
"""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_	:




      '''simple docstring'''







      @staticmethod
      def        UpperCamelCase__					(		*lowercase_				,   **lowercase_  ):

         """simple docstring"""
         pass


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




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Dict       =		MODEL_FOR_OBJECT_DETECTION_MAPPING







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    ObjectDetectionPipeline(model=_UpperCamelCase				,   image_processor=_UpperCamelCase  )
     return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,   threshold=0.0  )

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

     import datasets

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

     UpperCAmelCase_					:					List[str]			     =    [
         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"],
     ]
     UpperCAmelCase_					:					int			     =    object_detector(_UpperCamelCase				,   threshold=0.0  )

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







  @require_tf
  @unittest.skip("Object detection not implemented in TF"  )
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  @require_torch
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    "hf-internal-testing/tiny-detr-mobilenetsv3"

     UpperCAmelCase_					:					Union[str, Any]			     =    AutoModelForObjectDetection.from_pretrained(_UpperCamelCase  )
     UpperCAmelCase_					:					Optional[Any]			     =    AutoFeatureExtractor.from_pretrained(_UpperCamelCase  )
     UpperCAmelCase_					:					Any			     =    ObjectDetectionPipeline(model=_UpperCamelCase				,   feature_extractor=_UpperCamelCase  )

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

     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
             {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
         ]				,   )

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

     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             [
                 {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                 {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
             ],
             [
                 {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
                 {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
             ],
         ]				,   )







  @require_torch
  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    "facebook/detr-resnet-50"

     UpperCAmelCase_					:					int			     =    AutoModelForObjectDetection.from_pretrained(_UpperCamelCase  )
     UpperCAmelCase_					:					Any			     =    AutoFeatureExtractor.from_pretrained(_UpperCamelCase  )
     UpperCAmelCase_					:					int			     =    ObjectDetectionPipeline(model=_UpperCamelCase				,   feature_extractor=_UpperCamelCase  )

     UpperCAmelCase_					:					Optional[Any]			     =    object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"  )
     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
             {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
             {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
             {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
             {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
         ]				,   )

     UpperCAmelCase_					:					Union[str, Any]			     =    object_detector(
         [
             "http://images.cocodataset.org/val2017/000000039769.jpg",
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]  )
     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             [
                 {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                 {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                 {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                 {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                 {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
             [
                 {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                 {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                 {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                 {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                 {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         ]				,   )







  @require_torch
  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Tuple			     =    "facebook/detr-resnet-50"

     UpperCAmelCase_					:					List[Any]			     =    pipeline("object-detection"				,   model=_UpperCamelCase  )

     UpperCAmelCase_					:					str			     =    object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"  )
     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
             {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
             {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
             {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
             {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
         ]				,   )

     UpperCAmelCase_					:					int			     =    object_detector(
         [
             "http://images.cocodataset.org/val2017/000000039769.jpg",
             "http://images.cocodataset.org/val2017/000000039769.jpg",
         ]  )
     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             [
                 {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                 {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                 {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                 {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                 {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
             [
                 {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                 {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                 {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
                 {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
                 {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         ]				,   )







  @require_torch
  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    0.99_85
     UpperCAmelCase_					:					str			     =    "facebook/detr-resnet-50"

     UpperCAmelCase_					:					Union[str, Any]			     =    pipeline("object-detection"				,   model=_UpperCamelCase  )

     UpperCAmelCase_					:					Any			     =    object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,   threshold=_UpperCamelCase  )
     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
             {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
         ]				,   )







  @require_torch
  @require_pytesseract
  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    "Narsil/layoutlmv3-finetuned-funsd"
     UpperCAmelCase_					:					Optional[Any]			     =    0.99_93

     UpperCAmelCase_					:					List[Any]			     =    pipeline("object-detection"				,   model=_UpperCamelCase				,   threshold=_UpperCamelCase  )

     UpperCAmelCase_					:					List[Any]			     =    object_detector(
         "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"  )
     self.assertEqual(
         nested_simplify(_UpperCamelCase				,   decimals=4  )				,   [
             {"score": 0.99_93, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
             {"score": 0.99_93, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
         ]				,   )




from typing import TYPE_CHECKING

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


_A   =     {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XGLMForCausalLM",
			    "XGLMModel",
			    "XGLMPreTrainedModel",
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "FlaxXGLMForCausalLM",
			    "FlaxXGLMModel",
			    "FlaxXGLMPreTrainedModel",
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXGLMForCausalLM",
			    "TFXGLMModel",
			    "TFXGLMPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure)



