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_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Tuple=32     ,							__SCREAMING_SNAKE_CASE :      List[Any]=16     ,							__SCREAMING_SNAKE_CASE :      int=3     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=True     ,							__SCREAMING_SNAKE_CASE :      Dict=32     ,							__SCREAMING_SNAKE_CASE :      Tuple=4     ,							__SCREAMING_SNAKE_CASE :      List[str]=[0, 1, 2, 3]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=4     ,							__SCREAMING_SNAKE_CASE :      List[str]=37     ,							__SCREAMING_SNAKE_CASE :      Tuple="gelu"     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      str=3     ,							__SCREAMING_SNAKE_CASE :      int=[1, 3_84, 24, 24]     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							__SCREAMING_SNAKE_CASE :      List[str]=None     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  patch_size
   __a       =  num_channels
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  backbone_out_indices
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  initializer_range
   __a       =  num_labels
   __a       =  backbone_featmap_shape
   __a       =  scope
   __a       =  is_hybrid
   # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
   __a       =  (image_size // patch_size) ** 2
   __a       =  num_patches + 1

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

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

   __a       =  self.get_config()

   return config, pixel_values, labels

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  {
       "global_padding": "same",
       "layer_type": "bottleneck",
       "depths": [3, 4, 9],
       "out_features": ["stage1", "stage2", "stage3"],
       "embedding_dynamic_padding": True,
       "hidden_sizes": [96, 1_92, 3_84, 7_68],
       "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=__SCREAMING_SNAKE_CASE     ,							initializer_range=self.initializer_range     ,							is_hybrid=self.is_hybrid     ,							backbone_config=__SCREAMING_SNAKE_CASE     ,							backbone_featmap_shape=self.backbone_featmap_shape     ,							)

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  DPTModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  self.num_labels
   __a       =  DPTForDepthEstimation(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.predicted_depth.shape     ,							(self.batch_size, self.image_size, self.image_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  self.num_labels
   __a       =  DPTForSemanticSegmentation(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  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 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   __a       ,     __a       ,     __a       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE    				=			(
      {
          """depth-estimation""": DPTForDepthEstimation,
          """feature-extraction""": DPTModel,
          """image-segmentation""": DPTForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  DPTModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							has_text_modality=__SCREAMING_SNAKE_CASE     ,							hidden_size=37       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   self.config_tester.run_common_tests()

  @unittest.skip(reason="DPT does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   pass

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_depth_estimation(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      str       ):
   for model_class in self.all_model_classes:
    if model_class.__name__ == "DPTForDepthEstimation":
     continue

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

    if model_class in get_values(__SCREAMING_SNAKE_CASE       ):
     continue

    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.train()
    __a       =  self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							return_labels=__SCREAMING_SNAKE_CASE       )
    __a       =  model(**__SCREAMING_SNAKE_CASE       ).loss
    loss.backward()

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   for model_class in self.all_model_classes:
    if model_class.__name__ == "DPTForDepthEstimation":
     continue

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

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

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
    __a       =  DPTModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   __a       =  "add"
   with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
    __a       =  DPTForDepthEstimation(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


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






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

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"       )
   __a       =  DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"       ).to(__SCREAMING_SNAKE_CASE       )

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

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

   # verify the predicted depth
   __a       =  torch.Size((1, 3_84, 3_84)       )
   self.assertEqual(predicted_depth.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  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(__SCREAMING_SNAKE_CASE       )

   self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )



# Function to print upper half of diamond (pyramid)



def   __A		(			_A					):



 """simple docstring"""


 for i in range(0							,				_A					):
  for _ in range(0							,				n - i - 1					):  # printing spaces
   print(" "							,				end=""					)
  for _ in range(0							,				i + 1					):  # printing stars
   print("* "							,				end=""					)
  print()



def   __A		(			_A					):



 """simple docstring"""


 for i in range(_A							,				0							,				-1					):
  for _ in range(_A							,				0							,				-1					):  # printing stars
   print("* "							,				end=""					)
  print()
  for _ in range(n - i + 1							,				0							,				-1					):  # printing spaces
   print(" "							,				end=""					)



def   __A		(			_A					):



 """simple docstring"""


 if n <= 0:
  print("       ...       ....        nothing printing :("					)
  return
 floyd(_A					)  # upper half
 reverse_floyd(_A					)  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE :	Tuple							     =		1
  while K:
    SCREAMING_SNAKE_CASE :	Tuple							     =		int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE :	Optional[int]							     =		int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")


