

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 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def __init__(					self    :		List[Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		Optional[int]=13		,				UpperCAmelCase__    :		Tuple=30		,				UpperCAmelCase__    :		List[str]=2		,				UpperCAmelCase__    :		int=3		,				UpperCAmelCase__    :		str=True		,				UpperCAmelCase__    :		Dict=True		,				UpperCAmelCase__    :		Tuple=32		,				UpperCAmelCase__    :		Any=5		,				UpperCAmelCase__    :		Optional[int]=4		,				UpperCAmelCase__    :		int=37		,				UpperCAmelCase__    :		Any="gelu"		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		List[Any]=0.1		,				UpperCAmelCase__    :		str=10		,				UpperCAmelCase__    :		Dict=0.02		,				):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=parent
										lowercase   :       Optional[int]								=batch_size
										lowercase   :       List[str]								=image_size
										lowercase   :       Dict								=patch_size
										lowercase   :       int								=num_channels
										lowercase   :       Any								=is_training
										lowercase   :       Optional[Any]								=use_labels
										lowercase   :       Any								=hidden_size
										lowercase   :       int								=num_hidden_layers
										lowercase   :       Optional[int]								=num_attention_heads
										lowercase   :       Any								=intermediate_size
										lowercase   :       Dict								=hidden_act
										lowercase   :       List[str]								=hidden_dropout_prob
										lowercase   :       str								=attention_probs_dropout_prob
										lowercase   :       Optional[int]								=type_sequence_label_size
										lowercase   :       str								=initializer_range

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







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       int								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

										lowercase   :       Tuple								=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=UpperCAmelCase__		,				initializer_range=self.initializer_range		,				)

										return config, pixel_values







				def    lowerCamelCase_  (					self    :		List[Any]		,				UpperCAmelCase__    :		int		,				UpperCAmelCase__    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=FlaxViTModel(config=UpperCAmelCase__      )
										lowercase   :       Any								=model(UpperCAmelCase__      )
										# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
										lowercase   :       List[Any]								=(self.image_size, self.image_size)
										lowercase   :       Tuple								=(self.patch_size, self.patch_size)
										lowercase   :       str								=(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    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Any		,				UpperCAmelCase__    :		str      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.type_sequence_label_size
										lowercase   :       Dict								=FlaxViTForImageClassification(config=UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=model(UpperCAmelCase__      )
										self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)      )

										# test greyscale images
										lowercase   :       Tuple								=1
										lowercase   :       Any								=FlaxViTForImageClassification(UpperCAmelCase__      )

										lowercase   :       Optional[int]								=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
										lowercase   :       Optional[Any]								=model(UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										lowercase   :       str								=self.prepare_config_and_inputs()
										(
										    lowercase  
										)   :       Optional[Any]								=config_and_inputs
										lowercase   :       List[Any]								={'''pixel_values''': pixel_values}
										return config, inputs_dict





@require_flax
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







				def    lowerCamelCase_  (					self    :		Optional[int]      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=FlaxViTModelTester(self      )
										lowercase   :       List[str]								=ConfigTester(self		,				config_class=UpperCAmelCase__		,				has_text_modality=UpperCAmelCase__		,				hidden_size=37      )







				def    lowerCamelCase_  (					self    :		Dict      ):
										'''simple docstring'''
										self.config_tester.run_common_tests()







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       Optional[Any]								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																lowercase   :       Dict								=model_class(UpperCAmelCase__      )
																lowercase   :       Tuple								=inspect.signature(model.__call__      )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																lowercase   :       Tuple								=[*signature.parameters.keys()]

																lowercase   :       List[str]								=['''pixel_values''']
																self.assertListEqual(arg_names[:1]		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										lowercase   :       int								=self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__      ):
																						lowercase   :       Dict								=self._prepare_for_class(UpperCAmelCase__		,				UpperCAmelCase__      )
																						lowercase   :       Union[str, Any]								=model_class(UpperCAmelCase__      )

																						@jax.jit
																						def model_jitted(UpperCAmelCase__    :		Tuple		,				**UpperCAmelCase__    :		Dict      ):
																												return model(pixel_values=UpperCAmelCase__		,				**UpperCAmelCase__      )

																						with self.subTest('''JIT Enabled'''      ):
																												lowercase   :       Dict								=model_jitted(**UpperCAmelCase__      ).to_tuple()

																						with self.subTest('''JIT Disabled'''      ):
																												with jax.disable_jit():
																																		lowercase   :       Tuple								=model_jitted(**UpperCAmelCase__      ).to_tuple()

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







				@slow
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										for model_class_name in self.all_model_classes:
																lowercase   :       Union[str, Any]								=model_class_name.from_pretrained('''google/vit-base-patch16-224'''      )
																lowercase   :       Tuple								=model(np.ones((1, 3, 224, 224)      )      )
																self.assertIsNotNone(UpperCAmelCase__      )








'''simple docstring'''





def 							_lowerCAmelCase (      __magic_name__	:    int = 1000000	)							->     int:
						lowercase   :       Dict								=set(range(3      ,   __magic_name__      ,   2	)	)
						primes.add(2	)
						for p in range(3      ,   __magic_name__      ,   2	):
												if p not in primes:
																		continue
												primes.difference_update(set(range(p * p      ,   __magic_name__      ,   __magic_name__	)	)	)

						lowercase   :       List[Any]								=[float(__magic_name__	) for n in range(limit + 1	)]

						for p in primes:
												for n in range(__magic_name__      ,   limit + 1      ,   __magic_name__	):
																		phi[n] *= 1 - 1 / p

						return int(sum(phi[2:]	)	)


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





