

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				:

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





         A_							       =		{
             '''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=lowerCamelCase__			,						initializer_range=self.initializer_range			,						is_hybrid=self.is_hybrid			,						backbone_config=lowerCamelCase__			,						backbone_featmap_shape=self.backbone_featmap_shape			,						)

   def     UpperCamelCase					(				self	:    Dict			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[str]       ) ->			Tuple:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Any       ) ->			Any:
         """simple docstring"""





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

   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		DPTForSemanticSegmentation(lowerCamelCase__       )
         model.to(lowerCamelCase__       )
         model.eval()
         A_							       =		model(lowerCamelCase__			,						labels=lowerCamelCase__       )
         self.parent.assertEqual(
             result.logits.shape			,						(self.batch_size, self.num_labels, self.image_size, self.image_size)       )





   def     UpperCamelCase					(				self	:    Dict       ) ->			str:
         """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				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   _lowercase     :       Dict  					=    (
       {
           'depth-estimation': DPTForDepthEstimation,
           'feature-extraction': DPTModel,
           'image-segmentation': DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   _lowercase     :       Any  					=    False
   _lowercase     :       Dict  					=    False
   _lowercase     :       Dict  					=    False

   def     UpperCamelCase					(				self	:    Dict       ) ->			List[str]:
         """simple docstring"""





         A_							       =		DPTModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						has_text_modality=lowerCamelCase__			,						hidden_size=3_7       )

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





         self.config_tester.run_common_tests()

   @unittest.skip(reason='''DPT does not use inputs_embeds'''       )
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			List[str]:
         """simple docstring"""





         pass

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





         A_       ,A_							       =		self.model_tester.prepare_config_and_inputs_for_common()

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

   def     UpperCamelCase					(				self	:    Dict       ) ->			Any:
         """simple docstring"""





         A_       ,A_							       =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               A_							       =		model_class(lowerCamelCase__       )
               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]			,						lowerCamelCase__       )

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





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

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





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__       )

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





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Dict       ) ->			Union[str, Any]:
         """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(lowerCamelCase__       ):
                     continue

               A_							       =		model_class(lowerCamelCase__       )
               model.to(lowerCamelCase__       )
               model.train()
               A_							       =		self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__			,						return_labels=lowerCamelCase__       )
               A_							       =		model(**lowerCamelCase__       ).loss
               loss.backward()

   def     UpperCamelCase					(				self	:    Dict       ) ->			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(lowerCamelCase__       ) or not model_class.supports_gradient_checkpointing:
                     continue
               A_							       =		model_class(lowerCamelCase__       )
               model.to(lowerCamelCase__       )
               model.gradient_checkpointing_enable()
               model.train()
               A_							       =		self._prepare_for_class(lowerCamelCase__			,						lowerCamelCase__			,						return_labels=lowerCamelCase__       )
               A_							       =		model(**lowerCamelCase__       ).loss
               loss.backward()

   def     UpperCamelCase					(				self	:    Dict       ) ->			Optional[int]:
         """simple docstring"""





         A_       ,A_							       =		self.model_tester.prepare_config_and_inputs_for_common()

         A_							       =		_config_zero_init(lowerCamelCase__       )
         for model_class in self.all_model_classes:
               A_							       =		model_class(config=lowerCamelCase__       )
               # 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     UpperCamelCase					(				self	:    Optional[Any]       ) ->			str:
         """simple docstring"""





         pass

   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





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





   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





         A_       ,A_							       =		self.model_tester.prepare_config_and_inputs_for_common()
         A_							       =		'''add'''
         with self.assertRaises(lowerCamelCase__       ):
               A_							       =		DPTForDepthEstimation(lowerCamelCase__       )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







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





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

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





         A_							       =		DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''       )
         A_							       =		DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''       ).to(lowerCamelCase__       )

         A_							       =		prepare_img()
         A_							       =		image_processor(images=lowerCamelCase__			,						return_tensors='''pt'''       ).to(lowerCamelCase__       )

         # forward pass
         with torch.no_grad():
               A_							       =		model(**lowerCamelCase__       )
               A_							       =		outputs.predicted_depth

         # verify the predicted depth
         A_							       =		torch.Size((1, 3_8_4, 3_8_4)       )
         self.assertEqual(predicted_depth.shape			,						lowerCamelCase__       )

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

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








def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		len(SCREAMING_SNAKE_CASE   )
      A_							       =		(
          first_str_length if first_str_length > second_str_length else second_str_length
      )
      A_							       =		[]
      for char_count in range(SCREAMING_SNAKE_CASE   ):
            if char_count < first_str_length:
                  output_list.append(first_str[char_count]   )
            if char_count < second_str_length:
                  output_list.append(second_str[char_count]   )
      return "".join(SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)





