

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 	A_		:
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str=1_3       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=3_0       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=2       ,SCREAMING_SNAKE_CASE__		:      Any=3       ,SCREAMING_SNAKE_CASE__		:      int=True       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=True       ,SCREAMING_SNAKE_CASE__		:      Any=3_2       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=5       ,SCREAMING_SNAKE_CASE__		:      List[str]=4       ,SCREAMING_SNAKE_CASE__		:      int=3_7       ,SCREAMING_SNAKE_CASE__		:      Dict="gelu"       ,SCREAMING_SNAKE_CASE__		:      Dict=0.1       ,SCREAMING_SNAKE_CASE__		:      List[str]=0.1       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_0       ,SCREAMING_SNAKE_CASE__		:      Dict=0.02       ,SCREAMING_SNAKE_CASE__		:      Dict=3       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=0.6       ,SCREAMING_SNAKE_CASE__		:      List[str]=None       ,):
								__lowerCamelCase    :    Union[str, Any] 	=			parent
								__lowerCamelCase    :    Optional[Any] 	=			batch_size
								__lowerCamelCase    :    Dict 	=			image_size
								__lowerCamelCase    :    Dict 	=			patch_size
								__lowerCamelCase    :    List[str] 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			is_training
								__lowerCamelCase    :    int 	=			use_labels
								__lowerCamelCase    :    List[str] 	=			hidden_size
								__lowerCamelCase    :    Optional[int] 	=			num_hidden_layers
								__lowerCamelCase    :    List[str] 	=			num_attention_heads
								__lowerCamelCase    :    List[str] 	=			intermediate_size
								__lowerCamelCase    :    int 	=			hidden_act
								__lowerCamelCase    :    str 	=			hidden_dropout_prob
								__lowerCamelCase    :    List[Any] 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    Any 	=			type_sequence_label_size
								__lowerCamelCase    :    int 	=			initializer_range
								__lowerCamelCase    :    str 	=			mask_ratio
								__lowerCamelCase    :    Any 	=			scope

								# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
								# (we add 1 for the [CLS] token)
								__lowerCamelCase    :    Optional[int] 	=			(image_size // patch_size) ** 2
								__lowerCamelCase    :    Any 	=			int(math.ceil((1 - mask_ratio) * (num_patches + 1)))
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[Any] 	=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								__lowerCamelCase    :    Optional[Any] 	=			self.get_config()

								return config, pixel_values, labels
							def    lowerCAmelCase					(		self		:      Dict):
								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=A__       ,initializer_range=self.initializer_range       ,mask_ratio=self.mask_ratio       ,)
							def    lowerCAmelCase					(		self		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			ViTMAEModel(config=A__)
								model.to(A__)
								model.eval()
								__lowerCamelCase    :    Optional[int] 	=			model(A__)
								self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size))
							def    lowerCAmelCase					(		self		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Dict):
								__lowerCamelCase    :    str 	=			ViTMAEForPreTraining(A__)
								model.to(A__)
								model.eval()
								__lowerCamelCase    :    Any 	=			model(A__)
								__lowerCamelCase    :    Tuple 	=			(self.image_size // self.patch_size) ** 2
								__lowerCamelCase    :    Optional[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
								__lowerCamelCase    :    int 	=			1
								__lowerCamelCase    :    Dict 	=			ViTMAEForPreTraining(A__)
								model.to(A__)
								model.eval()
								__lowerCamelCase    :    Optional[int] 	=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								__lowerCamelCase    :    Any 	=			model(A__)
								__lowerCamelCase    :    Optional[Any] 	=			self.patch_size**2
								self.parent.assertEqual(result.logits.shape       ,(self.batch_size, num_patches, expected_num_channels))


							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Any 	=			self.prepare_config_and_inputs()
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			config_and_inputs
								__lowerCamelCase    :    Optional[Any] 	=			{'pixel_values': pixel_values}
								return config, inputs_dict






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

							_UpperCAmelCase   :  int										=				False
							_UpperCAmelCase   :  List[Any]										=				False
							_UpperCAmelCase   :  str										=				False
							_UpperCAmelCase   :  int										=				False
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    List[str] 	=			ViTMAEModelTester(self)
								__lowerCamelCase    :    int 	=			ConfigTester(self       ,config_class=A__       ,has_text_modality=A__       ,hidden_size=3_7)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								self.config_tester.run_common_tests()
							@unittest.skip(reason='ViTMAE does not use inputs_embeds')
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								pass
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Tuple 	=			model_class(A__)
									self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module))
									__lowerCamelCase    :    Dict 	=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A__       ,nn.Linear))
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			self.model_tester.prepare_config_and_inputs_for_common()

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

									__lowerCamelCase    :    Dict 	=			['pixel_values']
									self.assertListEqual(arg_names[:1]       ,A__)
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Any 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A__)
							def    lowerCAmelCase					(		self		:      Any):
								__lowerCamelCase    :    List[Any] 	=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_pretraining(*A__)
							def    lowerCAmelCase					(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]):
								# make masks reproducible
								np.random.seed(2)

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

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

								super().check_pt_tf_models(A__       ,A__       ,A__)
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									__lowerCamelCase    :    Dict 	=			model_class(A__)
									model.to(A__)
									model.eval()
									# make random mask reproducible
									torch.manual_seed(2)
									with torch.no_grad():
										__lowerCamelCase    :    str 	=			model(**self._prepare_for_class(A__       ,A__))

									__lowerCamelCase    :    int 	=			outputs[0].cpu().numpy()
									__lowerCamelCase    :    Optional[int] 	=			0

									with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(A__)
										__lowerCamelCase    :    Any 	=			model_class.from_pretrained(A__)
										model.to(A__)
										# make random mask reproducible
										torch.manual_seed(2)
										with torch.no_grad():
											__lowerCamelCase    :    Any 	=			model(**self._prepare_for_class(A__       ,A__))

										# Make sure we don't have nans
										__lowerCamelCase    :    Dict 	=			after_outputs[0].cpu().numpy()
										__lowerCamelCase    :    Optional[int] 	=			0
										__lowerCamelCase    :    List[Any] 	=			np.amax(np.abs(out_a - out_a))
										self.assertLessEqual(A__       ,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    lowerCAmelCase					(		self		:      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    lowerCAmelCase					(		self		:      List[str]):
								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    lowerCAmelCase					(		self		:      List[Any]):
								pass
							@unittest.skip(reason='ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load')
							def    lowerCAmelCase					(		self		:      Dict):
								pass
							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
							def    lowerCAmelCase					(		self		:      Optional[int]):
								pass


							@slow
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__lowerCamelCase    :    Optional[int] 	=			ViTMAEModel.from_pretrained(A__)
									self.assertIsNotNone(A__)






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






@require_torch
@require_vision
class 	A_		(	unittest.TestCase    ):
							@cached_property
							def    lowerCAmelCase					(		self		:      str):
								return ViTImageProcessor.from_pretrained('facebook/vit-mae-base') if is_vision_available() else None


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

								__lowerCamelCase    :    List[str] 	=			ViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base').to(A__)

								__lowerCamelCase    :    str 	=			self.default_image_processor
								__lowerCamelCase    :    Union[str, Any] 	=			prepare_img()
								__lowerCamelCase    :    Optional[int] 	=			image_processor(images=A__       ,return_tensors='pt').to(A__)

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

								# forward pass
								with torch.no_grad():
									__lowerCamelCase    :    int 	=			model(**A__       ,noise=torch.from_numpy(A__).to(device=A__))

								# verify the logits
								__lowerCamelCase    :    List[str] 	=			torch.Size((1, 1_9_6, 7_6_8))
								self.assertEqual(outputs.logits.shape       ,A__)

								__lowerCamelCase    :    Dict 	=			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(A__)       ,atol=1E-4))







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

a		=3






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	print('Generating primitive root of p'      )
	while True:
		__lowerCamelCase    :    Tuple 	=			random.randrange(3     ,			lowerCamelCase__      )
		if pow(lowerCamelCase__     ,			2     ,			lowerCamelCase__      ) == 1:
			continue
		if pow(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      ) == 1:
			continue
		return g






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					tuple[tuple[int, int, int, int], tuple[int, int]]:
	print('Generating prime p...'      )
	__lowerCamelCase    :    List[str] 	=			rabin_miller.generate_large_prime(lowerCamelCase__      )  # select large prime number.
	__lowerCamelCase    :    Dict 	=			primitive_root(lowerCamelCase__      )  # one primitive root on modulo p.
	__lowerCamelCase    :    Optional[int] 	=			random.randrange(3     ,			lowerCamelCase__      )  # private_key -> have to be greater than 2 for safety.
	__lowerCamelCase    :    List[Any] 	=			cryptomath.find_mod_inverse(pow(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )     ,			lowerCamelCase__      )

	__lowerCamelCase    :    int 	=			(key_size, e_a, e_a, p)
	__lowerCamelCase    :    str 	=			(key_size, d)

	return public_key, private_key






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					None:
	if os.path.exists(F"{name}_pubkey.txt"      ) or os.path.exists(F"{name}_privkey.txt"      ):
		print('\nWARNING:'      )
		print(
		    F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
		    'Use a different name or delete these files and re-run this program.'      )
		sys.exit()

	__lowerCamelCase	,			__lowerCamelCase    :    List[Any] 	=			generate_key(lowerCamelCase__      )
	print(F"\nWriting public key to file {name}_pubkey.txt..."      )
	with open(F"{name}_pubkey.txt"     ,			'w'      ) as fo:
		fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"      )

	print(F"Writing private key to file {name}_privkey.txt..."      )
	with open(F"{name}_privkey.txt"     ,			'w'      ) as fo:
		fo.write(F"{private_key[0]},{private_key[1]}"      )






def        SCREAMING_SNAKE_CASE__	(  )  ->					None:
	print('Making key files...'      )
	make_key_files('elgamal'     ,			2_0_4_8      )
	print('Key files generation successful'      )


if __name__ == "__main__":
					main()




