






'''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 					A__   (			*UpperCAmelCase      :		Optional[int]  ,   **UpperCAmelCase      :		Any		)	->	int:


             '''simple docstring'''

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




      """simple docstring"""




      UpperCamelCase_             =      MODEL_FOR_OBJECT_DETECTION_MAPPING






      def 					A__   (			self      :		Optional[Any]  ,   UpperCAmelCase      :		Any  ,   UpperCAmelCase      :		Optional[Any]  ,   UpperCAmelCase      :		Dict		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=ObjectDetectionPipeline(model=UpperCAmelCase  ,   image_processor=UpperCAmelCase		)
       return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






      def 					A__   (			self      :		Optional[Any]  ,   UpperCAmelCase      :		Tuple  ,   UpperCAmelCase      :		Optional[Any]		)	->	Any:


       '''simple docstring'''

       lowercase       : List[str]						=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

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

       lowercase       : 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(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 					A__   (			self      :		Any		)	->	Dict:


       '''simple docstring'''

       pass






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


       '''simple docstring'''

       lowercase       : Union[str, Any]						='''hf-internal-testing/tiny-detr-mobilenetsv3'''

       lowercase       : List[str]						=AutoModelForObjectDetection.from_pretrained(UpperCAmelCase		)
       lowercase       : List[Any]						=AutoFeatureExtractor.from_pretrained(UpperCAmelCase		)
       lowercase       : Tuple						=ObjectDetectionPipeline(model=UpperCAmelCase  ,   feature_extractor=UpperCAmelCase		)

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

       self.assertEqual(
           nested_simplify(UpperCAmelCase  ,   decimals=4		)  ,   [
               {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
               {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
           ]  ,   )

       lowercase       : Union[str, Any]						=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''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
                   {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
               ],
               [
                   {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
                   {'''score''': 0.3_3_7_6, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 159, '''ymin''': 120, '''xmax''': 480, '''ymax''': 359}},
               ],
           ]  ,   )






      @require_torch
      @slow
      def 					A__   (			self      :		Dict		)	->	str:


       '''simple docstring'''

       lowercase       : Optional[int]						='''facebook/detr-resnet-50'''

       lowercase       : Any						=AutoModelForObjectDetection.from_pretrained(UpperCAmelCase		)
       lowercase       : str						=AutoFeatureExtractor.from_pretrained(UpperCAmelCase		)
       lowercase       : int						=ObjectDetectionPipeline(model=UpperCAmelCase  ,   feature_extractor=UpperCAmelCase		)

       lowercase       : Optional[Any]						=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase  ,   decimals=4		)  ,   [
               {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
               {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
               {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
               {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
               {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
           ]  ,   )

       lowercase       : Optional[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''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                   {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                   {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                   {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                   {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
               ],
               [
                   {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                   {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                   {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                   {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                   {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
               ],
           ]  ,   )






      @require_torch
      @slow
      def 					A__   (			self      :		Optional[int]		)	->	Dict:


       '''simple docstring'''

       lowercase       : List[str]						='''facebook/detr-resnet-50'''

       lowercase       : List[str]						=pipeline('''object-detection'''  ,   model=UpperCAmelCase		)

       lowercase       : List[str]						=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase  ,   decimals=4		)  ,   [
               {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
               {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
               {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
               {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
               {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
           ]  ,   )

       lowercase       : 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''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                   {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                   {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                   {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                   {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
               ],
               [
                   {'''score''': 0.9_9_8_2, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 175, '''ymax''': 117}},
                   {'''score''': 0.9_9_6_0, '''label''': '''remote''', '''box''': {'''xmin''': 333, '''ymin''': 72, '''xmax''': 368, '''ymax''': 187}},
                   {'''score''': 0.9_9_5_5, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 639, '''ymax''': 473}},
                   {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
                   {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
               ],
           ]  ,   )






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


       '''simple docstring'''

       lowercase       : Any						=0.9_9_8_5
       lowercase       : int						='''facebook/detr-resnet-50'''

       lowercase       : Any						=pipeline('''object-detection'''  ,   model=UpperCAmelCase		)

       lowercase       : str						=object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,   threshold=UpperCAmelCase		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase  ,   decimals=4		)  ,   [
               {'''score''': 0.9_9_8_8, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 314, '''ymax''': 470}},
               {'''score''': 0.9_9_8_7, '''label''': '''cat''', '''box''': {'''xmin''': 345, '''ymin''': 23, '''xmax''': 640, '''ymax''': 368}},
           ]  ,   )






      @require_torch
      @require_pytesseract
      @slow
      def 					A__   (			self      :		Optional[Any]		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[int]						='''Narsil/layoutlmv3-finetuned-funsd'''
       lowercase       : Dict						=0.9_9_9_3

       lowercase       : Any						=pipeline('''object-detection'''  ,   model=UpperCAmelCase  ,   threshold=UpperCAmelCase		)

       lowercase       : Any						=object_detector(
           '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''		)
       self.assertEqual(
           nested_simplify(UpperCAmelCase  ,   decimals=4		)  ,   [
               {'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
               {'''score''': 0.9_9_9_3, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 294, '''ymin''': 254, '''xmax''': 343, '''ymax''': 264}},
           ]  ,   )





# Function to print upper half of diamond (pyramid)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(0		,    __magic_name__						):
								for _ in range(0		,    n - i - 1						):  # printing spaces
												print(" "		,    end=""						)
								for _ in range(0		,    i + 1						):  # printing stars
												print("* "		,    end=""						)
								print()

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				for i in range(__magic_name__		,    0		,    -1						):
								for _ in range(__magic_name__		,    0		,    -1						):  # printing stars
												print("* "		,    end=""						)
								print()
								for _ in range(n - i + 1		,    0		,    -1						):  # printing spaces
												print(" "		,    end=""						)

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				if n <= 0:
								print("       ...       ....        nothing printing :("						)
								return
				floyd(__magic_name__						)  # upper half
				reverse_floyd(__magic_name__						)  # lower half


if __name__ == "__main__":
						print(r"| /\ | |- |  |-  |--| |\  /| |-")
						print(r"|/  \| |- |_ |_  |__| | \/ | |_")
						_snake_case      :							Union[str, Any]     	=    1
						while K:
												_snake_case      :							Dict     	=    int(input("enter the number and , and see the magic : "))
												print()
												pretty_print(user_number)
												_snake_case      :							Tuple     	=    int(input("press 0 to exit... and 1 to continue..."))

						print("Good Bye...")
