






'''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}},
           ]  ,   )











def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  len(_UpperCamelCase) if (len(_UpperCamelCase) > 7) else 7

      # Print table header for output
      print(
          'Symbol'.center(8)       ,							'Stack'.center(_UpperCamelCase)       ,							'Postfix'.center(_UpperCamelCase)       ,							sep=' | '       ,							)
      print('-' * (print_width * 3 + 7))

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(_UpperCamelCase)  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(_UpperCamelCase)  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(_UpperCamelCase) == 0:
                        stack.append(_UpperCamelCase)  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(_UpperCamelCase) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop())  # pop stack & add to Postfix
                        stack.append(_UpperCamelCase)  # push x to stack

            print(
                x.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      while len(_UpperCamelCase) > 0:  # while stack is not empty
            post_fix.append(stack.pop())  # pop stack & add to Postfix
            print(
                ' '.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      return "".join(_UpperCamelCase)  # return Postfix as str
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

      return (infix_2_postfix(''.join(_UpperCamelCase)))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



