

'''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_		:

  '''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_=False				,	a_=None				,	)							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		parent
    UpperCAmelCase  							=		batch_size
    UpperCAmelCase  							=		image_size
    UpperCAmelCase  							=		patch_size
    UpperCAmelCase  							=		num_channels
    UpperCAmelCase  							=		is_training
    UpperCAmelCase  							=		use_labels
    UpperCAmelCase  							=		hidden_size
    UpperCAmelCase  							=		num_hidden_layers
    UpperCAmelCase  							=		backbone_out_indices
    UpperCAmelCase  							=		num_attention_heads
    UpperCAmelCase  							=		intermediate_size
    UpperCAmelCase  							=		hidden_act
    UpperCAmelCase  							=		hidden_dropout_prob
    UpperCAmelCase  							=		attention_probs_dropout_prob
    UpperCAmelCase  							=		initializer_range
    UpperCAmelCase  							=		num_labels
    UpperCAmelCase  							=		scope
    UpperCAmelCase  							=		is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    UpperCAmelCase  							=		(image_size // patch_size) ** 2
    UpperCAmelCase  							=		num_patches + 1
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

    UpperCAmelCase  							=		self.get_config()

    return config, pixel_values, labels
  def   snake_case_     (       self       )							->			List[str]:


    """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=a_				,	initializer_range=self.initializer_range				,	is_hybrid=self.is_hybrid				,	)
  def   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			int:


    """simple docstring"""
    UpperCAmelCase  							=		DPTModel(config=a_       )
    model.to(a_       )
    model.eval()
    UpperCAmelCase  							=		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_       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.num_labels
    UpperCAmelCase  							=		DPTForDepthEstimation(a_       )
    model.to(a_       )
    model.eval()
    UpperCAmelCase  							=		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_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.num_labels
    UpperCAmelCase  							=		DPTForSemanticSegmentation(a_       )
    model.to(a_       )
    model.eval()
    UpperCAmelCase  							=		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       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		self.prepare_config_and_inputs()
    UpperCAmelCase							, UpperCAmelCase							, UpperCAmelCase  							=		config_and_inputs
    UpperCAmelCase  							=		{'pixel_values': pixel_values}
    return config, inputs_dict




@require_torch
class     lowercase_		(						a	,    a	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	List[str]      					=				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  __lowerCAmelCase   :	List[str]      					=				(
      {
          "depth-estimation": DPTForDepthEstimation,
          "feature-extraction": DPTModel,
          "image-segmentation": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  __lowerCAmelCase   :	Dict      					=				False
  __lowerCAmelCase   :	Union[str, Any]      					=				False
  __lowerCAmelCase   :	List[str]      					=				False
  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		DPTModelTester(self       )
    UpperCAmelCase  							=		ConfigTester(self				,	config_class=a_				,	has_text_modality=a_				,	hidden_size=3_7       )
  def   snake_case_     (       self       )							->			int:


    """simple docstring"""
    self.config_tester.run_common_tests()
  @unittest.skip(reason='DPT does not use inputs_embeds'       )
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    pass
  def   snake_case_     (       self       )							->			Dict:


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

    for model_class in self.all_model_classes:
      UpperCAmelCase  							=		model_class(a_       )
      self.assertIsInstance(model.get_input_embeddings()				,	(nn.Module)       )
      UpperCAmelCase  							=		model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(a_				,	nn.Linear       )       )
  def   snake_case_     (       self       )							->			Any:


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

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

      UpperCAmelCase  							=		['pixel_values']
      self.assertListEqual(arg_names[:1]				,	a_       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*a_       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*a_       )
  def   snake_case_     (       self       )							->			int:


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

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

      if model_class in get_values(a_       ):
        continue

      UpperCAmelCase  							=		model_class(a_       )
      model.to(a_       )
      model.train()
      UpperCAmelCase  							=		self._prepare_for_class(a_				,	a_				,	return_labels=a_       )
      UpperCAmelCase  							=		model(**a_       ).loss
      loss.backward()
  def   snake_case_     (       self       )							->			Any:


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

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

      if model_class in get_values(a_       ) or not model_class.supports_gradient_checkpointing:
        continue
      UpperCAmelCase  							=		model_class(a_       )
      model.to(a_       )
      model.gradient_checkpointing_enable()
      model.train()
      UpperCAmelCase  							=		self._prepare_for_class(a_				,	a_				,	return_labels=a_       )
      UpperCAmelCase  							=		model(**a_       ).loss
      loss.backward()
  def   snake_case_     (       self       )							->			List[Any]:


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

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


    """simple docstring"""
    pass





  @slow
  def   snake_case_     (       self       )							->			str:


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




def  lowerCamelCase__						(  ):
  UpperCAmelCase  							=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image



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

  '''simple docstring'''





  def   snake_case_     (       self       )							->			str:


    """simple docstring"""
    UpperCAmelCase  							=		DPTImageProcessor.from_pretrained('Intel/dpt-large'       )
    UpperCAmelCase  							=		DPTForDepthEstimation.from_pretrained('Intel/dpt-large'       ).to(a_       )

    UpperCAmelCase  							=		prepare_img()
    UpperCAmelCase  							=		image_processor(images=a_				,	return_tensors='pt'       ).to(a_       )

    # forward pass
    with torch.no_grad():
      UpperCAmelCase  							=		model(**a_       )
      UpperCAmelCase  							=		outputs.predicted_depth

    # verify the predicted depth
    UpperCAmelCase  							=		torch.Size((1, 3_8_4, 3_8_4)       )
    self.assertEqual(predicted_depth.shape				,	a_       )

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

    self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]				,	a_				,	atol=1E-4       )       )
  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'       )
    UpperCAmelCase  							=		DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'       ).to(a_       )

    UpperCAmelCase  							=		prepare_img()
    UpperCAmelCase  							=		image_processor(images=a_				,	return_tensors='pt'       ).to(a_       )

    # forward pass
    with torch.no_grad():
      UpperCAmelCase  							=		model(**a_       )

    # verify the logits
    UpperCAmelCase  							=		torch.Size((1, 1_5_0, 4_8_0, 4_8_0)       )
    self.assertEqual(outputs.logits.shape				,	a_       )

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

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





  def   snake_case_     (       self       )							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'       )
    UpperCAmelCase  							=		DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'       ).to(a_       )

    UpperCAmelCase  							=		prepare_img()
    UpperCAmelCase  							=		image_processor(images=a_				,	return_tensors='pt'       ).to(a_       )

    # forward pass
    with torch.no_grad():
      UpperCAmelCase  							=		model(**a_       )

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

    UpperCAmelCase  							=		image_processor.post_process_semantic_segmentation(outputs=a_				,	target_sizes=[(5_0_0, 3_0_0)]       )
    UpperCAmelCase  							=		torch.Size((5_0_0, 3_0_0)       )
    self.assertEqual(segmentation[0].shape				,	a_       )

    UpperCAmelCase  							=		image_processor.post_process_semantic_segmentation(outputs=a_       )
    UpperCAmelCase  							=		torch.Size((4_8_0, 4_8_0)       )
    self.assertEqual(segmentation[0].shape				,	a_       )





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


