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       snake_case					:




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




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

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




  def 	__lowercase(      self     :	Any				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		ids_tensor([self.batch_size]     , self.type_sequence_label_size				)

       SCREAMING_SNAKE_CASE__				:				str			=		self.get_config()

       return config, pixel_values, labels




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




       """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=_a     , initializer_range=self.initializer_range     , )




  def 	__lowercase(      self     :	Dict     , a_     :	Tuple     , a_     :	int     , a_     :	Tuple				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		TFViTModel(config=_a				)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(_a     , training=_a				)
       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__				:				Tuple			=		self.image_size // 2
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		pixel_values[:, :, :image_size, :image_size]
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(_a     , interpolate_pos_encoding=_a     , training=_a				)
       SCREAMING_SNAKE_CASE__				:				List[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 	__lowercase(      self     :	Tuple     , a_     :	Union[str, Any]     , a_     :	int     , a_     :	List[str]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				List[str]			=		TFViTForImageClassification(_a				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(_a     , labels=_a     , training=_a				)
       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__				:				Union[str, Any]			=		self.image_size // 2
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		pixel_values[:, :, :image_size, :image_size]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(_a     , interpolate_pos_encoding=_a     , training=_a				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__				:				Any			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				List[str]			=		{"""pixel_values""": pixel_values}
       return config, inputs_dict




@require_tf
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
  lowercase_			=     (
      {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
      if is_tf_available()
      else {}
  )

  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     False




  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		TFViTModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				Tuple			=		ConfigTester(self     , config_class=_a     , has_text_modality=_a     , hidden_size=37				)




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




       """simple docstring"""
       self.config_tester.run_common_tests()




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




       """simple docstring"""
       pass




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




       """simple docstring"""
       pass




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model_class(_a				)
            self.assertIsInstance(model.get_input_embeddings()     , (tf.keras.layers.Layer)				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(_a     , tf.keras.layers.Layer				)				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Dict			=		model_class(_a				)
            SCREAMING_SNAKE_CASE__				:				List[str]			=		inspect.signature(model.call				)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            SCREAMING_SNAKE_CASE__				:				Tuple			=		[*signature.parameters.keys()]

            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		["""pixel_values"""]
            self.assertListEqual(arg_names[:1]     , _a				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a				)




  def 	__lowercase(      self     :	str				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_a				)




  @slow
  def 	__lowercase(      self     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		TFViTModel.from_pretrained('google/vit-base-patch16-224'				)
       self.assertIsNotNone(_a				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
     return image







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




  @cached_property
  def 	__lowercase(      self     :	str				)->       str:




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




  @slow
  def 	__lowercase(      self     :	Optional[int]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.default_image_processor
       SCREAMING_SNAKE_CASE__				:				int			=		prepare_img()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(images=_a     , return_tensors='tf'				)

       # forward pass
       SCREAMING_SNAKE_CASE__				:				Tuple			=		model(**_a				)

       # verify the logits
       SCREAMING_SNAKE_CASE__				:				Dict			=		tf.TensorShape((1, 1000)				)
       self.assertEqual(outputs.logits.shape     , _a				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		tf.constant([-0.2744, 0.8215, -0.0836]				)

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




def     _a				(      lowercase__  :			int = 1_00_00_00 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[i - 1 for i in range(limit + 1 )]

     for i in range(2     ,  limit + 1 ):
          if phi[i] == i - 1:
               for j in range(2 * i     ,  limit + 1     ,  lowercase__ ):
                    phi[j] -= phi[j] // i

     return sum(phi[2 : limit + 1] )


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



