


'''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      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			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      UpperCAmelCase_						:





    """simple docstring"""






    def __init__(							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=13    ,  SCREAMING_SNAKE_CASE_=64    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=4    ,  SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]    ,  SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]    ,  SCREAMING_SNAKE_CASE_=[16, 32, 64, 1_28]    ,  SCREAMING_SNAKE_CASE_=[1, 4, 8, 16]    ,  SCREAMING_SNAKE_CASE_=[1, 2, 4, 8]    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_=True    ,  SCREAMING_SNAKE_CASE_="gelu"    ,  SCREAMING_SNAKE_CASE_=0.1    ,  SCREAMING_SNAKE_CASE_=0.1    ,  SCREAMING_SNAKE_CASE_=0.0_2    ,  SCREAMING_SNAKE_CASE_=16    ,  SCREAMING_SNAKE_CASE_=3    ,  SCREAMING_SNAKE_CASE_=None    ,  )       ->  Dict:
         __lowerCamelCase     :			Any              =	parent
         __lowerCamelCase     :			List[str]              =	batch_size
         __lowerCamelCase     :			Optional[int]              =	image_size
         __lowerCamelCase     :			Any              =	num_channels
         __lowerCamelCase     :			List[str]              =	num_encoder_blocks
         __lowerCamelCase     :			int              =	sr_ratios
         __lowerCamelCase     :			str              =	depths
         __lowerCamelCase     :			Tuple              =	hidden_sizes
         __lowerCamelCase     :			str              =	downsampling_rates
         __lowerCamelCase     :			Tuple              =	num_attention_heads
         __lowerCamelCase     :			Union[str, Any]              =	is_training
         __lowerCamelCase     :			str              =	use_labels
         __lowerCamelCase     :			List[Any]              =	hidden_act
         __lowerCamelCase     :			int              =	hidden_dropout_prob
         __lowerCamelCase     :			Tuple              =	attention_probs_dropout_prob
         __lowerCamelCase     :			int              =	initializer_range
         __lowerCamelCase     :			Optional[int]              =	decoder_hidden_size
         __lowerCamelCase     :			Optional[int]              =	num_labels
         __lowerCamelCase     :			int              =	scope







    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			List[Any]              =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

         __lowerCamelCase     :			Any              =	self.get_config()
         return config, pixel_values, labels







    def    lowercase_       (							self			)       ->  Dict:
         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    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
         __lowerCamelCase     :			Optional[Any]              =	GLPNModel(config=SCREAMING_SNAKE_CASE_			)
         model.to(SCREAMING_SNAKE_CASE_			)
         model.eval()
         __lowerCamelCase     :			List[str]              =	model(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			str              =	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    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  Optional[int]:
         __lowerCamelCase     :			str              =	self.num_labels
         __lowerCamelCase     :			Optional[Any]              =	GLPNForDepthEstimation(SCREAMING_SNAKE_CASE_			)
         model.to(SCREAMING_SNAKE_CASE_			)
         model.eval()
         __lowerCamelCase     :			Union[str, Any]              =	model(SCREAMING_SNAKE_CASE_			)
         self.parent.assertEqual(result.predicted_depth.shape    ,  (self.batch_size, self.image_size, self.image_size)			)
         __lowerCamelCase     :			Optional[int]              =	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    lowercase_       (							self			)       ->  Optional[Any]:
         __lowerCamelCase     :			str              =	self.prepare_config_and_inputs()
         __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	config_and_inputs
         __lowerCamelCase     :			List[Any]              =	{'pixel_values': pixel_values}
         return config, inputs_dict







@require_torch
class      UpperCAmelCase_						(_UpperCAmelCase      ,	_UpperCAmelCase      ,	unittest.TestCase						):





    """simple docstring"""






    lowerCamelCase						:      Union[str, Any]							  =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
    lowerCamelCase						:      Dict							  =    (
        {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
    )

    lowerCamelCase						:      Optional[int]							  =    False
    lowerCamelCase						:      str							  =    False
    lowerCamelCase						:      str							  =    False







    def    lowercase_       (							self			)       ->  Tuple:
         __lowerCamelCase     :			Dict              =	GLPNModelTester(self			)
         __lowerCamelCase     :			Optional[Any]              =	GLPNConfigTester(self    ,  config_class=SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Optional[Any]:
         self.config_tester.run_common_tests()







    def    lowercase_       (							self			)       ->  Any:
         __lowerCamelCase     :			Optional[Any]              =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Tuple:
         __lowerCamelCase     :			Optional[int]              =	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    lowercase_       (							self			)       ->  Dict:
         pass







    @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'			)
    def    lowercase_       (							self			)       ->  int:
         pass







    def    lowercase_       (							self			)       ->  Any:
         __lowerCamelCase       , __lowerCamelCase     :			List[str]              =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase     :			Tuple              =	model_class(SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Any              =	inspect.signature(model.forward			)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowerCamelCase     :			Optional[int]              =	[*signature.parameters.keys()]

              __lowerCamelCase     :			List[str]              =	['pixel_values']
              self.assertListEqual(arg_names[:1]    ,  SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  List[Any]:
         __lowerCamelCase       , __lowerCamelCase     :			Optional[int]              =	self.model_tester.prepare_config_and_inputs_for_common()
         __lowerCamelCase     :			List[Any]              =	True

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

              __lowerCamelCase     :			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"]
              __lowerCamelCase     :			List[Any]              =	True
              __lowerCamelCase     :			int              =	model_class(SCREAMING_SNAKE_CASE_			)
              model.to(SCREAMING_SNAKE_CASE_			)
              model.eval()
              with torch.no_grad():
                   __lowerCamelCase     :			Tuple              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)
              __lowerCamelCase     :			Tuple              =	outputs.attentions

              self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)

              # verify the first attentions (first block, first layer)
              __lowerCamelCase     :			Any              =	(self.model_tester.image_size // 4) ** 2
              __lowerCamelCase     :			Dict              =	(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)
              __lowerCamelCase     :			Dict              =	(self.model_tester.image_size // 32) ** 2
              __lowerCamelCase     :			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]    ,  )
              __lowerCamelCase     :			Union[str, Any]              =	len(SCREAMING_SNAKE_CASE_			)

              # Check attention is always last and order is fine
              __lowerCamelCase     :			Dict              =	True
              __lowerCamelCase     :			List[Any]              =	True
              __lowerCamelCase     :			str              =	model_class(SCREAMING_SNAKE_CASE_			)
              model.to(SCREAMING_SNAKE_CASE_			)
              model.eval()
              with torch.no_grad():
                   __lowerCamelCase     :			Any              =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)			)

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

              __lowerCamelCase     :			Dict              =	outputs.attentions

              self.assertEqual(len(SCREAMING_SNAKE_CASE_			)    ,  SCREAMING_SNAKE_CASE_			)
              # verify the first attentions (first block, first layer)
              __lowerCamelCase     :			int              =	(self.model_tester.image_size // 4) ** 2
              __lowerCamelCase     :			str              =	(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    lowercase_       (							self			)       ->  Optional[Any]:
         def check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			):
              __lowerCamelCase     :			Tuple              =	model_class(SCREAMING_SNAKE_CASE_			)
              model.to(SCREAMING_SNAKE_CASE_			)
              model.eval()

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

              __lowerCamelCase     :			int              =	outputs.hidden_states

              __lowerCamelCase     :			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,
                  ]    ,  )

         __lowerCamelCase       , __lowerCamelCase     :			List[Any]              =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase     :			Union[str, Any]              =	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"]
              __lowerCamelCase     :			Tuple              =	True

              check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)







    def    lowercase_       (							self			)       ->  Optional[int]:
         if not self.model_tester.is_training:
              return

         __lowerCamelCase       , __lowerCamelCase     :			str              =	self.model_tester.prepare_config_and_inputs_for_common()
         __lowerCamelCase     :			List[str]              =	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":
                   __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			List[Any]              =	inputs_dict['pixel_values'].shape
                   __lowerCamelCase     :			List[str]              =	torch.zeros(
                       [self.model_tester.batch_size, height, width]    ,  device=SCREAMING_SNAKE_CASE_			).long()
              __lowerCamelCase     :			str              =	model_class(SCREAMING_SNAKE_CASE_			)
              model.to(SCREAMING_SNAKE_CASE_			)
              model.train()
              __lowerCamelCase     :			int              =	self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  return_labels=SCREAMING_SNAKE_CASE_			)
              __lowerCamelCase     :			Optional[Any]              =	model(**SCREAMING_SNAKE_CASE_			).loss
              loss.backward()







    @slow
    def    lowercase_       (							self			)       ->  List[Any]:
         for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase     :			List[str]              =	GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)







def     UpperCAmelCase__      (			)  ->	Union[str, Any]:
     __lowerCamelCase     :			Union[str, Any]              =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
     return image





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





    """simple docstring"""






    @slow
    def    lowercase_       (							self			)       ->  Union[str, Any]:
         __lowerCamelCase     :			Optional[Any]              =	GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
         __lowerCamelCase     :			str              =	GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]			).to(SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			int              =	prepare_img()
         __lowerCamelCase     :			Optional[Any]              =	image_processor(images=SCREAMING_SNAKE_CASE_    ,  return_tensors='pt'			).to(SCREAMING_SNAKE_CASE_			)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase     :			Tuple              =	model(**SCREAMING_SNAKE_CASE_			)

         # verify the predicted depth
         __lowerCamelCase     :			List[str]              =	torch.Size([1, 4_80, 6_40]			)
         self.assertEqual(outputs.predicted_depth.shape    ,  SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Tuple              =	torch.tensor(
             [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]			).to(SCREAMING_SNAKE_CASE_			)

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










'''simple docstring'''



def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
     __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							).strip()
     if not bin_string:
          raise ValueError('Empty string was passed to the function'							)
     __lowerCamelCase     :			Optional[int]              =	bin_string[0] == '-'
     if is_negative:
          __lowerCamelCase     :			List[Any]              =	bin_string[1:]
     if not all(char in '01' for char in bin_string							):
          raise ValueError('Non-binary value was passed to the function'							)
     __lowerCamelCase     :			Optional[int]              =	0
     for char in bin_string:
          __lowerCamelCase     :			str              =	2 * decimal_number + int(UpperCAmelCase_							)
     return -decimal_number if is_negative else decimal_number


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

      testmod()






