





"""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      lowercase_				:

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE=2   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=16   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE=True   ,					__SCREAMING_SNAKE_CASE=32   ,					__SCREAMING_SNAKE_CASE=4   ,					__SCREAMING_SNAKE_CASE=[0, 1, 2, 3]   ,					__SCREAMING_SNAKE_CASE=4   ,					__SCREAMING_SNAKE_CASE=37   ,					__SCREAMING_SNAKE_CASE="gelu"   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=0.1   ,					__SCREAMING_SNAKE_CASE=0.02   ,					__SCREAMING_SNAKE_CASE=3   ,					__SCREAMING_SNAKE_CASE=False   ,					__SCREAMING_SNAKE_CASE=None   ,					):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

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

        snake_case__    							=				self.get_config()

        return config, pixel_values, labels

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        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   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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



@require_torch
class      lowercase_				(     lowerCAmelCase__				,lowerCAmelCase__				,unittest.TestCase							):
       snake_case__				:							Any											=     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       snake_case__				:							Any											=     (
           {
               """depth-estimation""": DPTForDepthEstimation,
               """feature-extraction""": DPTModel,
               """image-segmentation""": DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       snake_case__				:							Optional[Any]											=     False
       snake_case__				:							Tuple											=     False
       snake_case__				:							Any											=     False

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				DPTModelTester(self     )
        snake_case__    							=				ConfigTester(self   ,					config_class=lowerCAmelCase_   ,					has_text_modality=lowerCAmelCase_   ,					hidden_size=37     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        self.config_tester.run_common_tests()

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






        '''simple docstring'''

        pass

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__							,   snake_case__    							=				self.model_tester.prepare_config_and_inputs_for_common()

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__							,   snake_case__    							=				self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case__    							=				["pixel_values"]
         self.assertListEqual(arg_names[:1]   ,					lowerCAmelCase_     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*lowerCAmelCase_     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

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

         if model_class in get_values(lowerCAmelCase_     ):
          continue

         snake_case__    							=				model_class(lowerCAmelCase_     )
         model.to(lowerCAmelCase_     )
         model.train()
         snake_case__    							=				self._prepare_for_class(lowerCAmelCase_   ,					lowerCAmelCase_   ,					return_labels=lowerCAmelCase_     )
         snake_case__    							=				model(**lowerCAmelCase_     ).loss
         loss.backward()

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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

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

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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__							,   snake_case__    							=				self.model_tester.prepare_config_and_inputs_for_common()

        snake_case__    							=				_config_zero_init(lowerCAmelCase_     )
        for model_class in self.all_model_classes:
         snake_case__    							=				model_class(config=lowerCAmelCase_     )
         # Skip the check for the backbone
         snake_case__    							=				[]
         for name, module in model.named_modules():
          if module.__class__.__name__ == "DPTViTHybridEmbeddings":
           snake_case__    							=				[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 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        pass

       @slow
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

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





def 							a__			(  )    ->  Dict:

 '''simple docstring'''






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



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

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				DPTImageProcessor.from_pretrained("Intel/dpt-large"     )
        snake_case__    							=				DPTForDepthEstimation.from_pretrained("Intel/dpt-large"     ).to(lowerCAmelCase_     )

        snake_case__    							=				prepare_img()
        snake_case__    							=				image_processor(images=lowerCAmelCase_   ,					return_tensors="pt"     ).to(lowerCAmelCase_     )

        # forward pass
        with torch.no_grad():
         snake_case__    							=				model(**lowerCAmelCase_     )
         snake_case__    							=				outputs.predicted_depth

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

        snake_case__    							=				torch.tensor(
            [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]     ).to(lowerCAmelCase_     )

        self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,					lowerCAmelCase_   ,					atol=1e-4     )     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"     )
        snake_case__    							=				DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"     ).to(lowerCAmelCase_     )

        snake_case__    							=				prepare_img()
        snake_case__    							=				image_processor(images=lowerCAmelCase_   ,					return_tensors="pt"     ).to(lowerCAmelCase_     )

        # forward pass
        with torch.no_grad():
         snake_case__    							=				model(**lowerCAmelCase_     )

        # verify the logits
        snake_case__    							=				torch.Size((1, 150, 480, 480)     )
        self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase_     )

        snake_case__    							=				torch.tensor(
            [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]     ).to(lowerCAmelCase_     )

        self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]   ,					lowerCAmelCase_   ,					atol=1e-4     )     )

       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        snake_case__    							=				DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"     )
        snake_case__    							=				DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"     ).to(lowerCAmelCase_     )

        snake_case__    							=				prepare_img()
        snake_case__    							=				image_processor(images=lowerCAmelCase_   ,					return_tensors="pt"     ).to(lowerCAmelCase_     )

        # forward pass
        with torch.no_grad():
         snake_case__    							=				model(**lowerCAmelCase_     )

        snake_case__    							=				outputs.logits.detach().cpu()

        snake_case__    							=				image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_   ,					target_sizes=[(500, 300)]     )
        snake_case__    							=				torch.Size((500, 300)     )
        self.assertEqual(segmentation[0].shape   ,					lowerCAmelCase_     )

        snake_case__    							=				image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase_     )
        snake_case__    							=				torch.Size((480, 480)     )
        self.assertEqual(segmentation[0].shape   ,					lowerCAmelCase_     )








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__					:     List[Any]							=				{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				["""BartTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[Any]							=				[
       """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BartForCausalLM""",
       """BartForConditionalGeneration""",
       """BartForQuestionAnswering""",
       """BartForSequenceClassification""",
       """BartModel""",
       """BartPretrainedModel""",
       """PretrainedBartModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     List[str]							=				[
       """TFBartForConditionalGeneration""",
       """TFBartForSequenceClassification""",
       """TFBartModel""",
       """TFBartPretrainedModel""",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a__					:     Any							=				[
       """FlaxBartDecoderPreTrainedModel""",
       """FlaxBartForCausalLM""",
       """FlaxBartForConditionalGeneration""",
       """FlaxBartForQuestionAnswering""",
       """FlaxBartForSequenceClassification""",
       """FlaxBartModel""",
       """FlaxBartPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
   from .tokenization_bart import BartTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_bart_fast import BartTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bart import (
          BART_PRETRAINED_MODEL_ARCHIVE_LIST,
          BartForCausalLM,
          BartForConditionalGeneration,
          BartForQuestionAnswering,
          BartForSequenceClassification,
          BartModel,
          BartPretrainedModel,
          PretrainedBartModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_bart import (
          TFBartForConditionalGeneration,
          TFBartForSequenceClassification,
          TFBartModel,
          TFBartPretrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_bart import (
          FlaxBartDecoderPreTrainedModel,
          FlaxBartForCausalLM,
          FlaxBartForConditionalGeneration,
          FlaxBartForQuestionAnswering,
          FlaxBartForSequenceClassification,
          FlaxBartModel,
          FlaxBartPreTrainedModel,
      )

else:
   import sys

   a__					:     int							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

