




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     SCREAMING_SNAKE_CASE__		:
           @staticmethod
           def        A__      (						*__lowerCamelCase	:		Optional[Any]       ,						**__lowerCamelCase	:		Tuple						):



             """simple docstring"""
             pass





@is_pipeline_test
@require_vision
class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     @require_torch
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''       ,						)
       lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       lowerCAmelCase__										=      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''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}],
               [{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''c'''}, {'''score''': 0.333, '''label''': '''b'''}],
           ]       ,						)

       lowerCAmelCase__										=      image_classifier([image] * 5       ,						candidate_labels=['''A''', '''B''', '''C''']       ,						batch_size=2						)
       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
           ]       ,						)
     @require_tf
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      pipeline(
           model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''       ,						framework='''tf'''						)
       lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       lowerCAmelCase__										=      image_classifier(__lowerCamelCase       ,						candidate_labels=['''a''', '''b''', '''c''']						)

       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[{'''score''': 0.333, '''label''': '''a'''}, {'''score''': 0.333, '''label''': '''b'''}, {'''score''': 0.333, '''label''': '''c'''}]       ,						)

       lowerCAmelCase__										=      image_classifier([image] * 5       ,						candidate_labels=['''A''', '''B''', '''C''']       ,						batch_size=2						)
       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
               [
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
                   {'''score''': 0.333, '''label''': ANY(__lowerCamelCase						)},
               ],
           ]       ,						)
     @slow
     @require_torch
     def        A__      (						self	:		Union[str, Any]						):



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

       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]       ,						)

       lowerCAmelCase__										=      image_classifier([image] * 5       ,						candidate_labels=['''cat''', '''plane''', '''remote''']       ,						batch_size=2						)
       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5       ,						)



     @slow
     @require_tf
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      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
       lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       lowerCAmelCase__										=      image_classifier(__lowerCamelCase       ,						candidate_labels=['''cat''', '''plane''', '''remote''']						)
       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[
               {'''score''': 0.511, '''label''': '''remote'''},
               {'''score''': 0.485, '''label''': '''cat'''},
               {'''score''': 0.004, '''label''': '''plane'''},
           ]       ,						)

       lowerCAmelCase__										=      image_classifier([image] * 5       ,						candidate_labels=['''cat''', '''plane''', '''remote''']       ,						batch_size=2						)
       self.assertEqual(
           nested_simplify(__lowerCamelCase						)       ,						[
               [
                   {'''score''': 0.511, '''label''': '''remote'''},
                   {'''score''': 0.485, '''label''': '''cat'''},
                   {'''score''': 0.004, '''label''': '''plane'''},
               ],
           ]
           * 5       ,						)







from __future__ import annotations
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase = None				):
  lowerCAmelCase__										=      word_bank or []
  # create a table
  lowerCAmelCase__										=      len(__lowerCAmelCase				) + 1

  lowerCAmelCase__										=      []
  for _ in range(__lowerCAmelCase				):
    table.append([]				)
  # seed value
  lowerCAmelCase__										=      [[]]  # because empty string has empty combination

  # iterate through the indices
  for i in range(__lowerCAmelCase				):
    # condition
    if table[i] != []:
      for word in word_bank:
        # slice condition
        if target[i : i + len(__lowerCAmelCase				)] == word:
          lowerCAmelCase__										=      [
              [word, *way] for way in table[i]
          ]
          # adds the word to every combination the current position holds
          # now,push that combination to the table[i+len(word)]
          table[i + len(__lowerCAmelCase				)] += new_combinations

    # combinations are in reverse order so reverse for better output
  for combination in table[len(__lowerCAmelCase				)]:
    combination.reverse()

  return table[len(__lowerCAmelCase				)]


if __name__ == "__main__":
      print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
      print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
      print(
          all_construct(
              """hexagonosaurus""",
              ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
          )
      )

