





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
     import jax

     from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class     __lowerCAmelCase	(   unittest.TestCase):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=1_3		,				lowerCAmelCase__=3_0		,				lowerCAmelCase__=2		,				lowerCAmelCase__=3		,				lowerCAmelCase__=True		,				lowerCAmelCase__=True		,				lowerCAmelCase__=3_2		,				lowerCAmelCase__=5		,				lowerCAmelCase__=4		,				lowerCAmelCase__=3_7		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=1_0		,				lowerCAmelCase__=0.02		,				)       ->						List[str]:



        '''simple docstring'''




        a__     : int  =parent
        a__     : str  =batch_size
        a__     : Any  =image_size
        a__     : Any  =patch_size
        a__     : Tuple  =num_channels
        a__     : Optional[int]  =is_training
        a__     : List[str]  =use_labels
        a__     : List[Any]  =hidden_size
        a__     : Tuple  =num_hidden_layers
        a__     : Optional[int]  =num_attention_heads
        a__     : Union[str, Any]  =intermediate_size
        a__     : str  =hidden_act
        a__     : Union[str, Any]  =hidden_dropout_prob
        a__     : List[str]  =attention_probs_dropout_prob
        a__     : Dict  =type_sequence_label_size
        a__     : Union[str, Any]  =initializer_range

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







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



        '''simple docstring'''




        a__     : Any  =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        a__     : List[str]  =ViTConfig(
            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=lowerCAmelCase__		,				initializer_range=self.initializer_range		,				)

        return config, pixel_values







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Optional[int]:



        '''simple docstring'''




        a__     : Optional[Any]  =FlaxViTModel(config=lowerCAmelCase__							)
        a__     : Union[str, Any]  =model(lowerCAmelCase__							)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        a__     : Optional[Any]  =(self.image_size, self.image_size)
        a__     : Tuple  =(self.patch_size, self.patch_size)
        a__     : Optional[int]  =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, num_patches + 1, self.hidden_size)							)







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						Dict:



        '''simple docstring'''




        a__     : int  =self.type_sequence_label_size
        a__     : str  =FlaxViTForImageClassification(config=lowerCAmelCase__							)
        a__     : Any  =model(lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)							)

        # test greyscale images
        a__     : List[Any]  =1
        a__     : int  =FlaxViTForImageClassification(lowerCAmelCase__							)

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







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



        '''simple docstring'''




        a__     : Dict  =self.prepare_config_and_inputs()
        (
            (
            a__
        ) ,							(
            a__
        ) ,							
        )     : Optional[Any]  =config_and_inputs
        a__     : Dict  ={"pixel_values": pixel_values}
        return config, inputs_dict







@require_flax
class     __lowerCAmelCase	(   UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : Any            =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







      def        _lowercase  (   self							)       ->						None:



        '''simple docstring'''




        a__     : str  =FlaxViTModelTester(self							)
        a__     : List[Any]  =ConfigTester(self		,				config_class=lowerCAmelCase__		,				has_text_modality=lowerCAmelCase__		,				hidden_size=3_7							)







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



        '''simple docstring'''




        self.config_tester.run_common_tests()







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



        '''simple docstring'''




        a__     : List[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						Any:



        '''simple docstring'''




        a__     : List[Any]  =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__ ,							a__     : Optional[int]  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__     : List[Any]  =model_class(lowerCAmelCase__							)
          a__     : Optional[Any]  =inspect.signature(model.__call__							)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          a__     : Tuple  =[*signature.parameters.keys()]

          a__     : int  =["pixel_values"]
          self.assertListEqual(arg_names[:1]		,				lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        a__ ,							a__     : Union[str, Any]  =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          with self.subTest(model_class.__name__							):
            a__     : Dict  =self._prepare_for_class(lowerCAmelCase__		,				lowerCAmelCase__							)
            a__     : str  =model_class(lowerCAmelCase__							)

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

            with self.subTest("JIT Enabled"							):
              a__     : List[str]  =model_jitted(**lowerCAmelCase__							).to_tuple()

            with self.subTest("JIT Disabled"							):
              with jax.disable_jit():
                a__     : Dict  =model_jitted(**lowerCAmelCase__							).to_tuple()

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







      @slow
      def        _lowercase  (   self							)       ->						str:



        '''simple docstring'''




        for model_class_name in self.all_model_classes:
          a__     : Any  =model_class_name.from_pretrained("google/vit-base-patch16-224"							)
          a__     : Any  =model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
          self.assertIsNotNone(lowerCAmelCase__							)













UpperCAmelCase     :     Dict							=	[0, 2, 4, 6, 8]
UpperCAmelCase     :     Tuple							=	[1, 3, 5, 7, 9]
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       list[int]	,	SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if remaining_length == 0:
    if digits[0] == 0 or digits[-1] == 0:
      return 0

    for i in range(length // 2 - 1	,	-1	,	-1						):
      remainder += digits[i] + digits[length - i - 1]

      if remainder % 2 == 0:
        return 0

      remainder //= 10

    return 1

  if remaining_length == 1:
    if remainder % 2 == 0:
      return 0

    a__     : str  =0
    for digit in range(10						):
      a__     : int  =digit
      result += reversible_numbers(
          0	,	(remainder + 2 * digit) // 10	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    return result

  a__     : List[str]  =0
  for digita in range(10						):
    a__     : Optional[int]  =digita

    if (remainder + digita) % 2 == 0:
      a__     : Dict  =ODD_DIGITS
    else:
      a__     : Any  =EVEN_DIGITS

    for digita in other_parity_digits:
      a__     : Union[str, Any]  =digita
      result += reversible_numbers(
          remaining_length - 2	,	(remainder + digita + digita) // 10	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	)
  return result
def 					_A	(		SCREAMING_SNAKE_CASE					:       int = 9						):





  """simple docstring"""



  a__     : List[str]  =0
  for length in range(1	,	max_power + 1						):
    result += reversible_numbers(SCREAMING_SNAKE_CASE	,	0	,	[0] * length	,	SCREAMING_SNAKE_CASE						)
  return result


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






