



"""simple docstring"""
import unittest

from transformers import is_vision_available
from transformers.pipelines import 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 					a	:



     @staticmethod
     def 	lowerCamelCase__       (						*lowerCAmelCase :					List[Any]       ,		**lowerCAmelCase :					Tuple		)     ->					Optional[int]:


        '''simple docstring'''




        pass
@is_pipeline_test
@require_vision
class 					a	(  unittest.TestCase       ):



  @require_torch
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=pipeline(
         model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""       ,		)
     SCREAMING_SNAKE_CASE_: str     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     SCREAMING_SNAKE_CASE_: Any     			=image_classifier(lowerCAmelCase       ,		candidate_labels=["""a""", """b""", """c"""]		)

     # The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
     # python and torch versions.
     self.assertIn(
         nested_simplify(lowerCAmelCase		)       ,		[
             [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}],
             [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """c"""}, {"""score""": 0.3_3_3, """label""": """b"""}],
         ]       ,		)

     SCREAMING_SNAKE_CASE_: str     			=image_classifier([image] * 5       ,		candidate_labels=["""A""", """B""", """C"""]       ,		batch_size=2		)
     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
         ]       ,		)



  @require_tf
  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=pipeline(
         model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""       ,		framework="""tf"""		)
     SCREAMING_SNAKE_CASE_: str     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     SCREAMING_SNAKE_CASE_: Dict     			=image_classifier(lowerCAmelCase       ,		candidate_labels=["""a""", """b""", """c"""]		)

     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}]       ,		)

     SCREAMING_SNAKE_CASE_: Dict     			=image_classifier([image] * 5       ,		candidate_labels=["""A""", """B""", """C"""]       ,		batch_size=2		)
     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
             [
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
                 {"""score""": 0.3_3_3, """label""": ANY(lowerCAmelCase		)},
             ],
         ]       ,		)



  @slow
  @require_torch
  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=pipeline(
         task="""zero-shot-image-classification"""       ,		model="""openai/clip-vit-base-patch32"""       ,		)
     # This is an image of 2 cats with remotes and no planes
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=image_classifier(lowerCAmelCase       ,		candidate_labels=["""cat""", """plane""", """remote"""]		)

     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[
             {"""score""": 0.5_1_1, """label""": """remote"""},
             {"""score""": 0.4_8_5, """label""": """cat"""},
             {"""score""": 0.0_0_4, """label""": """plane"""},
         ]       ,		)

     SCREAMING_SNAKE_CASE_: str     			=image_classifier([image] * 5       ,		candidate_labels=["""cat""", """plane""", """remote"""]       ,		batch_size=2		)
     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[
             [
                 {"""score""": 0.5_1_1, """label""": """remote"""},
                 {"""score""": 0.4_8_5, """label""": """cat"""},
                 {"""score""": 0.0_0_4, """label""": """plane"""},
             ],
         ]
         * 5       ,		)





  @slow
  @require_tf
  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=pipeline(
         task="""zero-shot-image-classification"""       ,		model="""openai/clip-vit-base-patch32"""       ,		framework="""tf"""		)
     # This is an image of 2 cats with remotes and no planes
     SCREAMING_SNAKE_CASE_: Tuple     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
     SCREAMING_SNAKE_CASE_: Tuple     			=image_classifier(lowerCAmelCase       ,		candidate_labels=["""cat""", """plane""", """remote"""]		)
     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[
             {"""score""": 0.5_1_1, """label""": """remote"""},
             {"""score""": 0.4_8_5, """label""": """cat"""},
             {"""score""": 0.0_0_4, """label""": """plane"""},
         ]       ,		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=image_classifier([image] * 5       ,		candidate_labels=["""cat""", """plane""", """remote"""]       ,		batch_size=2		)
     self.assertEqual(
         nested_simplify(lowerCAmelCase		)       ,		[
             [
                 {"""score""": 0.5_1_1, """label""": """remote"""},
                 {"""score""": 0.4_8_5, """label""": """cat"""},
                 {"""score""": 0.0_0_4, """label""": """plane"""},
             ],
         ]
         * 5       ,		)








"""simple docstring"""
import string





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=""""""
   for i in sequence:
      SCREAMING_SNAKE_CASE_: List[Any]     			=ord(lowercase )
      if 65 <= extract <= 90:
         output += chr(155 - extract )
      elif 97 <= extract <= 122:
         output += chr(219 - extract )
      else:
         output += i
   return output





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=string.ascii_letters
   SCREAMING_SNAKE_CASE_: Tuple     			=string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
   return "".join(
       letters_reversed[letters.index(lowercase )] if c in letters else c for c in sequence )





def      __magic_name__				(							):
   from timeit import timeit

   print("""Running performance benchmarks...""" )
   SCREAMING_SNAKE_CASE_: int     			="""from string import printable ; from __main__ import atbash, atbash_slow"""
   print(f'''> atbash_slow(): {timeit("atbash_slow(printable)"							,			setup=lowercase )} seconds''' )
   print(f'''>      atbash(): {timeit("atbash(printable)"							,			setup=lowercase )} seconds''' )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(f"""{example} encrypted in atbash: {atbash(example)}""")
   benchmark()



