


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				:     str   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Tuple=13   ,  UpperCAmelCase				:     Optional[int]=30   ,  UpperCAmelCase				:     Optional[int]=2   ,  UpperCAmelCase				:     str=3   ,  UpperCAmelCase				:     Optional[int]=True   ,  UpperCAmelCase				:     List[Any]=True   ,  UpperCAmelCase				:     Optional[int]=32   ,  UpperCAmelCase				:     Tuple=5   ,  UpperCAmelCase				:     Tuple=4   ,  UpperCAmelCase				:     Union[str, Any]=37   ,  UpperCAmelCase				:     Union[str, Any]="gelu"   ,  UpperCAmelCase				:     Optional[int]=0.1   ,  UpperCAmelCase				:     Optional[int]=0.1   ,  UpperCAmelCase				:     Union[str, Any]=10   ,  UpperCAmelCase				:     Tuple=0.02   ,  UpperCAmelCase				:     Optional[int]=3   ,  UpperCAmelCase				:     str=0.6   ,  UpperCAmelCase				:     List[str]=None   ,  ):
				SCREAMING_SNAKE_CASE_				:int				     =						parent
				SCREAMING_SNAKE_CASE_				:Any				     =						batch_size
				SCREAMING_SNAKE_CASE_				:Any				     =						image_size
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						patch_size
				SCREAMING_SNAKE_CASE_				:int				     =						num_channels
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						is_training
				SCREAMING_SNAKE_CASE_				:str				     =						use_labels
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						hidden_size
				SCREAMING_SNAKE_CASE_				:str				     =						num_hidden_layers
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						num_attention_heads
				SCREAMING_SNAKE_CASE_				:str				     =						intermediate_size
				SCREAMING_SNAKE_CASE_				:Any				     =						hidden_act
				SCREAMING_SNAKE_CASE_				:int				     =						hidden_dropout_prob
				SCREAMING_SNAKE_CASE_				:Tuple				     =						attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						type_sequence_label_size
				SCREAMING_SNAKE_CASE_				:str				     =						initializer_range
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						mask_ratio
				SCREAMING_SNAKE_CASE_				:Any				     =						scope

				# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
				# (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(image_size // patch_size) ** 2
				SCREAMING_SNAKE_CASE_				:str				     =						int(math.ceil((1 - mask_ratio) * (num_patches + 1)))






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

				SCREAMING_SNAKE_CASE_				:Any				     =						self.get_config()

				return config, pixel_values, labels






		def _snake_case		(						self				:     Union[str, Any]):
				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=UpperCAmelCase   ,  initializer_range=self.initializer_range   ,  mask_ratio=self.mask_ratio   ,  )






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     Tuple):
				SCREAMING_SNAKE_CASE_				:int				     =						ViTMAEModel(config=UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Dict				     =						model(UpperCAmelCase)
				self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size))






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Dict):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						ViTMAEForPreTraining(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Any				     =						model(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						(self.image_size // self.patch_size) ** 2
				SCREAMING_SNAKE_CASE_				:Dict				     =						self.patch_size**2 * self.num_channels
				self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, num_patches, expected_num_channels))

				# test greyscale images
				SCREAMING_SNAKE_CASE_				:int				     =						1
				SCREAMING_SNAKE_CASE_				:str				     =						ViTMAEForPreTraining(UpperCAmelCase)
				model.to(UpperCAmelCase)
				model.eval()
				SCREAMING_SNAKE_CASE_				:Dict				     =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
				SCREAMING_SNAKE_CASE_				:List[str]				     =						model(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Dict				     =						self.patch_size**2
				self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, num_patches, expected_num_channels))







		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:int				     =						config_and_inputs
				SCREAMING_SNAKE_CASE_				:List[str]				     =						{"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 						_UpperCAmelCase      (     lowercase		,		lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			Any       			=  (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
		lowerCamelCase_	:			int       			=  {"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

		lowerCamelCase_	:			int       			=  False
		lowerCamelCase_	:			List[str]       			=  False
		lowerCamelCase_	:			Dict       			=  False
		lowerCamelCase_	:			Dict       			=  False






		def _snake_case		(						self				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:int				     =						ViTMAEModelTester(self)
				SCREAMING_SNAKE_CASE_				:Any				     =						ConfigTester(self   ,  config_class=UpperCAmelCase   ,  has_text_modality=UpperCAmelCase   ,  hidden_size=37)






		def _snake_case		(						self				:     str):
				self.config_tester.run_common_tests()






		@unittest.skip(reason="ViTMAE does not use inputs_embeds")
		def _snake_case		(						self				:     Any):
				pass






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Dict				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model_class(UpperCAmelCase)
						self.assertIsInstance(model.get_input_embeddings()   ,  (nn.Module))
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(UpperCAmelCase   ,  nn.Linear))






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Any				     =						self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model_class(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:int				     =						inspect.signature(model.forward)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						SCREAMING_SNAKE_CASE_				:Tuple				     =						[*signature.parameters.keys()]

						SCREAMING_SNAKE_CASE_				:Tuple				     =						["pixel_values"]
						self.assertListEqual(arg_names[:1]   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:str				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Tuple   ,  UpperCAmelCase				:     List[Any]   ,  UpperCAmelCase				:     int):
				# make masks reproducible
				np.random.seed(2)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						int((pt_model.config.image_size // pt_model.config.patch_size) ** 2)
				SCREAMING_SNAKE_CASE_				:Any				     =						np.random.uniform(size=(self.model_tester.batch_size, num_patches))
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						torch.from_numpy(UpperCAmelCase)

				# Add `noise` argument.
				# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						pt_noise

				super().check_pt_tf_models(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase)






		def _snake_case		(						self				:     Optional[int]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.model_tester.prepare_config_and_inputs_for_common()

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

						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						outputs[0].cpu().numpy()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						0

						with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_pretrained(UpperCAmelCase)
								SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model_class.from_pretrained(UpperCAmelCase)
								model.to(UpperCAmelCase)
								# make random mask reproducible
								torch.manual_seed(2)
								with torch.no_grad():
										SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						model(**self._prepare_for_class(UpperCAmelCase   ,  UpperCAmelCase))

								# Make sure we don't have nans
								SCREAMING_SNAKE_CASE_				:str				     =						after_outputs[0].cpu().numpy()
								SCREAMING_SNAKE_CASE_				:Tuple				     =						0
								SCREAMING_SNAKE_CASE_				:Dict				     =						np.amax(np.abs(out_a - out_a))
								self.assertLessEqual(UpperCAmelCase   ,  1E-5)






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






		@unittest.skip(
		    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.")
		def _snake_case		(						self				:     Tuple):
				pass






		@unittest.skip(
		    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.")
		def _snake_case		(						self				:     List[str]):
				pass






		@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load")
		def _snake_case		(						self				:     str):
				pass






		@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
		def _snake_case		(						self				:     int):
				pass







		@slow
		def _snake_case		(						self				:     Optional[Any]):
				for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						ViTMAEModel.from_pretrained(UpperCAmelCase)
						self.assertIsNotNone(UpperCAmelCase)




def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[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				:     Tuple):
				return ViTImageProcessor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None







		@slow
		def _snake_case		(						self				:     List[str]):
				# make random mask reproducible across the PT and TF model
				np.random.seed(2)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.default_image_processor
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						prepare_img()
				SCREAMING_SNAKE_CASE_				:Dict				     =						image_processor(images=UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# 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)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ViTMAEConfig()
				SCREAMING_SNAKE_CASE_				:int				     =						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
				SCREAMING_SNAKE_CASE_				:int				     =						np.random.uniform(size=(1, num_patches))

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(**UpperCAmelCase   ,  noise=torch.from_numpy(UpperCAmelCase).to(device=UpperCAmelCase))

				# verify the logits
				SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.Size((1, 1_96, 7_68))
				self.assertEqual(outputs.logits.shape   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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(UpperCAmelCase)   ,  atol=1E-4))










from __future__ import annotations

import math


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						u
		for i in range(1     , a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp * (u - i)
		return temp


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						int(input("enter the numbers of values: " ) )
		SCREAMING_SNAKE_CASE_				:list[list[float]]				     =						[]
		for _ in range(a ):
				y.append([] )
		for i in range(a ):
				for j in range(a ):
						y[i].append(a )
						SCREAMING_SNAKE_CASE_				:Any				     =						0

		print("enter the values of parameters in a list: " )
		SCREAMING_SNAKE_CASE_				:Dict				     =						list(map(a     , input().split() ) )

		print("enter the values of corresponding parameters: " )
		for i in range(a ):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						float(input() )

		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						int(input("enter the value to interpolate: " ) )
		SCREAMING_SNAKE_CASE_				:str				     =						(value - x[0]) / (x[1] - x[0])

		# for calculating forward difference table

		for i in range(1     , a ):
				for j in range(n - i ):
						SCREAMING_SNAKE_CASE_				:List[str]				     =						y[j + 1][i - 1] - y[j][i - 1]

		SCREAMING_SNAKE_CASE_				:Tuple				     =						y[0][0]
		for i in range(1     , a ):
				summ += (ucal(a     , a ) * y[0][i]) / math.factorial(a )

		print(F"the value at {value} is {summ}" )


if __name__ == "__main__":
		main()






