


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        A_					(       __UpperCamelCase    ):




      '''simple docstring'''


      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							Dict							=						self.config_class(**self.inputs_dict				)
       self.parent.assertTrue(hasattr(a						,      'hidden_sizes'				)				)
       self.parent.assertTrue(hasattr(a						,      'num_attention_heads'				)				)
       self.parent.assertTrue(hasattr(a						,      'num_encoder_blocks'				)				)



class        A_					:




      '''simple docstring'''


      def __init__(				self:       Optional[int]						,      a:       List[Any]						,      a:       Union[str, Any]=13						,      a:       str=64						,      a:       Tuple=3						,      a:       List[Any]=4						,      a:       List[Any]=[2, 2, 2, 2]						,      a:       Optional[int]=[8, 4, 2, 1]						,      a:       Optional[int]=[16, 32, 64, 128]						,      a:       Any=[1, 4, 8, 16]						,      a:       Dict=[1, 2, 4, 8]						,      a:       Dict=True						,      a:       Optional[int]=True						,      a:       Optional[int]="gelu"						,      a:       Tuple=0.1						,      a:       Union[str, Any]=0.1						,      a:       int=0.0_2						,      a:       List[Any]=16						,      a:       List[Any]=3						,      a:       Union[str, Any]=None						,      ):
       __lowerCamelCase		:							Dict							=						parent
       __lowerCamelCase		:							str							=						batch_size
       __lowerCamelCase		:							Optional[Any]							=						image_size
       __lowerCamelCase		:							str							=						num_channels
       __lowerCamelCase		:							List[str]							=						num_encoder_blocks
       __lowerCamelCase		:							Any							=						sr_ratios
       __lowerCamelCase		:							Dict							=						depths
       __lowerCamelCase		:							List[str]							=						hidden_sizes
       __lowerCamelCase		:							int							=						downsampling_rates
       __lowerCamelCase		:							Union[str, Any]							=						num_attention_heads
       __lowerCamelCase		:							Union[str, Any]							=						is_training
       __lowerCamelCase		:							int							=						use_labels
       __lowerCamelCase		:							List[str]							=						hidden_act
       __lowerCamelCase		:							List[str]							=						hidden_dropout_prob
       __lowerCamelCase		:							str							=						attention_probs_dropout_prob
       __lowerCamelCase		:							Union[str, Any]							=						initializer_range
       __lowerCamelCase		:							Optional[Any]							=						decoder_hidden_size
       __lowerCamelCase		:							str							=						num_labels
       __lowerCamelCase		:							Any							=						scope




      def      _snake_case	(				self:       Union[str, Any]				):
       __lowerCamelCase		:							Optional[int]							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

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




      def      _snake_case	(				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      _snake_case	(				self:       Dict						,      a:       Optional[int]						,      a:       Tuple						,      a:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						GLPNModel(config=a				)
       model.to(a				)
       model.eval()
       __lowerCamelCase		:							Optional[int]							=						model(a				)
       __lowerCamelCase		:							Optional[int]							=						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      _snake_case	(				self:       Optional[Any]						,      a:       Union[str, Any]						,      a:       int						,      a:       Any				):
       __lowerCamelCase		:							List[Any]							=						self.num_labels
       __lowerCamelCase		:							Union[str, Any]							=						GLPNForDepthEstimation(a				)
       model.to(a				)
       model.eval()
       __lowerCamelCase		:							List[Any]							=						model(a				)
       self.parent.assertEqual(result.predicted_depth.shape						,      (self.batch_size, self.image_size, self.image_size)				)
       __lowerCamelCase		:							Any							=						model(a						,      labels=a				)
       self.parent.assertEqual(result.predicted_depth.shape						,      (self.batch_size, self.image_size, self.image_size)				)




      def      _snake_case	(				self:       List[str]				):
       __lowerCamelCase		:							Any							=						self.prepare_config_and_inputs()
       __lowerCamelCase					,     __lowerCamelCase					,     __lowerCamelCase		:							Any							=						config_and_inputs
       __lowerCamelCase		:							str							=						{'pixel_values': pixel_values}
       return config, inputs_dict



@require_torch
class        A_					(       __UpperCamelCase					,      __UpperCamelCase					,      unittest.TestCase    ):




      '''simple docstring'''


      __snake_case					       =   (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
      __snake_case					       =   (
          {"""depth-estimation""": GLPNForDepthEstimation, """feature-extraction""": GLPNModel} if is_torch_available() else {}
      )

      __snake_case					       =   False
      __snake_case					       =   False
      __snake_case					       =   False




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							List[str]							=						GLPNModelTester(self				)
       __lowerCamelCase		:							Any							=						GLPNConfigTester(self						,      config_class=a				)




      def      _snake_case	(				self:       Any				):
       self.config_tester.run_common_tests()




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							Optional[int]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a				)




      def      _snake_case	(				self:       str				):
       __lowerCamelCase		:							Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*a				)




      @unittest.skip('GLPN does not use inputs_embeds'				)
      def      _snake_case	(				self:       List[str]				):
       pass




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




      def      _snake_case	(				self:       Union[str, Any]				):
       __lowerCamelCase					,     __lowerCamelCase		:							Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase		:							Dict							=						model_class(a				)
        __lowerCamelCase		:							Union[str, Any]							=						inspect.signature(model.forward				)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        __lowerCamelCase		:							Dict							=						[*signature.parameters.keys()]

        __lowerCamelCase		:							Any							=						['pixel_values']
        self.assertListEqual(arg_names[:1]						,      a				)




      def      _snake_case	(				self:       str				):
       __lowerCamelCase					,     __lowerCamelCase		:							List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()
       __lowerCamelCase		:							List[str]							=						True

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

        __lowerCamelCase		:							Any							=						sum(self.model_tester.depths				)
        self.assertEqual(len(a				)						,      a				)

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

        self.assertEqual(len(a				)						,      a				)

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

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

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

        __lowerCamelCase		:							Union[str, Any]							=						outputs.attentions

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

        with torch.no_grad():
         __lowerCamelCase		:							Tuple							=						model(**self._prepare_for_class(a						,      a				)				)

        __lowerCamelCase		:							str							=						outputs.hidden_states

        __lowerCamelCase		:							Optional[Any]							=						self.model_tester.num_encoder_blocks
        self.assertEqual(len(a				)						,      a				)

        # 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		:							Dict							=						self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        __lowerCamelCase		:							int							=						True
        check_hidden_states_output(a						,      a						,      a				)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        __lowerCamelCase		:							str							=						True

        check_hidden_states_output(a						,      a						,      a				)




      def      _snake_case	(				self:       int				):
       if not self.model_tester.is_training:
        return

       __lowerCamelCase					,     __lowerCamelCase		:							Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()
       __lowerCamelCase		:							int							=						True

       for model_class in self.all_model_classes:
        if model_class in get_values(a				):
         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		:							Tuple							=						inputs_dict['pixel_values'].shape
         __lowerCamelCase		:							Any							=						torch.zeros(
             [self.model_tester.batch_size, height, width]						,      device=a				).long()
        __lowerCamelCase		:							Dict							=						model_class(a				)
        model.to(a				)
        model.train()
        __lowerCamelCase		:							str							=						self._prepare_for_class(a						,      a						,      return_labels=a				)
        __lowerCamelCase		:							str							=						model(**a				).loss
        loss.backward()




      @slow
      def      _snake_case	(				self:       str				):
       for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowerCamelCase		:							List[str]							=						GLPNModel.from_pretrained(a				)
        self.assertIsNotNone(a				)



def       UpperCamelCase__     (  ):
 __lowerCamelCase		:							List[Any]							=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
 return image



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




      '''simple docstring'''


      @slow
      def      _snake_case	(				self:       Tuple				):
       __lowerCamelCase		:							Optional[int]							=						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
       __lowerCamelCase		:							Dict							=						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(a				)

       __lowerCamelCase		:							int							=						prepare_img()
       __lowerCamelCase		:							int							=						image_processor(images=a						,      return_tensors='pt'				).to(a				)

       # forward pass
       with torch.no_grad():
        __lowerCamelCase		:							List[Any]							=						model(**a				)

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

       __lowerCamelCase		:							Optional[int]							=						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(a				)

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





def       UpperCamelCase__     (  SCREAMING_SNAKE_CASE__ = 100						):
 __lowerCamelCase		:							List[Any]							=						[[0 for _ in range(SCREAMING_SNAKE_CASE__						)] for _ in range(m + 1						)]
 for i in range(m + 1						):
  __lowerCamelCase		:							Any							=						1

 for n in range(m + 1						):
  for k in range(1			,       SCREAMING_SNAKE_CASE__						):
   memo[n][k] += memo[n][k - 1]
   if n > k:
    memo[n][k] += memo[n - k - 1][k]

 return memo[m][m - 1] - 1


if __name__ == "__main__":
       print(solution(int(input('Enter a number: ').strip())))

