperCamelCase      )

												if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	SCREAMING_SNAKE_CASE    			=       max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.MAX_LENGTH

												SCREAMING_SNAKE_CASE    			=       {}
												for i in range(_UpperCamelCase      ):
																	# padding
																	SCREAMING_SNAKE_CASE    			=       self._pad(
																	    truncated_inputs[i]	,							max_length=_UpperCamelCase	,							padding_strategy=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)

																	for key, value in outputs.items():
																						if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE    			=       []
																						if value.dtype is np.dtype(np.floataa      ):
																											SCREAMING_SNAKE_CASE    			=       value.astype(np.floataa      )
																						batch_outputs[key].append(_UpperCamelCase      )

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																	SCREAMING_SNAKE_CASE    			=       np.ones(len(_UpperCamelCase      )	,							dtype=np.intaa      )

												if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE    			=       max_length - len(_UpperCamelCase      )
																	if self.padding_side == "right":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(0, difference)      )
																						SCREAMING_SNAKE_CASE    			=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	elif self.padding_side == "left":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(difference, 0)      )
																						SCREAMING_SNAKE_CASE    			=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	else:
																						raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												if not truncation:
																	return processed_features
												elif truncation and max_length is None:
																	raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

												if needs_to_be_truncated:
																	SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																						SCREAMING_SNAKE_CASE    			=       processed_features["attention_mask"][:max_length]

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																						raise ValueError(
																						    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																	    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."      )

												return padding_strategy





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 DetaImageProcessor


class 						lowercase    (     unittest.TestCase     ):




							def __init__(						self   :					Optional[int]	,							_UpperCamelCase   :					Any	,							_UpperCamelCase   :					Dict=7	,							_UpperCamelCase   :					Union[str, Any]=3	,							_UpperCamelCase   :					Optional[int]=30	,							_UpperCamelCase   :					List[Any]=400	,							_UpperCamelCase   :					Dict=True	,							_UpperCamelCase   :					Union[str, Any]=None	,							_UpperCamelCase   :					Any=True	,							_UpperCamelCase   :					List[Any]=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=[0.5, 0.5, 0.5]	,							_UpperCamelCase   :					Tuple=True	,							_UpperCamelCase   :					List[Any]=1 / 255	,							_UpperCamelCase   :					Optional[Any]=True	,							)					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
												SCREAMING_SNAKE_CASE    			=       parent
												SCREAMING_SNAKE_CASE    			=       batch_size
												SCREAMING_SNAKE_CASE    			=       num_channels
												SCREAMING_SNAKE_CASE    			=       min_resolution
												SCREAMING_SNAKE_CASE    			=       max_resolution
												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       do_normalize
												SCREAMING_SNAKE_CASE    			=       image_mean
												SCREAMING_SNAKE_CASE    			=       image_std
												SCREAMING_SNAKE_CASE    			=       do_rescale
												SCREAMING_SNAKE_CASE    			=       rescale_factor
												SCREAMING_SNAKE_CASE    			=       do_pad




							def 	__snake_case(						self   :					List[str]      )					->				Union[str, Any]:



												'''simple docstring'''

												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 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]=False      )					->				List[Any]:



												'''simple docstring'''

												if not batched:
																	SCREAMING_SNAKE_CASE    			=       image_inputs[0]
																	if isinstance(_UpperCamelCase	,							Image.Image      ):
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       image.size
																	else:
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       image.shape[1], image.shape[2]
																	if w < h:
																						SCREAMING_SNAKE_CASE    			=       int(self.size["shortest_edge"] * h / w      )
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
																	elif w > h:
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
																						SCREAMING_SNAKE_CASE    			=       int(self.size["shortest_edge"] * w / h      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]
																						SCREAMING_SNAKE_CASE    			=       self.size["shortest_edge"]

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

												return expected_height, expected_width



@require_torch
@require_vision
class 						lowercase    (     a				,       unittest.TestCase     ):
							lowercase__						:       Optional[int]								=							DetaImageProcessor if is_vision_available() else None




							def 	__snake_case(						self   :					List[str]      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       DetaImageProcessingTester(self      )




							@property
							def 	__snake_case(						self   :					int      )					->				Tuple:



												'''simple docstring'''

												return self.image_processor_tester.prepare_image_processor_dict()




							def 	__snake_case(						self   :					List[Any]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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	,							"do_rescale"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"do_pad"      )      )
												self.assertTrue(hasattr(_UpperCamelCase	,							"size"      )      )




							def 	__snake_case(						self   :					Optional[Any]      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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      )




							def 	__snake_case(						self   :					str      )					->				List[Any]:



												'''simple docstring'''

												pass




							def 	__snake_case(						self   :					List[Any]      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PIL images
												SCREAMING_SNAKE_CASE    			=       prepare_image_inputs(self.image_processor_tester	,							equal_resolution=_UpperCamelCase      )
												for image in image_inputs:
																	self.assertIsInstance(_UpperCamelCase	,							Image.Image      )

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

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.image_processor_tester.get_expected_values(_UpperCamelCase	,							batched=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					List[str]      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					str      )					->				Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(image_inputs[0]	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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
												SCREAMING_SNAKE_CASE    			=       image_processing(_UpperCamelCase	,							return_tensors="pt"      ).pixel_values

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       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 	__snake_case(						self   :					Dict      )					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
												with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,							"r"      ) as f:
																	SCREAMING_SNAKE_CASE    			=       json.loads(f.read()      )

												SCREAMING_SNAKE_CASE    			=       {"image_id": 39_769, "annotations": target}

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor()
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							return_tensors="pt"      )

												# verify pixel values
												SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
												self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-4      )      )

												# verify area
												SCREAMING_SNAKE_CASE    			=       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"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-3      )      )
												# verify image_id
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([75, 75, 63, 65, 17, 17]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )





							@slow
							def 	__snake_case(						self   :					Dict      )					->				List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
												with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,							"r"      ) as f:
																	SCREAMING_SNAKE_CASE    			=       json.loads(f.read()      )

												SCREAMING_SNAKE_CASE    			=       {"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

												SCREAMING_SNAKE_CASE    			=       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"      )

												# encode them
												SCREAMING_SNAKE_CASE    			=       DetaImageProcessor(format="coco_panoptic"      )
												SCREAMING_SNAKE_CASE    			=       image_processing(images=_UpperCamelCase	,							annotations=_UpperCamelCase	,							masks_path=_UpperCamelCase	,							return_tensors="pt"      )

												# verify pixel values
												SCREAMING_SNAKE_CASE    			=       torch.Size([1, 3, 800, 1_066]      )
												self.assertEqual(encoding["pixel_values"].shape	,							_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-4      )      )

												# verify area
												SCREAMING_SNAKE_CASE    			=       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"]	,							_UpperCamelCase      )      )
												# verify boxes
												SCREAMING_SNAKE_CASE    			=       torch.Size([6, 4]      )
												self.assertEqual(encoding["labels"][0]["boxes"].shape	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       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]	,							_UpperCamelCase	,							atol=1e-3      )      )
												# verify image_id
												SCREAMING_SNAKE_CASE    			=       torch.tensor([39_769]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,							_UpperCamelCase      )      )
												# verify is_crowd
												SCREAMING_SNAKE_CASE    			=       torch.tensor([0, 0, 0, 0, 0, 0]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,							_UpperCamelCase      )      )
												# verify class_labels
												SCREAMING_SNAKE_CASE    			=       torch.tensor([17, 17, 63, 75, 75, 93]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,							_UpperCamelCase      )      )
												# verify masks
												SCREAMING_SNAKE_CASE    			=       822_873
												self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,							_UpperCamelCase      )
												# verify orig_size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([480, 640]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,							_UpperCamelCase      )      )
												# verify size
												SCREAMING_SNAKE_CASE    			=       torch.tensor([800, 1_066]      )
												self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,							_UpperCamelCase      )      )




