



'''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 	UpperCAmelCase	:


   '''simple docstring'''




   @staticmethod
   def       lowercase_  (   *lowerCAmelCase_							,  **lowerCAmelCase_) ->      Optional[int]:



        """simple docstring"""







        pass







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


 '''simple docstring'''




 __magic_name__					: int  =      MODEL_FOR_OBJECT_DETECTION_MAPPING




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







      a_       		=ObjectDetectionPipeline(model=lowerCAmelCase_							,  image_processor=lowerCAmelCase_)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Optional[int]:



      """simple docstring"""







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

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

      import datasets

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

      a_       		=[
          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_       		=object_detector(lowerCAmelCase_							,  threshold=0.0)

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




 @require_tf
 @unittest.skip("Object detection not implemented in TF")
 def       lowercase_  (   self) ->      List[Any]:



      """simple docstring"""







      pass




 @require_torch
 def       lowercase_  (   self) ->      List[str]:



      """simple docstring"""







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

      a_       		=AutoModelForObjectDetection.from_pretrained(lowerCAmelCase_)
      a_       		=AutoFeatureExtractor.from_pretrained(lowerCAmelCase_)
      a_       		=ObjectDetectionPipeline(model=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_)

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

      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
              {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
          ]							,  )

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

      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              [
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
              ],
              [
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
                  {"score": 0.3_3_7_6, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
              ],
          ]							,  )




 @require_torch
 @slow
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      a_       		="facebook/detr-resnet-50"

      a_       		=AutoModelForObjectDetection.from_pretrained(lowerCAmelCase_)
      a_       		=AutoFeatureExtractor.from_pretrained(lowerCAmelCase_)
      a_       		=ObjectDetectionPipeline(model=lowerCAmelCase_							,  feature_extractor=lowerCAmelCase_)

      a_       		=object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
              {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
              {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
              {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
              {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
          ]							,  )

      a_       		=object_detector(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ])
      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
          ]							,  )




 @require_torch
 @slow
 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		="facebook/detr-resnet-50"

      a_       		=pipeline("object-detection"							,  model=lowerCAmelCase_)

      a_       		=object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
              {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
              {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
              {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
              {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
          ]							,  )

      a_       		=object_detector(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ])
      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
              [
                  {"score": 0.9_9_8_2, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
                  {"score": 0.9_9_6_0, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
                  {"score": 0.9_9_5_5, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
                  {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
                  {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
              ],
          ]							,  )




 @require_torch
 @slow
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      a_       		=0.9_9_8_5
      a_       		="facebook/detr-resnet-50"

      a_       		=pipeline("object-detection"							,  model=lowerCAmelCase_)

      a_       		=object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"							,  threshold=lowerCAmelCase_)
      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              {"score": 0.9_9_8_8, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
              {"score": 0.9_9_8_7, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
          ]							,  )




 @require_torch
 @require_pytesseract
 @slow
 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







      a_       		="Narsil/layoutlmv3-finetuned-funsd"
      a_       		=0.9_9_9_3

      a_       		=pipeline("object-detection"							,  model=lowerCAmelCase_							,  threshold=lowerCAmelCase_)

      a_       		=object_detector(
          "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png")
      self.assertEqual(
          nested_simplify(lowerCAmelCase_							,  decimals=4)							,  [
              {"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
              {"score": 0.9_9_9_3, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
          ]							,  )







'''simple docstring'''


import os
from math import logaa






def  UpperCAmelCase_       (		lowercase__ = "base_exp.txt"       ):


     '''simple docstring'''







     a_       		=0
     a_       		=0
     for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__       )       ,	lowercase__       )       )       ):
          a_		,						a_       		=list(map(lowercase__       ,	line.split(","       )       )       )
          if x * logaa(lowercase__       ) > largest:
               a_       		=x * logaa(lowercase__       )
               a_       		=i + 1
     return result


if __name__ == "__main__":
  print(solution())


