
'''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 		_a     :







   """simple docstring"""




   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    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		parent
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		batch_size
    SCREAMING_SNAKE_CASE  :  List[str]								=		image_size
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		patch_size
    SCREAMING_SNAKE_CASE  :  Dict								=		num_channels
    SCREAMING_SNAKE_CASE  :  str								=		is_training
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		use_labels
    SCREAMING_SNAKE_CASE  :  List[Any]								=		hidden_size
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		num_hidden_layers
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		backbone_out_indices
    SCREAMING_SNAKE_CASE  :  List[str]								=		num_attention_heads
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		intermediate_size
    SCREAMING_SNAKE_CASE  :  List[str]								=		hidden_act
    SCREAMING_SNAKE_CASE  :  List[str]								=		hidden_dropout_prob
    SCREAMING_SNAKE_CASE  :  Tuple								=		attention_probs_dropout_prob
    SCREAMING_SNAKE_CASE  :  str								=		initializer_range
    SCREAMING_SNAKE_CASE  :  List[Any]								=		num_labels
    SCREAMING_SNAKE_CASE  :  int								=		scope
    SCREAMING_SNAKE_CASE  :  Dict								=		is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    SCREAMING_SNAKE_CASE  :  str								=		(image_size // patch_size) ** 2
    SCREAMING_SNAKE_CASE  :  List[str]								=		num_patches + 1






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

    SCREAMING_SNAKE_CASE  :  Tuple								=		self.get_config()

    return config, pixel_values, labels






   def 					__a			(       self      ):
    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 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		DPTModel(config=lowerCamelCase__      )
    model.to(lowerCamelCase__      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  Tuple								=		model(lowerCamelCase__      )
    self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.num_labels
    SCREAMING_SNAKE_CASE  :  Tuple								=		DPTForDepthEstimation(lowerCamelCase__      )
    model.to(lowerCamelCase__      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  Tuple								=		model(lowerCamelCase__      )
    self.parent.assertEqual(result.predicted_depth.shape    ,(self.batch_size, self.image_size, self.image_size)      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.num_labels
    SCREAMING_SNAKE_CASE  :  Dict								=		DPTForSemanticSegmentation(lowerCamelCase__      )
    model.to(lowerCamelCase__      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model(lowerCamelCase__    ,labels=lowerCamelCase__      )
    self.parent.assertEqual(
        result.logits.shape    ,(self.batch_size, self.num_labels, self.image_size, self.image_size)      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.prepare_config_and_inputs()
    SCREAMING_SNAKE_CASE  :  Tuple								=		config_and_inputs
    SCREAMING_SNAKE_CASE  :  List[str]								=		{'''pixel_values''': pixel_values}
    return config, inputs_dict



@require_torch
class 		_a     (							__lowerCAmelCase			,    __lowerCAmelCase			,    unittest.TestCase  ):







   """simple docstring"""




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

   A		= False
   A		= False
   A		= False






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		DPTModelTester(self      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		ConfigTester(self    ,config_class=lowerCamelCase__    ,has_text_modality=lowerCamelCase__    ,hidden_size=37      )






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






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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.model_tester.prepare_config_and_inputs_for_common()

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.model_tester.prepare_config_and_inputs_for_common()

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

     SCREAMING_SNAKE_CASE  :  List[str]								=		['''pixel_values''']
     self.assertListEqual(arg_names[:1]    ,lowerCamelCase__      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase__      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase__      )






   def 					__a			(       self      ):






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

     SCREAMING_SNAKE_CASE  :  List[Any]								=		self.model_tester.prepare_config_and_inputs_for_common()
     SCREAMING_SNAKE_CASE  :  int								=		True

     if model_class in get_values(lowerCamelCase__      ):
      continue

     SCREAMING_SNAKE_CASE  :  str								=		model_class(lowerCamelCase__      )
     model.to(lowerCamelCase__      )
     model.train()
     SCREAMING_SNAKE_CASE  :  str								=		self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__    ,return_labels=lowerCamelCase__      )
     SCREAMING_SNAKE_CASE  :  Dict								=		model(**lowerCamelCase__      ).loss
     loss.backward()






   def 					__a			(       self      ):






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

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

     if model_class in get_values(lowerCamelCase__      ) or not model_class.supports_gradient_checkpointing:
      continue
     SCREAMING_SNAKE_CASE  :  Dict								=		model_class(lowerCamelCase__      )
     model.to(lowerCamelCase__      )
     model.gradient_checkpointing_enable()
     model.train()
     SCREAMING_SNAKE_CASE  :  str								=		self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__    ,return_labels=lowerCamelCase__      )
     SCREAMING_SNAKE_CASE  :  Any								=		model(**lowerCamelCase__      ).loss
     loss.backward()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.model_tester.prepare_config_and_inputs_for_common()

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






   @slow
   def 					__a			(       self      ):






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



def        SCREAMING_SNAKE_CASE_  (	)       ->	List[str]:
 SCREAMING_SNAKE_CASE  :  Tuple								=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
 return image





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







   """simple docstring"""




   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		DPTImageProcessor.from_pretrained('Intel/dpt-large'      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		DPTForDepthEstimation.from_pretrained('Intel/dpt-large'      ).to(lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		prepare_img()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_processor(images=lowerCamelCase__    ,return_tensors='pt'      ).to(lowerCamelCase__      )

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

    # verify the predicted depth
    SCREAMING_SNAKE_CASE  :  str								=		torch.Size((1, 384, 384)      )
    self.assertEqual(predicted_depth.shape    ,lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'      )
    SCREAMING_SNAKE_CASE  :  str								=		DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'      ).to(lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  str								=		prepare_img()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_processor(images=lowerCamelCase__    ,return_tensors='pt'      ).to(lowerCamelCase__      )

    # forward pass
    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		model(**lowerCamelCase__      )

    # verify the logits
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		torch.Size((1, 150, 480, 480)      )
    self.assertEqual(outputs.logits.shape    ,lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'      ).to(lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		prepare_img()
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_processor(images=lowerCamelCase__    ,return_tensors='pt'      ).to(lowerCamelCase__      )

    # forward pass
    with torch.no_grad():
     SCREAMING_SNAKE_CASE  :  List[Any]								=		model(**lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		outputs.logits.detach().cpu()

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__    ,target_sizes=[(500, 300)]      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		torch.Size((500, 300)      )
    self.assertEqual(segmentation[0].shape    ,lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		torch.Size((480, 480)      )
    self.assertEqual(segmentation[0].shape    ,lowerCamelCase__      )






from typing import TYPE_CHECKING

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


__UpperCamelCase							:							List[Any]    							=					{
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					["MPNetTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							Union[str, Any]    							=					[
           "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MPNetForMaskedLM",
           "MPNetForMultipleChoice",
           "MPNetForQuestionAnswering",
           "MPNetForSequenceClassification",
           "MPNetForTokenClassification",
           "MPNetLayer",
           "MPNetModel",
           "MPNetPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase							:							List[Any]    							=					[
           "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMPNetEmbeddings",
           "TFMPNetForMaskedLM",
           "TFMPNetForMultipleChoice",
           "TFMPNetForQuestionAnswering",
           "TFMPNetForSequenceClassification",
           "TFMPNetForTokenClassification",
           "TFMPNetMainLayer",
           "TFMPNetModel",
           "TFMPNetPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
       from .tokenization_mpnet import MPNetTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mpnet_fast import MPNetTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mpnet import (
                  MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MPNetForMaskedLM,
                  MPNetForMultipleChoice,
                  MPNetForQuestionAnswering,
                  MPNetForSequenceClassification,
                  MPNetForTokenClassification,
                  MPNetLayer,
                  MPNetModel,
                  MPNetPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mpnet import (
                  TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMPNetEmbeddings,
                  TFMPNetForMaskedLM,
                  TFMPNetForMultipleChoice,
                  TFMPNetForQuestionAnswering,
                  TFMPNetForSequenceClassification,
                  TFMPNetForTokenClassification,
                  TFMPNetMainLayer,
                  TFMPNetModel,
                  TFMPNetPreTrainedModel,
              )

else:
       import sys

       __UpperCamelCase							:							Tuple    							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


