

import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


A									=  logging.getLogger(__name__)

A									=  50  # max width of layer names
A									=  70  # max width of quantizer names





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			parser.add_argument_group('quant_trainer arguments'						)
	group.add_argument('--wprec'      ,			type=lowercase__      ,			default=8      ,			help='weight precision'						)
	group.add_argument('--aprec'      ,			type=lowercase__      ,			default=8      ,			help='activation precision'						)
	group.add_argument('--quant-per-tensor'      ,			action='store_true'      ,			help='per tensor weight scaling'						)
	group.add_argument('--quant-disable'      ,			action='store_true'      ,			help='disable all quantizers'						)
	group.add_argument('--quant-disable-embeddings'      ,			action='store_true'      ,			help='disable all embeddings quantizers'						)
	group.add_argument('--quant-disable-keyword'      ,			type=lowercase__      ,			nargs='+'      ,			help='disable quantizers by keyword'						)
	group.add_argument('--quant-disable-layer-module'      ,			type=lowercase__      ,			help='disable quantizers by keyword under layer.'						)
	group.add_argument('--quant-enable-layer-module'      ,			type=lowercase__      ,			help='enable quantizers by keyword under layer'						)
	group.add_argument('--calibrator'      ,			default='max'      ,			help='which quantization range calibrator to use'						)
	group.add_argument('--percentile'      ,			default=lowercase__      ,			type=lowercase__      ,			help='percentile for PercentileCalibrator'						)
	group.add_argument('--fuse-qkv'      ,			action='store_true'      ,			help='use the same scale factor for qkv'						)
	group.add_argument('--clip-gelu'      ,			metavar='N'      ,			type=lowercase__      ,			help='clip gelu output maximum value to N'						)
	group.add_argument(
	    '--recalibrate-weights'      ,			action='store_true'      ,			help=(
	        'recalibrate weight amaxes by taking the max of the weights.'
	        ' amaxes will be computed with the current quantization granularity (axis).'
	    )      ,			)





def a(lowercase__						):





	'''simple docstring'''

	if args.calibrator == "max":
		snake_case_   		=			'max'
	elif args.calibrator == "percentile":
		if args.percentile is None:
			raise ValueError('Specify --percentile when using percentile calibrator'						)
		snake_case_   		=			'histogram'
	elif args.calibrator == "mse":
		snake_case_   		=			'histogram'
	else:
		raise ValueError(f"""Invalid calibrator {args.calibrator}"""						)

	snake_case_   		=			QuantDescriptor(num_bits=args.aprec      ,			calib_method=lowercase__						)
	snake_case_   		=			QuantDescriptor(num_bits=args.wprec      ,			axis=(None if args.quant_per_tensor else (0,))						)
	quant_nn.QuantLinear.set_default_quant_desc_input(lowercase__						)
	quant_nn.QuantLinear.set_default_quant_desc_weight(lowercase__						)





def a(lowercase__      ,			lowercase__      ,			lowercase__=False      ,			lowercase__=False						):





	'''simple docstring'''
	logger.info('Configuring Model for Quantization'						)
	logger.info(f"""using quantization package {pytorch_quantization.__file__}"""						)

	if not calib:
		if args.quant_disable_embeddings:
			set_quantizer_by_name(lowercase__      ,			['embeddings']      ,			which='weight'      ,			_disabled=lowercase__						)

		if args.quant_disable:
			set_quantizer_by_name(lowercase__      ,			['']      ,			_disabled=lowercase__						)

		if args.quant_disable_keyword:
			set_quantizer_by_name(lowercase__      ,			args.quant_disable_keyword      ,			_disabled=lowercase__						)

		if args.quant_disable_layer_module:
			set_quantizer_by_name(lowercase__      ,			[R'layer.\d+.' + args.quant_disable_layer_module]      ,			_disabled=lowercase__						)

		if args.quant_enable_layer_module:
			set_quantizer_by_name(lowercase__      ,			[R'layer.\d+.' + args.quant_enable_layer_module]      ,			_disabled=lowercase__						)

		if args.recalibrate_weights:
			recalibrate_weights(lowercase__						)

		if args.fuse_qkv:
			fuse_qkv(lowercase__      ,			lowercase__						)

	if args.clip_gelu:
		clip_gelu(lowercase__      ,			args.clip_gelu						)

	# if args.local_rank in [-1, 0] and not calib:
	print_quant_summary(lowercase__						)





def a(lowercase__						):





	'''simple docstring'''
	logger.info('Enabling Calibration'						)
	for name, module in model.named_modules():
		if name.endswith('_quantizer'						):
			if module._calibrator is not None:
				module.disable_quant()
				module.enable_calib()
			else:
				module.disable()
			logger.info(f"""{name:80}: {module}"""						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''
	logger.info('Loading calibrated amax'						)
	for name, module in model.named_modules():
		if name.endswith('_quantizer'						):
			if module._calibrator is not None:
				if isinstance(module._calibrator      ,			calib.MaxCalibrator						):
					module.load_calib_amax()
				else:
					module.load_calib_amax('percentile'      ,			percentile=args.percentile						)
				module.enable_quant()
				module.disable_calib()
			else:
				module.enable()
	model.cuda()
	print_quant_summary(lowercase__						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''

	def fusea(lowercase__      ,			lowercase__      ,			lowercase__						):
		for mod in [qq, qk, qv]:
			if not hasattr(lowercase__      ,			'_amax'						):
				print('          WARNING: NO AMAX BUFFER'						)
				return
		snake_case_   		=			qq._amax.detach().item()
		snake_case_   		=			qk._amax.detach().item()
		snake_case_   		=			qv._amax.detach().item()

		snake_case_   		=			max(lowercase__      ,			lowercase__      ,			lowercase__						)
		qq._amax.fill_(lowercase__						)
		qk._amax.fill_(lowercase__						)
		qv._amax.fill_(lowercase__						)
		logger.info(f"""          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"""						)

	for name, mod in model.named_modules():
		if name.endswith('.attention.self'						):
			logger.info(f"""FUSE_QKV: {name:{name_width}}"""						)
			fusea(mod.matmul_q_input_quantizer      ,			mod.matmul_k_input_quantizer      ,			mod.matmul_v_input_quantizer						)
			if args.quant_per_tensor:
				fusea(mod.query._weight_quantizer      ,			mod.key._weight_quantizer      ,			mod.value._weight_quantizer						)





def a(lowercase__      ,			lowercase__						):





	'''simple docstring'''

	for name, mod in model.named_modules():
		if name.endswith('.output.dense'						) and not name.endswith('attention.output.dense'						):
			snake_case_   		=			mod._input_quantizer._amax.data.detach().item()
			mod._input_quantizer._amax.data.detach().clamp_(max=lowercase__						)
			snake_case_   		=			mod._input_quantizer._amax.data.detach().item()
			logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""						)





def a(lowercase__						):





	'''simple docstring'''

	for name, mod in model.named_modules():
		if hasattr(lowercase__      ,			'_weight_quantizer'						) and mod._weight_quantizer.axis is not None:
			snake_case_   		=			mod.weight.shape[0]
			snake_case_   		=			mod._weight_quantizer._amax.detach()
			snake_case_   		=			torch.ones(lowercase__      ,			dtype=amax.dtype      ,			device=amax.device						) * amax
			print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}"""						)





def a(lowercase__						):





	'''simple docstring'''

	for name, mod in model.named_modules():
		if hasattr(lowercase__      ,			'_weight_quantizer'						):
			if not hasattr(mod.weight_quantizer      ,			'_amax'						):
				print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER'						)
				continue

			# determine which axes to reduce across
			# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
			snake_case_   		=			set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis						)
			snake_case_   		=			set(range(len(mod.weight.size()						)						)						) - axis_set
			snake_case_   		=			pytorch_quantization.utils.reduce_amax(mod.weight      ,			axis=lowercase__      ,			keepdims=lowercase__						).detach()
			logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""						)
			snake_case_   		=			amax





def a(lowercase__      ,			lowercase__=25      ,			lowercase__=180      ,			lowercase__=None						):





	'''simple docstring'''

	if ignore is None:
		snake_case_   		=			[]
	elif not isinstance(lowercase__      ,			lowercase__						):
		snake_case_   		=			[ignore]

	snake_case_   		=			0
	for name, mod in model.named_modules():
		if not hasattr(lowercase__      ,			'weight'						):
			continue
		snake_case_   		=			max(lowercase__      ,			len(lowercase__						)						)

	for name, mod in model.named_modules():
		snake_case_   		=			getattr(lowercase__      ,			'_input_quantizer'      ,			lowercase__						)
		snake_case_   		=			getattr(lowercase__      ,			'_weight_quantizer'      ,			lowercase__						)
		if not hasattr(lowercase__      ,			'weight'						):
			continue
		if type(lowercase__						) in ignore:
			continue
		if [True for s in ignore if type(lowercase__						) is str and s in name]:
			continue
		snake_case_   		=			f"""Act:{input_q.extra_repr()}"""
		snake_case_   		=			f"""Wgt:{weight_q.extra_repr()}"""
		snake_case_   		=			f"""{name:{name_width}} {act_str} {wgt_str}"""
		if len(lowercase__						) <= line_width:
			logger.info(lowercase__						)
		else:
			logger.info(f"""{name:{name_width}} {act_str}"""						)
			logger.info(f"""{'  ':{name_width}} {wgt_str}"""						)





def a(lowercase__						):





	'''simple docstring'''
	snake_case_   		=			0
	for name, mod in model.named_modules():
		if isinstance(lowercase__      ,			pytorch_quantization.nn.TensorQuantizer						):
			print(f"""{name:80} {mod}"""						)
			count += 1
	print(f"""{count} TensorQuantizers found in model"""						)





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__						):





	'''simple docstring'''
	snake_case_   		=			getattr(lowercase__      ,			lowercase__      ,			lowercase__						)
	if quantizer_mod is not None:
		assert hasattr(lowercase__      ,			lowercase__						)
		setattr(lowercase__      ,			lowercase__      ,			lowercase__						)
	else:
		logger.warning(f"""{name} has no {quantizer}"""						)





def a(lowercase__      ,			lowercase__      ,			lowercase__="both"      ,			**lowercase__						):





	'''simple docstring'''
	snake_case_   		=			f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
	for k, v in kwargs.items():
		s += f""" {k}={v}"""
		if which in ["input", "both"]:
			set_quantizer(lowercase__      ,			lowercase__      ,			'_input_quantizer'      ,			lowercase__      ,			lowercase__						)
		if which in ["weight", "both"]:
			set_quantizer(lowercase__      ,			lowercase__      ,			'_weight_quantizer'      ,			lowercase__      ,			lowercase__						)
	logger.info(lowercase__						)






def a(lowercase__      ,			lowercase__      ,			**lowercase__						):





	'''simple docstring'''

	for name, mod in model.named_modules():
		if hasattr(lowercase__      ,			'_input_quantizer'						) or hasattr(lowercase__      ,			'_weight_quantizer'						):
			for n in names:
				if re.search(lowercase__      ,			lowercase__						):
					set_quantizers(lowercase__      ,			lowercase__      ,			**lowercase__						)
		elif name.endswith('_quantizer'						):
			for n in names:
				if re.search(lowercase__      ,			lowercase__						):
					snake_case_   		=			f"""Warning: changing {name:{name_width}}"""
					for k, v in kwargs.items():
						s += f""" {k}={v}"""
						setattr(lowercase__      ,			lowercase__      ,			lowercase__						)
					logger.info(lowercase__						)



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				)				)
