






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							:      Any			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Optional[int]=2			,						UpperCamelCase_							:      Optional[Any]=32			,						UpperCamelCase_							:      int=16			,						UpperCamelCase_							:      Optional[Any]=3			,						UpperCamelCase_							:      Any=True			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      Tuple=32			,						UpperCamelCase_							:      int=4			,						UpperCamelCase_							:      Union[str, Any]=[0, 1, 2, 3]			,						UpperCamelCase_							:      Dict=4			,						UpperCamelCase_							:      List[Any]=37			,						UpperCamelCase_							:      int="gelu"			,						UpperCamelCase_							:      List[Any]=0.1			,						UpperCamelCase_							:      Dict=0.1			,						UpperCamelCase_							:      Dict=0.02			,						UpperCamelCase_							:      int=3			,						UpperCamelCase_							:      List[str]=[1, 384, 24, 24]			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Optional[Any]=None			,						):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

         __A       =						self.get_config()

         return config, pixel_values, labels




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						{
             """global_padding""": """same""",
             """layer_type""": """bottleneck""",
             """depths""": [3, 4, 9],
             """out_features""": ["""stage1""", """stage2""", """stage3"""],
             """embedding_dynamic_padding""": True,
             """hidden_sizes""": [96, 192, 384, 768],
             """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=UpperCamelCase_			,						initializer_range=self.initializer_range			,						is_hybrid=self.is_hybrid			,						backbone_config=UpperCamelCase_			,						backbone_featmap_shape=self.backbone_featmap_shape			,						)




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						DPTModel(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						DPTForDepthEstimation(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.predicted_depth.shape			,						(self.batch_size, self.image_size, self.image_size)					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						self.num_labels
         __A       =						DPTForSemanticSegmentation(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						labels=UpperCamelCase_					)
         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"""



         __A       =						self.prepare_config_and_inputs()
         __A     ,						__A     ,						__A       =						config_and_inputs
         __A       =						{"""pixel_values""": pixel_values}
         return config, inputs_dict


@require_torch
class    __lowercase   (						lowercase_      ,   lowercase_      ,   unittest.TestCase	):







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

     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A       =						DPTModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						has_text_modality=UpperCamelCase_			,						hidden_size=37					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         self.config_tester.run_common_tests()




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


         """simple docstring"""



         pass




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()

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




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()

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

             __A       =						["""pixel_values"""]
             self.assertListEqual(arg_names[:1]			,						UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



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

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

             if model_class in get_values(UpperCamelCase_					):
                 continue

             __A       =						model_class(UpperCamelCase_					)
             model.to(UpperCamelCase_					)
             model.train()
             __A       =						self._prepare_for_class(UpperCamelCase_			,						UpperCamelCase_			,						return_labels=UpperCamelCase_					)
             __A       =						model(**UpperCamelCase_					).loss
             loss.backward()




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



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

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

             if model_class in get_values(UpperCamelCase_					) or not model_class.supports_gradient_checkpointing:
                 continue
             __A       =						model_class(UpperCamelCase_					)
             model.to(UpperCamelCase_					)
             model.gradient_checkpointing_enable()
             model.train()
             __A       =						self._prepare_for_class(UpperCamelCase_			,						UpperCamelCase_			,						return_labels=UpperCamelCase_					)
             __A       =						model(**UpperCamelCase_					).loss
             loss.backward()




     def 		lowerCAmelCase_  (					self							:      Optional[int]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()

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


         """simple docstring"""



         pass




     @slow
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             __A       =						DPTModel.from_pretrained(UpperCamelCase_					)
             self.assertIsNotNone(UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()
         __A       =						"""add"""
         with self.assertRaises(UpperCamelCase_					):
             __A       =						DPTForDepthEstimation(UpperCamelCase_					)



def       _SCREAMING_SNAKE_CASE    (			)    ->   str:





    """simple docstring"""

    __A       =						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							:      List[Any]					):


         """simple docstring"""



         __A       =						DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""					)
         __A       =						DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""					).to(UpperCamelCase_					)

         __A       =						prepare_img()
         __A       =						image_processor(images=UpperCamelCase_			,						return_tensors="""pt"""					).to(UpperCamelCase_					)

         # forward pass
         with torch.no_grad():
             __A       =						model(**UpperCamelCase_					)
             __A       =						outputs.predicted_depth

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

         __A       =						torch.tensor(
             [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]					).to(UpperCamelCase_					)

         self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100			,						UpperCamelCase_			,						atol=1e-4					)					)












from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




