


"""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 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A			,   __A=13			,   __A=30			,   __A=2			,   __A=3			,   __A=True			,   __A=True			,   __A=32			,   __A=2			,   __A=4			,   __A=37			,   __A="gelu"			,   __A=0.1			,   __A=0.1			,   __A=10			,   __A=0.02			,   __A=3			,   __A=None			,   ) ->					Any:
     a					=parent
     a					=batch_size
     a					=image_size
     a					=patch_size
     a					=num_channels
     a					=is_training
     a					=use_labels
     a					=hidden_size
     a					=num_hidden_layers
     a					=num_attention_heads
     a					=intermediate_size
     a					=hidden_act
     a					=hidden_dropout_prob
     a					=attention_probs_dropout_prob
     a					=type_sequence_label_size
     a					=initializer_range
     a					=scope

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

     a					=self.get_config()

     return config, pixel_values, labels


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Union[str, Any]:
     a					=TFViTModel(config=__A						)
     a					=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.
     a					=self.image_size // 2
     a					=pixel_values[:, :, :image_size, :image_size]
     a					=model(__A			,   interpolate_pos_encoding=__A			,   training=__A						)
     a					=(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			,   __A			,   __A			,   __A						) ->					Tuple:
     a					=self.type_sequence_label_size
     a					=TFViTForImageClassification(__A						)
     a					=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.
     a					=self.image_size // 2
     a					=pixel_values[:, :, :image_size, :image_size]
     a					=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
     a					=1
     a					=TFViTForImageClassification(__A						)
     a					=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
     a					=model(__A						)
     self.parent.assertEqual(result.logits.shape			,   (self.batch_size, self.type_sequence_label_size)						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=self.prepare_config_and_inputs()
     a		,				a		,				a					=config_and_inputs
     a					={'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_tf
class 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 __lowerCAmelCase			 =  (
     {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False
 __lowerCAmelCase			 =  False


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[str]:
     a					=TFViTModelTester(self						)
     a					=ConfigTester(self			,   config_class=__A			,   has_text_modality=__A			,   hidden_size=37						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     self.config_tester.run_common_tests()


 @unittest.skip(reason='''ViT does not use inputs_embeds'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     pass


 @unittest.skip(reason='''ViT does not use inputs_embeds'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     pass


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

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


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a		,				a					=self.model_tester.prepare_config_and_inputs_for_common()

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

         a					=['''pixel_values''']
         self.assertListEqual(arg_names[:1]			,   __A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__A						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=TFViTModel.from_pretrained('''google/vit-base-patch16-224'''						)
     self.assertIsNotNone(__A						)

def _A						(						):







    """simple docstring"""
    a					=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image






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






 """simple docstring"""




 @cached_property
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''						) if is_vision_available() else None





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''						)

     a					=self.default_image_processor
     a					=prepare_img()
     a					=image_processor(images=__A			,   return_tensors='''tf'''						)

     # forward pass
     a					=model(**__A						)

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

     a					=tf.constant([-0.2_744, 0.8_215, -0.0_836]						)

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



"""simple docstring"""







def _A						(						lowercase = 2_00_00_00		):







    """simple docstring"""
    a					=[0 for i in range(n + 1		)]
    a					=1
    a					=1

    for i in range(2	,   int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i	,   n + 1	,   lowercase		):
                a					=1
    a					=0
    for i in range(lowercase		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
       print(F'{solution() = }')