


'''simple docstring'''







import inspect
import unittest

from transformers 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
   from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import GLPNImageProcessor


class 				__lowerCamelCase				( a_ ):
    """simple docstring"""


    def      A					(						self    :							Dict):
           _A  :			Optional[Any]											=      self.config_class(**self.inputs_dict)
           self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE   ,							'hidden_sizes'))
           self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE   ,							'num_attention_heads'))
           self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE   ,							'num_encoder_blocks'))




class 				__lowerCamelCase				:
    """simple docstring"""


    def __init__(						self    :							Any   ,							SCREAMING_SNAKE_CASE    :							str   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=13   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=64   ,							SCREAMING_SNAKE_CASE    :							str=3   ,							SCREAMING_SNAKE_CASE    :							List[Any]=4   ,							SCREAMING_SNAKE_CASE    :							str=[2, 2, 2, 2]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=[8, 4, 2, 1]   ,							SCREAMING_SNAKE_CASE    :							Union[str, Any]=[16, 32, 64, 128]   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]=[1, 4, 8, 16]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=[1, 2, 4, 8]   ,							SCREAMING_SNAKE_CASE    :							Any=True   ,							SCREAMING_SNAKE_CASE    :							str=True   ,							SCREAMING_SNAKE_CASE    :							List[str]="gelu"   ,							SCREAMING_SNAKE_CASE    :							Optional[int]=0.1   ,							SCREAMING_SNAKE_CASE    :							List[str]=0.1   ,							SCREAMING_SNAKE_CASE    :							Any=0.02   ,							SCREAMING_SNAKE_CASE    :							Dict=16   ,							SCREAMING_SNAKE_CASE    :							List[Any]=3   ,							SCREAMING_SNAKE_CASE    :							List[str]=None   ,							):
           _A  :			List[str]											=      parent
           _A  :			Tuple											=      batch_size
           _A  :			Optional[int]											=      image_size
           _A  :			Any											=      num_channels
           _A  :			Optional[int]											=      num_encoder_blocks
           _A  :			Tuple											=      sr_ratios
           _A  :			Tuple											=      depths
           _A  :			List[str]											=      hidden_sizes
           _A  :			str											=      downsampling_rates
           _A  :			int											=      num_attention_heads
           _A  :			Any											=      is_training
           _A  :			str											=      use_labels
           _A  :			List[str]											=      hidden_act
           _A  :			List[str]											=      hidden_dropout_prob
           _A  :			List[Any]											=      attention_probs_dropout_prob
           _A  :			Union[str, Any]											=      initializer_range
           _A  :			Optional[int]											=      decoder_hidden_size
           _A  :			Optional[Any]											=      num_labels
           _A  :			Dict											=      scope


    def      A					(						self    :							int):
           _A  :			Union[str, Any]											=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

           _A  :			Union[str, Any]											=      None
           if self.use_labels:
                  _A  :			Union[str, Any]											=      ids_tensor([self.batch_size, self.image_size, self.image_size]   ,							self.num_labels)

           _A  :			int											=      self.get_config()
           return config, pixel_values, labels


    def      A					(						self    :							List[Any]):
           return GLPNConfig(
               image_size=self.image_size   ,							num_channels=self.num_channels   ,							num_encoder_blocks=self.num_encoder_blocks   ,							depths=self.depths   ,							hidden_sizes=self.hidden_sizes   ,							num_attention_heads=self.num_attention_heads   ,							hidden_act=self.hidden_act   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							initializer_range=self.initializer_range   ,							decoder_hidden_size=self.decoder_hidden_size   ,							)


    def      A					(						self    :							Tuple   ,							SCREAMING_SNAKE_CASE    :							Dict   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							str):
           _A  :			List[str]											=      GLPNModel(config=SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			Any											=      model(SCREAMING_SNAKE_CASE)
           _A  :			Tuple											=      self.image_size // (self.downsampling_rates[-1] * 2)
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))


    def      A					(						self    :							str   ,							SCREAMING_SNAKE_CASE    :							Optional[Any]   ,							SCREAMING_SNAKE_CASE    :							Optional[int]   ,							SCREAMING_SNAKE_CASE    :							Dict):
           _A  :			Any											=      self.num_labels
           _A  :			Union[str, Any]											=      GLPNForDepthEstimation(SCREAMING_SNAKE_CASE)
           model.to(SCREAMING_SNAKE_CASE)
           model.eval()
           _A  :			int											=      model(SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size))
           _A  :			List[str]											=      model(SCREAMING_SNAKE_CASE   ,							labels=SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.predicted_depth.shape   ,							(self.batch_size, self.image_size, self.image_size))


    def      A					(						self    :							Union[str, Any]):
           _A  :			List[Any]											=      self.prepare_config_and_inputs()
           _A						,				_A						,				_A  :			Any											=      config_and_inputs
           _A  :			Dict											=      {'pixel_values': pixel_values}
           return config, inputs_dict




@require_torch
class 				__lowerCamelCase				( a_       ,      a_       ,      unittest.TestCase ):
    """simple docstring"""
    a									=					(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
    a									=					(
        {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
    )

    a									=					False
    a									=					False
    a									=					False


    def      A					(						self    :							Optional[Any]):
           _A  :			Tuple											=      GLPNModelTester(self)
           _A  :			str											=      GLPNConfigTester(self   ,							config_class=SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Dict):
           self.config_tester.run_common_tests()


    def      A					(						self    :							int):
           _A  :			Dict											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE)


    def      A					(						self    :							Any):
           _A  :			Dict											=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*SCREAMING_SNAKE_CASE)


    @unittest.skip('GLPN does not use inputs_embeds')
    def      A					(						self    :							str):
           pass


    @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods')
    def      A					(						self    :							str):
           pass


    def      A					(						self    :							Any):
           _A						,				_A  :			str											=      self.model_tester.prepare_config_and_inputs_for_common()

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

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


    def      A					(						self    :							int):
           _A						,				_A  :			Any											=      self.model_tester.prepare_config_and_inputs_for_common()
           _A  :			Union[str, Any]											=      True

           for model_class in self.all_model_classes:
                  _A  :			Any											=      True
                  _A  :			List[str]											=      False
                  _A  :			Dict											=      True
                  _A  :			List[str]											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  with torch.no_grad():
                         _A  :			List[str]											=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE))
                  _A  :			Tuple											=      outputs.attentions

                  _A  :			List[Any]											=      sum(self.model_tester.depths)
                  self.assertEqual(len(SCREAMING_SNAKE_CASE)   ,							SCREAMING_SNAKE_CASE)

                  # check that output_attentions also work using config
                  del inputs_dict["output_attentions"]
                  _A  :			List[str]											=      True
                  _A  :			str											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  with torch.no_grad():
                         _A  :			Tuple											=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE))
                  _A  :			int											=      outputs.attentions

                  self.assertEqual(len(SCREAMING_SNAKE_CASE)   ,							SCREAMING_SNAKE_CASE)

                  # verify the first attentions (first block, first layer)
                  _A  :			str											=      (self.model_tester.image_size // 4) ** 2
                  _A  :			int											=      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                  self.assertListEqual(
                      list(attentions[0].shape[-3:])   ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,							)

                  # verify the last attentions (last block, last layer)
                  _A  :			Optional[Any]											=      (self.model_tester.image_size // 32) ** 2
                  _A  :			List[Any]											=      (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                  self.assertListEqual(
                      list(attentions[-1].shape[-3:])   ,							[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]   ,							)
                  _A  :			Optional[Any]											=      len(SCREAMING_SNAKE_CASE)

                  # Check attention is always last and order is fine
                  _A  :			Optional[int]											=      True
                  _A  :			List[Any]											=      True
                  _A  :			Optional[Any]											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()
                  with torch.no_grad():
                         _A  :			Any											=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE))

                  self.assertEqual(out_len + 1   ,							len(SCREAMING_SNAKE_CASE))

                  _A  :			Optional[Any]											=      outputs.attentions

                  self.assertEqual(len(SCREAMING_SNAKE_CASE)   ,							SCREAMING_SNAKE_CASE)
                  # verify the first attentions (first block, first layer)
                  _A  :			Optional[Any]											=      (self.model_tester.image_size // 4) ** 2
                  _A  :			Any											=      (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                  self.assertListEqual(
                      list(self_attentions[0].shape[-3:])   ,							[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,							)


    def      A					(						self    :							Dict):
           def check_hidden_states_output(SCREAMING_SNAKE_CASE    :							List[Any]   ,							SCREAMING_SNAKE_CASE    :							int   ,							SCREAMING_SNAKE_CASE    :							List[str]):
                  _A  :			List[str]											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.eval()

                  with torch.no_grad():
                         _A  :			List[str]											=      model(**self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE))

                  _A  :			List[Any]											=      outputs.hidden_states

                  _A  :			Union[str, Any]											=      self.model_tester.num_encoder_blocks
                  self.assertEqual(len(SCREAMING_SNAKE_CASE)   ,							SCREAMING_SNAKE_CASE)

                  # verify the first hidden states (first block)
                  self.assertListEqual(
                      list(hidden_states[0].shape[-3:])   ,							[
                          self.model_tester.hidden_sizes[0],
                          self.model_tester.image_size // 4,
                          self.model_tester.image_size // 4,
                      ]   ,							)

           _A						,				_A  :			Dict											=      self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  _A  :			List[str]											=      True
                  check_hidden_states_output(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)

                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  _A  :			Union[str, Any]											=      True

                  check_hidden_states_output(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE)


    def      A					(						self    :							List[Any]):
           if not self.model_tester.is_training:
                  return

           _A						,				_A  :			Optional[Any]											=      self.model_tester.prepare_config_and_inputs_for_common()
           _A  :			int											=      True

           for model_class in self.all_model_classes:
                  if model_class in get_values(SCREAMING_SNAKE_CASE):
                         continue
                  # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
                  # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                  if model_class.__name__ == "GLPNForDepthEstimation":
                         _A						,				_A						,				_A						,				_A  :			int											=      inputs_dict['pixel_values'].shape
                         _A  :			Optional[int]											=      torch.zeros(
                             [self.model_tester.batch_size, height, width]   ,							device=SCREAMING_SNAKE_CASE).long()
                  _A  :			str											=      model_class(SCREAMING_SNAKE_CASE)
                  model.to(SCREAMING_SNAKE_CASE)
                  model.train()
                  _A  :			Dict											=      self._prepare_for_class(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							return_labels=SCREAMING_SNAKE_CASE)
                  _A  :			Union[str, Any]											=      model(**SCREAMING_SNAKE_CASE).loss
                  loss.backward()


    @slow
    def      A					(						self    :							Dict):
           for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A  :			int											=      GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)






def    lowerCAmelCase__ (       ):
       _A  :			List[str]											=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image


@require_torch
@require_vision
@slow
class 				__lowerCamelCase				( unittest.TestCase ):
    """simple docstring"""


    @slow
    def      A					(						self    :							Optional[int]):
           _A  :			Optional[int]											=      GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0])
           _A  :			str											=      GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(SCREAMING_SNAKE_CASE)

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

           # forward pass
           with torch.no_grad():
                  _A  :			str											=      model(**SCREAMING_SNAKE_CASE)

           # verify the predicted depth
           _A  :			Any											=      torch.Size([1, 480, 640])
           self.assertEqual(outputs.predicted_depth.shape   ,							SCREAMING_SNAKE_CASE)

           _A  :			Union[str, Any]											=      torch.tensor(
               [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]).to(SCREAMING_SNAKE_CASE)

           self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,							SCREAMING_SNAKE_CASE   ,							atol=1e-4))










'''simple docstring'''







A   :  Optional[Any]       		=				{
    '''0000''': '''0''',
    '''0001''': '''1''',
    '''0010''': '''2''',
    '''0011''': '''3''',
    '''0100''': '''4''',
    '''0101''': '''5''',
    '''0110''': '''6''',
    '''0111''': '''7''',
    '''1000''': '''8''',
    '''1001''': '''9''',
    '''1010''': '''a''',
    '''1011''': '''b''',
    '''1100''': '''c''',
    '''1101''': '''d''',
    '''1110''': '''e''',
    '''1111''': '''f''',
}






def    lowerCAmelCase__ (       lowerCamelCase  :    str     ):
       _A  :			Union[str, Any]											=      str(lowerCamelCase     ).strip()

       # Exceptions
       if not binary_str:
              raise ValueError('Empty string was passed to the function'     )
       _A  :			Any											=      binary_str[0] == '-'
       _A  :			str											=      binary_str[1:] if is_negative else binary_str
       if not all(char in '01' for char in binary_str     ):
              raise ValueError('Non-binary value was passed to the function'     )

       _A  :			Optional[Any]											=      (
           '0' * (4 * (divmod(len(lowerCamelCase     )						,4     )[0] + 1) - len(lowerCamelCase     )) + binary_str
       )

       _A  :			Optional[int]											=      []
       for x in range(0						,len(lowerCamelCase     )						,4     ):
              hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]     )
       _A  :			int											=      '0x' + ''.join(lowerCamelCase     )

       return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
   from doctest import testmod

   testmod()






