"""simple docstring"""




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 		__A							(				unittest.TestCase	):






     def __init__(	self       ,  a__       ,  a__=13       ,  a__=30       ,  a__=2       ,  a__=3       ,  a__=True       ,  a__=True       ,  a__=32       ,  a__=5       ,  a__=4       ,  a__=37       ,  a__="gelu"       ,  a__=0.1       ,  a__=0.1       ,  a__=10       ,  a__=0.0_2       ,  ):
       _lowerCAmelCase		: Tuple						    =  parent
       _lowerCAmelCase		: List[str]						    =  batch_size
       _lowerCAmelCase		: List[Any]						    =  image_size
       _lowerCAmelCase		: Optional[Any]						    =  patch_size
       _lowerCAmelCase		: Tuple						    =  num_channels
       _lowerCAmelCase		: List[str]						    =  is_training
       _lowerCAmelCase		: Optional[Any]						    =  use_labels
       _lowerCAmelCase		: List[str]						    =  hidden_size
       _lowerCAmelCase		: int						    =  num_hidden_layers
       _lowerCAmelCase		: Optional[Any]						    =  num_attention_heads
       _lowerCAmelCase		: str						    =  intermediate_size
       _lowerCAmelCase		: int						    =  hidden_act
       _lowerCAmelCase		: List[str]						    =  hidden_dropout_prob
       _lowerCAmelCase		: str						    =  attention_probs_dropout_prob
       _lowerCAmelCase		: Tuple						    =  type_sequence_label_size
       _lowerCAmelCase		: Union[str, Any]						    =  initializer_range

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






     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

       _lowerCAmelCase		: Any						    =  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=a__       ,  initializer_range=self.initializer_range       ,  )

       return config, pixel_values






     def 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[Any]						    =  FlaxViTModel(config=a__						)
       _lowerCAmelCase		: int						    =  model(a__						)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       _lowerCAmelCase		: Tuple						    =  (self.image_size, self.image_size)
       _lowerCAmelCase		: int						    =  (self.patch_size, self.patch_size)
       _lowerCAmelCase		: 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 					__A						(	self       ,  a__       ,  a__						):
       _lowerCAmelCase		: Any						    =  self.type_sequence_label_size
       _lowerCAmelCase		: Any						    =  FlaxViTForImageClassification(config=a__						)
       _lowerCAmelCase		: Optional[int]						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)						)

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

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  self.prepare_config_and_inputs()
       (
           (
           _lowerCAmelCase
       )    ,			(
           _lowerCAmelCase
       )    ,			
       )		: Tuple						    =  config_and_inputs
       _lowerCAmelCase		: int						    =  {"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_flax
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






     def 					__A						(	self						):
       _lowerCAmelCase		: Dict						    =  FlaxViTModelTester(self						)
       _lowerCAmelCase		: Dict						    =  ConfigTester(self       ,  config_class=a__       ,  has_text_modality=a__       ,  hidden_size=37						)






     def 					__A						(	self						):
       self.config_tester.run_common_tests()






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase		: Any						    =  ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]       ,  a__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__						):
           _lowerCAmelCase		: List[str]						    =  self._prepare_for_class(a__       ,  a__						)
           _lowerCAmelCase		: Optional[int]						    =  model_class(a__						)

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

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

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

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






     @slow
     def 					__A						(	self						):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase		: str						    =  model_class_name.from_pretrained("""google/vit-base-patch16-224"""						)
         _lowerCAmelCase		: Optional[Any]						    =  model(np.ones((1, 3, 224, 224)						)						)
         self.assertIsNotNone(a__						)





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change contrast to 170
          _a   :	str 					=     change_contrast(img, 170)
          cont_img.save('image_data/lena_high_contrast.png', format='png')




