

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       SCREAMING_SNAKE_CASE__					:





    def __init__(      self						,   a						,   a=2						,   a=32						,   a=16						,   a=3						,   a=True						,   a=True						,   a=32						,   a=4						,   a=[0, 1, 2, 3]						,   a=4						,   a=37						,   a="gelu"						,   a=0.1						,   a=0.1						,   a=0.02						,   a=3						,   a=[1, 384, 24, 24]						,   a=True						,   a=None						,   ):
         lowercase__    :   Tuple      =       parent
         lowercase__    :   Optional[int]      =       batch_size
         lowercase__    :   Optional[int]      =       image_size
         lowercase__    :   str      =       patch_size
         lowercase__    :   List[Any]      =       num_channels
         lowercase__    :   Any      =       is_training
         lowercase__    :   str      =       use_labels
         lowercase__    :   int      =       hidden_size
         lowercase__    :   int      =       num_hidden_layers
         lowercase__    :   Tuple      =       backbone_out_indices
         lowercase__    :   Union[str, Any]      =       num_attention_heads
         lowercase__    :   List[Any]      =       intermediate_size
         lowercase__    :   List[Any]      =       hidden_act
         lowercase__    :   Dict      =       hidden_dropout_prob
         lowercase__    :   int      =       attention_probs_dropout_prob
         lowercase__    :   Any      =       initializer_range
         lowercase__    :   List[str]      =       num_labels
         lowercase__    :   Optional[Any]      =       backbone_featmap_shape
         lowercase__    :   int      =       scope
         lowercase__    :   Any      =       is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         lowercase__    :   Dict      =       (image_size // patch_size) ** 2
         lowercase__    :   List[Any]      =       num_patches + 1





    def       snake_case_				(      self):
         lowercase__    :   Optional[int]      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__    :   Optional[Any]      =       None
         if self.use_labels:
              lowercase__    :   List[Any]      =       ids_tensor([self.batch_size, self.image_size, self.image_size]						,   self.num_labels)

         lowercase__    :   List[str]      =       self.get_config()

         return config, pixel_values, labels





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       {
             '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=a						,   initializer_range=self.initializer_range						,   is_hybrid=self.is_hybrid						,   backbone_config=a						,   backbone_featmap_shape=self.backbone_featmap_shape						,   )





    def       snake_case_				(      self						,   a						,   a						,   a):
         lowercase__    :   int      =       DPTModel(config=a)
         model.to(a)
         model.eval()
         lowercase__    :   Any      =       model(a)
         self.parent.assertEqual(result.last_hidden_state.shape						,   (self.batch_size, self.seq_length, self.hidden_size))





    def       snake_case_				(      self						,   a						,   a						,   a):
         lowercase__    :   str      =       self.num_labels
         lowercase__    :   int      =       DPTForDepthEstimation(a)
         model.to(a)
         model.eval()
         lowercase__    :   int      =       model(a)
         self.parent.assertEqual(result.predicted_depth.shape						,   (self.batch_size, self.image_size, self.image_size))





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





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       self.prepare_config_and_inputs()
         lowercase__      , lowercase__      , lowercase__    :   Optional[int]      =       config_and_inputs
         lowercase__    :   int      =       {'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       Optional[Any]														=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    __lowerCamelCase						:       List[Any]														=     (
        {
            """depth-estimation""": DPTForDepthEstimation,
            """feature-extraction""": DPTModel,
            """image-segmentation""": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    __lowerCamelCase						:       Any														=     False
    __lowerCamelCase						:       Union[str, Any]														=     False
    __lowerCamelCase						:       List[Any]														=     False





    def       snake_case_				(      self):
         lowercase__    :   List[str]      =       DPTModelTester(self)
         lowercase__    :   int      =       ConfigTester(self						,   config_class=a						,   has_text_modality=a						,   hidden_size=37)





    def       snake_case_				(      self):
         self.config_tester.run_common_tests()





    @unittest.skip(reason='DPT does not use inputs_embeds')
    def       snake_case_				(      self):
         pass





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

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





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   str      =       self.model_tester.prepare_config_and_inputs_for_common()

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

              lowercase__    :   int      =       ['pixel_values']
              self.assertListEqual(arg_names[:1]						,   a)





    def       snake_case_				(      self):
         lowercase__    :   Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    def       snake_case_				(      self):
         lowercase__    :   str      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*a)





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*a)





    def       snake_case_				(      self):
         for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                   continue

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

              if model_class in get_values(a):
                   continue

              lowercase__    :   int      =       model_class(a)
              model.to(a)
              model.train()
              lowercase__    :   Tuple      =       self._prepare_for_class(a						,   a						,   return_labels=a)
              lowercase__    :   str      =       model(**a).loss
              loss.backward()





    def       snake_case_				(      self):
         for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                   continue

              lowercase__      , lowercase__    :   Any      =       self.model_tester.prepare_config_and_inputs_for_common()
              lowercase__    :   List[str]      =       False
              lowercase__    :   List[Any]      =       True

              if model_class in get_values(a) or not model_class.supports_gradient_checkpointing:
                   continue
              lowercase__    :   Optional[int]      =       model_class(a)
              model.to(a)
              model.gradient_checkpointing_enable()
              model.train()
              lowercase__    :   Any      =       self._prepare_for_class(a						,   a						,   return_labels=a)
              lowercase__    :   Any      =       model(**a).loss
              loss.backward()





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   int      =       self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__    :   Optional[Any]      =       _config_zero_init(a)
         for model_class in self.all_model_classes:
              lowercase__    :   List[Any]      =       model_class(config=a)
              # Skip the check for the backbone
              lowercase__    :   Optional[int]      =       []
              for name, module in model.named_modules():
                   if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                        lowercase__    :   Optional[int]      =       [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       snake_case_				(      self):
         pass





    @slow
    def       snake_case_				(      self):
         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
              lowercase__    :   Union[str, Any]      =       DPTModel.from_pretrained(a)
              self.assertIsNotNone(a)





    def       snake_case_				(      self):
         # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
         lowercase__      , lowercase__    :   List[str]      =       self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__    :   Optional[Any]      =       'add'
         with self.assertRaises(a):
              lowercase__    :   int      =       DPTForDepthEstimation(a)



def 	snake_case__			(    ):



     '''simple docstring'''





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


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





    def       snake_case_				(      self):
         lowercase__    :   Union[str, Any]      =       DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas')
         lowercase__    :   Optional[int]      =       DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas').to(a)

         lowercase__    :   Optional[int]      =       prepare_img()
         lowercase__    :   str      =       image_processor(images=a						,   return_tensors='pt').to(a)

         # forward pass
         with torch.no_grad():
              lowercase__    :   Dict      =       model(**a)
              lowercase__    :   Optional[Any]      =       outputs.predicted_depth

         # verify the predicted depth
         lowercase__    :   List[Any]      =       torch.Size((1, 384, 384))
         self.assertEqual(predicted_depth.shape						,   a)

         lowercase__    :   Optional[int]      =       torch.tensor(
             [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]).to(a)

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




def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     if len(SCREAMING_SNAKE_CASE_			) != len(SCREAMING_SNAKE_CASE_			):
          raise ValueError('String lengths must match!'			)

     lowercase__    :   Union[str, Any]      =       0

     for chara, chara in zip(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_			):
          if chara != chara:
               count += 1

     return count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

