import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:




  class 			__magic_name__	:





     """simple docstring"""




     @staticmethod
     def 	lowerCAmelCase    (						*_lowercase       :	Optional[Any]		,	**_lowercase       :	Optional[int]				):



      """simple docstring"""



      pass




@is_pipeline_test
@require_vision
@require_torch
class 			__magic_name__	(		unittest.TestCase     ):





   """simple docstring"""




   lowerCAmelCase   :     Any	      = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



   def 	lowerCAmelCase    (						self       :	Tuple		,	_lowercase       :	Optional[Any]		,	_lowercase       :	Any		,	_lowercase       :	Optional[Any]				):



    """simple docstring"""



    _UpperCamelCase:  int    						=			pipeline(
        '''zero-shot-object-detection'''		,	model='''hf-internal-testing/tiny-random-owlvit-object-detection'''				)

    _UpperCamelCase:  Optional[int]    						=			[
        {
            """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
            """candidate_labels""": ["""cat""", """remote""", """couch"""],
        }
    ]
    return object_detector, examples



   def 	lowerCAmelCase    (						self       :	Optional[int]		,	_lowercase       :	Optional[int]		,	_lowercase       :	Tuple				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			object_detector(examples[0]		,	threshold=0.0				)

    _UpperCamelCase:  List[str]    						=			len(snake_case__				)
    self.assertGreater(snake_case__		,	0				)
    self.assertEqual(
        snake_case__		,	[
            {
                '''score''': ANY(snake_case__				),
                '''label''': ANY(snake_case__				),
                '''box''': {'''xmin''': ANY(snake_case__				), '''ymin''': ANY(snake_case__				), '''xmax''': ANY(snake_case__				), '''ymax''': ANY(snake_case__				)},
            }
            for i in range(snake_case__				)
        ]		,	)



   @require_tf
   @unittest.skip('''Zero Shot Object Detection not implemented in TF'''				)
   def 	lowerCAmelCase    (						self       :	Tuple				):



    """simple docstring"""



    pass



   @require_torch
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  List[Any]    						=			pipeline(
        '''zero-shot-object-detection'''		,	model='''hf-internal-testing/tiny-random-owlvit-object-detection'''				)

    _UpperCamelCase:  Any    						=			object_detector(
        '''./tests/fixtures/tests_samples/COCO/000000039769.png'''		,	candidate_labels=['''cat''', '''remote''', '''couch''']		,	threshold=0.64		,	)

    self.assertEqual(
        nested_simplify(snake_case__		,	decimals=4				)		,	[
            {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
            {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
            {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
            {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
            {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
            {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
            {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
            {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
        ]		,	)

    _UpperCamelCase:  Optional[int]    						=			object_detector(
        [
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]		,	threshold=0.64		,	)

    self.assertEqual(
        nested_simplify(snake_case__		,	decimals=4				)		,	[
            [
                {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            ]
        ]		,	)



   @require_torch
   @slow
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Tuple    						=			pipeline('''zero-shot-object-detection'''				)

    _UpperCamelCase:  List[Any]    						=			object_detector(
        '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,	candidate_labels=['''cat''', '''remote''', '''couch''']		,	)
    self.assertEqual(
        nested_simplify(snake_case__		,	decimals=4				)		,	[
            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
            {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
            {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
        ]		,	)

    _UpperCamelCase:  Optional[Any]    						=			object_detector(
        [
            {
                '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            },
            {
                '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            },
        ]		,	)
    self.assertEqual(
        nested_simplify(snake_case__		,	decimals=4				)		,	[
            [
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ],
            [
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ],
        ]		,	)



   @require_tf
   @unittest.skip('''Zero Shot Object Detection not implemented in TF'''				)
   def 	lowerCAmelCase    (						self       :	int				):



    """simple docstring"""



    pass



   @require_torch
   @slow
   def 	lowerCAmelCase    (						self       :	Union[str, Any]				):



    """simple docstring"""



    _UpperCamelCase:  Optional[int]    						=			0.2
    _UpperCamelCase:  int    						=			pipeline('''zero-shot-object-detection'''				)

    _UpperCamelCase:  List[Any]    						=			object_detector(
        '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,	candidate_labels=['''cat''', '''remote''', '''couch''']		,	threshold=snake_case__		,	)
    self.assertEqual(
        nested_simplify(snake_case__		,	decimals=4				)		,	[
            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
        ]		,	)



   @require_torch
   @slow
   def 	lowerCAmelCase    (						self       :	str				):



    """simple docstring"""



    _UpperCamelCase:  Union[str, Any]    						=			2
    _UpperCamelCase:  str    						=			pipeline('''zero-shot-object-detection'''				)

    _UpperCamelCase:  Tuple    						=			object_detector(
        '''http://images.cocodataset.org/val2017/000000039769.jpg'''		,	candidate_labels=['''cat''', '''remote''', '''couch''']		,	top_k=snake_case__		,	)
    self.assertEqual(
        nested_simplify(snake_case__		,	decimals=4				)		,	[
            {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
            {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
        ]		,	)






from string import ascii_uppercase

UpperCAmelCase_							=				{char: i for i, char in enumerate(ascii_uppercase)}
UpperCAmelCase_							=				dict(enumerate(ascii_uppercase))





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Dict  				=     len(lowercase				)
							lowercase_	:   Tuple  				=     0
							while True:
														if x == i:
																					lowercase_	:   Dict  				=     0
														if len(lowercase				) == len(lowercase				):
																					break
														key += key[i]
														i += 1
							return key





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Any  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in message:
														if letter == " ":
																					cipher_text += " "
														else:
																					lowercase_	:   Any  				=     (dicta[letter] - dicta[key_new[i]]) % 26
																					i += 1
																					cipher_text += dicta[x]
							return cipher_text





def  __magic_name__  (				lowercase				,       lowercase				)				-> str:

							"""simple docstring"""



							lowercase_	:   Optional[Any]  				=     """"""
							lowercase_	:   Optional[Any]  				=     0
							for letter in cipher_text:
														if letter == " ":
																					or_txt += " "
														else:
																					lowercase_	:   Optional[int]  				=     (dicta[letter] + dicta[key_new[i]] + 26) % 26
																					i += 1
																					or_txt += dicta[x]
							return or_txt





def  __magic_name__  (				)				-> None:

							"""simple docstring"""



							lowercase_	:   Union[str, Any]  				=     """THE GERMAN ATTACK"""
							lowercase_	:   str  				=     """SECRET"""
							lowercase_	:   int  				=     generate_key(lowercase				,       lowercase				)
							lowercase_	:   List[str]  				=     cipher_text(lowercase				,       lowercase				)
							print(f"""Encrypted Text = {s}"""				)
							print(f"""Original Text = {original_text(lowercase				,       lowercase				)}"""				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()