


import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:

  class   lowerCamelCase						:

         @staticmethod
         def 					UpperCAmelCase					(      *a__						,    **a__       ):






          '''simple docstring'''






          pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class   lowerCamelCase						(	unittest.TestCase  ):
       _UpperCamelCase     		=   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
       _UpperCamelCase     		=   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Dict							=   ImageClassificationPipeline(model=a__						,    image_processor=a__						,    top_k=2       )
        snake_case :			Optional[Any]							=   [
            Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
            """http://images.cocodataset.org/val2017/000000039769.jpg""",
        ]
        return image_classifier, examples

       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			int							=   image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )

        self.assertEqual(
            a__						,    [
                {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
            ]						,    )

        import datasets

        snake_case :			List[str]							=   datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''						,    '''image'''						,    split='''test'''       )

        # Accepts URL + PIL.Image + lists
        snake_case :			str							=   image_classifier(
            [
                Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ),
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                # RGBA
                dataset[0]['''file'''],
                # LA
                dataset[1]['''file'''],
                # L
                dataset[2]['''file'''],
            ]       )
        self.assertEqual(
            a__						,    [
                [
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                ],
                [
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                ],
                [
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                ],
                [
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                ],
                [
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                    {'''score''': ANY(a__       ), '''label''': ANY(a__       )},
                ],
            ]						,    )

       @require_torch
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   """hf-internal-testing/tiny-random-vit"""
        snake_case :			Optional[Any]							=   pipeline('''image-classification'''						,    model=a__       )

        snake_case :			int							=   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]						,    )

        snake_case :			Any							=   image_classifier(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ]						,    top_k=2						,    )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [
                [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
                [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
            ]						,    )

       @require_tf
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   """hf-internal-testing/tiny-random-vit"""
        snake_case :			Tuple							=   pipeline('''image-classification'''						,    model=a__						,    framework='''tf'''       )

        snake_case :			str							=   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]						,    )

        snake_case :			List[str]							=   image_classifier(
            [
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                '''http://images.cocodataset.org/val2017/000000039769.jpg''',
            ]						,    top_k=2						,    )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [
                [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
                [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
            ]						,    )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   PreTrainedTokenizer()

        # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
        snake_case :			Dict							=   pipeline(
            '''image-classification'''						,    model='''hf-internal-testing/tiny-random-vit'''						,    tokenizer=a__       )

        self.assertIs(image_classifier.tokenizer						,    a__       )



       @slow
       @require_torch
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   pipeline('''image-classification'''						,    model='''deepmind/vision-perceiver-conv'''       )
        snake_case :			Optional[int]							=   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [
                {'''score''': 0.4385, '''label''': '''tabby, tabby cat'''},
                {'''score''': 0.321, '''label''': '''tiger cat'''},
                {'''score''': 0.0502, '''label''': '''Egyptian cat'''},
                {'''score''': 0.0137, '''label''': '''crib, cot'''},
                {'''score''': 0.007, '''label''': '''radiator'''},
            ]						,    )

        snake_case :			str							=   pipeline('''image-classification'''						,    model='''deepmind/vision-perceiver-fourier'''       )
        snake_case :			List[Any]							=   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [
                {'''score''': 0.5658, '''label''': '''tabby, tabby cat'''},
                {'''score''': 0.1309, '''label''': '''tiger cat'''},
                {'''score''': 0.0722, '''label''': '''Egyptian cat'''},
                {'''score''': 0.0707, '''label''': '''remote control, remote'''},
                {'''score''': 0.0082, '''label''': '''computer keyboard, keypad'''},
            ]						,    )

        snake_case :			Tuple							=   pipeline('''image-classification'''						,    model='''deepmind/vision-perceiver-learned'''       )
        snake_case :			List[Any]							=   image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
        self.assertEqual(
            nested_simplify(a__						,    decimals=4       )						,    [
                {'''score''': 0.3022, '''label''': '''tabby, tabby cat'''},
                {'''score''': 0.2362, '''label''': '''Egyptian cat'''},
                {'''score''': 0.1856, '''label''': '''tiger cat'''},
                {'''score''': 0.0324, '''label''': '''remote control, remote'''},
                {'''score''': 0.0096, '''label''': '''quilt, comforter, comfort, puff'''},
            ]						,    )














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_     =				{
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				['''DeformableDetrFeatureExtractor''']
						A_     =				['''DeformableDetrImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_     =				[
						    '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DeformableDetrForObjectDetection''',
						    '''DeformableDetrModel''',
						    '''DeformableDetrPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
												from .image_processing_deformable_detr import DeformableDetrImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deformable_detr import (
												    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeformableDetrForObjectDetection,
												    DeformableDetrModel,
												    DeformableDetrPreTrainedModel,
												)

else:
						import sys

						A_     =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






