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   snake_case_       :






         @staticmethod
         def       __UpperCamelCase     (      *lowercase_				:     Optional[int]	,						**lowercase_				:     Optional[int]  )					->						List[Any]:
             pass



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   snake_case_       (		unittest.TestCase       ):
   __A	:		str         =				MODEL_FOR_OBJECT_DETECTION_MAPPING






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






   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     Optional[int]	,						lowercase_				:     List[str]  )					->						Union[str, Any]:
       lowercase__ :   Tuple    					=				object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"	,						threshold=0.0  )

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

       import datasets

       lowercase__ :   List[Any]    					=				datasets.load_dataset("hf-internal-testing/fixtures_image_utils"	,						"image"	,						split="test"  )

       lowercase__ :   Union[str, Any]    					=				[
           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"],
       ]
       lowercase__ :   Optional[Any]    					=				object_detector(lowercase_	,						threshold=0.0  )

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






   @require_tf
   @unittest.skip("Object detection not implemented in TF"  )
   def       __UpperCamelCase     (      self				:     Dict  )					->						int:
       pass






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Union[str, Any]:
       lowercase__ :   Optional[int]    					=				"hf-internal-testing/tiny-detr-mobilenetsv3"

       lowercase__ :   Optional[Any]    					=				AutoModelForObjectDetection.from_pretrained(lowercase_  )
       lowercase__ :   Tuple    					=				AutoFeatureExtractor.from_pretrained(lowercase_  )
       lowercase__ :   Any    					=				ObjectDetectionPipeline(model=lowercase_	,						feature_extractor=lowercase_  )

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

       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
               {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
           ]	,						)

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

       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               [
                   {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                   {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
               ],
               [
                   {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
                   {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
               ],
           ]	,						)






   @require_torch
   @slow
   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       lowercase__ :   List[str]    					=				"facebook/detr-resnet-50"

       lowercase__ :   str    					=				AutoModelForObjectDetection.from_pretrained(lowercase_  )
       lowercase__ :   int    					=				AutoFeatureExtractor.from_pretrained(lowercase_  )
       lowercase__ :   Any    					=				ObjectDetectionPipeline(model=lowercase_	,						feature_extractor=lowercase_  )

       lowercase__ :   Optional[int]    					=				object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"  )
       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
               {"score": 0.99_60, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
               {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
               {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
               {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
           ]	,						)

       lowercase__ :   List[str]    					=				object_detector(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]  )
       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               [
                   {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                   {"score": 0.99_60, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                   {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                   {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                   {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
               ],
               [
                   {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                   {"score": 0.99_60, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                   {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                   {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                   {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
               ],
           ]	,						)






   @require_torch
   @slow
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						List[Any]:
       lowercase__ :   Optional[Any]    					=				"facebook/detr-resnet-50"

       lowercase__ :   Tuple    					=				pipeline("object-detection"	,						model=lowercase_  )

       lowercase__ :   List[Any]    					=				object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"  )
       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
               {"score": 0.99_60, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
               {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
               {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
               {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
           ]	,						)

       lowercase__ :   Tuple    					=				object_detector(
           [
               "http://images.cocodataset.org/val2017/000000039769.jpg",
               "http://images.cocodataset.org/val2017/000000039769.jpg",
           ]  )
       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               [
                   {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                   {"score": 0.99_60, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                   {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                   {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                   {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
               ],
               [
                   {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
                   {"score": 0.99_60, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
                   {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
                   {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
                   {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
               ],
           ]	,						)






   @require_torch
   @slow
   def       __UpperCamelCase     (      self				:     str  )					->						List[Any]:
       lowercase__ :   int    					=				0.99_85
       lowercase__ :   Any    					=				"facebook/detr-resnet-50"

       lowercase__ :   List[str]    					=				pipeline("object-detection"	,						model=lowercase_  )

       lowercase__ :   int    					=				object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"	,						threshold=lowercase_  )
       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
               {"score": 0.99_87, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
           ]	,						)







   @require_torch
   @require_pytesseract
   @slow
   def       __UpperCamelCase     (      self				:     List[str]  )					->						List[str]:
       lowercase__ :   Any    					=				"Narsil/layoutlmv3-finetuned-funsd"
       lowercase__ :   List[str]    					=				0.99_93

       lowercase__ :   Optional[Any]    					=				pipeline("object-detection"	,						model=lowercase_	,						threshold=lowercase_  )

       lowercase__ :   Dict    					=				object_detector(
           "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"  )
       self.assertEqual(
           nested_simplify(lowercase_	,						decimals=4  )	,						[
               {"score": 0.99_93, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
               {"score": 0.99_93, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
           ]	,						)










"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__A											=		{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		["""ReformerTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=		[
      """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ReformerAttention""",
      """ReformerForMaskedLM""",
      """ReformerForQuestionAnswering""",
      """ReformerForSequenceClassification""",
      """ReformerLayer""",
      """ReformerModel""",
      """ReformerModelWithLMHead""",
      """ReformerPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer import ReformerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_reformer_fast import ReformerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_reformer import (
        REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ReformerAttention,
        ReformerForMaskedLM,
        ReformerForQuestionAnswering,
        ReformerForSequenceClassification,
        ReformerLayer,
        ReformerModel,
        ReformerModelWithLMHead,
        ReformerPreTrainedModel,
    )

else:
  import sys

  __A											=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
