
'''simple docstring'''





from __future__ import annotations
def 	snake_case_							(				_a  :				str			,       _a  :				dict[str, str]      ):
      snake_case_							,						snake_case_							,						snake_case_    							=				"", "", ""
      snake_case_    							=				[]

      for character in message_part:
            tmp.append(character_to_number[character]      )

      for each in tmp:
            one += each[0]
            two += each[1]
            three += each[2]

      return one + two + three
def 	snake_case_							(				_a  :				str			,       _a  :				dict[str, str]      ):
      snake_case_							,						snake_case_    							=				"", ""
      snake_case_    							=				[]

      for character in message_part:
            this_part += character_to_number[character]

      for digit in this_part:
            tmp += digit
            if len(_a      ) == len(_a      ):
                  result.append(_a      )
                  snake_case_    							=				""

      return result[0], result[1], result[2]
def 	snake_case_							(				_a  :				str			,       _a  :				str      ):
      # Validate message and alphabet, set to upper and remove spaces
      snake_case_    							=				alphabet.replace(" "			,       ""      ).upper()
      snake_case_    							=				message.replace(" "			,       ""      ).upper()

      # Check length and characters
      if len(_a      ) != 27:
            raise KeyError("Length of alphabet has to be 27."      )
      for each in message:
            if each not in alphabet:
                  raise ValueError("Each message character has to be included in alphabet!"      )

    # Generate dictionares
      snake_case_    							=				(
          "111",
          "112",
          "113",
          "121",
          "122",
          "123",
          "131",
          "132",
          "133",
          "211",
          "212",
          "213",
          "221",
          "222",
          "223",
          "231",
          "232",
          "233",
          "311",
          "312",
          "313",
          "321",
          "322",
          "323",
          "331",
          "332",
          "333",
      )
      snake_case_    							=				{}
      snake_case_    							=				{}
      for letter, number in zip(_a			,       _a      ):
            snake_case_    							=				number
            snake_case_    							=				letter

      return message, alphabet, character_to_number, number_to_character
def 	snake_case_							(				_a  :				str			,       _a  :				str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."			,       _a  :				int = 5      ):
      snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				__prepare(
          _a			,       _a      )
      snake_case_							,						snake_case_    							=				"", ""

      for i in range(0			,       len(_a      ) + 1			,       _a      ):
            encrypted_numeric += __encrypt_part(
                message[i : i + period]			,       _a      )

      for i in range(0			,       len(_a      )			,       3      ):
            encrypted += number_to_character[encrypted_numeric[i : i + 3]]

      return encrypted
def 	snake_case_							(				_a  :				str			,       _a  :				str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."			,       _a  :				int = 5      ):
      snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    							=				__prepare(
          _a			,       _a      )
      snake_case_    							=				[]
      snake_case_    							=				""

      for i in range(0			,       len(_a      ) + 1			,       _a      ):
            snake_case_							,						snake_case_							,						snake_case_    							=				__decrypt_part(message[i : i + period]			,       _a      )

            for j in range(0			,       len(_a      )      ):
                  decrypted_numeric.append(a[j] + b[j] + c[j]      )

      for each in decrypted_numeric:
            decrypted += number_to_character[each]

      return decrypted


if __name__ == "__main__":
     A							     = "DEFEND THE EAST WALL OF THE CASTLE."
     A							     = encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
     A							     = decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
     print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")









import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, _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 (
       MODEL_MAPPING,
       DataaVecVisionForImageClassification,
       DataaVecVisionForSemanticSegmentation,
       DataaVecVisionModel,
   )
   from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor


class 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=100							, _UpperCAmelCase=13							, _UpperCAmelCase=30							, _UpperCAmelCase=2							, _UpperCAmelCase=3							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=32							, _UpperCAmelCase=4							, _UpperCAmelCase=4							, _UpperCAmelCase=37							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.1							, _UpperCAmelCase=10							, _UpperCAmelCase=0.02							, _UpperCAmelCase=3							, _UpperCAmelCase=None							, _UpperCAmelCase=[0, 1, 2, 3]							, )		->				Dict:
        lowerCamelCase									=parent
        lowerCamelCase									=100
        lowerCamelCase									=batch_size
        lowerCamelCase									=image_size
        lowerCamelCase									=patch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=is_training
        lowerCamelCase									=use_labels
        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=type_sequence_label_size
        lowerCamelCase									=initializer_range
        lowerCamelCase									=scope
        lowerCamelCase									=out_indices
        lowerCamelCase									=num_labels

        # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(image_size // patch_size) ** 2
        lowerCamelCase									=num_patches + 1





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        lowerCamelCase									=None
        lowerCamelCase									=None
        if self.use_labels:
           lowerCamelCase									=ids_tensor([self.batch_size]							, self.type_sequence_label_size  )
           lowerCamelCase									=ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels  )

        lowerCamelCase									=self.get_config()

        return config, pixel_values, labels, pixel_labels





     def 						lowerCamelCase							(	self  )		->				str:
        return DataaVecVisionConfig(
            vocab_size=self.vocab_size							, 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							, out_indices=self.out_indices							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=DataaVecVisionModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, num_patches + 1, self.hidden_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Any:
        lowerCamelCase									=self.type_sequence_label_size
        lowerCamelCase									=DataaVecVisionForImageClassification(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        lowerCamelCase									=self.num_labels
        lowerCamelCase									=DataaVecVisionForSemanticSegmentation(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=self.prepare_config_and_inputs()
        lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=config_and_inputs
        lowerCamelCase									={"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class 		lowerCAmelCase_				(  UpperCamelCase				,     UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[int] 	=     (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __lowerCamelCase       :    str 	=     (
         {
             '''feature-extraction''': DataaVecVisionModel,
             '''image-classification''': DataaVecVisionForImageClassification,
             '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __lowerCamelCase       :    Any 	=     False
     __lowerCamelCase       :    List[str] 	=     False
     __lowerCamelCase       :    int 	=     False





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=DataaVecVisionModelTester(self  )
        lowerCamelCase									=ConfigTester(
            self							, config_class=_UpperCAmelCase							, has_text_modality=_UpperCAmelCase							, hidden_size=37  )





     def 						lowerCamelCase							(	self  )		->				int:
        self.config_tester.run_common_tests()





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Data2VecVision does not use inputs_embeds
        pass





     @require_torch_multi_gpu
     @unittest.skip(
         reason="""Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""  )
     def 						lowerCamelCase							(	self  )		->				Dict:
        pass





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

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





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

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

           lowerCamelCase									=["""pixel_values"""]
           self.assertListEqual(arg_names[:1]							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        if not self.model_tester.is_training:
           return

        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )]:
              continue

           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
           return

        lowerCamelCase									=False
        lowerCamelCase									=True

        for model_class in self.all_model_classes:
           if model_class in [*get_values(_UpperCAmelCase  )] or not model_class.supports_gradient_checkpointing:
              continue
           # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
           # this can then be incorporated into _prepare_for_class in test_modeling_common.py
           elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
              lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=inputs_dict["""pixel_values"""].shape
              lowerCamelCase									=torch.zeros(
                  [self.model_tester.batch_size, height, width]							, device=_UpperCAmelCase  ).long()
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.gradient_checkpointing_enable()
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase									=_config_zero_init(_UpperCAmelCase  )
        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(config=_UpperCAmelCase  )
           for name, param in model.named_parameters():
              # we skip lambda parameters as these require special initial values
              # determined by config.layer_scale_init_value
              if "lambda" 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 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase=2E-4							, _UpperCAmelCase="outputs"							, _UpperCAmelCase=None  )		->				int:
        # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
        super().check_pt_tf_outputs(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCamelCase									=DataaVecVisionModel.from_pretrained(_UpperCAmelCase  )
           self.assertIsNotNone(_UpperCAmelCase  )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   return image







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





     @cached_property
     def 						lowerCamelCase							(	self  )		->				List[str]:
        return (
            BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ) if is_vision_available() else None
        )





     @slow
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ).to(
            _UpperCAmelCase  )

        lowerCamelCase									=self.default_image_processor
        lowerCamelCase									=prepare_img()
        lowerCamelCase									=image_processor(images=_UpperCAmelCase							, return_tensors="""pt"""  ).to(_UpperCAmelCase  )

        # forward pass
        with torch.no_grad():
           lowerCamelCase									=model(**_UpperCAmelCase  )
        lowerCamelCase									=outputs.logits

        # verify the logits
        lowerCamelCase									=torch.Size((1, 1000)  )
        self.assertEqual(logits.shape							, _UpperCAmelCase  )

        lowerCamelCase									=torch.tensor([0.3277, -0.1395, 0.0911]  ).to(_UpperCAmelCase  )

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

        lowerCamelCase									=[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
        self.assertEqual(logits[0].topk(2  ).indices.cpu().tolist()							, _UpperCAmelCase  )