
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 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Optional[int]      ,							UpperCamelCase__      :			List[Any]      ,							UpperCamelCase__      :			Dict=2      ,							UpperCamelCase__      :			Optional[Any]=32      ,							UpperCamelCase__      :			Optional[Any]=16      ,							UpperCamelCase__      :			Union[str, Any]=3      ,							UpperCamelCase__      :			Optional[Any]=True      ,							UpperCamelCase__      :			Dict=True      ,							UpperCamelCase__      :			Union[str, Any]=32      ,							UpperCamelCase__      :			Dict=4      ,							UpperCamelCase__      :			Tuple=[0, 1, 2, 3]      ,							UpperCamelCase__      :			Optional[int]=4      ,							UpperCamelCase__      :			Optional[int]=37      ,							UpperCamelCase__      :			Optional[Any]="gelu"      ,							UpperCamelCase__      :			Optional[int]=0.1      ,							UpperCamelCase__      :			List[Any]=0.1      ,							UpperCamelCase__      :			Any=0.02      ,							UpperCamelCase__      :			Dict=3      ,							UpperCamelCase__      :			Union[str, Any]=False      ,							UpperCamelCase__      :			int=None      ,							)      ->     Tuple:







    """simple docstring"""






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







    """simple docstring"""






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

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

    __magic_name__    					=	self.get_config()

    return config, pixel_values, labels
  def 							_lowercase							(			self      :			Tuple   )      ->     Tuple:







    """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=__SCREAMING_SNAKE_CASE      ,							initializer_range=self.initializer_range      ,							is_hybrid=self.is_hybrid      ,							)
  def 							_lowercase							(			self      :			Any      ,							UpperCamelCase__      :			Any      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			Tuple   )      ->     Dict:







    """simple docstring"""






    __magic_name__    					=	DPTModel(config=__SCREAMING_SNAKE_CASE   )
    model.to(__SCREAMING_SNAKE_CASE   )
    model.eval()
    __magic_name__    					=	model(__SCREAMING_SNAKE_CASE   )
    self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			int      ,							UpperCamelCase__      :			Optional[int]   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	self.num_labels
    __magic_name__    					=	DPTForDepthEstimation(__SCREAMING_SNAKE_CASE   )
    model.to(__SCREAMING_SNAKE_CASE   )
    model.eval()
    __magic_name__    					=	model(__SCREAMING_SNAKE_CASE   )
    self.parent.assertEqual(result.predicted_depth.shape      ,							(self.batch_size, self.image_size, self.image_size)   )
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Optional[int]      ,							UpperCamelCase__      :			List[str]      ,							UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






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





  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Tuple:







    """simple docstring"""






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




@require_torch
class 	UpperCAmelCase_      (			UpperCamelCase_  ,	UpperCamelCase_  ,	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 							_lowercase							(			self      :			List[str]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	DPTModelTester(self   )
    __magic_name__    					=	ConfigTester(self      ,							config_class=__SCREAMING_SNAKE_CASE      ,							has_text_modality=__SCREAMING_SNAKE_CASE      ,							hidden_size=37   )
  def 							_lowercase							(			self      :			Optional[int]   )      ->     List[Any]:







    """simple docstring"""






    self.config_tester.run_common_tests()
  @unittest.skip(reason="""DPT does not use inputs_embeds"""   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[Any]:







    """simple docstring"""






    pass
  def 							_lowercase							(			self      :			List[Any]   )      ->     List[Any]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__    					=	model_class(__SCREAMING_SNAKE_CASE   )
      self.assertIsInstance(model.get_input_embeddings()      ,							(nn.Module)   )
      __magic_name__    					=	model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE      ,							nn.Linear   )   )
  def 							_lowercase							(			self      :			Dict   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

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

      __magic_name__    					=	["""pixel_values"""]
      self.assertListEqual(arg_names[:1]      ,							__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			int   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			List[Any]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Tuple:







    """simple docstring"""






    __magic_name__    					=	self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE   )
  def 							_lowercase							(			self      :			Any   )      ->     Dict:







    """simple docstring"""






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

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

      if model_class in get_values(__SCREAMING_SNAKE_CASE   ):
        continue

      __magic_name__    					=	model_class(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.train()
      __magic_name__    					=	self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							return_labels=__SCREAMING_SNAKE_CASE   )
      __magic_name__    					=	model(**__SCREAMING_SNAKE_CASE   ).loss
      loss.backward()
  def 							_lowercase							(			self      :			List[Any]   )      ->     Tuple:







    """simple docstring"""






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

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

      if model_class in get_values(__SCREAMING_SNAKE_CASE   ) or not model_class.supports_gradient_checkpointing:
        continue
      __magic_name__    					=	model_class(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.gradient_checkpointing_enable()
      model.train()
      __magic_name__    					=	self._prepare_for_class(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							return_labels=__SCREAMING_SNAKE_CASE   )
      __magic_name__    					=	model(**__SCREAMING_SNAKE_CASE   ).loss
      loss.backward()
  def 							_lowercase							(			self      :			List[str]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__					,   __magic_name__    					=	self.model_tester.prepare_config_and_inputs_for_common()

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







    """simple docstring"""






    pass





  @slow
  def 							_lowercase							(			self      :			Tuple   )      ->     Optional[Any]:







    """simple docstring"""






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

def a__   (	):






  '''simple docstring'''





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





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


  '''simple docstring'''







  def 							_lowercase							(			self      :			Tuple   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	DPTImageProcessor.from_pretrained("""Intel/dpt-large"""   )
    __magic_name__    					=	DPTForDepthEstimation.from_pretrained("""Intel/dpt-large"""   ).to(__SCREAMING_SNAKE_CASE   )

    __magic_name__    					=	prepare_img()
    __magic_name__    					=	image_processor(images=__SCREAMING_SNAKE_CASE      ,							return_tensors="""pt"""   ).to(__SCREAMING_SNAKE_CASE   )

    # forward pass
    with torch.no_grad():
      __magic_name__    					=	model(**__SCREAMING_SNAKE_CASE   )
      __magic_name__    					=	outputs.predicted_depth

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

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

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







    """simple docstring"""






    __magic_name__    					=	DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""   )
    __magic_name__    					=	DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""   ).to(__SCREAMING_SNAKE_CASE   )

    __magic_name__    					=	prepare_img()
    __magic_name__    					=	image_processor(images=__SCREAMING_SNAKE_CASE      ,							return_tensors="""pt"""   ).to(__SCREAMING_SNAKE_CASE   )

    # forward pass
    with torch.no_grad():
      __magic_name__    					=	model(**__SCREAMING_SNAKE_CASE   )

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

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

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





  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Tuple:







    """simple docstring"""






    __magic_name__    					=	DPTImageProcessor.from_pretrained("""Intel/dpt-large-ade"""   )
    __magic_name__    					=	DPTForSemanticSegmentation.from_pretrained("""Intel/dpt-large-ade"""   ).to(__SCREAMING_SNAKE_CASE   )

    __magic_name__    					=	prepare_img()
    __magic_name__    					=	image_processor(images=__SCREAMING_SNAKE_CASE      ,							return_tensors="""pt"""   ).to(__SCREAMING_SNAKE_CASE   )

    # forward pass
    with torch.no_grad():
      __magic_name__    					=	model(**__SCREAMING_SNAKE_CASE   )

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

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

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



import sys

sys.set_int_max_str_digits(0)  # type: ignore

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11
  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  if not f:
    return f

  # mark first 9 numbers
  __magic_name__    					=	int(str(A_ )[:9] )

  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10

  # check first 9 numbers for pandigitality

  for x in range(9 ):
    if not check_front[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers

  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers mod 1e9

  # mod m=1e9, done for fast optimisation
  __magic_name__    					=	[0] * 1000000
  __magic_name__    					=	1000000000

  for x in range(1000000 ):
    __magic_name__    					=	(aa + ba) % m
    __magic_name__    					=	ba % m
    __magic_name__    					=	ca % m
    if checka(A_ ):
      __magic_name__    					=	1

  for x in range(1000000 ):
    __magic_name__    					=	a + b
    __magic_name__    					=	b
    __magic_name__    					=	c
    # perform check only if in tocheck
    if tocheck[x + 3] and check(A_ ):
      return x + 3  # first 2 already done
  return -1


if __name__ == "__main__":
    print(F'''{solution() = }''')

