

import collections
import inspect
import unittest

from transformers import SwinConfig
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_backbone_common import BackboneTesterMixin
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
 from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor



class 		a_	:



      """simple docstring"""







      def __init__(				self					:							Any      ,snake_case					:							Tuple      ,snake_case					:							Any=13      ,snake_case					:							int=32      ,snake_case					:							List[str]=2      ,snake_case					:							Union[str, Any]=3      ,snake_case					:							str=16      ,snake_case					:							Tuple=[1, 2, 1]      ,snake_case					:							List[str]=[2, 2, 4]      ,snake_case					:							Optional[int]=2      ,snake_case					:							Union[str, Any]=2.0      ,snake_case					:							Any=True      ,snake_case					:							Union[str, Any]=0.0      ,snake_case					:							Optional[int]=0.0      ,snake_case					:							Any=0.1      ,snake_case					:							Optional[int]="gelu"      ,snake_case					:							str=False      ,snake_case					:							Tuple=True      ,snake_case					:							Optional[int]=0.02      ,snake_case					:							Optional[Any]=1e-5      ,snake_case					:							Optional[Any]=True      ,snake_case					:							Optional[int]=None      ,snake_case					:							str=True      ,snake_case					:							str=10      ,snake_case					:							List[str]=8      ,snake_case					:							Dict=["stage1", "stage2"]      ,snake_case					:							Union[str, Any]=[1, 2]      ,):
           SCREAMING_SNAKE_CASE										=parent
           SCREAMING_SNAKE_CASE										=batch_size
           SCREAMING_SNAKE_CASE										=image_size
           SCREAMING_SNAKE_CASE										=patch_size
           SCREAMING_SNAKE_CASE										=num_channels
           SCREAMING_SNAKE_CASE										=embed_dim
           SCREAMING_SNAKE_CASE										=depths
           SCREAMING_SNAKE_CASE										=num_heads
           SCREAMING_SNAKE_CASE										=window_size
           SCREAMING_SNAKE_CASE										=mlp_ratio
           SCREAMING_SNAKE_CASE										=qkv_bias
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=drop_path_rate
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=use_absolute_embeddings
           SCREAMING_SNAKE_CASE										=patch_norm
           SCREAMING_SNAKE_CASE										=layer_norm_eps
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=is_training
           SCREAMING_SNAKE_CASE										=scope
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=type_sequence_label_size
           SCREAMING_SNAKE_CASE										=encoder_stride
           SCREAMING_SNAKE_CASE										=out_features
           SCREAMING_SNAKE_CASE										=out_indices
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

           SCREAMING_SNAKE_CASE										=None
           if self.use_labels:
                SCREAMING_SNAKE_CASE										=ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )

           SCREAMING_SNAKE_CASE										=self.get_config()

           return config, pixel_values, labels
      def 	_lowerCAmelCase					(				self					:							int    ):
           return SwinConfig(
               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      ,encoder_stride=self.encoder_stride      ,out_features=self.out_features      ,out_indices=self.out_indices      ,)
      def 	_lowerCAmelCase					(				self					:							Optional[Any]      ,snake_case					:							Dict      ,snake_case					:							Tuple      ,snake_case					:							Dict    ):
           SCREAMING_SNAKE_CASE										=SwinModel(config=snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case    )

           SCREAMING_SNAKE_CASE										=((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
           SCREAMING_SNAKE_CASE										=int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, expected_seq_len, expected_dim)    )
      def 	_lowerCAmelCase					(				self					:							List[Any]      ,snake_case					:							List[Any]      ,snake_case					:							str      ,snake_case					:							str    ):
           SCREAMING_SNAKE_CASE										=SwinBackbone(config=snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case    )

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps    )      ,len(config.out_features    )    )
           self.parent.assertListEqual(list(result.feature_maps[0].shape    )      ,[self.batch_size, model.channels[0], 16, 16]    )

           # verify channels
           self.parent.assertEqual(len(model.channels    )      ,len(config.out_features    )    )

           # verify backbone works with out_features=None
           SCREAMING_SNAKE_CASE										=None
           SCREAMING_SNAKE_CASE										=SwinBackbone(config=snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case    )

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps    )      ,1    )
           self.parent.assertListEqual(list(result.feature_maps[0].shape    )      ,[self.batch_size, model.channels[-1], 4, 4]    )

           # verify channels
           self.parent.assertEqual(len(model.channels    )      ,1    )
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							Dict      ,snake_case					:							Optional[int]      ,snake_case					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=SwinForMaskedImageModeling(config=snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(
               result.logits.shape      ,(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

           # test greyscale images
           SCREAMING_SNAKE_CASE										=1
           SCREAMING_SNAKE_CASE										=SwinForMaskedImageModeling(snake_case    )
           model.to(snake_case    )
           model.eval()

           SCREAMING_SNAKE_CASE										=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, 1, self.image_size, self.image_size)    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]      ,snake_case					:							int      ,snake_case					:							Optional[int]      ,snake_case					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.type_sequence_label_size
           SCREAMING_SNAKE_CASE										=SwinForImageClassification(snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case      ,labels=snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)    )

           # test greyscale images
           SCREAMING_SNAKE_CASE										=1
           SCREAMING_SNAKE_CASE										=SwinForImageClassification(snake_case    )
           model.to(snake_case    )
           model.eval()

           SCREAMING_SNAKE_CASE										=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.type_sequence_label_size)    )


      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  (
               SCREAMING_SNAKE_CASE
           )						,  
           )										=config_and_inputs
           SCREAMING_SNAKE_CASE										={'pixel_values': pixel_values}
           return config, inputs_dict







@require_torch
class 		a_	(							lowerCamelCase_			,      lowerCamelCase_			,      unittest.TestCase					):



      """simple docstring"""







      __UpperCAmelCase      		=				(
          (
              SwinModel,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
          )
          if is_torch_available()
          else ()
      )
      __UpperCAmelCase      		=				(
          {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
          if is_torch_available()
          else {}
      )
      __UpperCAmelCase      		=				True

      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=SwinModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,embed_dim=37    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           self.create_and_test_config_common_properties()
           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 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           return
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case    )
      @unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'    )
      def 	_lowerCAmelCase					(				self					:							Dict    ):
           pass
      def 	_lowerCAmelCase					(				self					:							str    ):
           super().test_training_gradient_checkpointing()
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*snake_case    )
      @unittest.skip(reason='Swin does not use inputs_embeds'    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           pass
      @unittest.skip(reason='Swin Transformer does not use feedforward chunking'    )
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           pass
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                self.assertIsInstance(model.get_input_embeddings()      ,(nn.Module)    )
                SCREAMING_SNAKE_CASE										=model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case      ,nn.Linear    )    )
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()

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

                SCREAMING_SNAKE_CASE										=['pixel_values']
                self.assertListEqual(arg_names[:1]      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE										=True

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=False
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                model.to(snake_case    )
                model.eval()
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )
                SCREAMING_SNAKE_CASE										=outputs.attentions
                SCREAMING_SNAKE_CASE										=len(self.model_tester.depths    )
                self.assertEqual(len(snake_case    )      ,snake_case    )

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=config.window_size**2
                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                model.to(snake_case    )
                model.eval()
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )
                SCREAMING_SNAKE_CASE										=outputs.attentions
                self.assertEqual(len(snake_case    )      ,snake_case    )

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

                # Check attention is always last and order is fine
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=True
                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                model.to(snake_case    )
                model.eval()
                with torch.no_grad():
                     SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )

                # also another +1 for reshaped_hidden_states
                SCREAMING_SNAKE_CASE										=1 if model_class.__name__ == 'SwinBackbone' else 2
                self.assertEqual(out_len + added_hidden_states      ,len(snake_case    )    )

                SCREAMING_SNAKE_CASE										=outputs.attentions

                self.assertEqual(len(snake_case    )      ,snake_case    )

                self.assertListEqual(
                    list(self_attentions[0].shape[-3:]    )      ,[self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,)
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]      ,snake_case					:							Union[str, Any]      ,snake_case					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=model_class(snake_case    )
           model.to(snake_case    )
           model.eval()

           with torch.no_grad():
                SCREAMING_SNAKE_CASE										=model(**self._prepare_for_class(snake_case      ,snake_case    )    )

           SCREAMING_SNAKE_CASE										=outputs.hidden_states

           SCREAMING_SNAKE_CASE										=getattr(
               self.model_tester      ,'expected_num_hidden_layers'      ,len(self.model_tester.depths    ) + 1    )
           self.assertEqual(len(snake_case    )      ,snake_case    )

           # Swin has a different seq_length
           SCREAMING_SNAKE_CASE										=(
               config.patch_size
               if isinstance(config.patch_size      ,collections.abc.Iterable    )
               else (config.patch_size, config.patch_size)
           )

           SCREAMING_SNAKE_CASE										=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]    )      ,[num_patches, self.model_tester.embed_dim]      ,)

           if not model_class.__name__ == "SwinBackbone":
                SCREAMING_SNAKE_CASE										=outputs.reshaped_hidden_states
                self.assertEqual(len(snake_case    )      ,snake_case    )

                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=reshaped_hidden_states[0].shape
                SCREAMING_SNAKE_CASE										=(
                    reshaped_hidden_states[0].view(snake_case      ,snake_case      ,height * width    ).permute(0      ,2      ,1    )
                )
                self.assertListEqual(
                    list(reshaped_hidden_states.shape[-2:]    )      ,[num_patches, self.model_tester.embed_dim]      ,)
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE										=(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size      ,collections.abc.Iterable    )
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=True
                self.check_hidden_states_output(snake_case      ,snake_case      ,snake_case      ,snake_case    )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                SCREAMING_SNAKE_CASE										=True

                self.check_hidden_states_output(snake_case      ,snake_case      ,snake_case      ,snake_case    )
      def 	_lowerCAmelCase					(				self					:							Any    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE										=3

           SCREAMING_SNAKE_CASE										=(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size      ,collections.abc.Iterable    )
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           SCREAMING_SNAKE_CASE										=(
               config.patch_size
               if isinstance(config.patch_size      ,collections.abc.Iterable    )
               else (config.patch_size, config.patch_size)
           )

           SCREAMING_SNAKE_CASE										=image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           SCREAMING_SNAKE_CASE										=image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=True
                self.check_hidden_states_output(snake_case      ,snake_case      ,snake_case      ,(padded_height, padded_width)    )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                SCREAMING_SNAKE_CASE										=True
                self.check_hidden_states_output(snake_case      ,snake_case      ,snake_case      ,(padded_height, padded_width)    )
      @slow
      def 	_lowerCAmelCase					(				self					:							int    ):
           for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE										=SwinModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )


      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()

           SCREAMING_SNAKE_CASE										=_config_zero_init(snake_case    )
           for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE										=model_class(config=snake_case    )
                for name, param in model.named_parameters():
                     if "embeddings" not in name and 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'      ,)







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



      """simple docstring"""







      @cached_property
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           return (
               AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    )
               if is_vision_available()
               else None
           )


      @slow
      def 	_lowerCAmelCase					(				self					:							Any    ):
           SCREAMING_SNAKE_CASE										=SwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'    ).to(snake_case    )
           SCREAMING_SNAKE_CASE										=self.default_image_processor

           SCREAMING_SNAKE_CASE										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
           SCREAMING_SNAKE_CASE										=image_processor(images=snake_case      ,return_tensors='pt'    ).to(snake_case    )

           # forward pass
           with torch.no_grad():
                SCREAMING_SNAKE_CASE										=model(**snake_case    )

           # verify the logits
           SCREAMING_SNAKE_CASE										=torch.Size((1, 1000)    )
           self.assertEqual(outputs.logits.shape      ,snake_case    )
           SCREAMING_SNAKE_CASE										=torch.tensor([-0.0_948, -0.6_454, -0.0_921]    ).to(snake_case    )
           self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,snake_case      ,atol=1e-4    )    )







@require_torch
class 		a_	(							unittest.TestCase			,      lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				(SwinBackbone,) if is_torch_available() else ()
      __UpperCAmelCase      		=				SwinConfig
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=SwinModelTester(self    )




import random
import sys

_lowerCamelCase								="ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=input('Enter message: '      )
     SCREAMING_SNAKE_CASE										='LFWOAYUISVKMNXPBDCRJTQEGHZ'
     SCREAMING_SNAKE_CASE										=input('Encrypt/Decrypt [e/d]: '      )

     check_valid_key(lowerCAmelCase_      )

     if resp.lower().startswith('e'      ):
          SCREAMING_SNAKE_CASE										='encrypt'
          SCREAMING_SNAKE_CASE										=encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif resp.lower().startswith('d'      ):
          SCREAMING_SNAKE_CASE										='decrypt'
          SCREAMING_SNAKE_CASE										=decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     print(F'\n{mode.title()}ion: \n{translated}'      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     key_list.sort()
     letters_list.sort()

     if key_list != letters_list:
          sys.exit('Error in the key or symbol set.'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'encrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'decrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=''
     SCREAMING_SNAKE_CASE										=LETTERS
     SCREAMING_SNAKE_CASE										=key

     if mode == "decrypt":
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=chars_b, chars_a

     for symbol in message:
          if symbol.upper() in chars_a:
               SCREAMING_SNAKE_CASE										=chars_a.find(symbol.upper()      )
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               translated += symbol

     return translated







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     random.shuffle(lowerCAmelCase_      )
     return "".join(lowerCAmelCase_      )


if __name__ == "__main__":
 main()

