






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        _UpperCAmelCase						:




    def __init__(			self  :       Dict  ,     a  :       str  ,     a  :       str=2  ,     a  :       Optional[Any]=3_2  ,     a  :       int=1_6  ,     a  :       Dict=3  ,     a  :       Tuple=True  ,     a  :       Any=True  ,     a  :       List[Any]=3_2  ,     a  :       Optional[int]=4  ,     a  :       str=[0, 1, 2, 3]  ,     a  :       Optional[int]=4  ,     a  :       List[Any]=3_7  ,     a  :       Dict="gelu"  ,     a  :       Optional[int]=0.1  ,     a  :       Dict=0.1  ,     a  :       Optional[Any]=0.02  ,     a  :       List[Any]=3  ,     a  :       int=[1, 3_8_4, 2_4, 2_4]  ,     a  :       Any=True  ,     a  :       Union[str, Any]=None  ,     ):






       '''simple docstring'''






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




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

       lowercase_	:    int           =     self.get_config()

       return config, pixel_values, labels




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     {
           "global_padding": "same",
           "layer_type": "bottleneck",
           "depths": [3, 4, 9],
           "out_features": ["stage1", "stage2", "stage3"],
           "embedding_dynamic_padding": True,
           "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
           "num_groups": 2,
       }

       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=a  ,     initializer_range=self.initializer_range  ,     is_hybrid=self.is_hybrid  ,     backbone_config=a  ,     backbone_featmap_shape=self.backbone_featmap_shape  ,     )




    def 				lowerCAmelCase__				(			self  :       int  ,     a  :       Optional[int]  ,     a  :       Optional[Any]  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     DPTModel(config=a			)
       model.to(a			)
       model.eval()
       lowercase_	:    int           =     model(a			)
       self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)			)




    def 				lowerCAmelCase__				(			self  :       Tuple  ,     a  :       Any  ,     a  :       Union[str, Any]  ,     a  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.num_labels
       lowercase_	:    List[Any]           =     DPTForDepthEstimation(a			)
       model.to(a			)
       model.eval()
       lowercase_	:    Any           =     model(a			)
       self.parent.assertEqual(result.predicted_depth.shape  ,     (self.batch_size, self.image_size, self.image_size)			)




    def 				lowerCAmelCase__				(			self  :       Union[str, Any]  ,     a  :       Dict  ,     a  :       Union[str, Any]  ,     a  :       int			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.num_labels
       lowercase_	:    Any           =     DPTForSemanticSegmentation(a			)
       model.to(a			)
       model.eval()
       lowercase_	:    Tuple           =     model(a  ,     labels=a			)
       self.parent.assertEqual(
           result.logits.shape  ,     (self.batch_size, self.num_labels, self.image_size, self.image_size)			)





    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






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


@require_torch
class        _UpperCAmelCase						(		snake_case				,					snake_case				,					unittest.TestCase			):
    __lowerCamelCase:   Optional[Any]		       =    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    __lowerCamelCase:   Optional[int]		       =    (
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    __lowerCamelCase:   Tuple		       =    False
    __lowerCamelCase:   Tuple		       =    False
    __lowerCamelCase:   List[Any]		       =    False




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     DPTModelTester(self			)
       lowercase_	:    str           =     ConfigTester(self  ,     config_class=a  ,     has_text_modality=a  ,     hidden_size=3_7			)




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






       '''simple docstring'''






       self.config_tester.run_common_tests()




    @unittest.skip(reason="DPT does not use inputs_embeds"			)
    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       pass




    def 				lowerCAmelCase__				(			self  :       Optional[int]			):






       '''simple docstring'''






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

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




    def 				lowerCAmelCase__				(			self  :       Optional[Any]			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.model_tester.prepare_config_and_inputs_for_common()

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

          lowercase_	:    List[Any]           =     ["pixel_values"]
          self.assertListEqual(arg_names[:1]  ,     a			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*a			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Dict           =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*a			)




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






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

          lowercase_	:    str           =     self.model_tester.prepare_config_and_inputs_for_common()
          lowercase_	:    List[Any]           =     True

          if model_class in get_values(a			):
             continue

          lowercase_	:    Union[str, Any]           =     model_class(a			)
          model.to(a			)
          model.train()
          lowercase_	:    Any           =     self._prepare_for_class(a  ,     a  ,     return_labels=a			)
          lowercase_	:    str           =     model(**a			).loss
          loss.backward()




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






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

          lowercase_	:    Union[str, Any]           =     self.model_tester.prepare_config_and_inputs_for_common()
          lowercase_	:    Optional[int]           =     False
          lowercase_	:    List[str]           =     True

          if model_class in get_values(a			) or not model_class.supports_gradient_checkpointing:
             continue
          lowercase_	:    Dict           =     model_class(a			)
          model.to(a			)
          model.gradient_checkpointing_enable()
          model.train()
          lowercase_	:    Tuple           =     self._prepare_for_class(a  ,     a  ,     return_labels=a			)
          lowercase_	:    List[str]           =     model(**a			).loss
          loss.backward()




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






       '''simple docstring'''






       lowercase_	:    str           =     self.model_tester.prepare_config_and_inputs_for_common()

       lowercase_	:    Dict           =     _config_zero_init(a			)
       for model_class in self.all_model_classes:
          lowercase_	:    str           =     model_class(config=a			)
          # Skip the check for the backbone
          lowercase_	:    Any           =     []
          for name, module in model.named_modules():
             if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                lowercase_	:    Dict           =     [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[str]			):






       '''simple docstring'''






       pass




    @slow
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
          lowercase_	:    str           =     DPTModel.from_pretrained(a			)
          self.assertIsNotNone(a			)





    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Optional[int]           =     self.model_tester.prepare_config_and_inputs_for_common()
       lowercase_	:    str           =     "add"
       with self.assertRaises(a			):
          lowercase_	:    Optional[Any]           =     DPTForDepthEstimation(a			)




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    List[str]           =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
   return image





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




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"			)
       lowercase_	:    Optional[Any]           =     DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"			).to(a			)

       lowercase_	:    Optional[int]           =     prepare_img()
       lowercase_	:    List[str]           =     image_processor(images=a  ,     return_tensors="pt"			).to(a			)

       # forward pass
       with torch.no_grad():
          lowercase_	:    Optional[int]           =     model(**a			)
          lowercase_	:    Tuple           =     outputs.predicted_depth

       # verify the predicted depth
       lowercase_	:    List[Any]           =     torch.Size((1, 3_8_4, 3_8_4)			)
       self.assertEqual(predicted_depth.shape  ,     a			)

       lowercase_	:    int           =     torch.tensor(
           [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]			).to(a			)

       self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0  ,     a  ,     atol=1e-4			)			)












'''simple docstring'''
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Any           =     False
   while is_sorted is False:  # Until all the indices are traversed keep looping
      lowercase_	:    List[str]           =     True
      for i in range(0						,		len(_UpperCamelCase							) - 1						,		2							):  # iterating over all even indices
         if input_list[i] > input_list[i + 1]:
            lowercase_    ,		lowercase_	:    Union[str, Any]           =     input_list[i + 1], input_list[i]
            # swapping if elements not in order
            lowercase_	:    Any           =     False

      for i in range(1						,		len(_UpperCamelCase							) - 1						,		2							):  # iterating over all odd indices
         if input_list[i] > input_list[i + 1]:
            lowercase_    ,		lowercase_	:    Tuple           =     input_list[i + 1], input_list[i]
            # swapping if elements not in order
            lowercase_	:    List[Any]           =     False
   return input_list


if __name__ == "__main__":
 print('Enter list to be sorted')
 UpperCamelCase__ =			[int(x) for x in input().split()]
 # inputing elements of the list in one line
 UpperCamelCase__ =			odd_even_sort(input_list)
 print('The sorted list is')
 print(sorted_list)




