

"""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      _UpperCAmelCase   :



					@staticmethod
					def        a  (  *_lowercase     :       Optional[int]			,    **_lowercase     :       List[str]						):
						pass
@is_pipeline_test
@require_vision
class      _UpperCAmelCase   (							unittest.TestCase       ):



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

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



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

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

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



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

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

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



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

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


"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'				)

          if text is not None:
              UpperCAmelCase          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if text is not None and images is not None:
              UpperCAmelCase          =       image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_				) ,							tensor_type=lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



