




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 				a__ (				*A_					:  List[str]						,							**A_					:  str  )   ->	Optional[int]:

        """simple docstring"""






        pass







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



 '''simple docstring'''


 UpperCamelCase						   =						MODEL_FOR_OBJECT_DETECTION_MAPPING

 def 				a__ (				self					:  Union[str, Any]						,							A_					:  Optional[int]						,							A_					:  Optional[int]						,							A_					:  List[str]  )   ->	int:

       """simple docstring"""






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

 def 				a__ (				self					:  int						,							A_					:  Dict						,							A_					:  str  )   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'						,							threshold=0.0  )

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

       import datasets

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

       lowerCamelCase_		     =    [
           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_		     =    object_detector(A_						,							threshold=0.0  )

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

 @require_tf
 @unittest.skip('Object detection not implemented in TF'  )
 def 				a__ (				self					:  str  )   ->	Dict:

       """simple docstring"""






       pass

 @require_torch
 def 				a__ (				self					:  Union[str, Any]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    'hf-internal-testing/tiny-detr-mobilenetsv3'

       lowerCamelCase_		     =    AutoModelForObjectDetection.from_pretrained(A_  )
       lowerCamelCase_		     =    AutoFeatureExtractor.from_pretrained(A_  )
       lowerCamelCase_		     =    ObjectDetectionPipeline(model=A_						,							feature_extractor=A_  )

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

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

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

       self.assertEqual(
           nested_simplify(A_						,							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 				a__ (				self					:  Any  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    'facebook/detr-resnet-50'

       lowerCamelCase_		     =    AutoModelForObjectDetection.from_pretrained(A_  )
       lowerCamelCase_		     =    AutoFeatureExtractor.from_pretrained(A_  )
       lowerCamelCase_		     =    ObjectDetectionPipeline(model=A_						,							feature_extractor=A_  )

       lowerCamelCase_		     =    object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
       self.assertEqual(
           nested_simplify(A_						,							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}},
           ]						,							)

       lowerCamelCase_		     =    object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]  )
       self.assertEqual(
           nested_simplify(A_						,							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 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    'facebook/detr-resnet-50'

       lowerCamelCase_		     =    pipeline('object-detection'						,							model=A_  )

       lowerCamelCase_		     =    object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
       self.assertEqual(
           nested_simplify(A_						,							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}},
           ]						,							)

       lowerCamelCase_		     =    object_detector(
           [
               'http://images.cocodataset.org/val2017/000000039769.jpg',
               'http://images.cocodataset.org/val2017/000000039769.jpg',
           ]  )
       self.assertEqual(
           nested_simplify(A_						,							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 				a__ (				self					:  Optional[Any]  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    0.9985
       lowerCamelCase_		     =    'facebook/detr-resnet-50'

       lowerCamelCase_		     =    pipeline('object-detection'						,							model=A_  )

       lowerCamelCase_		     =    object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						,							threshold=A_  )
       self.assertEqual(
           nested_simplify(A_						,							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 				a__ (				self					:  Union[str, Any]  )   ->	Optional[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    'Narsil/layoutlmv3-finetuned-funsd'
       lowerCamelCase_		     =    0.9993

       lowerCamelCase_		     =    pipeline('object-detection'						,							model=A_						,							threshold=A_  )

       lowerCamelCase_		     =    object_detector(
           'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'  )
       self.assertEqual(
           nested_simplify(A_						,							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}},
           ]						,							)









from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
 lowerCamelCase				:		int     =   input("Enter image url: ").strip()
 print(F"""Downloading image from {url} ...""")
 lowerCamelCase				:		Tuple     =   BeautifulSoup(requests.get(url).content, "html.parser")
 # The image URL is in the content field of the first meta tag with property og:image
 lowerCamelCase				:		int     =   soup.find("meta", {"property": "og:image"})["content"]
 lowerCamelCase				:		Dict     =   requests.get(image_url).content
 lowerCamelCase				:		Optional[int]     =   F"""{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg"""
 with open(file_name, "wb") as fp:
  fp.write(image_data)
 print(F"""Done. Image saved to disk as {file_name}.""")



