



'''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     lowercase_ (   *__snake_case				:						List[Any]		,    **__snake_case				:						Union[str, Any]			):
           pass

@is_pipeline_test
@require_vision
class 							a__(      unittest.TestCase			):







    @require_torch
    def     lowercase_ (   self				:						Tuple			):
      a		:      List[str]       =  pipeline(
          model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'		,    )
      a		:      List[str]       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      a		:      List[str]       =  image_classifier(__snake_case		,    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(__snake_case			)		,    [
              [{'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'}],
          ]		,    )

      a		:      List[Any]       =  image_classifier([image] * 5		,    candidate_labels=['A', 'B', 'C']		,    batch_size=2			)
      self.assertEqual(
          nested_simplify(__snake_case			)		,    [
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
          ]		,    )







    @require_tf
    def     lowercase_ (   self				:						int			):
      a		:      Dict       =  pipeline(
          model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification'		,    framework='tf'			)
      a		:      Dict       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      a		:      Union[str, Any]       =  image_classifier(__snake_case		,    candidate_labels=['a', 'b', 'c']			)

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

      a		:      List[str]       =  image_classifier([image] * 5		,    candidate_labels=['A', 'B', 'C']		,    batch_size=2			)
      self.assertEqual(
          nested_simplify(__snake_case			)		,    [
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
              [
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
                  {'score': 0.333, 'label': ANY(__snake_case			)},
              ],
          ]		,    )







    @slow
    @require_torch
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Dict       =  pipeline(
          task='zero-shot-image-classification'		,    model='openai/clip-vit-base-patch32'		,    )
      # This is an image of 2 cats with remotes and no planes
      a		:      Optional[int]       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      a		:      Dict       =  image_classifier(__snake_case		,    candidate_labels=['cat', 'plane', 'remote']			)

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

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







    @slow
    @require_tf
    def     lowercase_ (   self				:						List[Any]			):
      a		:      int       =  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
      a		:      Optional[int]       =  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      a		:      Optional[int]       =  image_classifier(__snake_case		,    candidate_labels=['cat', 'plane', 'remote']			)
      self.assertEqual(
          nested_simplify(__snake_case			)		,    [
              {'score': 0.511, 'label': 'remote'},
              {'score': 0.485, 'label': 'cat'},
              {'score': 0.004, 'label': 'plane'},
          ]		,    )

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




'''simple docstring'''






from pathlib import Path

import fire
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  Path(_A       )
  a		:      Tuple       =  Path(_A       )
  dest_dir.mkdir(exist_ok=_A       )
  for path in src_dir.iterdir():
    a		:      Tuple       =  [x.rstrip() for x in list(path.open().readlines()       )][:n]
    a		:      List[str]       =  dest_dir.joinpath(path.name       )
    print(_A       )
    dest_path.open('w'       ).write('\n'.join(_A       )       )


if __name__ == "__main__":
     fire.Fire(minify)