




'''simple docstring'''




import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        MODEL_MAPPING,
        LevitForImageClassification,
        LevitForImageClassificationWithTeacher,
        LevitModel,
    )
    from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor

class     lowerCAmelCase_ (			__A ):







  '''simple docstring'''







  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.config_class(**self.inputs_dict			)
   self.parent.assertTrue(hasattr(snake_case_    ,		"""hidden_sizes"""			)			)
   self.parent.assertTrue(hasattr(snake_case_    ,		"""num_attention_heads"""			)			)

class     lowerCAmelCase_ :







  '''simple docstring'''







  def __init__(    self     :						List[Any]    ,		_UpperCAmelCase     :						str    ,		_UpperCAmelCase     :						int=13    ,		_UpperCAmelCase     :						Any=64    ,		_UpperCAmelCase     :						List[str]=3    ,		_UpperCAmelCase     :						Optional[Any]=3    ,		_UpperCAmelCase     :						List[str]=2    ,		_UpperCAmelCase     :						Dict=1    ,		_UpperCAmelCase     :						Union[str, Any]=16    ,		_UpperCAmelCase     :						int=[1_28, 2_56, 3_84]    ,		_UpperCAmelCase     :						int=[4, 6, 8]    ,		_UpperCAmelCase     :						Tuple=[2, 3, 4]    ,		_UpperCAmelCase     :						str=[16, 16, 16]    ,		_UpperCAmelCase     :						Tuple=0    ,		_UpperCAmelCase     :						Optional[int]=[2, 2, 2]    ,		_UpperCAmelCase     :						Dict=[2, 2, 2]    ,		_UpperCAmelCase     :						List[Any]=0.02    ,		_UpperCAmelCase     :						List[Any]=True    ,		_UpperCAmelCase     :						str=True    ,		_UpperCAmelCase     :						Optional[Any]=2    ,		):






   """simple docstring"""






   UpperCAmelCase__      				=     parent
   UpperCAmelCase__      				=     batch_size
   UpperCAmelCase__      				=     image_size
   UpperCAmelCase__      				=     num_channels
   UpperCAmelCase__      				=     kernel_size
   UpperCAmelCase__      				=     stride
   UpperCAmelCase__      				=     padding
   UpperCAmelCase__      				=     hidden_sizes
   UpperCAmelCase__      				=     num_attention_heads
   UpperCAmelCase__      				=     depths
   UpperCAmelCase__      				=     key_dim
   UpperCAmelCase__      				=     drop_path_rate
   UpperCAmelCase__      				=     patch_size
   UpperCAmelCase__      				=     attention_ratio
   UpperCAmelCase__      				=     mlp_ratio
   UpperCAmelCase__      				=     initializer_range
   UpperCAmelCase__      				=     [
       ["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
       ["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
   ]
   UpperCAmelCase__      				=     is_training
   UpperCAmelCase__      				=     use_labels
   UpperCAmelCase__      				=     num_labels
   UpperCAmelCase__      				=     initializer_range


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[int]			):






   """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.num_labels			)

   UpperCAmelCase__      				=     self.get_config()
   return config, pixel_values, labels


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Tuple			):






   """simple docstring"""






   return LevitConfig(
       image_size=self.image_size    ,		num_channels=self.num_channels    ,		kernel_size=self.kernel_size    ,		stride=self.stride    ,		padding=self.padding    ,		patch_size=self.patch_size    ,		hidden_sizes=self.hidden_sizes    ,		num_attention_heads=self.num_attention_heads    ,		depths=self.depths    ,		key_dim=self.key_dim    ,		drop_path_rate=self.drop_path_rate    ,		mlp_ratio=self.mlp_ratio    ,		attention_ratio=self.attention_ratio    ,		initializer_range=self.initializer_range    ,		down_ops=self.down_ops    ,		)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						str    ,		_UpperCAmelCase     :						Union[str, Any]    ,		_UpperCAmelCase     :						Union[str, Any]    ,		_UpperCAmelCase     :						Any			):






   """simple docstring"""






   UpperCAmelCase__      				=     LevitModel(config=snake_case_			)
   model.to(snake_case_			)
   model.eval()
   UpperCAmelCase__      				=     model(snake_case_			)
   UpperCAmelCase__      				=     (self.image_size, self.image_size)
   UpperCAmelCase__      				=     image_size[0], image_size[1]
   for _ in range(4			):
    UpperCAmelCase__      				=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1			)
    UpperCAmelCase__      				=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1			)
   self.parent.assertEqual(
       result.last_hidden_state.shape    ,		(self.batch_size, ceil(height / 4			) * ceil(width / 4			), self.hidden_sizes[-1])    ,		)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[Any]    ,		_UpperCAmelCase     :						Union[str, Any]    ,		_UpperCAmelCase     :						int    ,		_UpperCAmelCase     :						Any			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.num_labels
   UpperCAmelCase__      				=     LevitForImageClassification(snake_case_			)
   model.to(snake_case_			)
   model.eval()
   UpperCAmelCase__      				=     model(snake_case_    ,		labels=snake_case_			)
   self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)			)



  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]			):






   """simple docstring"""






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

@require_torch
class     lowerCAmelCase_ (			__A      ,	__A      ,	unittest.TestCase ):







  '''simple docstring'''







  lowerCAmelCase_					:       Dict					  =    (
      (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
      if is_torch_available()
      else ()
  )
  lowerCAmelCase_					:       List[Any]					  =    (
      {
          'feature-extraction': LevitModel,
          'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  lowerCAmelCase_					:       Optional[int]					  =    False
  lowerCAmelCase_					:       str					  =    False
  lowerCAmelCase_					:       Dict					  =    False
  lowerCAmelCase_					:       Any					  =    False
  lowerCAmelCase_					:       List[Any]					  =    False


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any			):






   """simple docstring"""






   UpperCAmelCase__      				=     LevitModelTester(self			)
   UpperCAmelCase__      				=     ConfigTester(self    ,		config_class=snake_case_    ,		has_text_modality=snake_case_    ,		hidden_size=37			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[int]			):






   """simple docstring"""






   self.create_and_test_config_common_properties()
   self.config_tester.create_and_test_config_to_json_string()
   self.config_tester.create_and_test_config_to_json_file()
   self.config_tester.create_and_test_config_from_and_save_pretrained()
   self.config_tester.create_and_test_config_with_num_labels()
   self.config_tester.check_config_can_be_init_without_params()
   self.config_tester.check_config_arguments_init()


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Dict			):






   """simple docstring"""






   return


  @unittest.skip(reason="""Levit does not use inputs_embeds"""			)
  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]			):






   """simple docstring"""






   pass


  @unittest.skip(reason="""Levit does not support input and output embeddings"""			)
  def 				SCREAMING_SNAKE_CASE__      (    self     :						str			):






   """simple docstring"""






   pass


  @unittest.skip(reason="""Levit does not output attentions"""			)
  def 				SCREAMING_SNAKE_CASE__      (    self     :						Any			):






   """simple docstring"""






   pass


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__      				=     model_class(snake_case_			)
    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]    ,		snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]			):






   """simple docstring"""






   def check_hidden_states_output(_UpperCAmelCase     :						Tuple    ,		_UpperCAmelCase     :						Any    ,		_UpperCAmelCase     :						List[Any]			):
    UpperCAmelCase__      				=     model_class(snake_case_			)
    model.to(snake_case_			)
    model.eval()

    with torch.no_grad():
     UpperCAmelCase__      				=     model(**self._prepare_for_class(snake_case_    ,		snake_case_			)			)

    UpperCAmelCase__      				=     outputs.hidden_states

    UpperCAmelCase__      				=     len(self.model_tester.depths			) + 1
    self.assertEqual(len(snake_case_			)    ,		snake_case_			)

    UpperCAmelCase__      				=     (self.model_tester.image_size, self.model_tester.image_size)
    UpperCAmelCase__      				=     image_size[0], image_size[1]
    for _ in range(4			):
     UpperCAmelCase__      				=     floor(
         (
             (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
             / self.model_tester.stride
         )
         + 1			)
     UpperCAmelCase__      				=     floor(
         (
             (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
             / self.model_tester.stride
         )
         + 1			)
    # verify the first hidden states (first block)
    self.assertListEqual(
        list(hidden_states[0].shape[-2:]			)    ,		[
            height * width,
            self.model_tester.hidden_sizes[0],
        ]    ,		)

   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    UpperCAmelCase__      				=     True
    check_hidden_states_output(snake_case_    ,		snake_case_    ,		snake_case_			)

    # check that output_hidden_states also work using config
    del inputs_dict["output_hidden_states"]
    UpperCAmelCase__      				=     True

    check_hidden_states_output(snake_case_    ,		snake_case_    ,		snake_case_			)


  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]			):






   """simple docstring"""






   pass


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]    ,		_UpperCAmelCase     :						Optional[int]    ,		_UpperCAmelCase     :						Dict    ,		_UpperCAmelCase     :						int=False			):






   """simple docstring"""






   UpperCAmelCase__      				=     super()._prepare_for_class(snake_case_    ,		snake_case_    ,		return_labels=snake_case_			)

   if return_labels:
    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
     del inputs_dict["labels"]

   return inputs_dict


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*snake_case_			)


  def 				SCREAMING_SNAKE_CASE__      (    self     :						int			):






   """simple docstring"""






   if not self.model_tester.is_training:
    return

   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs_for_common()
   UpperCAmelCase__      				=     True

   for model_class in self.all_model_classes:
    # LevitForImageClassificationWithTeacher supports inference-only
    if (
        model_class in get_values(snake_case_			)
        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
    ):
     continue
    UpperCAmelCase__      				=     model_class(snake_case_			)
    model.to(snake_case_			)
    model.train()
    UpperCAmelCase__      				=     self._prepare_for_class(snake_case_    ,		snake_case_    ,		return_labels=snake_case_			)
    UpperCAmelCase__      				=     model(**snake_case_			).loss
    loss.backward()


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs_for_common()
   if not self.model_tester.is_training:
    return

   UpperCAmelCase__      				=     False
   UpperCAmelCase__      				=     True

   for model_class in self.all_model_classes:
    if model_class in get_values(snake_case_			) or not model_class.supports_gradient_checkpointing:
     continue
    # LevitForImageClassificationWithTeacher supports inference-only
    if model_class.__name__ == "LevitForImageClassificationWithTeacher":
     continue
    UpperCAmelCase__      				=     model_class(snake_case_			)
    model.gradient_checkpointing_enable()
    model.to(snake_case_			)
    model.train()
    UpperCAmelCase__      				=     self._prepare_for_class(snake_case_    ,		snake_case_    ,		return_labels=snake_case_			)
    UpperCAmelCase__      				=     model(**snake_case_			).loss
    loss.backward()


  def 				SCREAMING_SNAKE_CASE__      (    self     :						Optional[Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     self.model_tester.prepare_config_and_inputs_for_common()

   UpperCAmelCase__      				=     [
       {"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
       {"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
       {"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
   ]

   for model_class in self.all_model_classes:
    if (
        model_class
        not in [
            *get_values(snake_case_			),
        ]
        or model_class.__name__ == "LevitForImageClassificationWithTeacher"
    ):
     continue

    for problem_type in problem_types:
     with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}'''			):
      UpperCAmelCase__      				=     problem_type["""title"""]
      UpperCAmelCase__      				=     problem_type["""num_labels"""]

      UpperCAmelCase__      				=     model_class(snake_case_			)
      model.to(snake_case_			)
      model.train()

      UpperCAmelCase__      				=     self._prepare_for_class(snake_case_    ,		snake_case_    ,		return_labels=snake_case_			)

      if problem_type["num_labels"] > 1:
       UpperCAmelCase__      				=     inputs["""labels"""].unsqueeze(1			).repeat(1    ,		problem_type["""num_labels"""]			)

      UpperCAmelCase__      				=     inputs["""labels"""].to(problem_type["""dtype"""]			)

      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
      # they have the same size." which is a symptom something in wrong for the regression problem.
      # See https://github.com/huggingface/transformers/issues/11780
      with warnings.catch_warnings(record=snake_case_			) as warning_list:
       UpperCAmelCase__      				=     model(**snake_case_			).loss
      for w in warning_list:
       if "Using a target size that is different to the input size" in str(w.message			):
        raise ValueError(
            f'''Something is going wrong in the regression problem: intercepted {w.message}'''			)

      loss.backward()



  @slow
  def 				SCREAMING_SNAKE_CASE__      (    self     :						List[str]			):






   """simple docstring"""






   for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    UpperCAmelCase__      				=     LevitModel.from_pretrained(snake_case_			)
    self.assertIsNotNone(snake_case_			)

def  _UpperCamelCase   (					):
 '''simple docstring'''





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







@require_torch
@require_vision
class     lowerCAmelCase_ (			unittest.TestCase ):







  '''simple docstring'''







  @cached_property
  def 				SCREAMING_SNAKE_CASE__      (    self     :						int			):






   """simple docstring"""






   return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)



  @slow
  def 				SCREAMING_SNAKE_CASE__      (    self     :						Union[str, Any]			):






   """simple docstring"""






   UpperCAmelCase__      				=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(
       snake_case_			)

   UpperCAmelCase__      				=     self.default_image_processor
   UpperCAmelCase__      				=     prepare_img()
   UpperCAmelCase__      				=     image_processor(images=snake_case_    ,		return_tensors="""pt"""			).to(snake_case_			)

   # forward pass
   with torch.no_grad():
    UpperCAmelCase__      				=     model(**snake_case_			)

   # verify the logits
   UpperCAmelCase__      				=     torch.Size((1, 10_00)			)
   self.assertEqual(outputs.logits.shape    ,		snake_case_			)

   UpperCAmelCase__      				=     torch.tensor([1.0448, -0.3745, -1.8317]			).to(snake_case_			)

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

"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





