






import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         DebertaVaForMaskedLM,
         DebertaVaForMultipleChoice,
         DebertaVaForQuestionAnswering,
         DebertaVaForSequenceClassification,
         DebertaVaForTokenClassification,
         DebertaVaModel,
     )
     from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST


class       __SCREAMING_SNAKE_CASE   (				_lowercase):






      def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=13       ,  _UpperCamelCase=7       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=99       ,  _UpperCamelCase=32       ,  _UpperCamelCase=5       ,  _UpperCamelCase=4       ,  _UpperCamelCase=37       ,  _UpperCamelCase="gelu"       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=5_12       ,  _UpperCamelCase=16       ,  _UpperCamelCase=2       ,  _UpperCamelCase=0.02       ,  _UpperCamelCase=False       ,  _UpperCamelCase=True       ,  _UpperCamelCase="None"       ,  _UpperCamelCase=3       ,  _UpperCamelCase=4       ,  _UpperCamelCase=None       ,  ):






            """simple docstring"""
            lowerCAmelCase__        =  parent
            lowerCAmelCase__        =  batch_size
            lowerCAmelCase__        =  seq_length
            lowerCAmelCase__        =  is_training
            lowerCAmelCase__        =  use_input_mask
            lowerCAmelCase__        =  use_token_type_ids
            lowerCAmelCase__        =  use_labels
            lowerCAmelCase__        =  vocab_size
            lowerCAmelCase__        =  hidden_size
            lowerCAmelCase__        =  num_hidden_layers
            lowerCAmelCase__        =  num_attention_heads
            lowerCAmelCase__        =  intermediate_size
            lowerCAmelCase__        =  hidden_act
            lowerCAmelCase__        =  hidden_dropout_prob
            lowerCAmelCase__        =  attention_probs_dropout_prob
            lowerCAmelCase__        =  max_position_embeddings
            lowerCAmelCase__        =  type_vocab_size
            lowerCAmelCase__        =  type_sequence_label_size
            lowerCAmelCase__        =  initializer_range
            lowerCAmelCase__        =  num_labels
            lowerCAmelCase__        =  num_choices
            lowerCAmelCase__        =  relative_attention
            lowerCAmelCase__        =  position_biased_input
            lowerCAmelCase__        =  pos_att_type
            lowerCAmelCase__        =  scope






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)

            lowerCAmelCase__        =  None
            if self.use_input_mask:
                  lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  vocab_size=2				)

            lowerCAmelCase__        =  None
            if self.use_token_type_ids:
                  lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.type_vocab_size				)

            lowerCAmelCase__        =  None
            lowerCAmelCase__        =  None
            lowerCAmelCase__        =  None
            if self.use_labels:
                  lowerCAmelCase__        =  ids_tensor([self.batch_size]       ,  self.type_sequence_label_size				)
                  lowerCAmelCase__        =  ids_tensor([self.batch_size, self.seq_length]       ,  self.num_labels				)
                  lowerCAmelCase__        =  ids_tensor([self.batch_size]       ,  self.num_choices				)

            lowerCAmelCase__        =  self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            return DebertaVaConfig(
                vocab_size=self.vocab_size       ,  hidden_size=self.hidden_size       ,  num_hidden_layers=self.num_hidden_layers       ,  num_attention_heads=self.num_attention_heads       ,  intermediate_size=self.intermediate_size       ,  hidden_act=self.hidden_act       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  max_position_embeddings=self.max_position_embeddings       ,  type_vocab_size=self.type_vocab_size       ,  initializer_range=self.initializer_range       ,  relative_attention=self.relative_attention       ,  position_biased_input=self.position_biased_input       ,  pos_att_type=self.pos_att_type       ,  )






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






            """simple docstring"""
            self.parent.assertListEqual(list(result.loss.size()				)       ,  []				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  DebertaVaModel(config=__UpperCamelCase				)
            model.to(__UpperCamelCase				)
            model.eval()
            lowerCAmelCase__        =  model(__UpperCamelCase       ,  attention_mask=__UpperCamelCase       ,  token_type_ids=__UpperCamelCase				)[0]
            lowerCAmelCase__        =  model(__UpperCamelCase       ,  token_type_ids=__UpperCamelCase				)[0]
            lowerCAmelCase__        =  model(__UpperCamelCase				)[0]

            self.parent.assertListEqual(list(sequence_output.size()				)       ,  [self.batch_size, self.seq_length, self.hidden_size]				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  DebertaVaForMaskedLM(config=__UpperCamelCase				)
            model.to(__UpperCamelCase				)
            model.eval()
            lowerCAmelCase__        =  model(__UpperCamelCase       ,  attention_mask=__UpperCamelCase       ,  token_type_ids=__UpperCamelCase       ,  labels=__UpperCamelCase				)

            self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  self.num_labels
            lowerCAmelCase__        =  DebertaVaForSequenceClassification(__UpperCamelCase				)
            model.to(__UpperCamelCase				)
            model.eval()
            lowerCAmelCase__        =  model(__UpperCamelCase       ,  attention_mask=__UpperCamelCase       ,  token_type_ids=__UpperCamelCase       ,  labels=__UpperCamelCase				)
            self.parent.assertListEqual(list(result.logits.size()				)       ,  [self.batch_size, self.num_labels]				)
            self.check_loss_output(__UpperCamelCase				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  self.num_labels
            lowerCAmelCase__        =  DebertaVaForTokenClassification(config=__UpperCamelCase				)
            model.to(__UpperCamelCase				)
            model.eval()
            lowerCAmelCase__        =  model(__UpperCamelCase       ,  attention_mask=__UpperCamelCase       ,  token_type_ids=__UpperCamelCase       ,  labels=__UpperCamelCase				)
            self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  DebertaVaForQuestionAnswering(config=__UpperCamelCase				)
            model.to(__UpperCamelCase				)
            model.eval()
            lowerCAmelCase__        =  model(
                __UpperCamelCase       ,  attention_mask=__UpperCamelCase       ,  token_type_ids=__UpperCamelCase       ,  start_positions=__UpperCamelCase       ,  end_positions=__UpperCamelCase       ,  )
            self.parent.assertEqual(result.start_logits.shape       ,  (self.batch_size, self.seq_length)				)
            self.parent.assertEqual(result.end_logits.shape       ,  (self.batch_size, self.seq_length)				)






      def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






            """simple docstring"""
            lowerCAmelCase__        =  DebertaVaForMultipleChoice(config=__UpperCamelCase				)
            model.to(__UpperCamelCase				)
            model.eval()
            lowerCAmelCase__        =  input_ids.unsqueeze(1				).expand(-1       ,  self.num_choices       ,  -1				).contiguous()
            lowerCAmelCase__        =  token_type_ids.unsqueeze(1				).expand(-1       ,  self.num_choices       ,  -1				).contiguous()
            lowerCAmelCase__        =  input_mask.unsqueeze(1				).expand(-1       ,  self.num_choices       ,  -1				).contiguous()
            lowerCAmelCase__        =  model(
                __UpperCamelCase       ,  attention_mask=__UpperCamelCase       ,  token_type_ids=__UpperCamelCase       ,  labels=__UpperCamelCase       ,  )
            self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices)				)







      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase__							
            )  ,    (
                lowerCAmelCase__							
            )  ,    (
                lowerCAmelCase__							
            )  ,    (
                lowerCAmelCase__							
            )  ,    (
                lowerCAmelCase__							
            )  ,    (
                lowerCAmelCase__							
            )  ,    (
                lowerCAmelCase__							
            )  ,    							
            )        =  config_and_inputs
            lowerCAmelCase__        =  {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
            return config, inputs_dict




@require_torch
class       __SCREAMING_SNAKE_CASE   (				_lowercase						,   _lowercase						,   unittest.TestCase):
      _SCREAMING_SNAKE_CASE		:    Dict						=   (
          (
              DebertaVaModel,
              DebertaVaForMaskedLM,
              DebertaVaForSequenceClassification,
              DebertaVaForTokenClassification,
              DebertaVaForQuestionAnswering,
              DebertaVaForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE		:    str						=   (
          {
              '''feature-extraction''': DebertaVaModel,
              '''fill-mask''': DebertaVaForMaskedLM,
              '''question-answering''': DebertaVaForQuestionAnswering,
              '''text-classification''': DebertaVaForSequenceClassification,
              '''token-classification''': DebertaVaForTokenClassification,
              '''zero-shot''': DebertaVaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE		:    Optional[Any]						=   True
      _SCREAMING_SNAKE_CASE		:    Dict						=   False
      _SCREAMING_SNAKE_CASE		:    Any						=   False
      _SCREAMING_SNAKE_CASE		:    int						=   False
      _SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   False






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  DebertaVaModelTester(self				)
            lowerCAmelCase__        =  ConfigTester(self       ,  config_class=__UpperCamelCase       ,  hidden_size=37				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            self.config_tester.run_common_tests()






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*__UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*__UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*__UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*__UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*__UpperCamelCase				)






      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_multiple_choice(*__UpperCamelCase				)







      @slow
      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase__        =  DebertaVaModel.from_pretrained(__UpperCamelCase				)
                  self.assertIsNotNone(__UpperCamelCase				)




@require_torch
@require_sentencepiece
@require_tokenizers
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






      @unittest.skip(reason='Model not available yet'				)
      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            pass







      @slow
      def  UpperCamelCase__ (		self				):






            """simple docstring"""
            lowerCAmelCase__        =  DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'				)

            lowerCAmelCase__        =  torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]				)
            lowerCAmelCase__        =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
            with torch.no_grad():
                  lowerCAmelCase__        =  model(__UpperCamelCase       ,  attention_mask=__UpperCamelCase				)[0]
            # compare the actual values for a slice.
            lowerCAmelCase__        =  torch.tensor(
                [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]				)
            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,  __UpperCamelCase       ,  atol=1E-4				)       ,  F"{output[:, 1:4, 1:4]}"				)













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 DeformableDetrImageProcessor


class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						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"""
												# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  num_channels
												lowerCAmelCase__        =  min_resolution
												lowerCAmelCase__        =  max_resolution
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  image_mean
												lowerCAmelCase__        =  image_std
												lowerCAmelCase__        =  do_rescale
												lowerCAmelCase__        =  rescale_factor
												lowerCAmelCase__        =  do_pad






						def  UpperCamelCase__ (		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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False				):






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

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

												return expected_height, expected_width




@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    Dict						=   DeformableDetrImageProcessor if is_vision_available() else None






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  DeformableDetrImageProcessingTester(self				)






						@property
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return self.image_processor_tester.prepare_image_processor_dict()






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  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				)

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






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												pass






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												lowerCAmelCase__        =  prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_UpperCamelCase				)
												for image in image_inputs:
																		self.assertIsInstance(_UpperCamelCase       ,  Image.Image				)

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

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

												lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(image_inputs[0]       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# Initialize image_processing
												lowerCAmelCase__        =  self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(image_inputs[0]       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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
												lowerCAmelCase__        =  image_processing(_UpperCamelCase       ,  return_tensors='pt'				).pixel_values

												lowerCAmelCase__  ,    lowerCAmelCase__        =  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  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# prepare image and target
												lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'       ,  'r'				) as f:
																		lowerCAmelCase__        =  json.loads(f.read()				)

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

												# encode them
												lowerCAmelCase__        =  DeformableDetrImageProcessor()
												lowerCAmelCase__        =  image_processing(images=_UpperCamelCase       ,  annotations=_UpperCamelCase       ,  return_tensors='pt'				)

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

												lowerCAmelCase__        =  torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  _UpperCamelCase       ,  atol=1E-4				)				)

												# verify area
												lowerCAmelCase__        =  torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  _UpperCamelCase				)				)
												# verify boxes
												lowerCAmelCase__        =  torch.Size([6, 4]				)
												self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  _UpperCamelCase       ,  atol=1E-3				)				)
												# verify image_id
												lowerCAmelCase__        =  torch.tensor([3_97_69]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  _UpperCamelCase				)				)
												# verify is_crowd
												lowerCAmelCase__        =  torch.tensor([0, 0, 0, 0, 0, 0]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  _UpperCamelCase				)				)
												# verify class_labels
												lowerCAmelCase__        =  torch.tensor([75, 75, 63, 65, 17, 17]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  _UpperCamelCase				)				)
												# verify orig_size
												lowerCAmelCase__        =  torch.tensor([4_80, 6_40]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  _UpperCamelCase				)				)
												# verify size
												lowerCAmelCase__        =  torch.tensor([8_00, 10_66]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  _UpperCamelCase				)				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												# prepare image, target and masks_path
												lowerCAmelCase__        =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
												with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'       ,  'r'				) as f:
																		lowerCAmelCase__        =  json.loads(f.read()				)

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

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

												# encode them
												lowerCAmelCase__        =  DeformableDetrImageProcessor(format='coco_panoptic'				)
												lowerCAmelCase__        =  image_processing(images=_UpperCamelCase       ,  annotations=_UpperCamelCase       ,  masks_path=_UpperCamelCase       ,  return_tensors='pt'				)

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

												lowerCAmelCase__        =  torch.tensor([0.27_96, 0.31_38, 0.34_81]				)
												self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]       ,  _UpperCamelCase       ,  atol=1E-4				)				)

												# verify area
												lowerCAmelCase__        =  torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['area']       ,  _UpperCamelCase				)				)
												# verify boxes
												lowerCAmelCase__        =  torch.Size([6, 4]				)
												self.assertEqual(encoding['labels'][0]['boxes'].shape       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]       ,  _UpperCamelCase       ,  atol=1E-3				)				)
												# verify image_id
												lowerCAmelCase__        =  torch.tensor([3_97_69]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']       ,  _UpperCamelCase				)				)
												# verify is_crowd
												lowerCAmelCase__        =  torch.tensor([0, 0, 0, 0, 0, 0]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']       ,  _UpperCamelCase				)				)
												# verify class_labels
												lowerCAmelCase__        =  torch.tensor([17, 17, 63, 75, 75, 93]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']       ,  _UpperCamelCase				)				)
												# verify masks
												lowerCAmelCase__        =  82_28_73
												self.assertEqual(encoding['labels'][0]['masks'].sum().item()       ,  _UpperCamelCase				)
												# verify orig_size
												lowerCAmelCase__        =  torch.tensor([4_80, 6_40]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']       ,  _UpperCamelCase				)				)
												# verify size
												lowerCAmelCase__        =  torch.tensor([8_00, 10_66]				)
												self.assertTrue(torch.allclose(encoding['labels'][0]['size']       ,  _UpperCamelCase				)				)





