


'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
       from PIL import Image

       from transformers import ViTImageProcessor

class       __magic_name__	:


     def __init__(      self							:     Optional[int]							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Dict=13							,	lowerCamelCase__							:     List[str]=30							,	lowerCamelCase__							:     List[str]=2							,	lowerCamelCase__							:     List[Any]=3							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     Optional[int]=True							,	lowerCamelCase__							:     List[str]=32							,	lowerCamelCase__							:     Dict=2							,	lowerCamelCase__							:     List[str]=4							,	lowerCamelCase__							:     List[str]=37							,	lowerCamelCase__							:     Optional[Any]="gelu"							,	lowerCamelCase__							:     Union[str, Any]=0.1							,	lowerCamelCase__							:     Union[str, Any]=0.1							,	lowerCamelCase__							:     Tuple=10							,	lowerCamelCase__							:     Optional[Any]=0.02							,	lowerCamelCase__							:     Optional[int]=3							,	lowerCamelCase__							:     Any=None							,	)  ->      Optional[int]:





      '''simple docstring'''






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

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

      UpperCamelCase__					:    str          =   self.get_config()

      return config, pixel_values, labels


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





      '''simple docstring'''






      return 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							,	)


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     str			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   TFViTModel(config=lowerCamelCase__			)
      UpperCamelCase__					:    Union[str, Any]          =   model(lowerCamelCase__							,	training=lowerCamelCase__			)
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)			)

      # Test with an image with different size than the one specified in config.
      UpperCamelCase__					:    str          =   self.image_size // 2
      UpperCamelCase__					:    List[str]          =   pixel_values[:, :, :image_size, :image_size]
      UpperCamelCase__					:    int          =   model(lowerCamelCase__							,	interpolate_pos_encoding=lowerCamelCase__							,	training=lowerCamelCase__			)
      UpperCamelCase__					:    Any          =   (image_size // self.patch_size) ** 2 + 1
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, seq_length, self.hidden_size)			)


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Tuple			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.type_sequence_label_size
      UpperCamelCase__					:    Dict          =   TFViTForImageClassification(lowerCamelCase__			)
      UpperCamelCase__					:    Dict          =   model(lowerCamelCase__							,	labels=lowerCamelCase__							,	training=lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)			)

      # Test with an image with different size than the one specified in config.
      UpperCamelCase__					:    List[str]          =   self.image_size // 2
      UpperCamelCase__					:    Optional[Any]          =   pixel_values[:, :, :image_size, :image_size]
      UpperCamelCase__					:    Dict          =   model(lowerCamelCase__							,	interpolate_pos_encoding=lowerCamelCase__							,	training=lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)			)

      # test greyscale images
      UpperCamelCase__					:    Optional[Any]          =   1
      UpperCamelCase__					:    Optional[int]          =   TFViTForImageClassification(lowerCamelCase__			)
      UpperCamelCase__					:    Optional[Any]          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      UpperCamelCase__					:    List[Any]          =   model(lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.prepare_config_and_inputs()
      UpperCamelCase__					:    List[Any]          =   config_and_inputs
      UpperCamelCase__					:    Any          =   {'''pixel_values''': pixel_values}
      return config, inputs_dict



@require_tf
class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		List[str]			=      (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     A:		int			=      (
         {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     A:		Dict			=      False
     A:		str			=      False
     A:		int			=      False


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   TFViTModelTester(self			)
      UpperCamelCase__					:    Dict          =   ConfigTester(self							,	config_class=lowerCamelCase__							,	has_text_modality=lowerCamelCase__							,	hidden_size=37			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[int]:





      '''simple docstring'''






      self.config_tester.run_common_tests()


     @unittest.skip(reason='''ViT does not use inputs_embeds'''			)
     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      List[Any]:





      '''simple docstring'''






      pass


     @unittest.skip(reason='''ViT does not use inputs_embeds'''			)
     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Any:





      '''simple docstring'''






      pass


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       UpperCamelCase__					:    Dict          =   model_class(lowerCamelCase__			)
       self.assertIsInstance(model.get_input_embeddings()							,	(tf.keras.layers.Layer)			)
       UpperCamelCase__					:    Tuple          =   model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(lowerCamelCase__							,	tf.keras.layers.Layer			)			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      List[Any]:





      '''simple docstring'''






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

      for model_class in self.all_model_classes:
       UpperCamelCase__					:    Optional[Any]          =   model_class(lowerCamelCase__			)
       UpperCamelCase__					:    Tuple          =   inspect.signature(model.call			)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       UpperCamelCase__					:    str          =   [*signature.parameters.keys()]

       UpperCamelCase__					:    Optional[Any]          =   ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__			)


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





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)


     @slow
     def      UpperCAmelCase__	(      self							:     int			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   TFViTModel.from_pretrained('''google/vit-base-patch16-224'''			)
      self.assertIsNotNone(lowerCamelCase__			)



def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image






@require_tf
@require_vision
class       __magic_name__	(     unittest.TestCase):


     @cached_property
     def      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Union[str, Any]:





      '''simple docstring'''






      return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''			) if is_vision_available() else None


     @slow
     def      UpperCAmelCase__	(      self							:     int			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''			)

      UpperCamelCase__					:    str          =   self.default_image_processor
      UpperCamelCase__					:    Optional[int]          =   prepare_img()
      UpperCamelCase__					:    str          =   image_processor(images=lowerCamelCase__							,	return_tensors='''tf'''			)

      # forward pass
      UpperCamelCase__					:    List[Any]          =   model(**lowerCamelCase__			)

      # verify the logits
      UpperCamelCase__					:    Optional[int]          =   tf.TensorShape((1, 1000)			)
      self.assertEqual(outputs.logits.shape							,	lowerCamelCase__			)

      UpperCamelCase__					:    Tuple          =   tf.constant([-0.2744, 0.8215, -0.0836]			)

      tf.debugging.assert_near(outputs.logits[0, :3]							,	lowerCamelCase__							,	atol=1E-4			)






import functools

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   len(SCREAMING_SNAKE_CASE     )

 @functools.cache
 def min_distance(SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase__					:    Tuple          =   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1    , SCREAMING_SNAKE_CASE     )    , 1 + min_distance(SCREAMING_SNAKE_CASE    , indexa + 1     )    , diff + min_distance(indexa + 1    , indexa + 1     )    , )

 return min_distance(0    , 0     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


