



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 	__lowerCAmelCase							(	*_lowerCAmelCase       ,     **_lowerCAmelCase			):
             pass





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





   @require_torch
   def 	__lowerCAmelCase							(	self			):
         _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 	__lowerCAmelCase							(	self			):
         _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 	__lowerCAmelCase							(	self			):
         _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 	__lowerCAmelCase							(	self			):
         _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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)