



import sys

from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core


# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers

UpperCAmelCase_		 =					"""python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
   pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
   pkgs_to_check_at_runtime.append("""importlib_metadata""")

for pkg in pkgs_to_check_at_runtime:
   if pkg in deps:
      if pkg == "tokenizers":
         # must be loaded here, or else tqdm check may fail
         from .utils import is_tokenizers_available

         if not is_tokenizers_available():
            continue  # not required, check version only if installed

      require_version_core(deps[pkg])
   else:
      raise ValueError(f'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:Union[str, Any]=None				)  ->     Any:
   require_version(deps[pkg]	,   _snake_case				)








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 	lowerCamelCase__	:



    """simple docstring"""

    @staticmethod
    def     snake_case_      (    *__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Any      )   ->	Any:
       pass


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



 """simple docstring"""

 @require_torch
 def     snake_case_      (    self					:					Tuple      )   ->	Tuple:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						)
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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'''}],
        ]			,						)

    _A      =       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     snake_case_      (    self					:					int      )   ->	Optional[int]:
    _A      =       pipeline(
        model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,						framework='''tf'''      )
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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'''}]			,						)

    _A      =       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     snake_case_      (    self					:					Optional[int]      )   ->	int:
    _A      =       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      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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'''},
        ]			,						)

    _A      =       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     snake_case_      (    self					:					Optional[int]      )   ->	Dict:
    _A      =       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      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    _A      =       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'''},
        ]			,						)

    _A      =       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			,						)



