


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  UpperCAmelCase_						:
         '''simple docstring'''






         @staticmethod
         def 			_lowercase (  *__SCREAMING_SNAKE_CASE			,       **__SCREAMING_SNAKE_CASE    ):




                """simple docstring"""




                pass



@is_pipeline_test
@require_torch_or_tf
@require_vision
class  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase   :  Tuple				=       MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
       __UpperCamelCase   :  Any				=       TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						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

              UpperCamelCase     :						Optional[Any] 						=		datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''			,       '''image'''			,       split='''test'''    )

              # Accepts URL + PIL.Image + lists
              UpperCamelCase     :						Union[str, Any] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		"""hf-internal-testing/tiny-random-vit"""
              UpperCamelCase     :						str 						=		pipeline('''image-classification'''			,       model=__A    )

              UpperCamelCase     :						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}]			,       )

              UpperCamelCase     :						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}],
                  ]			,       )






       @require_tf
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		"""hf-internal-testing/tiny-random-vit"""
              UpperCamelCase     :						Any 						=		pipeline('''image-classification'''			,       model=__A			,       framework='''tf'''    )

              UpperCamelCase     :						List[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}]			,       )

              UpperCamelCase     :						Optional[int] 						=		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 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Dict 						=		PreTrainedTokenizer()

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

              self.assertIs(image_classifier.tokenizer			,       __A    )






       @slow
       @require_torch
       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		pipeline('''image-classification'''			,       model='''deepmind/vision-perceiver-conv'''    )
              UpperCamelCase     :						Tuple 						=		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
              self.assertEqual(
                  nested_simplify(__A			,       decimals=4    )			,       [
                      {'''score''': 0.4_385, '''label''': '''tabby, tabby cat'''},
                      {'''score''': 0.321, '''label''': '''tiger cat'''},
                      {'''score''': 0.0_502, '''label''': '''Egyptian cat'''},
                      {'''score''': 0.0_137, '''label''': '''crib, cot'''},
                      {'''score''': 0.007, '''label''': '''radiator'''},
                  ]			,       )

              UpperCamelCase     :						str 						=		pipeline('''image-classification'''			,       model='''deepmind/vision-perceiver-fourier'''    )
              UpperCamelCase     :						Tuple 						=		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
              self.assertEqual(
                  nested_simplify(__A			,       decimals=4    )			,       [
                      {'''score''': 0.5_658, '''label''': '''tabby, tabby cat'''},
                      {'''score''': 0.1_309, '''label''': '''tiger cat'''},
                      {'''score''': 0.0_722, '''label''': '''Egyptian cat'''},
                      {'''score''': 0.0_707, '''label''': '''remote control, remote'''},
                      {'''score''': 0.0_082, '''label''': '''computer keyboard, keypad'''},
                  ]			,       )

              UpperCamelCase     :						Any 						=		pipeline('''image-classification'''			,       model='''deepmind/vision-perceiver-learned'''    )
              UpperCamelCase     :						List[str] 						=		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
              self.assertEqual(
                  nested_simplify(__A			,       decimals=4    )			,       [
                      {'''score''': 0.3_022, '''label''': '''tabby, tabby cat'''},
                      {'''score''': 0.2_362, '''label''': '''Egyptian cat'''},
                      {'''score''': 0.1_856, '''label''': '''tiger cat'''},
                      {'''score''': 0.0_324, '''label''': '''remote control, remote'''},
                      {'''score''': 0.0_096, '''label''': '''quilt, comforter, comfort, puff'''},
                  ]			,       )



"""simple docstring"""






import math as m






def 		_snake_case			(      lowercase__						:    Any ,	lowercase__						:    Any ,	lowercase__						:    Optional[int]=0.001						)    ->							Tuple:


			'''simple docstring'''





			return (f(a + h						) - f(a - h						)) / (2 * h)






def 		_snake_case			(      lowercase__						:    Optional[Any] ,	lowercase__						:    List[str]=0 ,	lowercase__						:    Union[str, Any]=1_0_0 ,	lowercase__						:    Any=0.0001 ,	lowercase__						:    List[str]=1E-6 ,	lowercase__						:    str=False						)    ->							List[str]:


			'''simple docstring'''





			lowerCAmelCase_       :Dict     	=    xa  # set the initial guess
			lowerCAmelCase_       :int     	=    [a]
			lowerCAmelCase_       :int     	=    abs(f(lowercase__						)						)
			lowerCAmelCase_       :Optional[int]     	=    lambda lowercase__						: calc_derivative(lowercase__ ,	lowercase__ ,	h=lowercase__						)  # noqa: E731  Derivative of f(x)
			for _ in range(lowercase__						):
						if fa(lowercase__						) == 0:
									raise ValueError("""No converging solution found"""						)
						lowerCAmelCase_       :Tuple     	=    a - f(lowercase__						) / fa(lowercase__						)  # Calculate the next estimate
						if logsteps:
									steps.append(lowercase__						)
						if error < maxerror:
									break
			else:
						raise ValueError("""Iteration limit reached, no converging solution found"""						)
			if logsteps:
						# If logstep is true, then log intermediate steps
						return a, error, steps
			return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				__UpperCAmelCase 					=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				__UpperCAmelCase					,					__UpperCAmelCase					,					__UpperCAmelCase 					=    newton_raphson(
				    f, xa=10, maxiter=10_00, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel('step')
				plt.ylabel('error')
				plt.show()
				print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")






