

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__       :







  """simple docstring"""




  def __init__(			self    : Any				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Optional[int]=1_3				,				UpperCAmelCase__    : Any=3_0				,				UpperCAmelCase__    : Any=2				,				UpperCAmelCase__    : Union[str, Any]=3				,				UpperCAmelCase__    : List[str]=True				,				UpperCAmelCase__    : List[str]=True				,				UpperCAmelCase__    : int=3_2				,				UpperCAmelCase__    : Optional[int]=5				,				UpperCAmelCase__    : List[Any]=4				,				UpperCAmelCase__    : Optional[int]=3_7				,				UpperCAmelCase__    : Optional[Any]="gelu"				,				UpperCAmelCase__    : Union[str, Any]=0.1				,				UpperCAmelCase__    : Dict=0.1				,				UpperCAmelCase__    : Any=1_0				,				UpperCAmelCase__    : str=0.02				,				UpperCAmelCase__    : List[str]=3				,				UpperCAmelCase__    : Dict=0.6				,				UpperCAmelCase__    : Any=None				,				)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   parent
        SCREAMING_SNAKE_CASE :      Any       =   batch_size
        SCREAMING_SNAKE_CASE :      List[str]       =   image_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   patch_size
        SCREAMING_SNAKE_CASE :      Dict       =   num_channels
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   is_training
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   use_labels
        SCREAMING_SNAKE_CASE :      Dict       =   hidden_size
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      int       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      str       =   intermediate_size
        SCREAMING_SNAKE_CASE :      Tuple       =   hidden_act
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      Dict       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      List[Any]       =   type_sequence_label_size
        SCREAMING_SNAKE_CASE :      Any       =   initializer_range
        SCREAMING_SNAKE_CASE :      Dict       =   mask_ratio
        SCREAMING_SNAKE_CASE :      Dict       =   scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        SCREAMING_SNAKE_CASE :      Any       =   (image_size // patch_size) ** 2
        SCREAMING_SNAKE_CASE :      List[Any]       =   int(math.ceil((1 - mask_ratio) * (num_patches + 1)		)		)


  def  _lowercase      (			self    : Dict		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        SCREAMING_SNAKE_CASE :      str       =   self.get_config()

        return config, pixel_values, labels


  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







        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=UpperCAmelCase__				,				initializer_range=self.initializer_range				,				mask_ratio=self.mask_ratio				,				)


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Union[str, Any]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   ViTMAEModel(config=UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      Dict       =   model(UpperCAmelCase__		)
        self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)		)


  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : Any				,				UpperCAmelCase__    : Tuple		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   ViTMAEForPreTraining(UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      Dict       =   model(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      int       =   (self.image_size // self.patch_size) ** 2
        SCREAMING_SNAKE_CASE :      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
        SCREAMING_SNAKE_CASE :      Tuple       =   1
        SCREAMING_SNAKE_CASE :      Any       =   ViTMAEForPreTraining(UpperCAmelCase__		)
        model.to(UpperCAmelCase__		)
        model.eval()
        SCREAMING_SNAKE_CASE :      Tuple       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   model(UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[Any]       =   self.patch_size**2
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, num_patches, expected_num_channels)		)




  def  _lowercase      (			self    : Optional[Any]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   config_and_inputs
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class        a__       (   UpperCAmelCase	,			UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[Any]			       =(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
  UpperCAmelCase__      : Union[str, Any]			       ={"""feature-extraction""": ViTMAEModel} if is_torch_available() else {}

  UpperCAmelCase__      : Optional[Any]			       =False
  UpperCAmelCase__      : Any			       =False
  UpperCAmelCase__      : str			       =False
  UpperCAmelCase__      : Union[str, Any]			       =False


  def  _lowercase      (			self    : Any		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   ViTMAEModelTester(self		)
        SCREAMING_SNAKE_CASE :      int       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				has_text_modality=UpperCAmelCase__				,				hidden_size=3_7		)


  def  _lowercase      (			self    : str		)					->int:
        """simple docstring"""







        self.config_tester.run_common_tests()


  @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""		)
  def  _lowercase      (			self    : Optional[Any]		)					->Union[str, Any]:
        """simple docstring"""







        pass


  def  _lowercase      (			self    : Optional[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   self.model_tester.prepare_config_and_inputs_for_common()

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


  def  _lowercase      (			self    : Tuple		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE :      Dict       =   ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : int		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__		)


  def  _lowercase      (			self    : Dict		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : List[str]		)					->Dict:
        """simple docstring"""







        np.random.seed(2		)

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

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

        super().check_pt_tf_models(UpperCAmelCase__				,				UpperCAmelCase__				,				UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Dict       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              SCREAMING_SNAKE_CASE :      Any       =   outputs[0].cpu().numpy()
              SCREAMING_SNAKE_CASE :      List[Any]       =   0

              with tempfile.TemporaryDirectory() as tmpdirname:
                    model.save_pretrained(UpperCAmelCase__		)
                    SCREAMING_SNAKE_CASE :      int       =   model_class.from_pretrained(UpperCAmelCase__		)
                    model.to(UpperCAmelCase__		)
                    # make random mask reproducible
                    torch.manual_seed(2		)
                    with torch.no_grad():
                          SCREAMING_SNAKE_CASE :      Optional[int]       =   model(**self._prepare_for_class(UpperCAmelCase__				,				UpperCAmelCase__		)		)

                    # Make sure we don't have nans
                    SCREAMING_SNAKE_CASE :      List[str]       =   after_outputs[0].cpu().numpy()
                    SCREAMING_SNAKE_CASE :      Optional[int]       =   0
                    SCREAMING_SNAKE_CASE :      Dict       =   np.amax(np.abs(out_a - out_a		)		)
                    self.assertLessEqual(UpperCAmelCase__				,				1e-5		)


  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
  def  _lowercase      (			self    : Optional[int]		)					->Any:
        """simple docstring"""







        pass


  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







        pass


  @unittest.skip(
      reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""		)
  def  _lowercase      (			self    : Optional[Any]		)					->str:
        """simple docstring"""







        pass


  @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""		)
  def  _lowercase      (			self    : Any		)					->Optional[int]:
        """simple docstring"""







        pass


  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
  def  _lowercase      (			self    : str		)					->List[str]:
        """simple docstring"""







        pass




  @slow
  def  _lowercase      (			self    : str		)					->Union[str, Any]:
        """simple docstring"""







        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE :      int       =   ViTMAEModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)






def 					__lowercase      (     )     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
      return image







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







  """simple docstring"""




  @cached_property
  def  _lowercase      (			self    : Tuple		)					->Union[str, Any]:
        """simple docstring"""







        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""		) if is_vision_available() else None




  @slow
  def  _lowercase      (			self    : Optional[Any]		)					->Union[str, Any]:
        """simple docstring"""







        np.random.seed(2		)

        SCREAMING_SNAKE_CASE :      Tuple       =   ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""		).to(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[Any]       =   self.default_image_processor
        SCREAMING_SNAKE_CASE :      Dict       =   prepare_img()
        SCREAMING_SNAKE_CASE :      str       =   image_processor(images=UpperCAmelCase__				,				return_tensors="""pt"""		).to(UpperCAmelCase__		)

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

        # forward pass
        with torch.no_grad():
              SCREAMING_SNAKE_CASE :      List[Any]       =   model(**UpperCAmelCase__				,				noise=torch.from_numpy(UpperCAmelCase__		).to(device=UpperCAmelCase__		)		)

        # verify the logits
        SCREAMING_SNAKE_CASE :      str       =   torch.Size((1, 1_9_6, 7_6_8)		)
        self.assertEqual(outputs.logits.shape				,				UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Any       =   torch.tensor(
            [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]		)

        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]				,				expected_slice.to(UpperCAmelCase__		)				,				atol=1e-4		)		)







def 					__lowercase      (     _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   """"""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 					__lowercase      (     _A			)     ->						dict[str, str]:
      SCREAMING_SNAKE_CASE :      int       =   [chr(i + 65			) for i in range(26			)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE :      List[str]       =   remove_duplicates(key.upper()			)
      SCREAMING_SNAKE_CASE :      Dict       =   len(_A			)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE :      int       =   {alphabet[i]: char for i, char in enumerate(_A			)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_A			)						,    26			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE :      str       =   alphabet[i - offset]
            SCREAMING_SNAKE_CASE :      List[Any]       =   char
      return cipher_alphabet






def 					__lowercase      (     _A						,    _A			)     ->						str:
      return "".join(cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      List[Any]       =   input("""Enter message to encode or decode: """			).strip()
      SCREAMING_SNAKE_CASE :      str       =   input("""Enter keyword: """			).strip()
      SCREAMING_SNAKE_CASE :      Dict       =   input("""Encipher or decipher? E/D:"""			).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE :      int       =   {"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   create_cipher_map(_A			)
      print(func(_A						,    _A			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




