




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							, A							, A=2							, A=3_2							, A=1_6							, A=3							, A=True							, A=True							, A=3_2							, A=4							, A=[0, 1, 2, 3]							, A=4							, A=3_7							, A="gelu"							, A=0.1							, A=0.1							, A=0.02							, A=3							, A=[1, 3_8_4, 2_4, 2_4]							, A=True							, A=None							, )						->							List[Any]:
         snake_case :							Tuple           =			parent
         snake_case :							Any           =			batch_size
         snake_case :							Optional[int]           =			image_size
         snake_case :							Tuple           =			patch_size
         snake_case :							Optional[Any]           =			num_channels
         snake_case :							List[Any]           =			is_training
         snake_case :							Dict           =			use_labels
         snake_case :							Tuple           =			hidden_size
         snake_case :							List[str]           =			num_hidden_layers
         snake_case :							List[str]           =			backbone_out_indices
         snake_case :							Any           =			num_attention_heads
         snake_case :							List[Any]           =			intermediate_size
         snake_case :							Optional[int]           =			hidden_act
         snake_case :							List[str]           =			hidden_dropout_prob
         snake_case :							List[Any]           =			attention_probs_dropout_prob
         snake_case :							str           =			initializer_range
         snake_case :							Optional[int]           =			num_labels
         snake_case :							Optional[Any]           =			backbone_featmap_shape
         snake_case :							List[str]           =			scope
         snake_case :							List[Any]           =			is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         snake_case :							Union[str, Any]           =			(image_size // patch_size) ** 2
         snake_case :							Dict           =			num_patches + 1

      def       UpperCAmelCase ( self      )						->							Tuple:
         snake_case :							List[str]           =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         snake_case :							Union[str, Any]           =			None
         if self.use_labels:
            snake_case :							Optional[Any]           =			ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels      )

         snake_case :							Any           =			self.get_config()

         return config, pixel_values, labels

      def       UpperCAmelCase ( self      )						->							Tuple:
         snake_case :							str           =			{
             """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=_UpperCamelCase							, initializer_range=self.initializer_range							, is_hybrid=self.is_hybrid							, backbone_config=_UpperCamelCase							, backbone_featmap_shape=self.backbone_featmap_shape							, )

      def       UpperCAmelCase ( self							, A							, A							, A      )						->							Optional[int]:
         snake_case :							Union[str, Any]           =			DPTModel(config=_UpperCamelCase      )
         model.to(_UpperCamelCase      )
         model.eval()
         snake_case :							Union[str, Any]           =			model(_UpperCamelCase      )
         self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, self.seq_length, self.hidden_size)      )

      def       UpperCAmelCase ( self							, A							, A							, A      )						->							List[Any]:
         snake_case :							Optional[int]           =			self.num_labels
         snake_case :							List[str]           =			DPTForDepthEstimation(_UpperCamelCase      )
         model.to(_UpperCamelCase      )
         model.eval()
         snake_case :							Optional[int]           =			model(_UpperCamelCase      )
         self.parent.assertEqual(result.predicted_depth.shape							, (self.batch_size, self.image_size, self.image_size)      )

      def       UpperCAmelCase ( self							, A							, A							, A      )						->							Union[str, Any]:
         snake_case :							Optional[Any]           =			self.num_labels
         snake_case :							Dict           =			DPTForSemanticSegmentation(_UpperCamelCase      )
         model.to(_UpperCamelCase      )
         model.eval()
         snake_case :							Optional[int]           =			model(_UpperCamelCase							, labels=_UpperCamelCase      )
         self.parent.assertEqual(
             result.logits.shape							, (self.batch_size, self.num_labels, self.image_size, self.image_size)      )

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case :							int           =			self.prepare_config_and_inputs()
         snake_case      ,   snake_case      ,   snake_case :							str           =			config_and_inputs
         snake_case :							Tuple           =			{"""pixel_values""": pixel_values}
         return config, inputs_dict




@require_torch
class 		__lowercase						(__a	,						__a	,						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       UpperCAmelCase ( self      )						->							List[str]:
         snake_case :							List[Any]           =			DPTModelTester(self      )
         snake_case :							Tuple           =			ConfigTester(self							, config_class=_UpperCamelCase							, has_text_modality=_UpperCamelCase							, hidden_size=3_7      )

      def       UpperCAmelCase ( self      )						->							str:
         self.config_tester.run_common_tests()

      @unittest.skip(reason="""DPT does not use inputs_embeds"""      )
      def       UpperCAmelCase ( self      )						->							Tuple:
         pass

      def       UpperCAmelCase ( self      )						->							Tuple:
         snake_case      ,   snake_case :							Dict           =			self.model_tester.prepare_config_and_inputs_for_common()

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

      def       UpperCAmelCase ( self      )						->							Any:
         snake_case      ,   snake_case :							Optional[int]           =			self.model_tester.prepare_config_and_inputs_for_common()

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

            snake_case :							List[str]           =			["""pixel_values"""]
            self.assertListEqual(arg_names[:1]							, _UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Tuple:
         snake_case :							Union[str, Any]           =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Dict:
         snake_case :							List[Any]           =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         snake_case :							Optional[int]           =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Union[str, Any]:
         for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
               continue

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

            if model_class in get_values(_UpperCamelCase      ):
               continue

            snake_case :							Dict           =			model_class(_UpperCamelCase      )
            model.to(_UpperCamelCase      )
            model.train()
            snake_case :							Union[str, Any]           =			self._prepare_for_class(_UpperCamelCase							, _UpperCamelCase							, return_labels=_UpperCamelCase      )
            snake_case :							int           =			model(**_UpperCamelCase      ).loss
            loss.backward()

      def       UpperCAmelCase ( self      )						->							int:
         for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
               continue

            snake_case      ,   snake_case :							Union[str, Any]           =			self.model_tester.prepare_config_and_inputs_for_common()
            snake_case :							Tuple           =			False
            snake_case :							Dict           =			True

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

      def       UpperCAmelCase ( self      )						->							Optional[int]:
         snake_case      ,   snake_case :							Union[str, Any]           =			self.model_tester.prepare_config_and_inputs_for_common()

         snake_case :							Dict           =			_config_zero_init(_UpperCamelCase      )
         for model_class in self.all_model_classes:
            snake_case :							Union[str, Any]           =			model_class(config=_UpperCamelCase      )
            # Skip the check for the backbone
            snake_case :							Dict           =			[]
            for name, module in model.named_modules():
               if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                  snake_case :							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       UpperCAmelCase ( self      )						->							List[str]:
         pass

      @slow
      def       UpperCAmelCase ( self      )						->							Optional[int]:
         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
            snake_case :							Dict           =			DPTModel.from_pretrained(_UpperCamelCase      )
            self.assertIsNotNone(_UpperCamelCase      )

      def       UpperCAmelCase ( self      )						->							Optional[Any]:
         # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
         snake_case      ,   snake_case :							List[str]           =			self.model_tester.prepare_config_and_inputs_for_common()
         snake_case :							int           =			"""add"""
         with self.assertRaises(_UpperCamelCase      ):
            snake_case :							Dict           =			DPTForDepthEstimation(_UpperCamelCase      )




def 		SCREAMING_SNAKE_CASE__   (       )    ->  str:
   snake_case :							str           =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
   return image




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




      """simple docstring"""





      def       UpperCAmelCase ( self      )						->							int:
         snake_case :							Dict           =			DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""      )
         snake_case :							str           =			DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""      ).to(_UpperCamelCase      )

         snake_case :							int           =			prepare_img()
         snake_case :							str           =			image_processor(images=_UpperCamelCase							, return_tensors="""pt"""      ).to(_UpperCamelCase      )

         # forward pass
         with torch.no_grad():
            snake_case :							int           =			model(**_UpperCamelCase      )
            snake_case :							Optional[Any]           =			outputs.predicted_depth

         # verify the predicted depth
         snake_case :							Optional[int]           =			torch.Size((1, 3_8_4, 3_8_4)      )
         self.assertEqual(predicted_depth.shape							, _UpperCamelCase      )

         snake_case :							List[Any]           =			torch.tensor(
             [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]      ).to(_UpperCamelCase      )

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

			_A  =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



