




"""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    :






     @staticmethod
     def 	_lowerCAmelCase   (		*UpperCamelCase       :		Union[str, Any]   ,     **UpperCamelCase       :		Dict   )			->						int:

           """simple docstring"""
           pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _lowerCamelCase    (					unittest.TestCase						):
   _lowerCamelCase    :int     				=     MODEL_FOR_OBJECT_DETECTION_MAPPING






   def 	_lowerCAmelCase   (		self       :		List[Any]   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		int   ,     UpperCamelCase       :		Any   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	ObjectDetectionPipeline(model=UpperCamelCase   ,     image_processor=UpperCamelCase   )
         return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






   def 	_lowerCAmelCase   (		self       :		Tuple   ,     UpperCamelCase       :		str   ,     UpperCamelCase       :		Tuple   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			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

         lowerCAmelCase__      :			Optional[int]   =	datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   ,     """image"""   ,     split="""test"""   )

         lowerCAmelCase__      :			Dict   =	[
             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"""],
         ]
         lowerCAmelCase__      :			List[Any]   =	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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						int:

         """simple docstring"""
         pass






   @require_torch
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	"""hf-internal-testing/tiny-detr-mobilenetsv3"""

         lowerCAmelCase__      :			Tuple   =	AutoModelForObjectDetection.from_pretrained(UpperCamelCase   )
         lowerCAmelCase__      :			Union[str, Any]   =	AutoFeatureExtractor.from_pretrained(UpperCamelCase   )
         lowerCAmelCase__      :			str   =	ObjectDetectionPipeline(model=UpperCamelCase   ,     feature_extractor=UpperCamelCase   )

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

         self.assertEqual(
             nested_simplify(UpperCamelCase   ,     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}},
             ]   ,     )

         lowerCAmelCase__      :			Dict   =	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""": 0.3376, """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 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Dict:

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

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

         lowerCAmelCase__      :			str   =	object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
         self.assertEqual(
             nested_simplify(UpperCamelCase   ,     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}},
             ]   ,     )

         lowerCAmelCase__      :			str   =	object_detector(
             [
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
                 """http://images.cocodataset.org/val2017/000000039769.jpg""",
             ]   )
         self.assertEqual(
             nested_simplify(UpperCamelCase   ,     decimals=4   )   ,     [
                 [
                     {"""score""": 0.9982, """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 	_lowerCAmelCase   (		self       :		Optional[int]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	"""facebook/detr-resnet-50"""

         lowerCAmelCase__      :			int   =	pipeline("""object-detection"""   ,     model=UpperCamelCase   )

         lowerCAmelCase__      :			int   =	object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   )
         self.assertEqual(
             nested_simplify(UpperCamelCase   ,     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}},
             ]   ,     )

         lowerCAmelCase__      :			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""": 0.9982, """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 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						int:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	0.9985
         lowerCAmelCase__      :			List[Any]   =	"""facebook/detr-resnet-50"""

         lowerCAmelCase__      :			List[str]   =	pipeline("""object-detection"""   ,     model=UpperCamelCase   )

         lowerCAmelCase__      :			List[str]   =	object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""   ,     threshold=UpperCamelCase   )
         self.assertEqual(
             nested_simplify(UpperCamelCase   ,     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 	_lowerCAmelCase   (		self       :		int   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	"""Narsil/layoutlmv3-finetuned-funsd"""
         lowerCAmelCase__      :			str   =	0.9993

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

         lowerCAmelCase__      :			Dict   =	object_detector(
             """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""   )
         self.assertEqual(
             nested_simplify(UpperCamelCase   ,     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}},
             ]   ,     )











"""simple docstring"""


import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    List[Any]:
      # Initialise PyTorch model
      lowerCAmelCase__      :			int   =	TaConfig.from_json_file(__UpperCAmelCase				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			Optional[int]   =	TaForConditionalGeneration(__UpperCAmelCase				)

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)

      # Save pytorch-model
      print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
      model.save_pretrained(__UpperCAmelCase				)


if __name__ == "__main__":
  _A													=      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A													=      parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





