

'''simple docstring'''







import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
      import jax

      from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
      from PIL import Image

      from transformers import BeitImageProcessor


class 					A__      (      unittest.TestCase			):



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=100		,				UpperCamelCase__=13		,				UpperCamelCase__=30		,				UpperCamelCase__=2		,				UpperCamelCase__=3		,				UpperCamelCase__=True		,				UpperCamelCase__=True		,				UpperCamelCase__=32		,				UpperCamelCase__=5		,				UpperCamelCase__=4		,				UpperCamelCase__=37		,				UpperCamelCase__="gelu"		,				UpperCamelCase__=0.1		,				UpperCamelCase__=0.1		,				UpperCamelCase__=10		,				UpperCamelCase__=0.02		,				UpperCamelCase__=3		,				)						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      parent
           A_						=      vocab_size
           A_						=      batch_size
           A_						=      image_size
           A_						=      patch_size
           A_						=      num_channels
           A_						=      is_training
           A_						=      use_labels
           A_						=      hidden_size
           A_						=      num_hidden_layers
           A_						=      num_attention_heads
           A_						=      intermediate_size
           A_						=      hidden_act
           A_						=      hidden_dropout_prob
           A_						=      attention_probs_dropout_prob
           A_						=      type_sequence_label_size
           A_						=      initializer_range

           # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
           A_						=      (image_size // patch_size) ** 2
           A_						=      num_patches + 1



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           A_						=      None
           if self.use_labels:
                  A_						=      ids_tensor([self.batch_size]		,				self.type_sequence_label_size       )

           A_						=      BeitConfig(
               vocab_size=self.vocab_size		,				image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				is_decoder=UpperCamelCase__		,				initializer_range=self.initializer_range		,				)

           return config, pixel_values, labels



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      Dict:





           '''simple docstring'''
           A_						=      FlaxBeitModel(config=UpperCamelCase__       )
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      str:





           '''simple docstring'''
           A_						=      FlaxBeitForMaskedImageModeling(config=UpperCamelCase__       )
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.seq_length - 1, self.vocab_size)       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )						->      List[Any]:





           '''simple docstring'''
           A_						=      self.type_sequence_label_size
           A_						=      FlaxBeitForImageClassification(config=UpperCamelCase__       )
           A_						=      model(UpperCamelCase__       )
           self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)       )

           # test greyscale images
           A_						=      1
           A_						=      FlaxBeitForImageClassification(UpperCamelCase__       )

           A_						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
           A_						=      model(UpperCamelCase__       )







    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      self.prepare_config_and_inputs()
           (
               (
               A_			
           )					,			(
               A_			
           )					,			(
               A_			
           )					,						
           )						=      config_and_inputs
           A_						=      {"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_flax
class 					A__      (      _snake_case						,						unittest.TestCase			):
    lowercase						=		(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )



    def 					snake_case_			(					self       )						->      None:





           '''simple docstring'''
           A_						=      FlaxBeitModelTester(self       )
           A_						=      ConfigTester(self		,				config_class=UpperCamelCase__		,				has_text_modality=UpperCamelCase__		,				hidden_size=37       )



    def 					snake_case_			(					self       )						->      Any:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_					,			A_						=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A_						=      model_class(UpperCamelCase__       )
                  A_						=      inspect.signature(model.__call__       )
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  A_						=      [*signature.parameters.keys()]

                  A_						=      ["""pixel_values"""]
                  self.assertListEqual(arg_names[:1]		,				UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Optional[Any]:





           '''simple docstring'''
           A_					,			A_						=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__       ):
                         A_						=      self._prepare_for_class(UpperCamelCase__		,				UpperCamelCase__       )
                         A_						=      model_class(UpperCamelCase__       )

                         @jax.jit
                         def model_jitted(UpperCamelCase__		,				**UpperCamelCase__       ):
                                return model(pixel_values=UpperCamelCase__		,				**UpperCamelCase__       )

                         with self.subTest("""JIT Enabled"""       ):
                                A_						=      model_jitted(**UpperCamelCase__       ).to_tuple()

                         with self.subTest("""JIT Disabled"""       ):
                                with jax.disable_jit():
                                       A_						=      model_jitted(**UpperCamelCase__       ).to_tuple()

                         self.assertEqual(len(UpperCamelCase__       )		,				len(UpperCamelCase__       )       )
                         for jitted_output, output in zip(UpperCamelCase__		,				UpperCamelCase__       ):
                                self.assertEqual(jitted_output.shape		,				output.shape       )



    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__       )



    def 					snake_case_			(					self       )						->      Union[str, Any]:





           '''simple docstring'''
           A_						=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )







    @slow
    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           for model_class_name in self.all_model_classes:
                  A_						=      model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""       )
                  A_						=      model(np.ones((1, 3, 224, 224)       )       )
                  self.assertIsNotNone(UpperCamelCase__       )

def       UpperCAmelCase__    (      )			->			List[str]:
       A_						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
       return image







@require_vision
@require_flax
class 					A__      (      unittest.TestCase			):



    @cached_property
    def 					snake_case_			(					self       )						->      int:





           '''simple docstring'''
           return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""       ) if is_vision_available() else None



    @slow
    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""       )

           A_						=      self.default_image_processor
           A_						=      prepare_img()
           A_						=      image_processor(images=UpperCamelCase__		,				return_tensors="""np"""       ).pixel_values

           # prepare bool_masked_pos
           A_						=      np.ones((1, 196)		,				dtype=UpperCamelCase__       )

           # forward pass
           A_						=      model(pixel_values=UpperCamelCase__		,				bool_masked_pos=UpperCamelCase__       )
           A_						=      outputs.logits

           # verify the logits
           A_						=      (1, 196, 8192)
           self.assertEqual(logits.shape		,				UpperCamelCase__       )

           A_						=      np.array(
               [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]       )

           self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]		,				UpperCamelCase__		,				atol=1e-2       )       )



    @slow
    def 					snake_case_			(					self       )						->      Dict:





           '''simple docstring'''
           A_						=      FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""       )

           A_						=      self.default_image_processor
           A_						=      prepare_img()
           A_						=      image_processor(images=UpperCamelCase__		,				return_tensors="""np"""       )

           # forward pass
           A_						=      model(**UpperCamelCase__       )
           A_						=      outputs.logits

           # verify the logits
           A_						=      (1, 1000)
           self.assertEqual(logits.shape		,				UpperCamelCase__       )

           A_						=      np.array([-1.2385, -1.0987, -1.0108]       )

           self.assertTrue(np.allclose(logits[0, :3]		,				UpperCamelCase__		,				atol=1e-4       )       )

           A_						=      281
           self.assertEqual(logits.argmax(-1       ).item()		,				UpperCamelCase__       )







    @slow
    def 					snake_case_			(					self       )						->      List[str]:





           '''simple docstring'''
           A_						=      FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""       )

           A_						=      self.default_image_processor
           A_						=      prepare_img()
           A_						=      image_processor(images=UpperCamelCase__		,				return_tensors="""np"""       )

           # forward pass
           A_						=      model(**UpperCamelCase__       )
           A_						=      outputs.logits

           # verify the logits
           A_						=      (1, 21841)
           self.assertEqual(logits.shape		,				UpperCamelCase__       )

           A_						=      np.array([1.6881, -0.2787, 0.5901]       )

           self.assertTrue(np.allclose(logits[0, :3]		,				UpperCamelCase__		,				atol=1e-4       )       )

           A_						=      2396
           self.assertEqual(logits.argmax(-1       ).item()		,				UpperCamelCase__       )



'''simple docstring'''







def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(a - b				) for a, b in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			None:
       if point:
              if isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
                     for item in point:
                            if not isinstance(UpperCAmelCase__,       (int, float)				):
                                   A_						=      (
                                       """Expected a list of numbers as input, found """
                                       F'''{type(UpperCAmelCase__				).__name__}'''
                                   )
                                   raise TypeError(UpperCAmelCase__				)
              else:
                     A_						=      F'''Expected a list of numbers as input, found {type(UpperCAmelCase__				).__name__}'''
                     raise TypeError(UpperCAmelCase__				)
       else:
              raise ValueError("""Missing an input"""				)
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__				)			->			float:
       _validate_point(UpperCAmelCase__				)
       _validate_point(UpperCAmelCase__				)
       if len(UpperCAmelCase__				) != len(UpperCAmelCase__				):
              raise ValueError("""Both points must be in the same n-dimensional space"""				)

       return float(sum(abs(x - y				) for x, y in zip(UpperCAmelCase__,       UpperCAmelCase__				)				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
