



'''simple docstring'''

import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
							import jax

							from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
							from PIL import Image

							from transformers import BeitImageProcessor

class       __magic_name__		(       unittest.TestCase  ):







							def __init__(				self   :			int      ,_UpperCAmelCase   :			Tuple      ,_UpperCAmelCase   :			Tuple=100      ,_UpperCAmelCase   :			List[Any]=13      ,_UpperCAmelCase   :			int=30      ,_UpperCAmelCase   :			int=2      ,_UpperCAmelCase   :			List[str]=3      ,_UpperCAmelCase   :			List[Any]=True      ,_UpperCAmelCase   :			Dict=True      ,_UpperCAmelCase   :			Any=32      ,_UpperCAmelCase   :			Optional[int]=5      ,_UpperCAmelCase   :			Optional[Any]=4      ,_UpperCAmelCase   :			Dict=37      ,_UpperCAmelCase   :			Any="gelu"      ,_UpperCAmelCase   :			Tuple=0.1      ,_UpperCAmelCase   :			List[Any]=0.1      ,_UpperCAmelCase   :			List[Any]=10      ,_UpperCAmelCase   :			Any=0.02      ,_UpperCAmelCase   :			Optional[Any]=3      ,):
									_a	: Union[str, Any]								=				parent
									_a	: Tuple								=				vocab_size
									_a	: str								=				batch_size
									_a	: Tuple								=				image_size
									_a	: Any								=				patch_size
									_a	: Optional[Any]								=				num_channels
									_a	: int								=				is_training
									_a	: List[Any]								=				use_labels
									_a	: int								=				hidden_size
									_a	: Optional[int]								=				num_hidden_layers
									_a	: Any								=				num_attention_heads
									_a	: Optional[int]								=				intermediate_size
									_a	: Optional[int]								=				hidden_act
									_a	: Dict								=				hidden_dropout_prob
									_a	: Optional[Any]								=				attention_probs_dropout_prob
									_a	: Optional[int]								=				type_sequence_label_size
									_a	: Union[str, Any]								=				initializer_range

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







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Any								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

									_a	: List[Any]								=				BeitConfig(
									    vocab_size=self.vocab_size      ,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, labels







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			int						):
									_a	: Optional[int]								=				FlaxBeitModel(config=_UpperCAmelCase						)
									_a	: Union[str, Any]								=				model(_UpperCAmelCase						)
									self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)						)







							def 				__lowercase     (				self   :			Dict      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			Any						):
									_a	: Optional[int]								=				FlaxBeitForMaskedImageModeling(config=_UpperCAmelCase						)
									_a	: List[Any]								=				model(_UpperCAmelCase						)
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length - 1, self.vocab_size)						)







							def 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			List[Any]      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			str						):
									_a	: Union[str, Any]								=				self.type_sequence_label_size
									_a	: Tuple								=				FlaxBeitForImageClassification(config=_UpperCAmelCase						)
									_a	: Union[str, Any]								=				model(_UpperCAmelCase						)
									self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)						)

									# test greyscale images
									_a	: Union[str, Any]								=				1
									_a	: Optional[Any]								=				FlaxBeitForImageClassification(_UpperCAmelCase						)

									_a	: Tuple								=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									_a	: Tuple								=				model(_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Union[str, Any]								=				self.prepare_config_and_inputs()
									(
									    (
									    _a
									)			,   (
									    _a
									)			,   (
									    _a
									)			,   
									)	: Tuple								=				config_and_inputs
									_a	: int								=				{'pixel_values': pixel_values}
									return config, inputs_dict

@require_flax
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Any  							=   (
							    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
							)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: List[str]								=				FlaxBeitModelTester(self						)
									_a	: Optional[int]								=				ConfigTester(self      ,config_class=_UpperCAmelCase      ,has_text_modality=_UpperCAmelCase      ,hidden_size=37						)







							def 				__lowercase     (				self   :			Tuple						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a			,   _a	: str								=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_a	: Any								=				model_class(_UpperCAmelCase						)
											_a	: Union[str, Any]								=				inspect.signature(model.__call__						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_a	: str								=				[*signature.parameters.keys()]

											_a	: Tuple								=				['pixel_values']
											self.assertListEqual(arg_names[:1]      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a			,   _a	: Union[str, Any]								=				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(_UpperCAmelCase      ,_UpperCAmelCase						)
													_a	: Union[str, Any]								=				model_class(_UpperCAmelCase						)

													@jax.jit
													def model_jitted(_UpperCAmelCase   :			Any      ,**_UpperCAmelCase   :			str						):
															return model(pixel_values=_UpperCAmelCase      ,**_UpperCAmelCase						)

													with self.subTest('JIT Enabled'						):
															_a	: List[Any]								=				model_jitted(**_UpperCAmelCase						).to_tuple()

													with self.subTest('JIT Disabled'						):
															with jax.disable_jit():
																	_a	: str								=				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						)







							def 				__lowercase     (				self   :			Any						):
									_a	: Optional[Any]								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Any						):
									_a	: str								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[Any]								=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Optional[Any]						):
									for model_class_name in self.all_model_classes:
											_a	: Any								=				model_class_name.from_pretrained('microsoft/beit-base-patch16-224'						)
											_a	: Optional[int]								=				model(np.ones((1, 3, 224, 224)						)						)
											self.assertIsNotNone(_UpperCAmelCase						)



def 	__lowerCamelCase	( )     -> Any:
		_a	: Optional[Any]								=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
		return image

@require_vision
@require_flax
class       __magic_name__		(       unittest.TestCase  ):







							@cached_property
							def 				__lowercase     (				self   :			Any						):
									return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'						) if is_vision_available() else None







							@slow
							def 				__lowercase     (				self   :			Dict						):
									_a	: int								=				FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'						)

									_a	: Dict								=				self.default_image_processor
									_a	: List[Any]								=				prepare_img()
									_a	: Optional[Any]								=				image_processor(images=_UpperCAmelCase      ,return_tensors='np'						).pixel_values

									# prepare bool_masked_pos
									_a	: str								=				np.ones((1, 196)      ,dtype=_UpperCAmelCase						)

									# forward pass
									_a	: List[str]								=				model(pixel_values=_UpperCAmelCase      ,bool_masked_pos=_UpperCAmelCase						)
									_a	: Optional[int]								=				outputs.logits

									# verify the logits
									_a	: Dict								=				(1, 196, 8192)
									self.assertEqual(logits.shape      ,_UpperCAmelCase						)

									_a	: Optional[Any]								=				np.array(
									    [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]						)

									self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]      ,_UpperCAmelCase      ,atol=1E-2						)						)







							@slow
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Optional[Any]								=				FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'						)

									_a	: int								=				self.default_image_processor
									_a	: List[Any]								=				prepare_img()
									_a	: Dict								=				image_processor(images=_UpperCAmelCase      ,return_tensors='np'						)

									# forward pass
									_a	: Union[str, Any]								=				model(**_UpperCAmelCase						)
									_a	: Union[str, Any]								=				outputs.logits

									# verify the logits
									_a	: Union[str, Any]								=				(1, 1000)
									self.assertEqual(logits.shape      ,_UpperCAmelCase						)

									_a	: Optional[Any]								=				np.array([-1.23_85, -1.09_87, -1.01_08]						)

									self.assertTrue(np.allclose(logits[0, :3]      ,_UpperCAmelCase      ,atol=1E-4						)						)

									_a	: List[str]								=				281
									self.assertEqual(logits.argmax(-1						).item()      ,_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Tuple						):
									_a	: Dict								=				FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'						)

									_a	: Dict								=				self.default_image_processor
									_a	: List[Any]								=				prepare_img()
									_a	: List[str]								=				image_processor(images=_UpperCAmelCase      ,return_tensors='np'						)

									# forward pass
									_a	: Optional[Any]								=				model(**_UpperCAmelCase						)
									_a	: List[str]								=				outputs.logits

									# verify the logits
									_a	: Union[str, Any]								=				(1, 21841)
									self.assertEqual(logits.shape      ,_UpperCAmelCase						)

									_a	: Optional[int]								=				np.array([1.68_81, -0.27_87, 0.59_01]						)

									self.assertTrue(np.allclose(logits[0, :3]      ,_UpperCAmelCase      ,atol=1E-4						)						)

									_a	: Tuple								=				2396
									self.assertEqual(logits.argmax(-1						).item()      ,_UpperCAmelCase						)









'''simple docstring'''

def 	__lowerCamelCase	( lowerCAmelCase_ = 1000000    )     -> int:
		_a	: Optional[int]								=				set(range(3		,     lowerCAmelCase_		,     2    )    )
		primes.add(2    )
		for p in range(3		,     lowerCAmelCase_		,     2    ):
				if p not in primes:
						continue
				primes.difference_update(set(range(p * p		,     lowerCAmelCase_		,     lowerCAmelCase_    )    )    )

		_a	: Tuple								=				[float(lowerCAmelCase_    ) for n in range(limit + 1    )]

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

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


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




