


import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union

import torch

from .tensor_utils import tensor_tree_map, tree_map






def 					UpperCamelCase   (      __magic_name__	:				Union[dict, list, tuple, torch.Tensor]	)  ->    List[Tuple[int, ...]]:
		"""simple docstring"""




		lowercase__	     =						[]
		if isinstance(__magic_name__       ,       __magic_name__	):
				for v in tree.values():
						shapes.extend(_fetch_dims(__magic_name__	)	)
		elif isinstance(__magic_name__       ,       (list, tuple)	):
				for t in tree:
						shapes.extend(_fetch_dims(__magic_name__	)	)
		elif isinstance(__magic_name__       ,       torch.Tensor	):
				shapes.append(tree.shape	)
		else:
				raise ValueError("""Not supported"""	)

		return shapes






@torch.jit.ignore
def 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				Tuple[int, ...]	)  ->    Tuple[int, ...]:
		"""simple docstring"""




		lowercase__	     =						[]
		for d in reversed(__magic_name__	):
				idx.append(flat_idx % d	)
				lowercase__	     =						flat_idx // d

		return tuple(reversed(__magic_name__	)	)






@torch.jit.ignore
def 					UpperCamelCase   (      __magic_name__	:				Sequence[int]       ,       __magic_name__	:				Sequence[int]       ,       __magic_name__	:				Sequence[int]       ,       __magic_name__	:				Optional[Sequence[bool]] = None       ,       __magic_name__	:				Optional[Sequence[bool]] = None       ,       )  ->    List[Tuple[slice, ...]]:
		"""simple docstring"""




		def reduce_edge_list(__magic_name__	:				List[bool]	) -> None:
				lowercase__	     =						True
				for i in range(len(__magic_name__	)	):
						lowercase__	     =						-1 * (i + 1)
						l[reversed_idx] &= tally
						lowercase__	     =						l[reversed_idx]

		if start_edges is None:
				lowercase__	     =						[s == 0 for s in start]
				reduce_edge_list(__magic_name__	)
		if end_edges is None:
				lowercase__	     =						[e == (d - 1) for e, d in zip(__magic_name__       ,       __magic_name__	)]
				reduce_edge_list(__magic_name__	)

		# Base cases. Either start/end are empty and we're done, or the final,
		# one-dimensional tensor can be simply sliced
		if len(__magic_name__	) == 0:
				return [()]
		elif len(__magic_name__	) == 1:
				return [(slice(start[0]       ,       end[0] + 1	),)]

		lowercase__	     =						[]
		lowercase__	     =						[]

		# Dimensions common to start and end can be selected directly
		for s, e in zip(__magic_name__       ,       __magic_name__	):
				if s == e:
						path_list.append(slice(__magic_name__       ,       s + 1	)	)
				else:
						break

		lowercase__	     =						tuple(__magic_name__	)
		lowercase__	     =						len(__magic_name__	)

		# start == end, and we're done
		if divergence_idx == len(__magic_name__	):
				return [path]

		def upper() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				lowercase__	     =						start[divergence_idx]
				return tuple(
				    path + (slice(__magic_name__       ,       sdi + 1	),) + s
				    for s in _get_minimal_slice_set(
				        start[divergence_idx + 1 :]       ,       [d - 1 for d in dims[divergence_idx + 1 :]]       ,       dims[divergence_idx + 1 :]       ,       start_edges=start_edges[divergence_idx + 1 :]       ,       end_edges=[True for _ in end_edges[divergence_idx + 1 :]]       ,       )	)

		def lower() -> Tuple[Tuple[slice, ...], ...]:
				assert start_edges is not None
				assert end_edges is not None

				lowercase__	     =						end[divergence_idx]
				return tuple(
				    path + (slice(__magic_name__       ,       edi + 1	),) + s
				    for s in _get_minimal_slice_set(
				        [0 for _ in start[divergence_idx + 1 :]]       ,       end[divergence_idx + 1 :]       ,       dims[divergence_idx + 1 :]       ,       start_edges=[True for _ in start_edges[divergence_idx + 1 :]]       ,       end_edges=end_edges[divergence_idx + 1 :]       ,       )	)

		# If both start and end are at the edges of the subtree rooted at
		# divergence_idx, we can just select the whole subtree at once
		if start_edges[divergence_idx] and end_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx]       ,       end[divergence_idx] + 1	),)	)
		# If just start is at the edge, we can grab almost all of the subtree,
		# treating only the ragged bottom edge as an edge case
		elif start_edges[divergence_idx]:
				slices.append(path + (slice(start[divergence_idx]       ,       end[divergence_idx]	),)	)
				slices.extend(lower()	)
		# Analogous to the previous case, but the top is ragged this time
		elif end_edges[divergence_idx]:
				slices.extend(upper()	)
				slices.append(path + (slice(start[divergence_idx] + 1       ,       end[divergence_idx] + 1	),)	)
		# If both sides of the range are ragged, we need to handle both sides
		# separately. If there's contiguous meat in between them, we can index it
		# in one big chunk
		else:
				slices.extend(upper()	)
				lowercase__	     =						end[divergence_idx] - start[divergence_idx]
				if middle_ground > 1:
						slices.append(path + (slice(start[divergence_idx] + 1       ,       end[divergence_idx]	),)	)
				slices.extend(lower()	)

		return slices






@torch.jit.ignore
def 					UpperCamelCase   (      __magic_name__	:				torch.Tensor       ,       __magic_name__	:				int       ,       __magic_name__	:				int       ,       __magic_name__	:				int	)  ->    torch.Tensor:
		"""simple docstring"""




		lowercase__	     =						t.shape[:no_batch_dims]
		lowercase__	     =						list(_flat_idx_to_idx(__magic_name__       ,       __magic_name__	)	)
		# _get_minimal_slice_set is inclusive
		lowercase__	     =						list(_flat_idx_to_idx(flat_end - 1       ,       __magic_name__	)	)

		# Get an ordered list of slices to perform
		lowercase__	     =						_get_minimal_slice_set(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       )

		lowercase__	     =						[t[s] for s in slices]

		return torch.cat([s.view((-1,) + t.shape[no_batch_dims:]	) for s in sliced_tensors]	)






def 					UpperCamelCase   (      __magic_name__	:				Callable       ,       __magic_name__	:				Dict[str, Any]       ,       __magic_name__	:				int       ,       __magic_name__	:				int       ,       __magic_name__	:				bool = False       ,       __magic_name__	:				Any = None       ,       __magic_name__	:				bool = False       ,       )  ->    Any:
		"""simple docstring"""




		if not (len(__magic_name__	) > 0):
				raise ValueError("""Must provide at least one input"""	)

		lowercase__	     =						[shape[:no_batch_dims] for shape in _fetch_dims(__magic_name__	)]
		lowercase__	     =						tuple([max(__magic_name__	) for s in zip(*__magic_name__	)]	)

		def _prep_inputs(__magic_name__	:				torch.Tensor	) -> torch.Tensor:
				if not low_mem:
						if not sum(t.shape[:no_batch_dims]	) == no_batch_dims:
								lowercase__	     =						t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
						lowercase__	     =						t.reshape(-1       ,       *t.shape[no_batch_dims:]	)
				else:
						lowercase__	     =						t.expand(orig_batch_dims + t.shape[no_batch_dims:]	)
				return t

		lowercase__	     =						tensor_tree_map(_prep_inputs       ,       __magic_name__	)
		lowercase__	     =						None
		if _out is not None:
				lowercase__	     =						tensor_tree_map(lambda __magic_name__	: t.view([-1] + list(t.shape[no_batch_dims:]	)	)       ,       _out	)

		lowercase__	     =						1
		for d in orig_batch_dims:
				flat_batch_dim *= d

		lowercase__	     =						flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)

		def _select_chunk(__magic_name__	:				torch.Tensor	) -> torch.Tensor:
				return t[i : i + chunk_size] if t.shape[0] != 1 else t

		lowercase__	     =						0
		lowercase__	     =						prepped_outputs
		for _ in range(__magic_name__	):
				# Chunk the input
				if not low_mem:
						lowercase__	     =						_select_chunk
				else:
						lowercase__	     =						partial(
						    _chunk_slice       ,       flat_start=__magic_name__       ,       flat_end=min(__magic_name__       ,       i + chunk_size	)       ,       no_batch_dims=len(__magic_name__	)       ,       )

				lowercase__	     =						tensor_tree_map(__magic_name__       ,       __magic_name__	)

				# Run the layer on the chunk
				lowercase__	     =						layer(**__magic_name__	)

				# Allocate space for the output
				if out is None:
						lowercase__	     =						tensor_tree_map(lambda __magic_name__	: t.new_zeros((flat_batch_dim,) + t.shape[1:]	)       ,       __magic_name__	)

				# Put the chunk in its pre-allocated space
				if isinstance(__magic_name__       ,       __magic_name__	):

						def assign(__magic_name__	:				dict       ,       __magic_name__	:				dict	) -> None:
								for k, v in da.items():
										if isinstance(__magic_name__       ,       __magic_name__	):
												assign(__magic_name__       ,       da[k]	)
										else:
												if _add_into_out:
														v[i : i + chunk_size] += da[k]
												else:
														lowercase__	     =						da[k]

						assign(__magic_name__       ,       __magic_name__	)
				elif isinstance(__magic_name__       ,       __magic_name__	):
						for xa, xa in zip(__magic_name__       ,       __magic_name__	):
								if _add_into_out:
										xa[i : i + chunk_size] += xa
								else:
										lowercase__	     =						xa
				elif isinstance(__magic_name__       ,       torch.Tensor	):
						if _add_into_out:
								out[i : i + chunk_size] += output_chunk
						else:
								lowercase__	     =						output_chunk
				else:
						raise ValueError("""Not supported"""	)

				i += chunk_size

		lowercase__	     =						tensor_tree_map(lambda __magic_name__	: t.view(orig_batch_dims + t.shape[1:]	)       ,       __magic_name__	)

		return out







class 				A       :
					'''simple docstring'''






					def __init__(self       :		List[str] ,   _UpperCAmelCase       :		int = 512 ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						max_chunk_size
							lowercase__	     =						None
							lowercase__	     =						None

					def     lowerCamelCase__	(self       :		List[Any] ,   _UpperCAmelCase       :		Callable ,   _UpperCAmelCase       :		tuple ,   _UpperCAmelCase       :		int						)		->  int:




							"""simple docstring"""


							logging.info("""Tuning chunk size..."""						)

							if min_chunk_size >= self.max_chunk_size:
									return min_chunk_size

							lowercase__	     =						[2**l for l in range(int(math.log(self.max_chunk_size ,   2						)						) + 1						)]
							lowercase__	     =						[c for c in candidates if c > min_chunk_size]
							lowercase__	     =						[min_chunk_size] + candidates
							candidates[-1] += 4

							def test_chunk_size(_UpperCAmelCase       :		int						) -> bool:
									try:
											with torch.no_grad():
													fn(*_UpperCAmelCase ,   chunk_size=_UpperCAmelCase						)
											return True
									except RuntimeError:
											return False

							lowercase__	     =						0
							lowercase__	     =						len(_UpperCAmelCase						) - 1
							while i > min_viable_chunk_size_index:
									lowercase__	     =						test_chunk_size(candidates[i]						)
									if not viable:
											lowercase__	     =						(min_viable_chunk_size_index + i) // 2
									else:
											lowercase__	     =						i
											lowercase__	     =						(i + len(_UpperCAmelCase						) - 1) // 2

							return candidates[min_viable_chunk_size_index]

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		Iterable ,   _UpperCAmelCase       :		Iterable						)		->  bool:




							"""simple docstring"""


							lowercase__	     =						True
							for aa, aa in zip(_UpperCAmelCase ,   _UpperCAmelCase						):
									assert type(_UpperCAmelCase						) == type(_UpperCAmelCase						)
									if isinstance(_UpperCAmelCase ,   (list, tuple)						):
											consistent &= self._compare_arg_caches(_UpperCAmelCase ,   _UpperCAmelCase						)
									elif isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											lowercase__	     =						[v for _, v in sorted(aa.items() ,   key=lambda _UpperCAmelCase						: x[0]						)]
											lowercase__	     =						[v for _, v in sorted(aa.items() ,   key=lambda _UpperCAmelCase						: x[0]						)]
											consistent &= self._compare_arg_caches(_UpperCAmelCase ,   _UpperCAmelCase						)
									else:
											consistent &= aa == aa

							return consistent


					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Callable ,   _UpperCAmelCase       :		tuple ,   _UpperCAmelCase       :		int ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						True
							lowercase__	     =						tree_map(lambda _UpperCAmelCase						: a.shape if isinstance(_UpperCAmelCase ,   torch.Tensor						) else a ,   _UpperCAmelCase ,   _UpperCAmelCase						)
							if self.cached_arg_data is not None:
									# If args have changed shape/value, we need to re-tune
									assert len(self.cached_arg_data						) == len(_UpperCAmelCase						)
									lowercase__	     =						self._compare_arg_caches(self.cached_arg_data ,   _UpperCAmelCase						)
							else:
									# Otherwise, we can reuse the precomputed value
									lowercase__	     =						False

							if not consistent:
									lowercase__	     =						self._determine_favorable_chunk_size(
									    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   )
									lowercase__	     =						arg_data

							assert self.cached_chunk_size is not None

							return self.cached_chunk_size




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 SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=7			,					__UpperCamelCase=3			,					__UpperCamelCase=30			,					__UpperCamelCase=4_00			,					__UpperCamelCase=True			,					__UpperCamelCase=None			,					__UpperCamelCase=True			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=True			,					__UpperCamelCase=1 / 2_55			,					__UpperCamelCase=True			,					):

				"""simple docstring"""


				snake_case_   		=			size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			num_channels
				snake_case_   		=			min_resolution
				snake_case_   		=			max_resolution
				snake_case_   		=			do_resize
				snake_case_   		=			size
				snake_case_   		=			do_normalize
				snake_case_   		=			image_mean
				snake_case_   		=			image_std
				snake_case_   		=			do_rescale
				snake_case_   		=			rescale_factor
				snake_case_   		=			do_pad


			def       __lowerCAmelCase			(     self				):

				"""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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=False				):

				"""simple docstring"""


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

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

				return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	DetaImageProcessor if is_vision_available() else None


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			DetaImageProcessingTester(self				)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class.from_dict(self.image_processor_dict				)
				self.assertEqual(image_processor.size			,					{'shortest_edge': 18, 'longest_edge': 13_33}				)
				self.assertEqual(image_processor.do_pad			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PIL images
				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
				snake_case_   		=			image_processing(image_inputs[0]			,					return_tensors='pt'				).pixel_values

				snake_case_		,							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
				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

				snake_case_		,							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
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

				snake_case_		,							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
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

				snake_case_   		=			{'image_id': 3_97_69, 'annotations': target}

				# encode them
				snake_case_   		=			DetaImageProcessor()
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			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
				snake_case_   		=			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
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			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
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([75, 75, 63, 65, 17, 17]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				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:
					snake_case_   		=			json.loads(f.read()				)

				snake_case_   		=			{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

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

				# encode them
				snake_case_   		=			DetaImageProcessor(format='coco_panoptic'				)
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					masks_path=__UpperCamelCase			,					return_tensors='pt'				)

				# verify pixel values
				snake_case_   		=			torch.Size([1, 3, 8_00, 10_66]				)
				self.assertEqual(encoding['pixel_values'].shape			,					__UpperCamelCase				)

				snake_case_   		=			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
				snake_case_   		=			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
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			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
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([17, 17, 63, 75, 75, 93]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify masks
				snake_case_   		=			82_28_73
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,					__UpperCamelCase				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)
