"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_UpperCamelCase     :		Any											=					{"LayoutLMv2Config", "LayoutLMv3Config"}


@is_pipeline_test
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					List[Any]										=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      lowerCamelCase__	:					Union[str, Any]										=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            lowerCamelCase__	:					Dict										=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            lowerCamelCase__	:					List[str]										=						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	Optional[Any]     					=			pipeline(
                 task='text-classification'	,    model='hf-internal-testing/tiny-random-distilbert'	,    framework='pt'							)

             lowercase__    :	Tuple     					=			text_classifier('This is great !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'LABEL_0', 'score': 0.504}]							)

             lowercase__    :	int     					=			text_classifier('This is great !'	,    top_k=2							)
             self.assertEqual(
                 nested_simplify(a							)	,    [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]							)

             lowercase__    :	List[str]     					=			text_classifier(['This is great !', 'This is bad']	,    top_k=2							)
             self.assertEqual(
                 nested_simplify(a							)	,    [
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]	,    )

             lowercase__    :	int     					=			text_classifier('This is great !'	,    top_k=1							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'LABEL_0', 'score': 0.504}]							)

             # Legacy behavior
             lowercase__    :	Optional[Any]     					=			text_classifier('This is great !'	,    return_all_scores=a							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'LABEL_0', 'score': 0.504}]							)

             lowercase__    :	List[str]     					=			text_classifier('This is great !'	,    return_all_scores=a							)
             self.assertEqual(
                 nested_simplify(a							)	,    [[{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}]]							)

             lowercase__    :	Union[str, Any]     					=			text_classifier(['This is great !', 'Something else']	,    return_all_scores=a							)
             self.assertEqual(
                 nested_simplify(a							)	,    [
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                     [{'label': 'LABEL_0', 'score': 0.504}, {'label': 'LABEL_1', 'score': 0.496}],
                 ]	,    )

             lowercase__    :	List[str]     					=			text_classifier(['This is great !', 'Something else']	,    return_all_scores=a							)
             self.assertEqual(
                 nested_simplify(a							)	,    [
                     {'label': 'LABEL_0', 'score': 0.504},
                     {'label': 'LABEL_0', 'score': 0.504},
                 ]	,    )


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             import torch

             lowercase__    :	Tuple     					=			pipeline(
                 task='text-classification'	,    model='hf-internal-testing/tiny-random-distilbert'	,    framework='pt'	,    device=torch.device('cpu'							)	,    )

             lowercase__    :	str     					=			text_classifier('This is great !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'LABEL_0', 'score': 0.504}]							)


      @require_tf
      def 	_UpperCAmelCase (     self							) ->      Optional[Any]:
             lowercase__    :	Any     					=			pipeline(
                 task='text-classification'	,    model='hf-internal-testing/tiny-random-distilbert'	,    framework='tf'							)

             lowercase__    :	Tuple     					=			text_classifier('This is great !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'LABEL_0', 'score': 0.504}]							)


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             lowercase__    :	str     					=			pipeline('text-classification'							)

             lowercase__    :	str     					=			text_classifier('This is great !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'POSITIVE', 'score': 1.0}]							)
             lowercase__    :	Tuple     					=			text_classifier('This is bad !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'NEGATIVE', 'score': 1.0}]							)
             lowercase__    :	Optional[int]     					=			text_classifier('Birds are a type of animal'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'POSITIVE', 'score': 0.988}]							)


      @slow
      @require_tf
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			pipeline('text-classification'	,    framework='tf'							)

             lowercase__    :	str     					=			text_classifier('This is great !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'POSITIVE', 'score': 1.0}]							)
             lowercase__    :	Dict     					=			text_classifier('This is bad !'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'NEGATIVE', 'score': 1.0}]							)
             lowercase__    :	str     					=			text_classifier('Birds are a type of animal'							)
             self.assertEqual(nested_simplify(a							)	,    [{'label': 'POSITIVE', 'score': 0.988}]							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Tuple:
             lowercase__    :	List[str]     					=			TextClassificationPipeline(model=a	,    tokenizer=a							)
             return text_classifier, ["HuggingFace is in", "This is another test"]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Dict:
             lowercase__    :	Dict     					=			text_classifier.model
             # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
             lowercase__    :	int     					=			"""HuggingFace is in"""
             lowercase__    :	Optional[Any]     					=			text_classifier(a							)

             self.assertEqual(nested_simplify(a							)	,    [{'label': ANY(a							), 'score': ANY(a							)}]							)
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()							)

             lowercase__    :	List[Any]     					=			["""HuggingFace is in """, """Paris is in France"""]
             lowercase__    :	Dict     					=			text_classifier(a							)
             self.assertEqual(
                 nested_simplify(a							)	,    [{'label': ANY(a							), 'score': ANY(a							)}, {'label': ANY(a							), 'score': ANY(a							)}]	,    )
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()							)
             self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()							)

             # Forcing to get all results with `top_k=None`
             # This is NOT the legacy format
             lowercase__    :	Any     					=			text_classifier(a	,    top_k=a							)
             lowercase__    :	Dict     					=			len(model.config.idalabel.values()							)
             self.assertEqual(
                 nested_simplify(a							)	,    [[{'label': ANY(a							), 'score': ANY(a							)}] * N, [{'label': ANY(a							), 'score': ANY(a							)}] * N]	,    )

             lowercase__    :	Tuple     					=			{"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
             lowercase__    :	Dict     					=			text_classifier(a							)
             self.assertEqual(
                 nested_simplify(a							)	,    {'label': ANY(a							), 'score': ANY(a							)}	,    )
             self.assertTrue(outputs['label'] in model.config.idalabel.values()							)

             # This might be used a text pair, but tokenizer + pipe interaction
             # makes it hard to understand that it's not using the pair properly
             # https://github.com/huggingface/transformers/issues/17305
             # We disabled this usage instead as it was outputting wrong outputs.
             lowercase__    :	Union[str, Any]     					=			[["""HuggingFace is in """, """Paris is in France"""]]
             with self.assertRaises(a							):
                    text_classifier(a							)

             # This used to be valid for doing text pairs
             # We're keeping it working because of backward compatibility
             lowercase__    :	Any     					=			text_classifier([[['HuggingFace is in ', 'Paris is in France']]]							)
             self.assertEqual(
                 nested_simplify(a							)	,    [{'label': ANY(a							), 'score': ANY(a							)}]	,    )
             self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()							)


"""simple docstring"""





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image
else:


       class   UpperCAmelCase_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			depth_estimator(
                 [
                     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(
                 [
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                     {'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)},
                 ]	,    a	,    )


      @require_tf
      @unittest.skip('Depth estimation is not implemented in TF'							)
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[Any]     					=			hashimage(outputs['depth']							)

             # This seems flaky.
             # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
             self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)	,    29.304							)
             self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)	,    2.662							)


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

