



'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


a__  :	str								=	logging.get_logger(__name__)

a__  :	int								=	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

a__  :	Optional[int]								=	{
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

a__  :	int								=	{
    'google/realm-cc-news-pretrained-embedder': 5_1_2,
    'google/realm-cc-news-pretrained-encoder': 5_1_2,
    'google/realm-cc-news-pretrained-scorer': 5_1_2,
    'google/realm-cc-news-pretrained-openqa': 5_1_2,
    'google/realm-orqa-nq-openqa': 5_1_2,
    'google/realm-orqa-nq-reader': 5_1_2,
    'google/realm-orqa-wq-openqa': 5_1_2,
    'google/realm-orqa-wq-reader': 5_1_2,
}

a__  :	Union[str, Any]								=	{
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}
class 				UpperCAmelCase__     (			UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE    					= VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE    					= PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE    					= PRETRAINED_INIT_CONFIGURATION
 __SCREAMING_SNAKE_CASE    					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE    					= RealmTokenizer



 def __init__(     self						,		lowercase=None						,		lowercase=None						,		lowercase=True						,		lowercase="[UNK]"						,		lowercase="[SEP]"						,		lowercase="[PAD]"						,		lowercase="[CLS]"						,		lowercase="[MASK]"						,		lowercase=True						,		lowercase=None						,		**lowercase						,		)     ->							List[Any]:
  super().__init__(
      __SCREAMING_SNAKE_CASE						,		tokenizer_file=__SCREAMING_SNAKE_CASE						,		do_lower_case=__SCREAMING_SNAKE_CASE						,		unk_token=__SCREAMING_SNAKE_CASE						,		sep_token=__SCREAMING_SNAKE_CASE						,		pad_token=__SCREAMING_SNAKE_CASE						,		cls_token=__SCREAMING_SNAKE_CASE						,		mask_token=__SCREAMING_SNAKE_CASE						,		tokenize_chinese_chars=__SCREAMING_SNAKE_CASE						,		strip_accents=__SCREAMING_SNAKE_CASE						,		**__SCREAMING_SNAKE_CASE						,		)

  __UpperCamelCase 					=     json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
  if (
      normalizer_state.get("""lowercase"""						,		__SCREAMING_SNAKE_CASE	) != do_lower_case
      or normalizer_state.get("""strip_accents"""						,		__SCREAMING_SNAKE_CASE	) != strip_accents
      or normalizer_state.get("""handle_chinese_chars"""						,		__SCREAMING_SNAKE_CASE	) != tokenize_chinese_chars
  ):
   __UpperCamelCase 					=     getattr(__SCREAMING_SNAKE_CASE						,		normalizer_state.pop("""type"""	)	)
   __UpperCamelCase 					=     do_lower_case
   __UpperCamelCase 					=     strip_accents
   __UpperCamelCase 					=     tokenize_chinese_chars
   __UpperCamelCase 					=     normalizer_class(**__SCREAMING_SNAKE_CASE	)

  __UpperCamelCase 					=     do_lower_case



 def       __lowerCamelCase (     self						,		lowercase						,		**lowercase	)     ->							Optional[Any]:
  __UpperCamelCase 					=     PaddingStrategy.MAX_LENGTH

  __UpperCamelCase 					=     text
  __UpperCamelCase 					=     kwargs.pop("""text_pair"""						,		__SCREAMING_SNAKE_CASE	)
  __UpperCamelCase 					=     kwargs.pop("""return_tensors"""						,		__SCREAMING_SNAKE_CASE	)

  __UpperCamelCase 					=     {
      """input_ids""": [],
      """attention_mask""": [],
      """token_type_ids""": [],
  }

  for idx, candidate_text in enumerate(__SCREAMING_SNAKE_CASE	):
   if batch_text_pair is not None:
    __UpperCamelCase 					=     batch_text_pair[idx]
   else:
    __UpperCamelCase 					=     None

   __UpperCamelCase 					=     super().__call__(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE						,		return_tensors=__SCREAMING_SNAKE_CASE						,		**__SCREAMING_SNAKE_CASE	)

   __UpperCamelCase 					=     encoded_candidates.get("""input_ids"""	)
   __UpperCamelCase 					=     encoded_candidates.get("""attention_mask"""	)
   __UpperCamelCase 					=     encoded_candidates.get("""token_type_ids"""	)

   if encoded_input_ids is not None:
    output_data["input_ids"].append(__SCREAMING_SNAKE_CASE	)
   if encoded_attention_mask is not None:
    output_data["attention_mask"].append(__SCREAMING_SNAKE_CASE	)
   if encoded_token_type_ids is not None:
    output_data["token_type_ids"].append(__SCREAMING_SNAKE_CASE	)

  __UpperCamelCase 					=     {key: item for key, item in output_data.items() if len(__SCREAMING_SNAKE_CASE	) != 0}

  return BatchEncoding(__SCREAMING_SNAKE_CASE						,		tensor_type=__SCREAMING_SNAKE_CASE	)



 def       __lowerCamelCase (     self						,		lowercase						,		lowercase=None	)     ->							Optional[Any]:
  __UpperCamelCase 					=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

  if token_ids_a:
   output += token_ids_a + [self.sep_token_id]

  return output



 def       __lowerCamelCase (     self						,		lowercase						,		lowercase = None	)     ->							List[int]:
  __UpperCamelCase 					=     [self.sep_token_id]
  __UpperCamelCase 					=     [self.cls_token_id]
  if token_ids_a is None:
   return len(cls + token_ids_a + sep	) * [0]
  return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]




 def       __lowerCamelCase (     self						,		lowercase						,		lowercase = None	)     ->							Tuple[str]:
  __UpperCamelCase 					=     self._tokenizer.model.save(__SCREAMING_SNAKE_CASE						,		name=__SCREAMING_SNAKE_CASE	)
  return tuple(__SCREAMING_SNAKE_CASE	)








'''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 						lowerCAmelCase__   :



   """simple docstring"""



   @staticmethod
   def       UpperCAmelCase__		(		*__SCREAMING_SNAKE_CASE					:       Tuple					,							**__SCREAMING_SNAKE_CASE					:       Union[str, Any]			)  -> List[str]:


        """simple docstring"""




        pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 lowerCAmelCase__  					=      MODEL_FOR_OBJECT_DETECTION_MAPPING
 def       UpperCAmelCase__		(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       Any					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Tuple			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
 def       UpperCAmelCase__		(		self					:       Optional[int]					,							__SCREAMING_SNAKE_CASE					:       Dict					,							__SCREAMING_SNAKE_CASE					:       List[Any]			)  -> Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					,							threshold=0.0			)

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

      import datasets

      __SCREAMING_SNAKE_CASE     =      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""					,							"""image"""					,							split="""test"""			)

      __SCREAMING_SNAKE_CASE     =      [
          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"""],
      ]
      __SCREAMING_SNAKE_CASE     =      object_detector(__SCREAMING_SNAKE_CASE					,							threshold=0.0			)

      self.assertEqual(len(__SCREAMING_SNAKE_CASE			)					,							len(__SCREAMING_SNAKE_CASE			)			)
      for outputs in batch_outputs:
           self.assertGreater(len(__SCREAMING_SNAKE_CASE			)					,							0			)
           for detected_object in outputs:
                self.assertEqual(
                    __SCREAMING_SNAKE_CASE					,							{
                        """score""": ANY(__SCREAMING_SNAKE_CASE			),
                        """label""": ANY(__SCREAMING_SNAKE_CASE			),
                        """box""": {"""xmin""": ANY(__SCREAMING_SNAKE_CASE			), """ymin""": ANY(__SCREAMING_SNAKE_CASE			), """xmax""": ANY(__SCREAMING_SNAKE_CASE			), """ymax""": ANY(__SCREAMING_SNAKE_CASE			)},
                    }					,							)
 @require_tf
 @unittest.skip("""Object detection not implemented in TF"""			)
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> str:


      """simple docstring"""




      pass
 @require_torch
 def       UpperCAmelCase__		(		self					:       str			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """hf-internal-testing/tiny-detr-mobilenetsv3"""

      __SCREAMING_SNAKE_CASE     =      AutoModelForObjectDetection.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							feature_extractor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,							threshold=0.0			)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]					,							threshold=0.0					,							)

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              ],
              [
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
                  {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      AutoModelForObjectDetection.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      AutoFeatureExtractor.from_pretrained(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      ObjectDetectionPipeline(model=__SCREAMING_SNAKE_CASE					,							feature_extractor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
              {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
              {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
              {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
              {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          [
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
              [
                  {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
                  {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
                  {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
                  {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
                  {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
              ],
          ]					,							)
 @require_torch
 @slow
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      0.9985
      __SCREAMING_SNAKE_CASE     =      """facebook/detr-resnet-50"""

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""					,							threshold=__SCREAMING_SNAKE_CASE			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
              {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
          ]					,							)


 @require_torch
 @require_pytesseract
 @slow
 def       UpperCAmelCase__		(		self					:       int			)  -> Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      """Narsil/layoutlmv3-finetuned-funsd"""
      __SCREAMING_SNAKE_CASE     =      0.9993

      __SCREAMING_SNAKE_CASE     =      pipeline("""object-detection"""					,							model=__SCREAMING_SNAKE_CASE					,							threshold=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      object_detector(
          """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""			)
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE					,							decimals=4			)					,							[
              {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
              {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
          ]					,							)






