




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Any       =      HfArgumentParser(_lowercase				)
     UpperCAmelCase_      :							Tuple       =      parser.parse_args_into_dataclasses()[0]
     UpperCAmelCase_      :							Tuple       =      TensorFlowBenchmark(args=_lowercase				)
     try:
          UpperCAmelCase_      :							Tuple       =      parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          UpperCAmelCase_      :							int       =      '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          UpperCAmelCase_      :							List[Any]       =      ''' '''.join(str(_lowercase				).split(''' '''				)[:-1]				)
          UpperCAmelCase_      :							Union[str, Any]       =      ''''''
          UpperCAmelCase_      :							Any       =      eval(str(_lowercase				).split(''' '''				)[-1]				)
          UpperCAmelCase_      :							str       =      []
          for arg in depreciated_args:
               # arg[2:] removes '--'
               if arg[2:] in TensorFlowBenchmark.deprecated_args:
                    # arg[5:] removes '--no_'
                    full_error_msg += arg_error_msg.format(arg[5:]				)
               else:
                    wrong_args.append(_lowercase				)
          if len(_lowercase				) > 0:
               UpperCAmelCase_      :							Tuple       =      full_error_msg + begin_error_msg + str(_lowercase				)
          raise ValueError(_lowercase				)
     benchmark.run()


if __name__ == "__main__":
     main()





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       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=100       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=30       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=3       ,)			->		Dict:
         UpperCAmelCase_      :							Union[str, Any]       =      parent
         UpperCAmelCase_      :							Tuple       =      vocab_size
         UpperCAmelCase_      :							List[str]       =      batch_size
         UpperCAmelCase_      :							Any       =      image_size
         UpperCAmelCase_      :							Union[str, Any]       =      patch_size
         UpperCAmelCase_      :							Union[str, Any]       =      num_channels
         UpperCAmelCase_      :							Any       =      is_training
         UpperCAmelCase_      :							Union[str, Any]       =      use_labels
         UpperCAmelCase_      :							Union[str, Any]       =      hidden_size
         UpperCAmelCase_      :							Union[str, Any]       =      num_hidden_layers
         UpperCAmelCase_      :							int       =      num_attention_heads
         UpperCAmelCase_      :							Union[str, Any]       =      intermediate_size
         UpperCAmelCase_      :							Any       =      hidden_act
         UpperCAmelCase_      :							Optional[int]       =      hidden_dropout_prob
         UpperCAmelCase_      :							List[Any]       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							List[str]       =      type_sequence_label_size
         UpperCAmelCase_      :							Union[str, Any]       =      initializer_range

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





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[Any]       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         UpperCAmelCase_      :							Tuple       =      None
         if self.use_labels:
              UpperCAmelCase_      :							Union[str, Any]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )

         UpperCAmelCase_      :							Optional[int]       =      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=_SCREAMING_SNAKE_CASE       ,initializer_range=self.initializer_range       ,)

         return config, pixel_values, labels





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      FlaxBeitModel(config=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Union[str, Any]       =      FlaxBeitForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.seq_length - 1, self.vocab_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		str:
         UpperCAmelCase_      :							Dict       =      self.type_sequence_label_size
         UpperCAmelCase_      :							int       =      FlaxBeitForImageClassification(config=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)  )

         # test greyscale images
         UpperCAmelCase_      :							Any       =      1
         UpperCAmelCase_      :							List[Any]       =      FlaxBeitForImageClassification(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							str       =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
         UpperCAmelCase_      :							Optional[Any]       =      model(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   (
             UpperCAmelCase_
         ),   
         )      :							List[str]       =      config_and_inputs
         UpperCAmelCase_      :							int       =      {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_flax
class   __a(					_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )





    def 							a__				(				self  )			->		None:
         UpperCAmelCase_      :							List[Any]       =      FlaxBeitModelTester(self  )
         UpperCAmelCase_      :							List[str]       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,has_text_modality=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self  )			->		Optional[int]:
         self.config_tester.run_common_tests()





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_,   UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Union[str, Any]       =      model_class(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Union[str, Any]       =      inspect.signature(model.__call__  )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_      :							Optional[Any]       =      [*signature.parameters.keys()]

              UpperCAmelCase_      :							Optional[int]       =      ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__  ):
                   UpperCAmelCase_      :							List[Any]       =      self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Union[str, Any]       =      model_class(_SCREAMING_SNAKE_CASE  )

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

                   with self.subTest('''JIT Enabled'''  ):
                        UpperCAmelCase_      :							Dict       =      model_jitted(**_SCREAMING_SNAKE_CASE  ).to_tuple()

                   with self.subTest('''JIT Disabled'''  ):
                        with jax.disable_jit():
                             UpperCAmelCase_      :							List[str]       =      model_jitted(**_SCREAMING_SNAKE_CASE  ).to_tuple()

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





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Dict       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		List[Any]:
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_      :							List[Any]       =      model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''  )
              UpperCAmelCase_      :							Optional[int]       =      model(np.ones((1, 3, 224, 224)  )  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image



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






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		Dict:
         return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''  ) if is_vision_available() else None





    @slow
    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''  )

         UpperCAmelCase_      :							List[Any]       =      self.default_image_processor
         UpperCAmelCase_      :							Optional[Any]       =      prepare_img()
         UpperCAmelCase_      :							Optional[Any]       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  ).pixel_values

         # prepare bool_masked_pos
         UpperCAmelCase_      :							Union[str, Any]       =      np.ones((1, 196)       ,dtype=_SCREAMING_SNAKE_CASE  )

         # forward pass
         UpperCAmelCase_      :							Optional[int]       =      model(pixel_values=_SCREAMING_SNAKE_CASE       ,bool_masked_pos=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      outputs.logits

         # verify the logits
         UpperCAmelCase_      :							List[str]       =      (1, 196, 8_192)
         self.assertEqual(logits.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[str]       =      np.array(
             [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]  )

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





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Any       =      FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''  )

         UpperCAmelCase_      :							Any       =      self.default_image_processor
         UpperCAmelCase_      :							Any       =      prepare_img()
         UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         # forward pass
         UpperCAmelCase_      :							Optional[Any]       =      model(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      outputs.logits

         # verify the logits
         UpperCAmelCase_      :							Dict       =      (1, 1_000)
         self.assertEqual(logits.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      np.array([-1.23_85, -1.09_87, -1.01_08]  )

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

         UpperCAmelCase_      :							Dict       =      281
         self.assertEqual(logits.argmax(-1  ).item()       ,_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							str       =      FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''  )

         UpperCAmelCase_      :							Tuple       =      self.default_image_processor
         UpperCAmelCase_      :							Any       =      prepare_img()
         UpperCAmelCase_      :							Dict       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''np'''  )

         # forward pass
         UpperCAmelCase_      :							Dict       =      model(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Optional[Any]       =      outputs.logits

         # verify the logits
         UpperCAmelCase_      :							Union[str, Any]       =      (1, 21_841)
         self.assertEqual(logits.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      np.array([1.68_81, -0.27_87, 0.59_01]  )

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

         UpperCAmelCase_      :							Dict       =      2_396
         self.assertEqual(logits.argmax(-1  ).item()       ,_SCREAMING_SNAKE_CASE  )