

"""simple docstring"""






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTMAEForPreTraining, ViTMAEModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor






class      _UpperCAmelCase      :
			def __init__(		self   ,		_A   ,		_A=13   ,		_A=30   ,		_A=2   ,		_A=3   ,		_A=True   ,		_A=True   ,		_A=32   ,		_A=5   ,		_A=4   ,		_A=37   ,		_A="gelu"   ,		_A=0.1   ,		_A=0.1   ,		_A=10   ,		_A=0.02   ,		_A=3   ,		_A=0.6   ,		_A=None   ,		)     ->     Any:






				'''simple docstring'''







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

				# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
				# (we add 1 for the [CLS] token)
				_UpperCAmelCase     :				Tuple          =       (image_size // patch_size) ** 2
				_UpperCAmelCase     :				int          =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

				_UpperCAmelCase     :				Dict          =       None
				if self.use_labels:
					_UpperCAmelCase     :				Tuple          =       ids_tensor([self.batch_size]   ,		self.type_sequence_label_size  )

				_UpperCAmelCase     :				List[str]          =       self.get_config()

				return config, pixel_values, labels
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				return ViTMAEConfig(
				    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=lowerCamelCase__   ,		initializer_range=self.initializer_range   ,		mask_ratio=self.mask_ratio   ,		)
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       ViTMAEModel(config=lowerCamelCase__  )
				model.to(lowerCamelCase__  )
				model.eval()
				_UpperCAmelCase     :				List[Any]          =       model(lowerCamelCase__  )
				self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)  )
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       ViTMAEForPreTraining(lowerCamelCase__  )
				model.to(lowerCamelCase__  )
				model.eval()
				_UpperCAmelCase     :				List[Any]          =       model(lowerCamelCase__  )
				_UpperCAmelCase     :				str          =       (self.image_size // self.patch_size) ** 2
				_UpperCAmelCase     :				int          =       self.patch_size**2 * self.num_channels
				self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, num_patches, expected_num_channels)  )

				# test greyscale images
				_UpperCAmelCase     :				int          =       1
				_UpperCAmelCase     :				int          =       ViTMAEForPreTraining(lowerCamelCase__  )
				model.to(lowerCamelCase__  )
				model.eval()
				_UpperCAmelCase     :				Any          =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
				_UpperCAmelCase     :				str          =       model(lowerCamelCase__  )
				_UpperCAmelCase     :				List[str]          =       self.patch_size**2
				self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, num_patches, expected_num_channels)  )
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[Any]          =       self.prepare_config_and_inputs()
				_UpperCAmelCase     :				Optional[int]          =       config_and_inputs
				_UpperCAmelCase     :				List[Any]          =       {"""pixel_values""": pixel_values}
				return config, inputs_dict






@require_torch
class      _UpperCAmelCase      (		__UpperCAmelCase     ,  __UpperCAmelCase     ,  unittest.TestCase):
			__a			:							int					       =				(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
			__a			:							Optional[Any]					       =				{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

			__a			:							List[Any]					       =				False
			__a			:							Optional[Any]					       =				False
			__a			:							int					       =				False
			__a			:							Union[str, Any]					       =				False
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				Tuple          =       ViTMAEModelTester(self  )
				_UpperCAmelCase     :				List[Any]          =       ConfigTester(self   ,		config_class=lowerCamelCase__   ,		has_text_modality=lowerCamelCase__   ,		hidden_size=37  )
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				self.config_tester.run_common_tests()
			@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""  )
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				pass
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase     :				Tuple          =       model_class(lowerCamelCase__  )
					self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module)  )
					_UpperCAmelCase     :				Optional[Any]          =       model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(lowerCamelCase__   ,		nn.Linear  )  )
			def 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       self.model_tester.prepare_config_and_inputs_for_common()

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

					_UpperCAmelCase     :				Optional[Any]          =       ["""pixel_values"""]
					self.assertListEqual(arg_names[:1]   ,		lowerCamelCase__  )
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCamelCase__  )
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Union[str, Any]          =       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				np.random.seed(2  )

				_UpperCAmelCase     :				Tuple          =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2  )
				_UpperCAmelCase     :				Dict          =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
				_UpperCAmelCase     :				str          =       torch.from_numpy(lowerCamelCase__  )

				# Add `noise` argument.
				# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
				_UpperCAmelCase     :				Tuple          =       pt_noise

				super().check_pt_tf_models(lowerCamelCase__   ,		lowerCamelCase__   ,		lowerCamelCase__  )
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_UpperCAmelCase     :				List[str]          =       model_class(lowerCamelCase__  )
					model.to(lowerCamelCase__  )
					model.eval()
					# make random mask reproducible
					torch.manual_seed(2  )
					with torch.no_grad():
						_UpperCAmelCase     :				Tuple          =       model(**self._prepare_for_class(lowerCamelCase__   ,		lowerCamelCase__  )  )

					_UpperCAmelCase     :				Union[str, Any]          =       outputs[0].cpu().numpy()
					_UpperCAmelCase     :				List[str]          =       0

					with tempfile.TemporaryDirectory() as tmpdirname:
						model.save_pretrained(lowerCamelCase__  )
						_UpperCAmelCase     :				List[str]          =       model_class.from_pretrained(lowerCamelCase__  )
						model.to(lowerCamelCase__  )
						# make random mask reproducible
						torch.manual_seed(2  )
						with torch.no_grad():
							_UpperCAmelCase     :				Tuple          =       model(**self._prepare_for_class(lowerCamelCase__   ,		lowerCamelCase__  )  )

						# Make sure we don't have nans
						_UpperCAmelCase     :				int          =       after_outputs[0].cpu().numpy()
						_UpperCAmelCase     :				List[Any]          =       0
						_UpperCAmelCase     :				List[str]          =       np.amax(np.abs(out_a - out_a  )  )
						self.assertLessEqual(lowerCamelCase__   ,		1e-5  )
			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""  )
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				pass
			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""  )
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				pass
			@unittest.skip(
			    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""  )
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				pass
			@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""  )
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







				pass
			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				pass
			@slow
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_UpperCAmelCase     :				Dict          =       ViTMAEModel.from_pretrained(lowerCamelCase__  )
					self.assertIsNotNone(lowerCamelCase__  )
def    UpperCamelCase       (      )					->      Dict:
	_UpperCAmelCase     :				str          =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
	return image






@require_torch
@require_vision
class      _UpperCAmelCase      (		unittest.TestCase):
			@cached_property
			def 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""  ) if is_vision_available() else None
			@slow
			def 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				np.random.seed(2  )

				_UpperCAmelCase     :				List[str]          =       ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""  ).to(lowerCamelCase__  )

				_UpperCAmelCase     :				List[str]          =       self.default_image_processor
				_UpperCAmelCase     :				List[Any]          =       prepare_img()
				_UpperCAmelCase     :				List[Any]          =       image_processor(images=lowerCamelCase__   ,		return_tensors="""pt"""  ).to(lowerCamelCase__  )

				# prepare a noise vector that will be also used for testing the TF model
				# (this way we can ensure that the PT and TF models operate on the same inputs)
				_UpperCAmelCase     :				str          =       ViTMAEConfig()
				_UpperCAmelCase     :				Optional[int]          =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
				_UpperCAmelCase     :				Tuple          =       np.random.uniform(size=(1, num_patches)  )

				# forward pass
				with torch.no_grad():
					_UpperCAmelCase     :				List[str]          =       model(**lowerCamelCase__   ,		noise=torch.from_numpy(lowerCamelCase__  ).to(device=lowerCamelCase__  )  )

				# verify the logits
				_UpperCAmelCase     :				List[Any]          =       torch.Size((1, 1_96, 7_68)  )
				self.assertEqual(outputs.logits.shape   ,		lowerCamelCase__  )

				_UpperCAmelCase     :				int          =       torch.tensor(
				    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

				self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,		expected_slice.to(lowerCamelCase__  )   ,		atol=1e-4  )  )









"""simple docstring"""


from decimal import Decimal, getcontext
from math import ceil, factorial


def    _lowerCamelCase   (     lowerCamelCase__	:							int  ):

 if not isinstance(lowerCamelCase__       ,		lowerCamelCase__  ):
  raise TypeError("""Undefined for non-integers"""  )
 elif precision < 1:
  raise ValueError("""Undefined for non-natural numbers"""  )

 lowercase__       :       List[str]				=						precision
 lowercase__       :       Optional[int]				=						ceil(precision / 14  )
 lowercase__       :       Union[str, Any]				=						42_68_80 * Decimal(1_00_05  ).sqrt()
 lowercase__       :       Optional[Any]				=						1
 lowercase__       :       Optional[Any]				=						13_59_14_09
 lowercase__       :       List[Any]				=						Decimal(lowerCamelCase__  )
 for k in range(1       ,		lowerCamelCase__  ):
  lowercase__       :       Optional[int]				=						factorial(6 * k  ) // (factorial(3 * k  ) * factorial(lowerCamelCase__  ) ** 3)
  linear_term += 5_45_14_01_34
  exponential_term *= -26_25_37_41_26_40_76_80_00
  partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
 return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
   __snake_case    					=					50
   print(F"The first {n} digits of pi is: {pi(n)}")