

'''simple docstring'''

import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor




class 	A_			:






     '''simple docstring'''



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

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


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Any:
      UpperCAmelCase	:     Tuple						 =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      UpperCAmelCase	:     str						 =			None
      if self.use_labels:
       UpperCAmelCase	:     Optional[Any]						 =			ids_tensor([self.batch_size]					,      self.type_sequence_label_size	)

      UpperCAmelCase	:     int						 =			self.get_config()

      return config, pixel_values, labels


     def        UpperCAmelCase_	(		self			:					Dict	)						->     List[Any]:
      return ViTMSNConfig(
          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					,      initializer_range=self.initializer_range					,      )


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Optional[Any]	)						->     int:
      UpperCAmelCase	:     str						 =			ViTMSNModel(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Union[str, Any]						 =			model(lowercase_	)
      self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					Dict					,      lowercase_			:					Any	)						->     Optional[Any]:
      UpperCAmelCase	:     Tuple						 =			self.type_sequence_label_size
      UpperCAmelCase	:     int						 =			ViTMSNForImageClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Any						 =			model(lowercase_					,      labels=lowercase_	)
      print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'	)
      print('Labels: {labels}'	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)	)

      # test greyscale images
      UpperCAmelCase	:     Optional[Any]						 =			1
      UpperCAmelCase	:     Optional[int]						 =			ViTMSNForImageClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()

      UpperCAmelCase	:     Optional[Any]						 =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
      UpperCAmelCase	:     Any						 =			model(lowercase_	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.type_sequence_label_size)	)




     def        UpperCAmelCase_	(		self			:					List[str]	)						->     List[Any]:
      UpperCAmelCase	:     Tuple						 =			self.prepare_config_and_inputs()
      UpperCAmelCase	:     Optional[int]						 =			config_and_inputs
      UpperCAmelCase	:     Union[str, Any]						 =			{'pixel_values': pixel_values}
      return config, inputs_dict




@require_torch
class 	A_			(    _snake_case	,       _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     UpperCAmelCase_						:     int  		=  (
         {"""feature-extraction""": ViTMSNModel, """image-classification""": ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase_						:     List[Any]  		=  False
     UpperCAmelCase_						:     Tuple  		=  False
     UpperCAmelCase_						:     Union[str, Any]  		=  False
     UpperCAmelCase_						:     Optional[int]  		=  False


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     str:
      UpperCAmelCase	:     Tuple						 =			ViTMSNModelTester(self	)
      UpperCAmelCase	:     List[Any]						 =			ConfigTester(self					,      config_class=lowercase_					,      has_text_modality=lowercase_					,      hidden_size=37	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Dict:
      self.config_tester.run_common_tests()


     @unittest.skip(reason='ViTMSN does not use inputs_embeds'	)
     def        UpperCAmelCase_	(		self			:					Dict	)						->     Union[str, Any]:
      pass


     def        UpperCAmelCase_	(		self			:					Any	)						->     Any:
      UpperCAmelCase	:     Tuple						 =			self.model_tester.prepare_config_and_inputs_for_common()

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


     def        UpperCAmelCase_	(		self			:					int	)						->     List[Any]:
      UpperCAmelCase	:     int						 =			self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCAmelCase	:     int						 =			['pixel_values']
       self.assertListEqual(arg_names[:1]					,      lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Tuple:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Any:
      UpperCAmelCase	:     Union[str, Any]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowercase_	)




     @slow
     def        UpperCAmelCase_	(		self			:					str	)						->     Any:
      for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:     Union[str, Any]						 =			ViTMSNModel.from_pretrained(lowercase_	)
       self.assertIsNotNone(lowercase_	)




def 				UpperCamelCase(					):
 UpperCAmelCase	:     Optional[Any]						 =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
 return image




@require_torch
@require_vision
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @cached_property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Union[str, Any]:
      return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'	) if is_vision_available() else None




     @slow
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      torch.manual_seed(2	)
      UpperCAmelCase	:     str						 =			ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'	).to(lowercase_	)

      UpperCAmelCase	:     Optional[Any]						 =			self.default_image_processor
      UpperCAmelCase	:     Optional[int]						 =			prepare_img()
      UpperCAmelCase	:     Tuple						 =			image_processor(images=lowercase_					,      return_tensors='pt'	).to(lowercase_	)

      # forward pass
      with torch.no_grad():
       UpperCAmelCase	:     Any						 =			model(**lowercase_	)

      # verify the logits
      UpperCAmelCase	:     Any						 =			torch.Size((1, 1_000)	)
      self.assertEqual(outputs.logits.shape					,      lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			torch.tensor([-0.0803, -0.4454, -0.2375]	).to(lowercase_	)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]					,      lowercase_					,      atol=1E-4	)	)







'''simple docstring'''

import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

lowercase__         =      3




def 				UpperCamelCase(					UpperCAmelCase_     ):
 print('Generating primitive root of p'     )
 while True:
  UpperCAmelCase	:     Union[str, Any]						 =			random.randrange(3  ,	UpperCAmelCase_     )
  if pow(UpperCAmelCase_  ,	2  ,	UpperCAmelCase_     ) == 1:
   continue
  if pow(UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ) == 1:
   continue
  return g




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

 UpperCAmelCase	:     Tuple						 =			(key_size, e_a, e_a, p)
 UpperCAmelCase	:     Optional[int]						 =			(key_size, d)

 return public_key, private_key




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 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()

 UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			generate_key(UpperCAmelCase_     )
 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 				UpperCamelCase(					):
 print('Making key files...'     )
 make_key_files('elgamal'  ,	20_48     )
 print('Key files generation successful'     )


if __name__ == "__main__":
 main()




