
"""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							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       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      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_  ,							snake_case_=13  ,							snake_case_=64  ,							snake_case_=3  ,							snake_case_=4  ,							snake_case_=[2, 2, 2, 2]  ,							snake_case_=[8, 4, 2, 1]  ,							snake_case_=[16, 32, 64, 128]  ,							snake_case_=[1, 4, 8, 16]  ,							snake_case_=[1, 2, 4, 8]  ,							snake_case_=True  ,							snake_case_=True  ,							snake_case_="gelu"  ,							snake_case_=0.1  ,							snake_case_=0.1  ,							snake_case_=0.02  ,							snake_case_=16  ,							snake_case_=3  ,							snake_case_=None  ,							)   ->       Any:
													__lowerCAmelCase        =       parent
													__lowerCAmelCase        =       batch_size
													__lowerCAmelCase        =       image_size
													__lowerCAmelCase        =       num_channels
													__lowerCAmelCase        =       num_encoder_blocks
													__lowerCAmelCase        =       sr_ratios
													__lowerCAmelCase        =       depths
													__lowerCAmelCase        =       hidden_sizes
													__lowerCAmelCase        =       downsampling_rates
													__lowerCAmelCase        =       num_attention_heads
													__lowerCAmelCase        =       is_training
													__lowerCAmelCase        =       use_labels
													__lowerCAmelCase        =       hidden_act
													__lowerCAmelCase        =       hidden_dropout_prob
													__lowerCAmelCase        =       attention_probs_dropout_prob
													__lowerCAmelCase        =       initializer_range
													__lowerCAmelCase        =       decoder_hidden_size
													__lowerCAmelCase        =       num_labels
													__lowerCAmelCase        =       scope




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

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




							def  A__		(				self							)   ->       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  ,							snake_case_  ,							snake_case_  ,							snake_case_							)   ->       str:
													__lowerCAmelCase        =       GLPNModel(config=snake_case_							)
													model.to(snake_case_							)
													model.eval()
													__lowerCAmelCase        =       model(snake_case_							)
													__lowerCAmelCase        =       __lowerCAmelCase        =       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  ,							snake_case_  ,							snake_case_  ,							snake_case_							)   ->       Dict:
													__lowerCAmelCase        =       self.num_labels
													__lowerCAmelCase        =       GLPNForDepthEstimation(snake_case_							)
													model.to(snake_case_							)
													model.eval()
													__lowerCAmelCase        =       model(snake_case_							)
													self.parent.assertEqual(result.predicted_depth.shape  ,							(self.batch_size, self.image_size, self.image_size)							)
													__lowerCAmelCase        =       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							)   ->       Any:
													__lowerCAmelCase        =       self.prepare_config_and_inputs()
													__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       config_and_inputs
													__lowerCAmelCase        =       {"""pixel_values""": pixel_values}
													return config, inputs_dict
@require_torch
class      lowerCAmelCase_						(							A__							,							A__							,							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  A__		(				self							)   ->       Optional[Any]:
													__lowerCAmelCase        =       GLPNModelTester(self							)
													__lowerCAmelCase        =       GLPNConfigTester(self  ,							config_class=snake_case_							)




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




							def  A__		(				self							)   ->       Tuple:
													__lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*snake_case_							)




							def  A__		(				self							)   ->       Optional[Any]:
													__lowerCAmelCase        =       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							)   ->       int:
													pass




							@unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""							)
							def  A__		(				self							)   ->       Any:
													pass




							def  A__		(				self							)   ->       Optional[int]:
													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()

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

																			__lowerCAmelCase        =       ["""pixel_values"""]
																			self.assertListEqual(arg_names[:1]  ,							snake_case_							)




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

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

																			__lowerCAmelCase        =       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"]
																			__lowerCAmelCase        =       True
																			__lowerCAmelCase        =       model_class(snake_case_							)
																			model.to(snake_case_							)
																			model.eval()
																			with torch.no_grad():
																									__lowerCAmelCase        =       model(**self._prepare_for_class(snake_case_  ,							snake_case_							)							)
																			__lowerCAmelCase        =       outputs.attentions

																			self.assertEqual(len(snake_case_							)  ,							snake_case_							)

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

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

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

																			__lowerCAmelCase        =       outputs.attentions

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

																			with torch.no_grad():
																									__lowerCAmelCase        =       model(**self._prepare_for_class(snake_case_  ,							snake_case_							)							)

																			__lowerCAmelCase        =       outputs.hidden_states

																			__lowerCAmelCase        =       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,
																			    ]  ,							)

													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()

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

																			check_hidden_states_output(snake_case_  ,							snake_case_  ,							snake_case_							)




							def  A__		(				self							)   ->       List[Any]:
													if not self.model_tester.is_training:
																			return

													__lowerCAmelCase ,      __lowerCAmelCase        =       self.model_tester.prepare_config_and_inputs_for_common()
													__lowerCAmelCase        =       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":
																									__lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase ,      __lowerCAmelCase        =       inputs_dict["""pixel_values"""].shape
																									__lowerCAmelCase        =       torch.zeros(
																									    [self.model_tester.batch_size, height, width]  ,							device=snake_case_							).long()
																			__lowerCAmelCase        =       model_class(snake_case_							)
																			model.to(snake_case_							)
																			model.train()
																			__lowerCAmelCase        =       self._prepare_for_class(snake_case_  ,							snake_case_  ,							return_labels=snake_case_							)
																			__lowerCAmelCase        =       model(**snake_case_							).loss
																			loss.backward()




							@slow
							def  A__		(				self							)   ->       List[str]:
													for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__lowerCAmelCase        =       GLPNModel.from_pretrained(snake_case_							)
																			self.assertIsNotNone(snake_case_							)

def 				lowercase      ():
						__lowerCAmelCase        =       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							)   ->       Any:
													__lowerCAmelCase        =       GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
													__lowerCAmelCase        =       GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(snake_case_							)

													__lowerCAmelCase        =       prepare_img()
													__lowerCAmelCase        =       image_processor(images=snake_case_  ,							return_tensors="""pt"""							).to(snake_case_							)

													# forward pass
													with torch.no_grad():
																			__lowerCAmelCase        =       model(**snake_case_							)

													# verify the predicted depth
													__lowerCAmelCase        =       torch.Size([1, 480, 640]							)
													self.assertEqual(outputs.predicted_depth.shape  ,							snake_case_							)

													__lowerCAmelCase        =       torch.tensor(
													    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]							).to(snake_case_							)

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


"""simple docstring"""
from __future__ import annotations
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self  ,							snake_case_							)   ->       None:
													__lowerCAmelCase        =       data
													__lowerCAmelCase        =       None
class      lowerCAmelCase_						:



							'''simple docstring'''





							def __init__(				self							)   ->       List[str]:
													__lowerCAmelCase        =       None




							def  A__		(				self  ,							snake_case_							)   ->       int:
													__lowerCAmelCase        =       Node(snake_case_							)
													__lowerCAmelCase        =       self.head
													__lowerCAmelCase        =       new_node
													return self.head.data




							def  A__		(				self							)   ->       int | None:
													__lowerCAmelCase        =       self.head
													__lowerCAmelCase        =       self.head
													if self.head:
																			while fast_pointer and fast_pointer.next:
																									__lowerCAmelCase        =       fast_pointer.next.next
																									__lowerCAmelCase        =       slow_pointer.next
																			return slow_pointer.data
													else:
																			print("""No element found."""							)
																			return None


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_			      =					LinkedList()
			for _ in range(int(input().strip())):
						SCREAMING_SNAKE_CASE_			      =					int(input().strip())
						link.push(data)
			print(link.middle_element())
