


import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
    from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor







class       _lowercase			:


       """simple docstring"""




       def __init__(       self							:      Dict ,				__lowerCamelCase							:      Tuple ,				__lowerCamelCase							:      List[Any]=2 ,				__lowerCamelCase							:      List[str]=32 ,				__lowerCamelCase							:      Tuple=16 ,				__lowerCamelCase							:      int=3 ,				__lowerCamelCase							:      Optional[Any]=True ,				__lowerCamelCase							:      List[Any]=True ,				__lowerCamelCase							:      str=32 ,				__lowerCamelCase							:      Any=4 ,				__lowerCamelCase							:      Union[str, Any]=[0, 1, 2, 3] ,				__lowerCamelCase							:      Tuple=4 ,				__lowerCamelCase							:      Union[str, Any]=37 ,				__lowerCamelCase							:      Dict="gelu" ,				__lowerCamelCase							:      Any=0.1 ,				__lowerCamelCase							:      str=0.1 ,				__lowerCamelCase							:      List[str]=0.0_2 ,				__lowerCamelCase							:      Optional[int]=3 ,				__lowerCamelCase							:      Dict=False ,				__lowerCamelCase							:      str=None ,				):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[Any]   =  parent
             lowerCamelCase__		:    int   =  batch_size
             lowerCamelCase__		:    Optional[int]   =  image_size
             lowerCamelCase__		:    Union[str, Any]   =  patch_size
             lowerCamelCase__		:    Optional[int]   =  num_channels
             lowerCamelCase__		:    List[Any]   =  is_training
             lowerCamelCase__		:    Tuple   =  use_labels
             lowerCamelCase__		:    Tuple   =  hidden_size
             lowerCamelCase__		:    List[Any]   =  num_hidden_layers
             lowerCamelCase__		:    Dict   =  backbone_out_indices
             lowerCamelCase__		:    Any   =  num_attention_heads
             lowerCamelCase__		:    Optional[Any]   =  intermediate_size
             lowerCamelCase__		:    Optional[Any]   =  hidden_act
             lowerCamelCase__		:    List[Any]   =  hidden_dropout_prob
             lowerCamelCase__		:    Any   =  attention_probs_dropout_prob
             lowerCamelCase__		:    str   =  initializer_range
             lowerCamelCase__		:    Any   =  num_labels
             lowerCamelCase__		:    List[str]   =  scope
             lowerCamelCase__		:    List[Any]   =  is_hybrid
             # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
             lowerCamelCase__		:    Optional[int]   =  (image_size // patch_size) ** 2
             lowerCamelCase__		:    Optional[int]   =  num_patches + 1





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

             lowerCamelCase__		:    Any   =  None
             if self.use_labels:
                   lowerCamelCase__		:    str   =  ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels	)

             lowerCamelCase__		:    int   =  self.get_config()

             return config, pixel_values, labels





       def 						lowerCAmelCase					(       self							:      str	):


             '''simple docstring'''



             return DPTConfig(
                 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 ,				backbone_out_indices=self.backbone_out_indices ,				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=__lowerCamelCase ,				initializer_range=self.initializer_range ,				is_hybrid=self.is_hybrid ,				)





       def 						lowerCAmelCase					(       self							:      Tuple ,				__lowerCamelCase							:      Union[str, Any] ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  DPTModel(config=__lowerCamelCase	)
             model.to(__lowerCamelCase	)
             model.eval()
             lowerCamelCase__		:    Dict   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)





       def 						lowerCAmelCase					(       self							:      str ,				__lowerCamelCase							:      Optional[Any] ,				__lowerCamelCase							:      Any ,				__lowerCamelCase							:      Tuple	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  self.num_labels
             lowerCamelCase__		:    List[str]   =  DPTForDepthEstimation(__lowerCamelCase	)
             model.to(__lowerCamelCase	)
             model.eval()
             lowerCamelCase__		:    List[Any]   =  model(__lowerCamelCase	)
             self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)





       def 						lowerCAmelCase					(       self							:      Optional[Any] ,				__lowerCamelCase							:      List[Any] ,				__lowerCamelCase							:      Dict ,				__lowerCamelCase							:      int	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.num_labels
             lowerCamelCase__		:    Optional[Any]   =  DPTForSemanticSegmentation(__lowerCamelCase	)
             model.to(__lowerCamelCase	)
             model.eval()
             lowerCamelCase__		:    Union[str, Any]   =  model(__lowerCamelCase ,				labels=__lowerCamelCase	)
             self.parent.assertEqual(
                 result.logits.shape ,				(self.batch_size, self.num_labels, self.image_size, self.image_size)	)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



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







@require_torch
class       _lowercase			(							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__ 						=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       A__ 						=      (
           {
               "depth-estimation": DPTForDepthEstimation,
               "feature-extraction": DPTModel,
               "image-segmentation": DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       A__ 						=      False
       A__ 						=      False
       A__ 						=      False





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    Optional[int]   =  DPTModelTester(self	)
             lowerCamelCase__		:    Dict   =  ConfigTester(self ,				config_class=__lowerCamelCase ,				has_text_modality=__lowerCamelCase ,				hidden_size=37	)





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       @unittest.skip(reason="DPT does not use inputs_embeds"	)
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase					(       self							:      Optional[Any]	):


             '''simple docstring'''



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

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:    List[str]   =  model_class(__lowerCamelCase	)
                   self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)	)
                   lowerCamelCase__		:    Any   =  model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(__lowerCamelCase ,				nn.Linear	)	)





       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__   ,       lowerCamelCase__		:    Union[str, Any]   =  self.model_tester.prepare_config_and_inputs_for_common()

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

                   lowerCamelCase__		:    Union[str, Any]   =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1] ,				__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[Any]   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      List[str]	):


             '''simple docstring'''



             lowerCamelCase__		:    int   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             lowerCamelCase__		:    Any   =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase	)





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             for model_class in self.all_model_classes:
                   if model_class.__name__ == "DPTForDepthEstimation":
                         continue

                   lowerCamelCase__   ,       lowerCamelCase__		:    Optional[int]   =  self.model_tester.prepare_config_and_inputs_for_common()
                   lowerCamelCase__		:    str   =  True

                   if model_class in get_values(__lowerCamelCase	):
                         continue

                   lowerCamelCase__		:    str   =  model_class(__lowerCamelCase	)
                   model.to(__lowerCamelCase	)
                   model.train()
                   lowerCamelCase__		:    Optional[Any]   =  self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase ,				return_labels=__lowerCamelCase	)
                   lowerCamelCase__		:    List[Any]   =  model(**__lowerCamelCase	).loss
                   loss.backward()





       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             for model_class in self.all_model_classes:
                   if model_class.__name__ == "DPTForDepthEstimation":
                         continue

                   lowerCamelCase__   ,       lowerCamelCase__		:    Any   =  self.model_tester.prepare_config_and_inputs_for_common()
                   lowerCamelCase__		:    Tuple   =  False
                   lowerCamelCase__		:    Optional[int]   =  True

                   if model_class in get_values(__lowerCamelCase	) or not model_class.supports_gradient_checkpointing:
                         continue
                   lowerCamelCase__		:    List[Any]   =  model_class(__lowerCamelCase	)
                   model.to(__lowerCamelCase	)
                   model.gradient_checkpointing_enable()
                   model.train()
                   lowerCamelCase__		:    Union[str, Any]   =  self._prepare_for_class(__lowerCamelCase ,				__lowerCamelCase ,				return_labels=__lowerCamelCase	)
                   lowerCamelCase__		:    Union[str, Any]   =  model(**__lowerCamelCase	).loss
                   loss.backward()





       def 						lowerCAmelCase					(       self							:      int	):


             '''simple docstring'''



             lowerCamelCase__   ,       lowerCamelCase__		:    Optional[Any]   =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:    Dict   =  _config_zero_init(__lowerCamelCase	)
             for model_class in self.all_model_classes:
                   lowerCamelCase__		:    List[Any]   =  model_class(config=__lowerCamelCase	)
                   # Skip the check for the backbone
                   lowerCamelCase__		:    str   =  []
                   for name, module in model.named_modules():
                         if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                               lowerCamelCase__		:    Optional[Any]   =  [f"{name}.{key}" for key in module.state_dict().keys()]
                               break

                   for name, param in model.named_parameters():
                         if param.requires_grad:
                               if name in backbone_params:
                                     continue
                               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" ,				)





       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
       def 						lowerCAmelCase					(       self							:      List[Any]	):


             '''simple docstring'''



             pass





       @slow
       def 						lowerCAmelCase					(       self							:      Any	):


             '''simple docstring'''



             for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__		:    Union[str, Any]   =  DPTModel.from_pretrained(__lowerCamelCase	)
                   self.assertIsNotNone(__lowerCamelCase	)



def 				lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:    str   =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
      return image







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


       """simple docstring"""




       def 						lowerCAmelCase					(       self							:      Dict	):


             '''simple docstring'''



             lowerCamelCase__		:    Dict   =  DPTImageProcessor.from_pretrained("Intel/dpt-large"	)
             lowerCamelCase__		:    Optional[int]   =  DPTForDepthEstimation.from_pretrained("Intel/dpt-large"	).to(__lowerCamelCase	)

             lowerCamelCase__		:    Union[str, Any]   =  prepare_img()
             lowerCamelCase__		:    Tuple   =  image_processor(images=__lowerCamelCase ,				return_tensors="pt"	).to(__lowerCamelCase	)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__		:    List[Any]   =  model(**__lowerCamelCase	)
                   lowerCamelCase__		:    Dict   =  outputs.predicted_depth

             # verify the predicted depth
             lowerCamelCase__		:    Tuple   =  torch.Size((1, 384, 384)	)
             self.assertEqual(predicted_depth.shape ,				__lowerCamelCase	)

             lowerCamelCase__		:    List[Any]   =  torch.tensor(
                 [[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]	).to(__lowerCamelCase	)

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





       def 						lowerCAmelCase					(       self							:      Union[str, Any]	):


             '''simple docstring'''



             lowerCamelCase__		:    Tuple   =  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
             lowerCamelCase__		:    Tuple   =  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(__lowerCamelCase	)

             lowerCamelCase__		:    str   =  prepare_img()
             lowerCamelCase__		:    Optional[int]   =  image_processor(images=__lowerCamelCase ,				return_tensors="pt"	).to(__lowerCamelCase	)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__		:    str   =  model(**__lowerCamelCase	)

             # verify the logits
             lowerCamelCase__		:    int   =  torch.Size((1, 150, 480, 480)	)
             self.assertEqual(outputs.logits.shape ,				__lowerCamelCase	)

             lowerCamelCase__		:    Optional[Any]   =  torch.tensor(
                 [[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]	).to(__lowerCamelCase	)

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





       def 						lowerCAmelCase					(       self							:      Optional[int]	):


             '''simple docstring'''



             lowerCamelCase__		:    List[str]   =  DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"	)
             lowerCamelCase__		:    List[str]   =  DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"	).to(__lowerCamelCase	)

             lowerCamelCase__		:    List[Any]   =  prepare_img()
             lowerCamelCase__		:    List[Any]   =  image_processor(images=__lowerCamelCase ,				return_tensors="pt"	).to(__lowerCamelCase	)

             # forward pass
             with torch.no_grad():
                   lowerCamelCase__		:    Any   =  model(**__lowerCamelCase	)

             lowerCamelCase__		:    Tuple   =  outputs.logits.detach().cpu()

             lowerCamelCase__		:    Optional[int]   =  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase ,				target_sizes=[(500, 300)]	)
             lowerCamelCase__		:    Any   =  torch.Size((500, 300)	)
             self.assertEqual(segmentation[0].shape ,				__lowerCamelCase	)

             lowerCamelCase__		:    str   =  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase	)
             lowerCamelCase__		:    Tuple   =  torch.Size((480, 480)	)
             self.assertEqual(segmentation[0].shape ,				__lowerCamelCase	)






A				:   Union[str, Any]		= {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}

def 				lowercase_		(		_A      :   str						):

      """simple docstring"""




      return [alphabet[char] for char in key.upper()]

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[str]   =  ""
      lowerCamelCase__		:    Union[str, Any]   =  0
      lowerCamelCase__		:    Any   =  generate_table(_A						)
      for char in words.upper():
            cipher += get_opponent(table[count]   , _A						)
            lowerCamelCase__		:    Optional[int]   =  (count + 1) % len(_A						)
      return cipher

def 				lowercase_		(		_A      :   str   , _A      :   str						):

      """simple docstring"""




      return encrypt(_A   , _A						)

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__		:    List[Any]   =  0 if char in table[0] else 1
      lowerCamelCase__		:    List[Any]   =  table[row].index(_A						)
      return row, col

def 				lowercase_		(		_A      :   tuple[str, str]   , _A      :   str						):

      """simple docstring"""




      lowerCamelCase__   ,       lowerCamelCase__		:    List[str]   =  get_position(_A   , char.upper()						)
      if row == 1:
            return table[0][col]
      else:
            return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    A				:   List[str]		= input("Enter key: ").strip()
    A				:   Tuple		= input("Enter text to encrypt: ").strip()
    A				:   Dict		= encrypt(key, text)

    print(f'Encrypted: {cipher_text}')
    print(f'Decrypted with key: {decrypt(key, cipher_text)}')


