




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:    Union[str, Any]				,    _lowerCAmelCase:    Any				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Dict=30				,    _lowerCAmelCase:    Optional[int]=2				,    _lowerCAmelCase:    Any=3				,    _lowerCAmelCase:    Dict=True				,    _lowerCAmelCase:    List[Any]=True				,    _lowerCAmelCase:    str=32				,    _lowerCAmelCase:    Tuple=5				,    _lowerCAmelCase:    Dict=4				,    _lowerCAmelCase:    str=37				,    _lowerCAmelCase:    Optional[int]="gelu"				,    _lowerCAmelCase:    Tuple=0.1				,    _lowerCAmelCase:    Optional[int]=0.1				,    _lowerCAmelCase:    int=10				,    _lowerCAmelCase:    str=0.02				,    ):
         lowercase			:List[Any]							=  parent
         lowercase			:Dict							=  batch_size
         lowercase			:Optional[Any]							=  image_size
         lowercase			:Optional[Any]							=  patch_size
         lowercase			:List[Any]							=  num_channels
         lowercase			:Tuple							=  is_training
         lowercase			:int							=  use_labels
         lowercase			:List[Any]							=  hidden_size
         lowercase			:List[str]							=  num_hidden_layers
         lowercase			:List[Any]							=  num_attention_heads
         lowercase			:Any							=  intermediate_size
         lowercase			:Optional[int]							=  hidden_act
         lowercase			:str							=  hidden_dropout_prob
         lowercase			:str							=  attention_probs_dropout_prob
         lowercase			:Optional[int]							=  type_sequence_label_size
         lowercase			:Any							=  initializer_range

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






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[Any]							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         lowercase			:int							=  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     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Union[str, Any] ):
         lowercase			:Optional[Any]							=  FlaxViTModel(config=_lowerCAmelCase )
         lowercase			:int							=  model(_lowerCAmelCase )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         lowercase			:List[str]							=  (self.image_size, self.image_size)
         lowercase			:Union[str, Any]							=  (self.patch_size, self.patch_size)
         lowercase			:Any							=  (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     SCREAMING_SNAKE_CASE			(      self:    str				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    List[str] ):
         lowercase			:Dict							=  self.type_sequence_label_size
         lowercase			:str							=  FlaxViTForImageClassification(config=_lowerCAmelCase )
         lowercase			:List[Any]							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         lowercase			:Optional[Any]							=  1
         lowercase			:Optional[int]							=  FlaxViTForImageClassification(_lowerCAmelCase )

         lowercase			:Tuple							=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         lowercase			:Optional[Any]							=  model(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[int]							=  self.prepare_config_and_inputs()
         (
             (
             lowercase
         )		,			(
             lowercase
         )		,			
         )			:Any							=  config_and_inputs
         lowercase			:List[str]							=  {"pixel_values": pixel_values}
         return config, inputs_dict







@require_flax
class       __lowerCAmelCase				(							lowerCAmelCase					,       unittest.TestCase):
       _a           =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:List[str]							=  FlaxViTModelTester(self )
         lowercase			:List[Any]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    has_text_modality=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Any							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase			:Any							=  self.model_tester.prepare_config_and_inputs_for_common()

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

           lowercase			:Optional[int]							=  ["pixel_values"]
           self.assertListEqual(arg_names[:1]				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         lowercase		,			lowercase			: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__ ):
             lowercase			:Dict							=  self._prepare_for_class(_lowerCAmelCase				,    _lowerCAmelCase )
             lowercase			:Any							=  model_class(_lowerCAmelCase )

             @jax.jit
             def model_jitted(_lowerCAmelCase:    Union[str, Any]				,    **_lowerCAmelCase:    Dict ):
               return model(pixel_values=_lowerCAmelCase				,    **_lowerCAmelCase )

             with self.subTest("JIT Enabled" ):
               lowercase			:Any							=  model_jitted(**_lowerCAmelCase ).to_tuple()

             with self.subTest("JIT Disabled" ):
               with jax.disable_jit():
                 lowercase			:str							=  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     SCREAMING_SNAKE_CASE			(      self:    str ):
         for model_class_name in self.all_model_classes:
           lowercase			:List[Any]							=  model_class_name.from_pretrained("google/vit-base-patch16-224" )
           lowercase			:int							=  model(np.ones((1, 3, 2_24, 2_24) ) )
           self.assertIsNotNone(_lowerCAmelCase )









_UpperCAmelCase		:  Tuple	=   {str(digit): digit**5 for digit in range(10)}



def 					UpperCAmelCase__     (   lowerCamelCase      ):
  return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCamelCase      )      )



def 					UpperCAmelCase__     (   ):
  return sum(
      number
      for number in range(1000,	1000000      )
      if number == digits_fifth_powers_sum(lowerCamelCase      )      )


if __name__ == "__main__":
     print(solution())



