



'''simple docstring'''






import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
         LiltForQuestionAnswering,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltModel,
     )
     from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class 							a__:







    def __init__(   self				:						List[str]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Union[str, Any]=13		,    __snake_case				:						List[Any]=7		,    __snake_case				:						int=True		,    __snake_case				:						Optional[int]=True		,    __snake_case				:						int=True		,    __snake_case				:						List[Any]=True		,    __snake_case				:						List[Any]=99		,    __snake_case				:						Union[str, Any]=24		,    __snake_case				:						Any=2		,    __snake_case				:						str=6		,    __snake_case				:						Optional[Any]=37		,    __snake_case				:						str="gelu"		,    __snake_case				:						Dict=0.1		,    __snake_case				:						int=0.1		,    __snake_case				:						int=5_12		,    __snake_case				:						Dict=16		,    __snake_case				:						List[Any]=2		,    __snake_case				:						Dict=0.02		,    __snake_case				:						Union[str, Any]=3		,    __snake_case				:						str=None		,    __snake_case				:						str=10_00		,    ):
      a		:      str       =  parent
      a		:      List[str]       =  batch_size
      a		:      Dict       =  seq_length
      a		:      Tuple       =  is_training
      a		:      List[str]       =  use_input_mask
      a		:      Dict       =  use_token_type_ids
      a		:      List[str]       =  use_labels
      a		:      Union[str, Any]       =  vocab_size
      a		:      str       =  hidden_size
      a		:      Tuple       =  num_hidden_layers
      a		:      int       =  num_attention_heads
      a		:      List[str]       =  intermediate_size
      a		:      Dict       =  hidden_act
      a		:      Dict       =  hidden_dropout_prob
      a		:      Union[str, Any]       =  attention_probs_dropout_prob
      a		:      int       =  max_position_embeddings
      a		:      List[Any]       =  type_vocab_size
      a		:      List[Any]       =  type_sequence_label_size
      a		:      int       =  initializer_range
      a		:      List[Any]       =  num_labels
      a		:      Dict       =  scope
      a		:      Dict       =  range_bbox







    def     lowercase_ (   self				:						List[Any]			):
      a		:      Any       =  ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size			)

      a		:      List[str]       =  ids_tensor([self.batch_size, self.seq_length, 4]		,    self.range_bbox			)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]			):
        for j in range(bbox.shape[1]			):
          if bbox[i, j, 3] < bbox[i, j, 1]:
            a		:      Tuple       =  bbox[i, j, 3]
            a		:      str       =  bbox[i, j, 1]
            a		:      int       =  t
          if bbox[i, j, 2] < bbox[i, j, 0]:
            a		:      Dict       =  bbox[i, j, 2]
            a		:      Union[str, Any]       =  bbox[i, j, 0]
            a		:      Union[str, Any]       =  t

      a		:      List[str]       =  None
      if self.use_input_mask:
        a		:      Optional[Any]       =  ids_tensor([self.batch_size, self.seq_length]		,    vocab_size=2			)

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

      a		:      Any       =  None
      a		:      Union[str, Any]       =  None
      if self.use_labels:
        a		:      Tuple       =  ids_tensor([self.batch_size]		,    self.type_sequence_label_size			)
        a		:      str       =  ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels			)

      a		:      Optional[Any]       =  self.get_config()

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







    def     lowercase_ (   self				:						Union[str, Any]			):
      return LiltConfig(
          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		,    )







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						Dict		,    __snake_case				:						List[Any]		,    __snake_case				:						int		,    __snake_case				:						Tuple		,    __snake_case				:						str		,    __snake_case				:						str		,    __snake_case				:						Dict		,    ):
      a		:      Tuple       =  LiltModel(config=UpperCamelCase__			)
      model.to(UpperCamelCase__			)
      model.eval()
      a		:      Optional[int]       =  model(UpperCamelCase__		,    bbox=UpperCamelCase__		,    attention_mask=UpperCamelCase__		,    token_type_ids=UpperCamelCase__			)
      a		:      Any       =  model(UpperCamelCase__		,    bbox=UpperCamelCase__		,    token_type_ids=UpperCamelCase__			)
      a		:      Any       =  model(UpperCamelCase__		,    bbox=UpperCamelCase__			)

      self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)			)
      self.parent.assertEqual(result.pooler_output.shape		,    (self.batch_size, self.hidden_size)			)







    def     lowercase_ (   self				:						Any		,    __snake_case				:						List[Any]		,    __snake_case				:						int		,    __snake_case				:						List[str]		,    __snake_case				:						List[Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[Any]		,    __snake_case				:						Any		,    ):
      a		:      str       =  self.num_labels
      a		:      Any       =  LiltForTokenClassification(config=UpperCamelCase__			)
      model.to(UpperCamelCase__			)
      model.eval()
      a		:      Optional[Any]       =  model(
          UpperCamelCase__		,    bbox=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     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						Tuple		,    __snake_case				:						List[str]		,    __snake_case				:						Optional[int]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						str		,    __snake_case				:						Dict		,    ):
      a		:      Union[str, Any]       =  LiltForQuestionAnswering(config=UpperCamelCase__			)
      model.to(UpperCamelCase__			)
      model.eval()
      a		:      List[Any]       =  model(
          UpperCamelCase__		,    bbox=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     lowercase_ (   self				:						str			):
      a		:      Union[str, Any]       =  self.prepare_config_and_inputs()
      (
          a						
      )		:      List[Any]       =  config_and_inputs
      a		:      List[str]       =  {
          '''input_ids''': input_ids,
          '''bbox''': bbox,
          '''token_type_ids''': token_type_ids,
          '''attention_mask''': input_mask,
      }
      return config, inputs_dict

@require_torch
class 							a__(      UpperCamelCase_	,				UpperCamelCase_	,				UpperCamelCase_	,				unittest.TestCase			):
    lowercase__				=					(
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowercase__				=					(
        {
            """feature-extraction""": LiltModel,
            """question-answering""": LiltForQuestionAnswering,
            """text-classification""": LiltForSequenceClassification,
            """token-classification""": LiltForTokenClassification,
            """zero-shot""": LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowercase__				=					False
    lowercase__				=					False







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						Optional[int]		,    __snake_case				:						str		,    __snake_case				:						List[str]		,    __snake_case				:						List[str]			):
      return True







    def     lowercase_ (   self				:						List[str]			):
      a		:      Any       =  LiltModelTester(self			)
      a		:      Dict       =  ConfigTester(self		,    config_class=UpperCamelCase__		,    hidden_size=37			)







    def     lowercase_ (   self				:						Any			):
      self.config_tester.run_common_tests()







    def     lowercase_ (   self				:						Dict			):
      a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase__			)







    def     lowercase_ (   self				:						List[Any]			):
      a		:      List[Any]       =  self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
        a		:      Any       =  type
        self.model_tester.create_and_check_model(*UpperCamelCase__			)







    def     lowercase_ (   self				:						Dict			):
      a		:      str       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__			)







    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[int]       =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__			)







    @slow
    def     lowercase_ (   self				:						str			):





      for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a		:      Union[str, Any]       =  LiltModel.from_pretrained(UpperCamelCase__			)
        self.assertIsNotNone(UpperCamelCase__			)

@require_torch
@slow
class 							a__(      unittest.TestCase			):







    def     lowercase_ (   self				:						str			):
      a		:      Dict       =  LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'			).to(UpperCamelCase__			)

      a		:      Dict       =  torch.tensor([[1, 2]]		,    device=UpperCamelCase__			)
      a		:      str       =  torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,    device=UpperCamelCase__			)

      # forward pass
      with torch.no_grad():
        a		:      Dict       =  model(input_ids=UpperCamelCase__		,    bbox=UpperCamelCase__			)

      a		:      str       =  torch.Size([1, 2, 7_68]			)
      a		:      Tuple       =  torch.tensor(
          [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]		,    device=UpperCamelCase__		,    )

      self.assertTrue(outputs.last_hidden_state.shape		,    UpperCamelCase__			)
      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]		,    UpperCamelCase__		,    atol=1e-3			)			)




'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image
else:

     class 							a__:







         @staticmethod
         def     lowercase_ (   *__snake_case				:						int		,    **__snake_case				:						str			):
           pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							a__(      unittest.TestCase			):
    lowercase__				=					MODEL_FOR_OBJECT_DETECTION_MAPPING







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Tuple			):
      a		:      Tuple       =  ObjectDetectionPipeline(model=__snake_case		,    image_processor=__snake_case			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[Any]			):
      a		:      Any       =  object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'		,    threshold=0.0			)

      self.assertGreater(len(__snake_case			)		,    0			)
      for detected_object in outputs:
        self.assertEqual(
            __snake_case		,    {
                'score': ANY(__snake_case			),
                'label': ANY(__snake_case			),
                'box': {'xmin': ANY(__snake_case			), 'ymin': ANY(__snake_case			), 'xmax': ANY(__snake_case			), 'ymax': ANY(__snake_case			)},
            }		,    )

      import datasets

      a		:      Any       =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,    'image'		,    split='test'			)

      a		:      Tuple       =  [
          Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			),
          'http://images.cocodataset.org/val2017/000000039769.jpg',
          # RGBA
          dataset[0]['file'],
          # LA
          dataset[1]['file'],
          # L
          dataset[2]['file'],
      ]
      a		:      List[Any]       =  object_detector(__snake_case		,    threshold=0.0			)

      self.assertEqual(len(__snake_case			)		,    len(__snake_case			)			)
      for outputs in batch_outputs:
        self.assertGreater(len(__snake_case			)		,    0			)
        for detected_object in outputs:
          self.assertEqual(
              __snake_case		,    {
                  'score': ANY(__snake_case			),
                  'label': ANY(__snake_case			),
                  'box': {'xmin': ANY(__snake_case			), 'ymin': ANY(__snake_case			), 'xmax': ANY(__snake_case			), 'ymax': ANY(__snake_case			)},
              }		,    )







    @require_tf
    @unittest.skip('Object detection not implemented in TF'			)
    def     lowercase_ (   self				:						List[str]			):
      pass







    @require_torch
    def     lowercase_ (   self				:						Tuple			):
      a		:      Union[str, Any]       =  'hf-internal-testing/tiny-detr-mobilenetsv3'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      str       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      Any       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      str       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=0.0			)

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
          ]		,    )

      a		:      Dict       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]		,    threshold=0.0		,    )

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Union[str, Any]       =  'facebook/detr-resnet-50'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      List[Any]       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      str       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      List[Any]       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      List[str]       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Any			):
      a		:      Any       =  'facebook/detr-resnet-50'

      a		:      int       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Dict       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      int       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Optional[Any]       =  0.9985
      a		:      Optional[int]       =  'facebook/detr-resnet-50'

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Any       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=__snake_case			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )







    @require_torch
    @require_pytesseract
    @slow
    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[int]       =  'Narsil/layoutlmv3-finetuned-funsd'
      a		:      Optional[int]       =  0.9993

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case		,    threshold=__snake_case			)

      a		:      Union[str, Any]       =  object_detector(
          'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
          ]		,    )