


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 	UpperCamelCase__				:



     '''simple docstring'''

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




           '''simple docstring'''


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

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Dict:




           '''simple docstring'''


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

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

           SCREAMING_SNAKE_CASE	=		self.get_config()

           return config, pixel_values, labels






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFViTModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		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.
           SCREAMING_SNAKE_CASE	=		self.image_size // 2
           SCREAMING_SNAKE_CASE	=		pixel_values[:, :, :image_size, :image_size]
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,interpolate_pos_encoding=lowerCamelCase__		,training=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		(image_size // self.patch_size) ** 2 + 1
           self.parent.assertEqual(result.last_hidden_state.shape		,(self.batch_size, seq_length, self.hidden_size) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					str		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Dict )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.type_sequence_label_size
           SCREAMING_SNAKE_CASE	=		TFViTForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		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.
           SCREAMING_SNAKE_CASE	=		self.image_size // 2
           SCREAMING_SNAKE_CASE	=		pixel_values[:, :, :image_size, :image_size]
           SCREAMING_SNAKE_CASE	=		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
           SCREAMING_SNAKE_CASE	=		1
           SCREAMING_SNAKE_CASE	=		TFViTForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.type_sequence_label_size) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_tf
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Optional[Any]				=    (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     __snake_case					:			Any				=    (
         {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			Any				=    False
     __snake_case					:			Any				=    False






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFViTModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__		,hidden_size=37 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	str:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     @unittest.skip(reason="""ViT does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[str]:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ViT does not use inputs_embeds""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[int]:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE	=		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFViTModel.from_pretrained("""google/vit-base-patch16-224""" )
           self.assertIsNotNone(lowerCamelCase__ )



def      __lowercase     (   )				->     int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image



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



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-0.2744, 0.8215, -0.0836] )

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      def get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> str:
            SCREAMING_SNAKE_CASE	=		[]
            SCREAMING_SNAKE_CASE	=		min(len(_stra			)				,		len(_stra			)			) // 2
            for i, l in enumerate(_stra			):
                  SCREAMING_SNAKE_CASE	=		int(max(0				,		i - limit			)			)
                  SCREAMING_SNAKE_CASE	=		int(min(i + limit + 1				,		len(_stra			)			)			)
                  if l in _stra[left:right]:
                        matched.append(_SCREAMING_SNAKE_CASE			)
                        SCREAMING_SNAKE_CASE	=		F"""{_stra[0:_stra.index(_SCREAMING_SNAKE_CASE			)]} {_stra[_stra.index(_SCREAMING_SNAKE_CASE			) + 1:]}"""

            return "".join(_SCREAMING_SNAKE_CASE			)

      # matching characters
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		get_matched_characters(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
      SCREAMING_SNAKE_CASE	=		len(_SCREAMING_SNAKE_CASE			)

      # transposition
      SCREAMING_SNAKE_CASE	=		(
          len([(ca, ca) for ca, ca in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) if ca != ca]			) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE	=		0.0
      else:
            SCREAMING_SNAKE_CASE	=		(
                1
                / 3
                * (
                    match_count / len(_SCREAMING_SNAKE_CASE			)
                    + match_count / len(_SCREAMING_SNAKE_CASE			)
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE	=		0
      for ca, ca in zip(stra[:4]				,		stra[:4]			):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("""hello""", """world"""))


