

from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase_							:					Any		 =   logging.get_logger(__name__)

UpperCAmelCase_							:					str		 =   {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class      lowerCamelCase_      (	_lowercase	):
	_lowercase		:   Dict     					=				'''blenderbot-small'''
	_lowercase		:   List[Any]     					=				['''past_key_values''']
	_lowercase		:   str     					=				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




	def __init__(     self						:     Optional[Any]					,      __A						:     Union[str, Any]=5_0265					,      __A						:     str=512					,      __A						:     Any=8					,      __A						:     str=2048					,      __A						:     List[Any]=16					,      __A						:     Dict=8					,      __A						:     Union[str, Any]=2048					,      __A						:     Union[str, Any]=16					,      __A						:     Optional[int]=0.0					,      __A						:     Dict=0.0					,      __A						:     Dict=True					,      __A						:     Any=True					,      __A						:     Dict="gelu"					,      __A						:     int=512					,      __A						:     str=0.1					,      __A						:     Optional[int]=0.0					,      __A						:     Union[str, Any]=0.0					,      __A						:     Optional[int]=0.0_2					,      __A						:     Union[str, Any]=1					,      __A						:     str=False					,      __A						:     Tuple=0					,      __A						:     Union[str, Any]=1					,      __A						:     Tuple=2					,      __A						:     Optional[int]=2					,      **__A						:     List[Any]					,      ):
		__A :			List[str]		    =			vocab_size
		__A :			int		    =			max_position_embeddings
		__A :			int		    =			d_model
		__A :			int		    =			encoder_ffn_dim
		__A :			Any		    =			encoder_layers
		__A :			Any		    =			encoder_attention_heads
		__A :			List[Any]		    =			decoder_ffn_dim
		__A :			Any		    =			decoder_layers
		__A :			int		    =			decoder_attention_heads
		__A :			int		    =			dropout
		__A :			str		    =			attention_dropout
		__A :			Any		    =			activation_dropout
		__A :			List[str]		    =			activation_function
		__A :			Optional[int]		    =			init_std
		__A :			int		    =			encoder_layerdrop
		__A :			List[str]		    =			decoder_layerdrop
		__A :			List[str]		    =			use_cache
		__A :			int		    =			encoder_layers
		__A :			int		    =			scale_embedding  # scale factor will be sqrt(d_model) if True

		super().__init__(
		    pad_token_id=__A					,      bos_token_id=__A					,      eos_token_id=__A					,      is_encoder_decoder=__A					,      decoder_start_token_id=__A					,      forced_eos_token_id=__A					,      **__A					,      )






class      lowerCamelCase_      (	_lowercase	):




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		if self.task in ["default", "seq2seq-lm"]:
			__A :			Dict		    =			OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
			    ]							)

			if self.use_past:
				__A :			Optional[Any]		    =			{0: """batch"""}
				__A :			Union[str, Any]		    =			{0: """batch""", 1: """past_decoder_sequence + sequence"""}
			else:
				__A :			Tuple		    =			{0: """batch""", 1: """decoder_sequence"""}
				__A :			str		    =			{0: """batch""", 1: """decoder_sequence"""}

			if self.use_past:
				self.fill_with_past_key_values_(__A					,      direction="""inputs"""							)
		elif self.task == "causal-lm":
			# TODO: figure this case out.
			__A :			Union[str, Any]		    =			OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
			    ]							)
			if self.use_past:
				__A    ,				__A :			Dict		    =			self.num_layers
				for i in range(__A							):
					__A :			Dict		    =			{0: """batch""", 2: """past_sequence + sequence"""}
					__A :			Union[str, Any]		    =			{0: """batch""", 2: """past_sequence + sequence"""}
		else:
			__A :			int		    =			OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
			        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
			        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
			    ]							)

		return common_inputs




	@property
	def        lowerCAmelCase_       (     self						:     Any							):
		if self.task in ["default", "seq2seq-lm"]:
			__A :			Any		    =			super().outputs
		else:
			__A :			Dict		    =			super(__A					,      self							).outputs
			if self.use_past:
				__A    ,				__A :			Tuple		    =			self.num_layers
				for i in range(__A							):
					__A :			Tuple		    =			{0: """batch""", 2: """past_sequence + sequence"""}
					__A :			int		    =			{0: """batch""", 2: """past_sequence + sequence"""}
		return common_outputs




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     PreTrainedTokenizer					,      __A						:     int = -1					,      __A						:     int = -1					,      __A						:     bool = False					,      __A						:     Optional[TensorType] = None					,      ):
		__A :			str		    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
		    __A					,      __A					,      __A					,      __A					,      __A							)

		# Generate decoder inputs
		__A :			str		    =			seq_length if not self.use_past else 1
		__A :			Any		    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
		    __A					,      __A					,      __A					,      __A					,      __A							)
		__A :			Dict		    =			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
		__A :			str		    =			dict(**__A					,      **__A							)

		if self.use_past:
			if not is_torch_available():
				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
			else:
				import torch
			__A    ,				__A :			List[Any]		    =			common_inputs["""input_ids"""].shape
			__A :			List[str]		    =			common_inputs["""decoder_input_ids"""].shape[1]
			__A    ,				__A :			int		    =			self.num_attention_heads
			__A :			Optional[Any]		    =			(
			    batch,
			    num_encoder_attention_heads,
			    encoder_seq_length,
			    self._config.hidden_size // num_encoder_attention_heads,
			)
			__A :			Optional[Any]		    =			decoder_seq_length + 3
			__A :			str		    =			(
			    batch,
			    num_decoder_attention_heads,
			    decoder_past_length,
			    self._config.hidden_size // num_decoder_attention_heads,
			)

			__A :			List[str]		    =			torch.cat(
			    [common_inputs["""decoder_attention_mask"""], torch.ones(__A					,      __A							)]					,      dim=1							)

			__A :			int		    =			[]
			# If the number of encoder and decoder layers are present in the model configuration, both are considered
			__A    ,				__A :			Any		    =			self.num_layers
			__A :			int		    =			min(__A					,      __A							)
			__A :			Tuple		    =			max(__A					,      __A							) - min_num_layers
			__A :			Dict		    =			"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

			for _ in range(__A							):
				common_inputs["past_key_values"].append(
				    (
				        torch.zeros(__A							),
				        torch.zeros(__A							),
				        torch.zeros(__A							),
				        torch.zeros(__A							),
				    )							)
			# TODO: test this.
			__A :			List[str]		    =			encoder_shape if remaining_side_name == """encoder""" else decoder_shape
			for _ in range(__A					,      __A							):
				common_inputs["past_key_values"].append((torch.zeros(__A							), torch.zeros(__A							))							)
		return common_inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     PreTrainedTokenizer					,      __A						:     int = -1					,      __A						:     int = -1					,      __A						:     bool = False					,      __A						:     Optional[TensorType] = None					,      ):
		__A :			str		    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
		    __A					,      __A					,      __A					,      __A					,      __A							)

		if self.use_past:
			if not is_torch_available():
				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
			else:
				import torch
			__A    ,				__A :			Union[str, Any]		    =			common_inputs["""input_ids"""].shape
			# Not using the same length for past_key_values
			__A :			int		    =			seqlen + 2
			__A    ,				__A :			Optional[Any]		    =			self.num_layers
			__A    ,				__A :			Optional[Any]		    =			self.num_attention_heads
			__A :			List[str]		    =			(
			    batch,
			    num_encoder_attention_heads,
			    past_key_values_length,
			    self._config.hidden_size // num_encoder_attention_heads,
			)

			__A :			Union[str, Any]		    =			common_inputs["""attention_mask"""].dtype
			__A :			int		    =			torch.cat(
			    [common_inputs["""attention_mask"""], torch.ones(__A					,      __A					,      dtype=__A							)]					,      dim=1							)
			__A :			Tuple		    =			[
			    (torch.zeros(__A							), torch.zeros(__A							)) for _ in range(__A							)
			]
		return common_inputs




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     PreTrainedTokenizer					,      __A						:     int = -1					,      __A						:     int = -1					,      __A						:     bool = False					,      __A						:     Optional[TensorType] = None					,      ):
		# Copied from OnnxConfig.generate_dummy_inputs
		# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		__A :			List[str]		    =			compute_effective_axis_dimension(
		    __A					,      fixed_dimension=OnnxConfig.default_fixed_batch					,      num_token_to_add=0							)

		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
		__A :			List[str]		    =			tokenizer.num_special_tokens_to_add(__A							)
		__A :			Tuple		    =			compute_effective_axis_dimension(
		    __A					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=__A							)

		# Generate dummy inputs according to compute batch and sequence
		__A :			Any		    =			[""" """.join([tokenizer.unk_token]							) * seq_length] * batch_size
		__A :			Any		    =			dict(tokenizer(__A					,      return_tensors=__A							)							)
		return common_inputs




	def        lowerCAmelCase_       (     self						:     List[str]					,      __A						:     PreTrainedTokenizer					,      __A						:     int = -1					,      __A						:     int = -1					,      __A						:     bool = False					,      __A						:     Optional[TensorType] = None					,      ):
		if self.task in ["default", "seq2seq-lm"]:
			__A :			Any		    =			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
			    __A					,      batch_size=__A					,      seq_length=__A					,      is_pair=__A					,      framework=__A							)

		elif self.task == "causal-lm":
			__A :			List[str]		    =			self._generate_dummy_inputs_for_causal_lm(
			    __A					,      batch_size=__A					,      seq_length=__A					,      is_pair=__A					,      framework=__A							)
		else:
			__A :			List[Any]		    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    __A					,      batch_size=__A					,      seq_length=__A					,      is_pair=__A					,      framework=__A							)

		return common_inputs




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     int					,      __A						:     Dict					,      __A						:     str					,      __A						:     Dict							):
		if self.task in ["default", "seq2seq-lm"]:
			__A :			Union[str, Any]		    =			super()._flatten_past_key_values_(__A					,      __A					,      __A					,      __A							)
		else:
			__A :			Union[str, Any]		    =			super(__A					,      self							)._flatten_past_key_values_(
			    __A					,      __A					,      __A					,      __A							)








import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import DetaImageProcessor






class      lowerCamelCase_      (	unittest.TestCase	):




	def __init__(     self						:     Optional[int]					,      __A						:     Union[str, Any]					,      __A						:     int=7					,      __A						:     int=3					,      __A						:     int=30					,      __A						:     Dict=400					,      __A						:     str=True					,      __A						:     str=None					,      __A						:     str=True					,      __A						:     Optional[int]=[0.5, 0.5, 0.5]					,      __A						:     List[str]=[0.5, 0.5, 0.5]					,      __A						:     Optional[Any]=True					,      __A						:     int=1 / 255					,      __A						:     List[Any]=True					,      ):
		# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
		__A :			Union[str, Any]		    =			size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
		__A :			Union[str, Any]		    =			parent
		__A :			Union[str, Any]		    =			batch_size
		__A :			Union[str, Any]		    =			num_channels
		__A :			Optional[Any]		    =			min_resolution
		__A :			Union[str, Any]		    =			max_resolution
		__A :			Any		    =			do_resize
		__A :			Union[str, Any]		    =			size
		__A :			Optional[int]		    =			do_normalize
		__A :			Dict		    =			image_mean
		__A :			Optional[int]		    =			image_std
		__A :			Tuple		    =			do_rescale
		__A :			Optional[Any]		    =			rescale_factor
		__A :			Tuple		    =			do_pad




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

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

		return expected_height, expected_width






@require_torch
@require_vision
class      lowerCamelCase_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Tuple     					=				DetaImageProcessor if is_vision_available() else None




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			Tuple		    =			DetaImageProcessingTester(self							)




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return self.image_processor_tester.prepare_image_processor_dict()




	def        lowerCAmelCase_       (     self						:     List[str]							):
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		self.assertTrue(hasattr(__A					,      """image_mean"""							)							)
		self.assertTrue(hasattr(__A					,      """image_std"""							)							)
		self.assertTrue(hasattr(__A					,      """do_normalize"""							)							)
		self.assertTrue(hasattr(__A					,      """do_resize"""							)							)
		self.assertTrue(hasattr(__A					,      """do_rescale"""							)							)
		self.assertTrue(hasattr(__A					,      """do_pad"""							)							)
		self.assertTrue(hasattr(__A					,      """size"""							)							)




	def        lowerCAmelCase_       (     self						:     Any							):
		__A :			Dict		    =			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					,      __A							)




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		pass




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		# Initialize image_processing
		__A :			Tuple		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PIL images
		__A :			List[Any]		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      Image.Image							)

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

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

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

		# Test batched
		__A    ,				__A :			List[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

		__A :			List[str]		    =			image_processing(__A					,      return_tensors="""pt"""							).pixel_values
		self.assertEqual(
		    encoded_images.shape					,      (
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        expected_height,
		        expected_width,
		    )					,      )




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

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

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

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

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

		__A    ,				__A :			Optional[Any]		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

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




	def        lowerCAmelCase_       (     self						:     Dict							):
		# Initialize image_processing
		__A :			int		    =			self.image_processing_class(**self.image_processor_dict							)
		# create random PyTorch tensors
		__A :			Dict		    =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__A					,      torchify=__A							)
		for image in image_inputs:
			self.assertIsInstance(__A					,      torch.Tensor							)

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

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

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

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

		__A    ,				__A :			Any		    =			self.image_processor_tester.get_expected_values(__A					,      batched=__A							)

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




	@slow
	def        lowerCAmelCase_       (     self						:     Tuple							):
		# prepare image and target
		__A :			Tuple		    =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
		with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""					,      """r"""							) as f:
			__A :			Any		    =			json.loads(f.read()							)

		__A :			int		    =			{"""image_id""": 3_9769, """annotations""": target}

		# encode them
		__A :			List[str]		    =			DetaImageProcessor()
		__A :			List[str]		    =			image_processing(images=__A					,      annotations=__A					,      return_tensors="""pt"""							)

		# verify pixel values
		__A :			Tuple		    =			torch.Size([1, 3, 800, 1066]							)
		self.assertEqual(encoding["""pixel_values"""].shape					,      __A							)

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

		# verify area
		__A :			Tuple		    =			torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,      __A							)							)
		# verify boxes
		__A :			Tuple		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			List[str]		    =			torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,      __A					,      atol=1e-3							)							)
		# verify image_id
		__A :			List[str]		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Dict		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			List[Any]		    =			torch.tensor([75, 75, 63, 65, 17, 17]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify orig_size
		__A :			str		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)




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

		__A :			Optional[int]		    =			{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

		__A :			Any		    =			pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

		# encode them
		__A :			Any		    =			DetaImageProcessor(format="""coco_panoptic"""							)
		__A :			int		    =			image_processing(images=__A					,      annotations=__A					,      masks_path=__A					,      return_tensors="""pt"""							)

		# verify pixel values
		__A :			Tuple		    =			torch.Size([1, 3, 800, 1066]							)
		self.assertEqual(encoding["""pixel_values"""].shape					,      __A							)

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

		# verify area
		__A :			Union[str, Any]		    =			torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]					,      __A							)							)
		# verify boxes
		__A :			Union[str, Any]		    =			torch.Size([6, 4]							)
		self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape					,      __A							)
		__A :			Union[str, Any]		    =			torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]					,      __A					,      atol=1e-3							)							)
		# verify image_id
		__A :			Any		    =			torch.tensor([3_9769]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]					,      __A							)							)
		# verify is_crowd
		__A :			Any		    =			torch.tensor([0, 0, 0, 0, 0, 0]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]					,      __A							)							)
		# verify class_labels
		__A :			Optional[Any]		    =			torch.tensor([17, 17, 63, 75, 75, 93]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]					,      __A							)							)
		# verify masks
		__A :			List[str]		    =			82_2873
		self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()					,      __A							)
		# verify orig_size
		__A :			Tuple		    =			torch.tensor([480, 640]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]					,      __A							)							)
		# verify size
		__A :			Any		    =			torch.tensor([800, 1066]							)
		self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]					,      __A							)							)





