




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       lowercase_		(SCREAMING_SNAKE_CASE__   ):



      def   __a				( self  :      Tuple     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.config_class(**self.inputs_dict     )
        self.parent.assertTrue(hasattr(snake_case__				,       'hidden_sizes'     )     )
        self.parent.assertTrue(hasattr(snake_case__				,       'num_attention_heads'     )     )
        self.parent.assertTrue(hasattr(snake_case__				,       'num_encoder_blocks'     )     )



class       lowercase_		:



      def __init__( self  :      Tuple				,       snake_case__  :      Optional[int]				,       snake_case__  :      Union[str, Any]=13				,       snake_case__  :      Optional[Any]=64				,       snake_case__  :      Any=3				,       snake_case__  :      Optional[Any]=4				,       snake_case__  :      Dict=[2, 2, 2, 2]				,       snake_case__  :      Any=[8, 4, 2, 1]				,       snake_case__  :      List[str]=[16, 32, 64, 1_28]				,       snake_case__  :      Dict=[1, 4, 8, 16]				,       snake_case__  :      Any=[1, 2, 4, 8]				,       snake_case__  :      Tuple=True				,       snake_case__  :      str=True				,       snake_case__  :      Dict="gelu"				,       snake_case__  :      List[Any]=0.1				,       snake_case__  :      List[Any]=0.1				,       snake_case__  :      Tuple=0.02				,       snake_case__  :      Optional[Any]=16				,       snake_case__  :      Optional[int]=3				,       snake_case__  :      List[Any]=None				,       ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       parent
        SCREAMING_SNAKE_CASE_							=       batch_size
        SCREAMING_SNAKE_CASE_							=       image_size
        SCREAMING_SNAKE_CASE_							=       num_channels
        SCREAMING_SNAKE_CASE_							=       num_encoder_blocks
        SCREAMING_SNAKE_CASE_							=       sr_ratios
        SCREAMING_SNAKE_CASE_							=       depths
        SCREAMING_SNAKE_CASE_							=       hidden_sizes
        SCREAMING_SNAKE_CASE_							=       downsampling_rates
        SCREAMING_SNAKE_CASE_							=       num_attention_heads
        SCREAMING_SNAKE_CASE_							=       is_training
        SCREAMING_SNAKE_CASE_							=       use_labels
        SCREAMING_SNAKE_CASE_							=       hidden_act
        SCREAMING_SNAKE_CASE_							=       hidden_dropout_prob
        SCREAMING_SNAKE_CASE_							=       attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_							=       initializer_range
        SCREAMING_SNAKE_CASE_							=       decoder_hidden_size
        SCREAMING_SNAKE_CASE_							=       num_labels
        SCREAMING_SNAKE_CASE_							=       scope



      def   __a				( self  :      Any     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

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



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

        """simple docstring"""

        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  :      Dict				,       snake_case__  :      Union[str, Any]				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Optional[int]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GLPNModel(config=snake_case__     )
        model.to(snake_case__     )
        model.eval()
        SCREAMING_SNAKE_CASE_							=       model(snake_case__     )
        SCREAMING_SNAKE_CASE_							=       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  :      Any				,       snake_case__  :      Dict				,       snake_case__  :      Union[str, Any]				,       snake_case__  :      Union[str, Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.num_labels
        SCREAMING_SNAKE_CASE_							=       GLPNForDepthEstimation(snake_case__     )
        model.to(snake_case__     )
        model.eval()
        SCREAMING_SNAKE_CASE_							=       model(snake_case__     )
        self.parent.assertEqual(result.predicted_depth.shape				,       (self.batch_size, self.image_size, self.image_size)     )
        SCREAMING_SNAKE_CASE_							=       model(snake_case__				,       labels=snake_case__     )
        self.parent.assertEqual(result.predicted_depth.shape				,       (self.batch_size, self.image_size, self.image_size)     )






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

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_							=       config_and_inputs
        SCREAMING_SNAKE_CASE_							=       {'''pixel_values''': pixel_values}
        return config, inputs_dict



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

      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False



      def   __a				( self  :      List[str]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GLPNModelTester(self     )
        SCREAMING_SNAKE_CASE_							=       GLPNConfigTester(self				,       config_class=snake_case__     )



      def   __a				( self  :      Any     ):

        """simple docstring"""

        self.config_tester.run_common_tests()



      def   __a				( self  :      List[Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case__     )



      def   __a				( self  :      str     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*snake_case__     )



      @unittest.skip('GLPN does not use inputs_embeds'     )
      def   __a				( self  :      Optional[int]     ):

        """simple docstring"""

        pass



      @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'     )
      def   __a				( self  :      Tuple     ):

        """simple docstring"""

        pass



      def   __a				( self  :      str     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

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

          SCREAMING_SNAKE_CASE_							=       ['''pixel_values''']
          self.assertListEqual(arg_names[:1]				,       snake_case__     )



      def   __a				( self  :      str     ):

        """simple docstring"""

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

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

          SCREAMING_SNAKE_CASE_							=       sum(self.model_tester.depths     )
          self.assertEqual(len(snake_case__     )				,       snake_case__     )

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

          self.assertEqual(len(snake_case__     )				,       snake_case__     )

          # verify the first attentions (first block, first layer)
          SCREAMING_SNAKE_CASE_							=       (self.model_tester.image_size // 4) ** 2
          SCREAMING_SNAKE_CASE_							=       (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)
          SCREAMING_SNAKE_CASE_							=       (self.model_tester.image_size // 32) ** 2
          SCREAMING_SNAKE_CASE_							=       (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]				,       )
          SCREAMING_SNAKE_CASE_							=       len(snake_case__     )

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

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

          SCREAMING_SNAKE_CASE_							=       outputs.attentions

          self.assertEqual(len(snake_case__     )				,       snake_case__     )
          # verify the first attentions (first block, first layer)
          SCREAMING_SNAKE_CASE_							=       (self.model_tester.image_size // 4) ** 2
          SCREAMING_SNAKE_CASE_							=       (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  :      Tuple     ):

        """simple docstring"""

        def check_hidden_states_output(snake_case__  :      str				,       snake_case__  :      List[Any]				,       snake_case__  :      List[Any]     ):
          SCREAMING_SNAKE_CASE_							=       model_class(snake_case__     )
          model.to(snake_case__     )
          model.eval()

          with torch.no_grad():
            SCREAMING_SNAKE_CASE_							=       model(**self._prepare_for_class(snake_case__				,       snake_case__     )     )

          SCREAMING_SNAKE_CASE_							=       outputs.hidden_states

          SCREAMING_SNAKE_CASE_							=       self.model_tester.num_encoder_blocks
          self.assertEqual(len(snake_case__     )				,       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,
              ]				,       )

        SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          SCREAMING_SNAKE_CASE_							=       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"]
          SCREAMING_SNAKE_CASE_							=       True

          check_hidden_states_output(snake_case__				,       snake_case__				,       snake_case__     )



      def   __a				( self  :      Dict     ):

        """simple docstring"""

        if not self.model_tester.is_training:
          return

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

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






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

        """simple docstring"""

        for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          SCREAMING_SNAKE_CASE_							=       GLPNModel.from_pretrained(snake_case__     )
          self.assertIsNotNone(snake_case__     )


def 				_a       (				)->  Tuple:
  SCREAMING_SNAKE_CASE_							=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
  return image




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



      @slow
      def   __a				( self  :      Tuple     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
        SCREAMING_SNAKE_CASE_							=       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(snake_case__     )

        SCREAMING_SNAKE_CASE_							=       prepare_img()
        SCREAMING_SNAKE_CASE_							=       image_processor(images=snake_case__				,       return_tensors='pt'     ).to(snake_case__     )

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

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

        SCREAMING_SNAKE_CASE_							=       torch.tensor(
            [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]     ).to(snake_case__     )

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





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[str]											=   input('''Enter numbers separated by a comma:\n''').strip()
						SCREAMING_SNAKE_CASE:    Any											=   [int(item) for item in user_input.split(''',''')]
						print(merge_insertion_sort(unsorted))