
"""simple docstring"""





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 __lowerCAmelCase				:




   """simple docstring"""







   def __init__(				self						:					List[str]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Union[str, Any]=2       ,   _snake_case						:					Dict=32       ,   _snake_case						:					Any=16       ,   _snake_case						:					Dict=3       ,   _snake_case						:					Optional[Any]=True       ,   _snake_case						:					Any=True       ,   _snake_case						:					Any=32       ,   _snake_case						:					Tuple=4       ,   _snake_case						:					str=[0, 1, 2, 3]       ,   _snake_case						:					Tuple=4       ,   _snake_case						:					List[str]=37       ,   _snake_case						:					str="gelu"       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					int=0.0_2       ,   _snake_case						:					Optional[Any]=3       ,   _snake_case						:					List[Any]=[1, 384, 24, 24]       ,   _snake_case						:					Optional[int]=True       ,   _snake_case						:					Optional[int]=None       ,   )      -> Dict:







        """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						:					List[Any]	)      -> Tuple:







        """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						:					Dict	)      -> int:







        """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=_snake_case       ,   initializer_range=self.initializer_range       ,   is_hybrid=self.is_hybrid       ,   backbone_config=_snake_case       ,   backbone_featmap_shape=self.backbone_featmap_shape       ,   )




   def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					str	)      -> Union[str, Any]:







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




   def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Dict       ,   _snake_case						:					List[str]	)      -> List[str]:







        """simple docstring"""
        A_				 =						self.num_labels
        A_				 =						DPTForDepthEstimation(_snake_case	)
        model.to(_snake_case	)
        model.eval()
        A_				 =						model(_snake_case	)
        self.parent.assertEqual(result.predicted_depth.shape       ,   (self.batch_size, self.image_size, self.image_size)	)




   def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[Any]	)      -> Optional[Any]:







        """simple docstring"""
        A_				 =						self.num_labels
        A_				 =						DPTForSemanticSegmentation(_snake_case	)
        model.to(_snake_case	)
        model.eval()
        A_				 =						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						:					Union[str, Any]	)      -> 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 __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




   """simple docstring"""







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

   snake_case	      =				False
   snake_case	      =				False
   snake_case	      =				False




   def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







        """simple docstring"""
        A_				 =						DPTModelTester(self	)
        A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case       ,   hidden_size=37	)




   def      lowerCamelCase__     (				self						:					Tuple	)      -> str:







        """simple docstring"""
        self.config_tester.run_common_tests()




   @unittest.skip(reason="DPT does not use inputs_embeds"	)
   def      lowerCamelCase__     (				self						:					str	)      -> Optional[int]:







        """simple docstring"""
        pass




   def      lowerCamelCase__     (				self						:					str	)      -> str:







        """simple docstring"""
        A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

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




   def      lowerCamelCase__     (				self						:					Dict	)      -> 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(_snake_case	)
             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]       ,   _snake_case	)




   def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







        """simple docstring"""
        A_				 =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_snake_case	)




   def      lowerCamelCase__     (				self						:					Any	)      -> Any:







        """simple docstring"""
        A_				 =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*_snake_case	)




   def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







        """simple docstring"""
        A_				 =						self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case	)




   def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> List[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(_snake_case	):
                  continue

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




   def      lowerCamelCase__     (				self						:					str	)      -> List[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_				 =						False
             A_				 =						True

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




   def      lowerCamelCase__     (				self						:					Dict	)      -> Any:







        """simple docstring"""
        A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

        A_				 =						_config_zero_init(_snake_case	)
        for model_class in self.all_model_classes:
             A_				 =						model_class(config=_snake_case	)
             # 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						:					List[Any]	)      -> Optional[Any]:







        """simple docstring"""
        pass




   @slow
   def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> int:







        """simple docstring"""
        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             A_				 =						DPTModel.from_pretrained(_snake_case	)
             self.assertIsNotNone(_snake_case	)






   def      lowerCamelCase__     (				self						:					int	)      -> str:







        """simple docstring"""
        A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
        A_				 =						"add"
        with self.assertRaises(_snake_case	):
             A_				 =						DPTForDepthEstimation(_snake_case	)





def 							A_     ():

     '''simple docstring'''





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




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




   """simple docstring"""







   def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> List[Any]:







        """simple docstring"""
        A_				 =						DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
        A_				 =						DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(_snake_case	)

        A_				 =						prepare_img()
        A_				 =						image_processor(images=_snake_case       ,   return_tensors="pt"	).to(_snake_case	)

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

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

        A_				 =						torch.tensor(
            [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]	).to(_snake_case	)

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






"""simple docstring"""





from __future__ import annotations

import math

UpperCamelCase_					:						List[str]				= '''2020.9.26'''
UpperCamelCase_					:						List[Any]				= '''xcodz-dot, cclaus, dhruvmanila'''
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not all(isinstance(__a				,   (float, int)				) for val in locals().values()				):
										A_				 =						f'Input values must either be float or int: {list(locals().values()				)}'
										raise TypeError(__a				)
					A_				 =						((x * distance) / (z + distance)) * scale
					A_				 =						((y * distance) / (z + distance)) * scale
					return projected_x, projected_y
def 							A_     (__a				,   __a				,   __a				,   __a				,   __a				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				):
										raise TypeError("Axis must be a str"				)
					A_				 =						locals()
					del input_variables["axis"]
					if not all(isinstance(__a				,   (float, int)				) for val in input_variables.values()				):
										A_				 =						(
										    "Input values except axis must either be float or int: "
										    f'{list(input_variables.values()				)}'
										)
										raise TypeError(__a				)
					A_				 =						(angle % 360) / 450 * 180 / math.pi
					if axis == "z":
										A_				 =						x * math.cos(__a				) - y * math.sin(__a				)
										A_				 =						y * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						z
					elif axis == "x":
										A_				 =						y * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + y * math.sin(__a				)
										A_				 =						x
					elif axis == "y":
										A_				 =						x * math.cos(__a				) - z * math.sin(__a				)
										A_				 =						z * math.cos(__a				) + x * math.sin(__a				)
										A_				 =						y
					else:
										raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'"				)

					return new_x, new_y, new_z


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
		print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")




