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   ,				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:





       '''simple docstring'''





       UpperCamelCase__										=      parent
       UpperCamelCase__										=      vocab_size
       UpperCamelCase__										=      batch_size
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      is_training
       UpperCamelCase__										=      use_labels
       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      type_sequence_label_size
       UpperCamelCase__										=      initializer_range

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






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





       '''simple docstring'''





       UpperCamelCase__										=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       UpperCamelCase__										=      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_	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxBeitModel(config=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      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_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      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_	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      self.type_sequence_label_size
       UpperCamelCase__										=      FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      model(SCREAMING_SNAKE_CASE_	)
       self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)	)

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

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






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       (
           UpperCamelCase__
       )						,       
       )										=      config_and_inputs
       UpperCamelCase__										=      {'''pixel_values''': pixel_values}
       return config, inputs_dict



@require_flax
class     _A   (    __UpperCamelCase	, unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=(
      (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
  )






  def 		_a					(self	)		->      None:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxBeitModelTester(self	)
       UpperCamelCase__										=      ConfigTester(self   ,				config_class=SCREAMING_SNAKE_CASE_   ,				has_text_modality=SCREAMING_SNAKE_CASE_   ,				hidden_size=37	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       self.config_tester.run_common_tests()






  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

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

            UpperCamelCase__										=      ['''pixel_values''']
            self.assertListEqual(arg_names[:1]   ,				SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__						,       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__	):
                 UpperCamelCase__										=      self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)
                 UpperCamelCase__										=      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__										=      model_jitted(**SCREAMING_SNAKE_CASE_	).to_tuple()

                 with self.subTest('''JIT Disabled'''	):
                      with jax.disable_jit():
                           UpperCamelCase__										=      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	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_	)






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





       '''simple docstring'''





       UpperCamelCase__										=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_	)






  @slow
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





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







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image



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






  @cached_property
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





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






  @slow
  def 		_a					(self	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	).pixel_values

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

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

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

       UpperCamelCase__										=      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]   ,				SCREAMING_SNAKE_CASE_   ,				atol=1E-2	)	)






  @slow
  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)

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

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

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

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

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






  @slow
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''	)

       UpperCamelCase__										=      self.default_image_processor
       UpperCamelCase__										=      prepare_img()
       UpperCamelCase__										=      image_processor(images=SCREAMING_SNAKE_CASE_   ,				return_tensors='''np'''	)

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

       # verify the logits
       UpperCamelCase__										=      (1, 2_1841)
       self.assertEqual(logits.shape   ,				SCREAMING_SNAKE_CASE_	)

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

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

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




def  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



