



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			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=13 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=5 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=37 ,					__UpperCAmelCase="gelu" ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=10 ,					__UpperCAmelCase=0.0_2 ,					):







											"""simple docstring"""



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

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



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

											a__	:							Optional[int]      						= 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=SCREAMING_SNAKE_CASE_ ,					initializer_range=self.initializer_range ,					)

											return config, pixel_values



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= FlaxViTModel(config=SCREAMING_SNAKE_CASE_				)
											a__	:							str      						= model(SCREAMING_SNAKE_CASE_				)
											# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
											a__	:							str      						= (self.image_size, self.image_size)
											a__	:							str      						= (self.patch_size, self.patch_size)
											a__	:							Any      						= (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 ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= self.type_sequence_label_size
											a__	:							Any      						= FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_				)
											a__	:							Dict      						= model(SCREAMING_SNAKE_CASE_				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.type_sequence_label_size)				)

											# test greyscale images
											a__	:							List[str]      						= 1
											a__	:							List[Any]      						= FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_				)

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




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.prepare_config_and_inputs()
											(
											    a__			
											)	:							int      						= config_and_inputs
											a__	:							Union[str, Any]      						= {'pixel_values': pixel_values}
											return config, inputs_dict





@require_flax
class 				_a						(       _UpperCAmelCase       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:str =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= FlaxViTModelTester(self				)
											a__	:							Union[str, Any]      						= ConfigTester(self ,					config_class=SCREAMING_SNAKE_CASE_ ,					has_text_modality=SCREAMING_SNAKE_CASE_ ,					hidden_size=37				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.config_tester.run_common_tests()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



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

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

															a__	:							Optional[int]      						= ['pixel_values']
															self.assertListEqual(arg_names[:1] ,					SCREAMING_SNAKE_CASE_				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.model_tester.prepare_config_and_inputs_for_common()

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

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

																			with self.subTest("JIT Enabled"				):
																							a__	:							Optional[int]      						= model_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

																			with self.subTest("JIT Disabled"				):
																							with jax.disable_jit():
																											a__	:							Union[str, Any]      						= model_jitted(**SCREAMING_SNAKE_CASE_				).to_tuple()

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




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											for model_class_name in self.all_model_classes:
															a__	:							Union[str, Any]      						= model_class_name.from_pretrained("google/vit-base-patch16-224"				)
															a__	:							Union[str, Any]      						= model(np.ones((1, 3, 224, 224)				)				)
															self.assertIsNotNone(SCREAMING_SNAKE_CASE_				)






'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 1_00				)	->						int:
     __lowerCamelCase  :      Union[str, Any]														=						n * (n + 1) * (2 * n + 1) / 6
     __lowerCamelCase  :      Union[str, Any]														=						(n * (n + 1) / 2) ** 2
     return int(square_of_sum - sum_of_squares				)


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






