




'''simple docstring'''





import inspect
import unittest

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

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


if is_torch_available():
  import torch
  from torch import nn

  from transformers import SwinaSRForImageSuperResolution, SwinaSRModel
  from transformers.models.swinasr.modeling_swinasr import SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import SwinaSRImageProcessor




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=13		,     __lowerCAmelCase=32		,     __lowerCAmelCase=1		,     __lowerCAmelCase=3		,     __lowerCAmelCase=16		,     __lowerCAmelCase=[1, 2, 1]		,     __lowerCAmelCase=[2, 2, 4]		,     __lowerCAmelCase=2		,     __lowerCAmelCase=2.0		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.0		,     __lowerCAmelCase=0.1		,     __lowerCAmelCase="gelu"		,     __lowerCAmelCase=False		,     __lowerCAmelCase=True		,     __lowerCAmelCase=0.02		,     __lowerCAmelCase=1e-5		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=2		,     ):







      '''simple docstring'''


      _a				:							str							       =      parent
      _a				:							str							       =      batch_size
      _a				:							int							       =      image_size
      _a				:							int							       =      patch_size
      _a				:							Any							       =      num_channels
      _a				:							Optional[Any]							       =      embed_dim
      _a				:							List[Any]							       =      depths
      _a				:							Union[str, Any]							       =      num_heads
      _a				:							List[Any]							       =      window_size
      _a				:							Optional[Any]							       =      mlp_ratio
      _a				:							Dict							       =      qkv_bias
      _a				:							Optional[int]							       =      hidden_dropout_prob
      _a				:							List[Any]							       =      attention_probs_dropout_prob
      _a				:							List[str]							       =      drop_path_rate
      _a				:							str							       =      hidden_act
      _a				:							Optional[int]							       =      use_absolute_embeddings
      _a				:							List[str]							       =      patch_norm
      _a				:							List[str]							       =      layer_norm_eps
      _a				:							Any							       =      initializer_range
      _a				:							Any							       =      is_training
      _a				:							Any							       =      scope
      _a				:							int							       =      use_labels
      _a				:							Union[str, Any]							       =      upscale

      # here we set some attributes to make tests pass
      _a				:							List[str]							       =      len(__lowerCAmelCase )
      _a				:							str							       =      embed_dim
      _a				:							Optional[int]							       =      (image_size // patch_size) ** 2



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

      _a				:							str							       =      self.get_config()

      return config, pixel_values, labels



  def     a_							(			self ):







      '''simple docstring'''


      return SwinaSRConfig(
          image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     embed_dim=self.embed_dim		,     depths=self.depths		,     num_heads=self.num_heads		,     window_size=self.window_size		,     mlp_ratio=self.mlp_ratio		,     qkv_bias=self.qkv_bias		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     drop_path_rate=self.drop_path_rate		,     hidden_act=self.hidden_act		,     use_absolute_embeddings=self.use_absolute_embeddings		,     path_norm=self.patch_norm		,     layer_norm_eps=self.layer_norm_eps		,     initializer_range=self.initializer_range		,     upscale=self.upscale		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Union[str, Any]							       =      SwinaSRModel(config=__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							List[str]							       =      model(__lowerCAmelCase )

      self.parent.assertEqual(
          result.last_hidden_state.shape		,     (self.batch_size, self.embed_dim, self.image_size, self.image_size) )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Dict							       =      SwinaSRForImageSuperResolution(__lowerCAmelCase )
      model.to(__lowerCAmelCase )
      model.eval()
      _a				:							Tuple							       =      model(__lowerCAmelCase )

      _a				:							Dict							       =      self.image_size * self.upscale
      self.parent.assertEqual(
          result.reconstruction.shape		,     (self.batch_size, self.num_channels, expected_image_size, expected_image_size) )



  def     a_							(			self ):







      '''simple docstring'''


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






@require_torch
class      UpperCamelCase       (	snake_case__      ,		snake_case__      ,		unittest.TestCase       ):
  a     :Dict          =					(SwinaSRModel, SwinaSRForImageSuperResolution) if is_torch_available() else ()
  a     :List[str]          =					{'feature-extraction': SwinaSRModel} if is_torch_available() else {}

  a     :List[Any]          =					False
  a     :Tuple          =					False
  a     :Any          =					False
  a     :Union[str, Any]          =					False
  a     :Union[str, Any]          =					False



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      SwinaSRModelTester(self )
      _a				:							Optional[int]							       =      ConfigTester(self		,     config_class=__lowerCAmelCase		,     embed_dim=37 )



  def     a_							(			self ):







      '''simple docstring'''


      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_super_resolution(*__lowerCAmelCase )



  @unittest.skip(reason="""Got `CUDA error: misaligned address` with PyTorch 2.0.0.""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not use inputs_embeds""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  @unittest.skip(reason="""Swin2SR does not support training yet""" )
  def     a_							(			self ):







      '''simple docstring'''


      pass



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							int							       =      self.model_tester.prepare_config_and_inputs_for_common()

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



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Tuple							       =      self.model_tester.prepare_config_and_inputs_for_common()

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

          _a				:							List[Any]							       =      ["""pixel_values"""]
          self.assertListEqual(arg_names[:1]		,     __lowerCAmelCase )



  @slow
  def     a_							(			self ):







      '''simple docstring'''


      for model_name in SWIN2SR_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _a				:							List[str]							       =      SwinaSRModel.from_pretrained(__lowerCAmelCase )
          self.assertIsNotNone(__lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							str							       =      self.model_tester.prepare_config_and_inputs_for_common()

      _a				:							Union[str, Any]							       =      _config_zero_init(__lowerCAmelCase )
      for model_class in self.all_model_classes:
          _a				:							Any							       =      model_class(config=__lowerCAmelCase )
          for name, param in model.named_parameters():
              if "logit_scale" in name:
                  continue
              if param.requires_grad:
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''		,     )



  def     a_							(			self ):







      '''simple docstring'''


      _a       , _a				:							Dict							       =      self.model_tester.prepare_config_and_inputs_for_common()
      _a				:							List[str]							       =      True

      for model_class in self.all_model_classes:
          _a				:							Dict							       =      True
          _a				:							Optional[int]							       =      False
          _a				:							Any							       =      True
          _a				:							Dict							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          _a				:							Tuple							       =      len(self.model_tester.depths )
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          _a				:							List[str]							       =      True
          _a				:							str							       =      config.window_size**2
          _a				:							Union[str, Any]							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Any							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )
          _a				:							Optional[int]							       =      outputs.attentions
          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )
          _a				:							Dict							       =      len(__lowerCAmelCase )

          # Check attention is always last and order is fine
          _a				:							Tuple							       =      True
          _a				:							Any							       =      True
          _a				:							str							       =      model_class(__lowerCAmelCase )
          model.to(__lowerCAmelCase )
          model.eval()
          with torch.no_grad():
              _a				:							Optional[Any]							       =      model(**self._prepare_for_class(__lowerCAmelCase		,     __lowerCAmelCase ) )

          self.assertEqual(out_len + 1		,     len(__lowerCAmelCase ) )

          _a				:							List[str]							       =      outputs.attentions

          self.assertEqual(len(__lowerCAmelCase )		,     __lowerCAmelCase )

          self.assertListEqual(
              list(self_attentions[0].shape[-3:] )		,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]		,     )






@require_vision
@require_torch
@slow
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							int							       =      SwinaSRImageProcessor()
      _a				:							Optional[Any]							       =      SwinaSRForImageSuperResolution.from_pretrained("""caidas/swin2SR-classical-sr-x2-64""" ).to(__lowerCAmelCase )

      _a				:							Tuple							       =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      _a				:							Optional[int]							       =      processor(images=__lowerCAmelCase		,     return_tensors="""pt""" ).to(__lowerCAmelCase )

      # forward pass
      with torch.no_grad():
          _a				:							str							       =      model(**__lowerCAmelCase )

      # verify the logits
      _a				:							List[Any]							       =      torch.Size([1, 3, 976, 1296] )
      self.assertEqual(outputs.reconstruction.shape		,     __lowerCAmelCase )
      _a				:							List[str]							       =      torch.tensor(
          [[0.5458, 0.5546, 0.5638], [0.5526, 0.5565, 0.5651], [0.5396, 0.5426, 0.5621]] ).to(__lowerCAmelCase )
      self.assertTrue(torch.allclose(outputs.reconstruction[0, 0, :3, :3]		,     __lowerCAmelCase		,     atol=1e-4 ) )






from __future__ import annotations

from string import ascii_letters






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a = None			):




		"""simple docstring"""






		lowerCAmelCase__  :     List[Any]       	=   alphabet or ascii_letters

		# The final result string
		lowerCAmelCase__  :     Optional[Any]       	=   ''''''

		for character in input_string:
				if character not in alpha:
						# Append without encryption if character is not in the alphabet
						result += character
				else:
						# Get the index of the new key and make sure it isn't too large
						lowerCAmelCase__  :     int       	=   (alpha.index(_a			) + key) % len(_a			)

						# Append the encoded character to the alphabet
						result += alpha[new_key]

		return result






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a = None			):




		"""simple docstring"""






		key *= -1

		return encrypt(_a      ,  _a      ,  _a			)






def 	lowerCamelCase_       (			_a      ,  _a = None			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   alphabet or ascii_letters

		# To store data on all the combinations
		lowerCAmelCase__  :     Optional[int]       	=   {}

		# Cycle through each combination
		for key in range(1      ,  len(_a			) + 1			):
				# Decrypt the message and store the result in the data
				lowerCAmelCase__  :     Optional[int]       	=   decrypt(_a      ,  _a      ,  _a			)

		return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 10}\n Menu\n{"-" * 10}''')
				print(*['''1.Encrypt''', '''2.Decrypt''', '''3.BruteForce''', '''4.Quit'''], sep='''\n''')

				# get user input
				lowerCamelCase		=						input('''\nWhat would you like to do?: ''').strip() or '''4'''

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print('''Invalid choice, please enter a valid choice''')
				elif choice == "1":
						lowerCamelCase		=						input('''Please enter the string to be encrypted: ''')
						lowerCamelCase		=						int(input('''Please enter off-set: ''').strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						lowerCamelCase		=						input('''Please enter the string to be decrypted: ''')
						lowerCamelCase		=						int(input('''Please enter off-set: ''').strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						lowerCamelCase		=						input('''Please enter the string to be decrypted: ''')
						lowerCamelCase		=						brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f'''Key: {key} | Message: {value}''')

				elif choice == "4":
						print('''Goodbye.''')
						break




