

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 		a_	:



      """simple docstring"""







      def __init__(				self					:							Optional[int]      ,snake_case					:							str      ,snake_case					:							Optional[Any]=2      ,snake_case					:							Optional[int]=32      ,snake_case					:							Union[str, Any]=16      ,snake_case					:							Tuple=3      ,snake_case					:							Tuple=True      ,snake_case					:							Optional[int]=True      ,snake_case					:							List[str]=32      ,snake_case					:							Dict=4      ,snake_case					:							str=[0, 1, 2, 3]      ,snake_case					:							Optional[int]=4      ,snake_case					:							Optional[int]=37      ,snake_case					:							List[str]="gelu"      ,snake_case					:							Tuple=0.1      ,snake_case					:							Optional[int]=0.1      ,snake_case					:							Optional[Any]=0.02      ,snake_case					:							List[Any]=3      ,snake_case					:							Optional[int]=False      ,snake_case					:							Optional[Any]=None      ,):
           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										=is_training
           SCREAMING_SNAKE_CASE										=use_labels
           SCREAMING_SNAKE_CASE										=hidden_size
           SCREAMING_SNAKE_CASE										=num_hidden_layers
           SCREAMING_SNAKE_CASE										=backbone_out_indices
           SCREAMING_SNAKE_CASE										=num_attention_heads
           SCREAMING_SNAKE_CASE										=intermediate_size
           SCREAMING_SNAKE_CASE										=hidden_act
           SCREAMING_SNAKE_CASE										=hidden_dropout_prob
           SCREAMING_SNAKE_CASE										=attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE										=initializer_range
           SCREAMING_SNAKE_CASE										=num_labels
           SCREAMING_SNAKE_CASE										=scope
           SCREAMING_SNAKE_CASE										=is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           SCREAMING_SNAKE_CASE										=(image_size // patch_size) ** 2
           SCREAMING_SNAKE_CASE										=num_patches + 1
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           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.image_size, self.image_size]      ,self.num_labels    )

           SCREAMING_SNAKE_CASE										=self.get_config()

           return config, pixel_values, labels
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           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=snake_case      ,initializer_range=self.initializer_range      ,is_hybrid=self.is_hybrid      ,)
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]      ,snake_case					:							List[str]      ,snake_case					:							List[Any]      ,snake_case					:							Any    ):
           SCREAMING_SNAKE_CASE										=DPTModel(config=snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.last_hidden_state.shape      ,(self.batch_size, self.seq_length, self.hidden_size)    )
      def 	_lowerCAmelCase					(				self					:							Any      ,snake_case					:							Any      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[Any]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=DPTForDepthEstimation(snake_case    )
           model.to(snake_case    )
           model.eval()
           SCREAMING_SNAKE_CASE										=model(snake_case    )
           self.parent.assertEqual(result.predicted_depth.shape      ,(self.batch_size, self.image_size, self.image_size)    )
      def 	_lowerCAmelCase					(				self					:							str      ,snake_case					:							str      ,snake_case					:							Optional[Any]      ,snake_case					:							Optional[int]    ):
           SCREAMING_SNAKE_CASE										=self.num_labels
           SCREAMING_SNAKE_CASE										=DPTForSemanticSegmentation(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.num_labels, self.image_size, self.image_size)    )


      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           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      		=				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      __UpperCAmelCase      		=				(
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      __UpperCAmelCase      		=				False
      def 	_lowerCAmelCase					(				self					:							str    ):
           SCREAMING_SNAKE_CASE										=DPTModelTester(self    )
           SCREAMING_SNAKE_CASE										=ConfigTester(self      ,config_class=snake_case      ,has_text_modality=snake_case      ,hidden_size=37    )
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           self.config_tester.run_common_tests()
      @unittest.skip(reason='DPT does not use inputs_embeds'    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           pass
      def 	_lowerCAmelCase					(				self					:							int    ):
           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					:							Tuple    ):
           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					:							Tuple    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							List[Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*snake_case    )
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

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

                if model_class in get_values(snake_case    ):
                     continue

                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                model.to(snake_case    )
                model.train()
                SCREAMING_SNAKE_CASE										=self._prepare_for_class(snake_case      ,snake_case      ,return_labels=snake_case    )
                SCREAMING_SNAKE_CASE										=model(**snake_case    ).loss
                loss.backward()
      def 	_lowerCAmelCase					(				self					:							Optional[int]    ):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

                SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=self.model_tester.prepare_config_and_inputs_for_common()
                SCREAMING_SNAKE_CASE										=False
                SCREAMING_SNAKE_CASE										=True

                if model_class in get_values(snake_case    ) or not model_class.supports_gradient_checkpointing:
                     continue
                SCREAMING_SNAKE_CASE										=model_class(snake_case    )
                model.to(snake_case    )
                model.gradient_checkpointing_enable()
                model.train()
                SCREAMING_SNAKE_CASE										=self._prepare_for_class(snake_case      ,snake_case      ,return_labels=snake_case    )
                SCREAMING_SNAKE_CASE										=model(**snake_case    ).loss
                loss.backward()
      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    )
                # Skip the check for the backbone
                SCREAMING_SNAKE_CASE										=[]
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          SCREAMING_SNAKE_CASE										=[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					:							Optional[int]    ):
           pass


      @slow
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE										=DPTModel.from_pretrained(snake_case    )
                self.assertIsNotNone(snake_case    )







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image



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



      """simple docstring"""







      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=DPTImageProcessor.from_pretrained('Intel/dpt-large'    )
           SCREAMING_SNAKE_CASE										=DPTForDepthEstimation.from_pretrained('Intel/dpt-large'    ).to(snake_case    )

           SCREAMING_SNAKE_CASE										=prepare_img()
           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    )
                SCREAMING_SNAKE_CASE										=outputs.predicted_depth

           # verify the predicted depth
           SCREAMING_SNAKE_CASE										=torch.Size((1, 384, 384)    )
           self.assertEqual(predicted_depth.shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=torch.tensor(
               [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]    ).to(snake_case    )

           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]      ,snake_case      ,atol=1e-4    )    )
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           SCREAMING_SNAKE_CASE										=DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'    )
           SCREAMING_SNAKE_CASE										=DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'    ).to(snake_case    )

           SCREAMING_SNAKE_CASE										=prepare_img()
           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, 150, 480, 480)    )
           self.assertEqual(outputs.logits.shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=torch.tensor(
               [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]    ).to(snake_case    )

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


      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           SCREAMING_SNAKE_CASE										=DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'    )
           SCREAMING_SNAKE_CASE										=DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'    ).to(snake_case    )

           SCREAMING_SNAKE_CASE										=prepare_img()
           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    )

           SCREAMING_SNAKE_CASE										=outputs.logits.detach().cpu()

           SCREAMING_SNAKE_CASE										=image_processor.post_process_semantic_segmentation(outputs=snake_case      ,target_sizes=[(500, 300)]    )
           SCREAMING_SNAKE_CASE										=torch.Size((500, 300)    )
           self.assertEqual(segmentation[0].shape      ,snake_case    )

           SCREAMING_SNAKE_CASE										=image_processor.post_process_semantic_segmentation(outputs=snake_case    )
           SCREAMING_SNAKE_CASE										=torch.Size((480, 480)    )
           self.assertEqual(segmentation[0].shape      ,snake_case    )




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()

