


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__       (	lowerCAmelCase):





     def 					__A (self	)					->       Tuple:
      _lowercase											=self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(UpperCAmelCase		,					'''hidden_sizes'''	)	)
      self.parent.assertTrue(hasattr(UpperCAmelCase		,					'''num_attention_heads'''	)	)
      self.parent.assertTrue(hasattr(UpperCAmelCase		,					'''num_encoder_blocks'''	)	)







class 				lowerCamelCase__       :





     def __init__(self		,					UpperCAmelCase		,					UpperCAmelCase=1_3		,					UpperCAmelCase=6_4		,					UpperCAmelCase=3		,					UpperCAmelCase=4		,					UpperCAmelCase=[2, 2, 2, 2]		,					UpperCAmelCase=[8, 4, 2, 1]		,					UpperCAmelCase=[1_6, 3_2, 6_4, 1_2_8]		,					UpperCAmelCase=[1, 4, 8, 1_6]		,					UpperCAmelCase=[1, 2, 4, 8]		,					UpperCAmelCase=True		,					UpperCAmelCase=True		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1_6		,					UpperCAmelCase=3		,					UpperCAmelCase=None		,					)					->       int:
      _lowercase											=parent
      _lowercase											=batch_size
      _lowercase											=image_size
      _lowercase											=num_channels
      _lowercase											=num_encoder_blocks
      _lowercase											=sr_ratios
      _lowercase											=depths
      _lowercase											=hidden_sizes
      _lowercase											=downsampling_rates
      _lowercase											=num_attention_heads
      _lowercase											=is_training
      _lowercase											=use_labels
      _lowercase											=hidden_act
      _lowercase											=hidden_dropout_prob
      _lowercase											=attention_probs_dropout_prob
      _lowercase											=initializer_range
      _lowercase											=decoder_hidden_size
      _lowercase											=num_labels
      _lowercase											=scope





     def 					__A (self	)					->       Optional[Any]:
      _lowercase											=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

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





     def 					__A (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 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       List[str]:
      _lowercase											=GLPNModel(config=UpperCAmelCase	)
      model.to(UpperCAmelCase	)
      model.eval()
      _lowercase											=model(UpperCAmelCase	)
      _lowercase											=_lowercase											=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		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       Tuple:
      _lowercase											=self.num_labels
      _lowercase											=GLPNForDepthEstimation(UpperCAmelCase	)
      model.to(UpperCAmelCase	)
      model.eval()
      _lowercase											=model(UpperCAmelCase	)
      self.parent.assertEqual(result.predicted_depth.shape		,					(self.batch_size, self.image_size, self.image_size)	)
      _lowercase											=model(UpperCAmelCase		,					labels=UpperCAmelCase	)
      self.parent.assertEqual(result.predicted_depth.shape		,					(self.batch_size, self.image_size, self.image_size)	)





     def 					__A (self	)					->       Any:
      _lowercase											=self.prepare_config_and_inputs()
      _lowercase				,   _lowercase				,   _lowercase											=config_and_inputs
      _lowercase											={'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
class 				lowerCamelCase__       (	lowerCAmelCase       ,							lowerCAmelCase       ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__				      =      (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE__				      =      (
         {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
     )

     SCREAMING_SNAKE_CASE__				      =      False
     SCREAMING_SNAKE_CASE__				      =      False
     SCREAMING_SNAKE_CASE__				      =      False





     def 					__A (self	)					->       Dict:
      _lowercase											=GLPNModelTester(self	)
      _lowercase											=GLPNConfigTester(self		,					config_class=UpperCAmelCase	)





     def 					__A (self	)					->       Any:
      self.config_tester.run_common_tests()





     def 					__A (self	)					->       List[str]:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCAmelCase	)





     def 					__A (self	)					->       Any:
      _lowercase											=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*UpperCAmelCase	)





     @unittest.skip('''GLPN does not use inputs_embeds'''	)
     def 					__A (self	)					->       Optional[Any]:
      pass





     @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''	)
     def 					__A (self	)					->       Optional[Any]:
      pass





     def 					__A (self	)					->       List[str]:
      _lowercase				,   _lowercase											=self.model_tester.prepare_config_and_inputs_for_common()

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

       _lowercase											=['''pixel_values''']
       self.assertListEqual(arg_names[:1]		,					UpperCAmelCase	)





     def 					__A (self	)					->       Optional[int]:
      _lowercase				,   _lowercase											=self.model_tester.prepare_config_and_inputs_for_common()
      _lowercase											=True

      for model_class in self.all_model_classes:
       _lowercase											=True
       _lowercase											=False
       _lowercase											=True
       _lowercase											=model_class(UpperCAmelCase	)
       model.to(UpperCAmelCase	)
       model.eval()
       with torch.no_grad():
        _lowercase											=model(**self._prepare_for_class(UpperCAmelCase		,					UpperCAmelCase	)	)
       _lowercase											=outputs.attentions

       _lowercase											=sum(self.model_tester.depths	)
       self.assertEqual(len(UpperCAmelCase	)		,					UpperCAmelCase	)

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

       self.assertEqual(len(UpperCAmelCase	)		,					UpperCAmelCase	)

       # verify the first attentions (first block, first layer)
       _lowercase											=(self.model_tester.image_size // 4) ** 2
       _lowercase											=(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)
       _lowercase											=(self.model_tester.image_size // 3_2) ** 2
       _lowercase											=(self.model_tester.image_size // (3_2 * 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]		,					)
       _lowercase											=len(UpperCAmelCase	)

       # Check attention is always last and order is fine
       _lowercase											=True
       _lowercase											=True
       _lowercase											=model_class(UpperCAmelCase	)
       model.to(UpperCAmelCase	)
       model.eval()
       with torch.no_grad():
        _lowercase											=model(**self._prepare_for_class(UpperCAmelCase		,					UpperCAmelCase	)	)

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

       _lowercase											=outputs.attentions

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

       with torch.no_grad():
        _lowercase											=model(**self._prepare_for_class(UpperCAmelCase		,					UpperCAmelCase	)	)

       _lowercase											=outputs.hidden_states

       _lowercase											=self.model_tester.num_encoder_blocks
       self.assertEqual(len(UpperCAmelCase	)		,					UpperCAmelCase	)

       # 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,
           ]		,					)

      _lowercase				,   _lowercase											=self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       _lowercase											=True
       check_hidden_states_output(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)

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

       check_hidden_states_output(UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)





     def 					__A (self	)					->       Union[str, Any]:
      if not self.model_tester.is_training:
       return

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

      for model_class in self.all_model_classes:
       if model_class in get_values(UpperCAmelCase	):
        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":
        _lowercase				,   _lowercase				,   _lowercase				,   _lowercase											=inputs_dict['''pixel_values'''].shape
        _lowercase											=torch.zeros(
            [self.model_tester.batch_size, height, width]		,					device=UpperCAmelCase	).long()
       _lowercase											=model_class(UpperCAmelCase	)
       model.to(UpperCAmelCase	)
       model.train()
       _lowercase											=self._prepare_for_class(UpperCAmelCase		,					UpperCAmelCase		,					return_labels=UpperCAmelCase	)
       _lowercase											=model(**UpperCAmelCase	).loss
       loss.backward()





     @slow
     def 					__A (self	)					->       Optional[int]:
      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase											=GLPNModel.from_pretrained(UpperCAmelCase	)
       self.assertIsNotNone(UpperCAmelCase	)



def     UpperCAmelCase_ (							)					->      Any:







 """simple docstring"""


 _lowercase											=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
 return image







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





     @slow
     def 					__A (self	)					->       Optional[Any]:
      _lowercase											=GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      _lowercase											=GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(UpperCAmelCase	)

      _lowercase											=prepare_img()
      _lowercase											=image_processor(images=UpperCAmelCase		,					return_tensors='''pt'''	).to(UpperCAmelCase	)

      # forward pass
      with torch.no_grad():
       _lowercase											=model(**UpperCAmelCase	)

      # verify the predicted depth
      _lowercase											=torch.Size([1, 4_8_0, 6_4_0]	)
      self.assertEqual(outputs.predicted_depth.shape		,					UpperCAmelCase	)

      _lowercase											=torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	).to(UpperCAmelCase	)

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





from __future__ import annotations

UpperCAmelCase__            =							[2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]







class 				lowerCamelCase__       :





     def __init__(self		,					UpperCAmelCase	)					->       None:
      _lowercase											=-1
      _lowercase											=-1
      _lowercase											=[-1] * length
      _lowercase											=None
      _lowercase											=None





     def __repr__(self	)					->       str:
      return f"Node(min_value={self.minn} max_value={self.maxx})"



def     UpperCAmelCase_ (							__snake_case						)					->      Node | None:







 """simple docstring"""


 _lowercase											=Node(len(__snake_case						)						)
 _lowercase				,   _lowercase											=min(__snake_case						), max(__snake_case						)
 # Leaf node case where the node contains only one unique value
 if root.minn == root.maxx:
  return root
 _lowercase											=(root.minn + root.maxx) // 2

 _lowercase											=[]
 _lowercase											=[]

 for index, num in enumerate(__snake_case						):
  if num <= pivot:
   left_arr.append(__snake_case						)
  else:
   right_arr.append(__snake_case						)
  _lowercase											=len(__snake_case						)
 _lowercase											=build_tree(__snake_case						)
 _lowercase											=build_tree(__snake_case						)
 return root
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if index < 0 or node is None:
  return 0
 # Leaf node cases
 if node.minn == node.maxx:
  return index + 1 if node.minn == num else 0
 _lowercase											=(node.minn + node.maxx) // 2
 if num <= pivot:
  # go the left subtree and map index to the left subtree
  return rank_till_index(node.left					,						__snake_case					,						node.map_left[index] - 1						)
 else:
  # go to the right subtree and map index to the right subtree
  return rank_till_index(node.right					,						__snake_case					,						index - node.map_left[index]						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if start > end:
  return 0
 _lowercase											=rank_till_index(__snake_case					,						__snake_case					,						__snake_case						)
 _lowercase											=rank_till_index(__snake_case					,						__snake_case					,						start - 1						)
 return rank_till_end - rank_before_start
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if index > (end - start) or start > end or node is None:
  return -1
 # Leaf node case
 if node.minn == node.maxx:
  return node.minn
 # Number of elements in the left subtree in interval [start, end]
 _lowercase											=node.map_left[end] - (
     node.map_left[start - 1] if start else 0
 )
 if num_elements_in_left_tree > index:
  return quantile(
      node.left					,						__snake_case					,						(node.map_left[start - 1] if start else 0)					,						node.map_left[end] - 1					,						)
 else:
  return quantile(
      node.right					,						index - num_elements_in_left_tree					,						start - (node.map_left[start - 1] if start else 0)					,						end - node.map_left[end]					,						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 if (
     start > end
     or node is None
     or start_num > end_num
     or node.minn > end_num
     or node.maxx < start_num
 ):
  return 0
 if start_num <= node.minn and node.maxx <= end_num:
  return end - start + 1
 _lowercase											=range_counting(
     node.left					,						(node.map_left[start - 1] if start else 0)					,						node.map_left[end] - 1					,						__snake_case					,						__snake_case					,						)
 _lowercase											=range_counting(
     node.right					,						start - (node.map_left[start - 1] if start else 0)					,						end - node.map_left[end]					,						__snake_case					,						__snake_case					,						)
 return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()

