






"""simple docstring"""



import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    LayoutLMForMaskedLM,
						    LayoutLMForQuestionAnswering,
						    LayoutLMForSequenceClassification,
						    LayoutLMForTokenClassification,
						    LayoutLMModel,
						)





class  _UpperCAmelCase	:




					def __init__(    self					:						Tuple   ,						A					:						Union[str, Any]   ,						A					:						Optional[Any]=13   ,						A					:						List[Any]=7   ,						A					:						List[Any]=True   ,						A					:						Dict=True   ,						A					:						List[str]=True   ,						A					:						Tuple=True   ,						A					:						Optional[Any]=99   ,						A					:						Dict=32   ,						A					:						str=5   ,						A					:						Union[str, Any]=4   ,						A					:						Tuple=37   ,						A					:						Any="gelu"   ,						A					:						Union[str, Any]=0.1   ,						A					:						Optional[Any]=0.1   ,						A					:						List[Any]=5_12   ,						A					:						Union[str, Any]=16   ,						A					:						Union[str, Any]=2   ,						A					:						Optional[int]=0.02   ,						A					:						List[str]=3   ,						A					:						Optional[Any]=4   ,						A					:						int=None   ,						A					:						Tuple=10_00   ,						)  ->  str:
							lowercase_			:			str								=       parent
							lowercase_			:			str								=       batch_size
							lowercase_			:			List[Any]								=       seq_length
							lowercase_			:			int								=       is_training
							lowercase_			:			Union[str, Any]								=       use_input_mask
							lowercase_			:			str								=       use_token_type_ids
							lowercase_			:			Optional[Any]								=       use_labels
							lowercase_			:			str								=       vocab_size
							lowercase_			:			Any								=       hidden_size
							lowercase_			:			str								=       num_hidden_layers
							lowercase_			:			List[str]								=       num_attention_heads
							lowercase_			:			Optional[int]								=       intermediate_size
							lowercase_			:			str								=       hidden_act
							lowercase_			:			Optional[int]								=       hidden_dropout_prob
							lowercase_			:			int								=       attention_probs_dropout_prob
							lowercase_			:			Any								=       max_position_embeddings
							lowercase_			:			Dict								=       type_vocab_size
							lowercase_			:			Any								=       type_sequence_label_size
							lowercase_			:			Optional[Any]								=       initializer_range
							lowercase_			:			Union[str, Any]								=       num_labels
							lowercase_			:			Dict								=       num_choices
							lowercase_			:			Dict								=       scope
							lowercase_			:			str								=       range_bbox




					def   A  (    self					:						Dict       )  ->  Tuple:
							lowercase_			:			str								=       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size       )

							lowercase_			:			Any								=       ids_tensor([self.batch_size, self.seq_length, 4]   ,						self.range_bbox       )
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]       ):
									for j in range(bbox.shape[1]       ):
											if bbox[i, j, 3] < bbox[i, j, 1]:
													lowercase_			:			Union[str, Any]								=       bbox[i, j, 3]
													lowercase_			:			List[Any]								=       bbox[i, j, 1]
													lowercase_			:			Union[str, Any]								=       t
											if bbox[i, j, 2] < bbox[i, j, 0]:
													lowercase_			:			Tuple								=       bbox[i, j, 2]
													lowercase_			:			Union[str, Any]								=       bbox[i, j, 0]
													lowercase_			:			List[str]								=       t

							lowercase_			:			Dict								=       None
							if self.use_input_mask:
									lowercase_			:			Any								=       ids_tensor([self.batch_size, self.seq_length]   ,						vocab_size=2       )

							lowercase_			:			Optional[Any]								=       None
							if self.use_token_type_ids:
									lowercase_			:			List[Any]								=       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size       )

							lowercase_			:			Tuple								=       None
							lowercase_			:			Dict								=       None
							lowercase_			:			Tuple								=       None
							if self.use_labels:
									lowercase_			:			Any								=       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size       )
									lowercase_			:			Tuple								=       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels       )
									lowercase_			:			Tuple								=       ids_tensor([self.batch_size]   ,						self.num_choices       )

							lowercase_			:			Union[str, Any]								=       self.get_config()

							return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




					def   A  (    self					:						Union[str, Any]       )  ->  Optional[Any]:
							return LayoutLMConfig(
							    vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						initializer_range=self.initializer_range   ,						)




					def   A  (    self					:						Optional[Any]   ,						A					:						str   ,						A					:						Optional[Any]   ,						A					:						Any   ,						A					:						str   ,						A					:						List[Any]   ,						A					:						str   ,						A					:						List[Any]   ,						A					:						str       )  ->  List[Any]:
							lowercase_			:			int								=       LayoutLMModel(config=A       )
							model.to(A       )
							model.eval()
							lowercase_			:			List[str]								=       model(A   ,						A   ,						attention_mask=A   ,						token_type_ids=A       )
							lowercase_			:			Optional[Any]								=       model(A   ,						A   ,						token_type_ids=A       )
							lowercase_			:			Any								=       model(A   ,						A       )
							self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)       )
							self.parent.assertEqual(result.pooler_output.shape   ,						(self.batch_size, self.hidden_size)       )




					def   A  (    self					:						int   ,						A					:						Tuple   ,						A					:						Optional[int]   ,						A					:						Dict   ,						A					:						Union[str, Any]   ,						A					:						Tuple   ,						A					:						str   ,						A					:						Any   ,						A					:						str       )  ->  Optional[Any]:
							lowercase_			:			int								=       LayoutLMForMaskedLM(config=A       )
							model.to(A       )
							model.eval()
							lowercase_			:			Dict								=       model(A   ,						A   ,						attention_mask=A   ,						token_type_ids=A   ,						labels=A       )
							self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)       )




					def   A  (    self					:						Any   ,						A					:						Any   ,						A					:						Dict   ,						A					:						Tuple   ,						A					:						Tuple   ,						A					:						Optional[int]   ,						A					:						int   ,						A					:						int   ,						A					:						List[Any]       )  ->  Optional[Any]:
							lowercase_			:			int								=       self.num_labels
							lowercase_			:			Tuple								=       LayoutLMForSequenceClassification(A       )
							model.to(A       )
							model.eval()
							lowercase_			:			Optional[int]								=       model(
							    A   ,						A   ,						attention_mask=A   ,						token_type_ids=A   ,						labels=A       )
							self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)       )




					def   A  (    self					:						str   ,						A					:						List[str]   ,						A					:						List[Any]   ,						A					:						int   ,						A					:						Optional[Any]   ,						A					:						List[str]   ,						A					:						Optional[int]   ,						A					:						List[str]   ,						A					:						List[Any]       )  ->  Union[str, Any]:
							lowercase_			:			int								=       self.num_labels
							lowercase_			:			int								=       LayoutLMForTokenClassification(config=A       )
							model.to(A       )
							model.eval()
							lowercase_			:			List[Any]								=       model(A   ,						A   ,						attention_mask=A   ,						token_type_ids=A   ,						labels=A       )
							self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)       )




					def   A  (    self					:						Dict   ,						A					:						List[str]   ,						A					:						Optional[int]   ,						A					:						Tuple   ,						A					:						Optional[Any]   ,						A					:						Dict   ,						A					:						Union[str, Any]   ,						A					:						Optional[int]   ,						A					:						int       )  ->  Any:
							lowercase_			:			Optional[int]								=       LayoutLMForQuestionAnswering(config=A       )
							model.to(A       )
							model.eval()
							lowercase_			:			int								=       model(
							    A   ,						bbox=A   ,						attention_mask=A   ,						token_type_ids=A   ,						start_positions=A   ,						end_positions=A   ,						)
							self.parent.assertEqual(result.start_logits.shape   ,						(self.batch_size, self.seq_length)       )
							self.parent.assertEqual(result.end_logits.shape   ,						(self.batch_size, self.seq_length)       )






					def   A  (    self					:						Union[str, Any]       )  ->  int:
							lowercase_			:			Any								=       self.prepare_config_and_inputs()
							(
							    (
							    lowercase_
							)	, (
							    lowercase_
							)	, (
							    lowercase_
							)	, (
							    lowercase_
							)	, (
							    lowercase_
							)	, (
							    lowercase_
							)	, (
							    lowercase_
							)	, (
							    lowercase_
							)	, 
							)			:			int								=       config_and_inputs
							lowercase_			:			Any								=       {
							    '''input_ids''': input_ids,
							    '''bbox''': bbox,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict





@require_torch
class  _UpperCAmelCase	(			_A       ,						_A       ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       (
					    (
					        LayoutLMModel,
					        LayoutLMForMaskedLM,
					        LayoutLMForSequenceClassification,
					        LayoutLMForTokenClassification,
					        LayoutLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					SCREAMING_SNAKE_CASE_		:    Union[str, Any]							   =       (
					    {
					        "document-question-answering": LayoutLMForQuestionAnswering,
					        "feature-extraction": LayoutLMModel,
					        "fill-mask": LayoutLMForMaskedLM,
					        "text-classification": LayoutLMForSequenceClassification,
					        "token-classification": LayoutLMForTokenClassification,
					        "zero-shot": LayoutLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					SCREAMING_SNAKE_CASE_		:    Any							   =       True




					def   A  (    self					:						Dict       )  ->  Union[str, Any]:
							lowercase_			:			Any								=       LayoutLMModelTester(self       )
							lowercase_			:			Optional[Any]								=       ConfigTester(self   ,						config_class=A   ,						hidden_size=37       )




					def   A  (    self					:						str       )  ->  Any:
							self.config_tester.run_common_tests()




					def   A  (    self					:						List[str]       )  ->  List[Any]:
							lowercase_			:			Optional[Any]								=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A       )




					def   A  (    self					:						Tuple       )  ->  List[Any]:
							lowercase_			:			List[Any]								=       self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									lowercase_			:			List[str]								=       type
									self.model_tester.create_and_check_model(*A       )




					def   A  (    self					:						str       )  ->  Dict:
							lowercase_			:			Any								=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A       )




					def   A  (    self					:						Optional[int]       )  ->  str:
							lowercase_			:			Tuple								=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*A       )




					def   A  (    self					:						Union[str, Any]       )  ->  Tuple:
							lowercase_			:			List[str]								=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A       )




					def   A  (    self					:						int       )  ->  Union[str, Any]:
							lowercase_			:			Union[str, Any]								=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*A       )






					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''       )
					def   A  (    self					:						str       )  ->  List[Any]:
							pass



def 						lowercase   (    ):
		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
		# fmt: off
		lowercase_			:			int								=       torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]		, device=__snake_case     )  # noqa: E231
		lowercase_			:			Tuple								=       torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]		, device=__snake_case     )  # noqa: E231
		lowercase_			:			str								=       torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]		, device=__snake_case     )  # noqa: E231
		lowercase_			:			Union[str, Any]								=       torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]		, device=__snake_case     )  # noqa: E231
		# these are sequence labels (i.e. at the token level)
		lowercase_			:			Union[str, Any]								=       torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]		, device=__snake_case     )  # noqa: E231
		# fmt: on

		return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class  _UpperCAmelCase	(			unittest.TestCase							):




					@slow
					def   A  (    self					:						Any       )  ->  Any:
							lowercase_			:			List[str]								=       LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''       ).to(A       )

							lowercase_	, lowercase_	, lowercase_	, lowercase_	, lowercase_			:			List[Any]								=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_			:			Tuple								=       model(input_ids=A   ,						bbox=A   ,						attention_mask=A   ,						token_type_ids=A       )

							# test the sequence output on [0, :3, :3]
							lowercase_			:			List[Any]								=       torch.tensor(
							    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]   ,						device=A   ,						)

							self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,						A   ,						atol=1e-3       )       )

							# test the pooled output on [1, :3]
							lowercase_			:			List[str]								=       torch.tensor([-0.6580, -0.0214, 0.8552]   ,						device=A       )

							self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]   ,						A   ,						atol=1e-3       )       )




					@slow
					def   A  (    self					:						Tuple       )  ->  Optional[int]:
							# initialize model with randomly initialized sequence classification head
							lowercase_			:			List[Any]								=       LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''   ,						num_labels=2       ).to(
							    A       )

							lowercase_	, lowercase_	, lowercase_	, lowercase_	, lowercase_			:			Dict								=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_			:			int								=       model(
							    input_ids=A   ,						bbox=A   ,						attention_mask=A   ,						token_type_ids=A   ,						labels=torch.tensor([1, 1]   ,						device=A       )   ,						)

							# test whether we get a loss as a scalar
							lowercase_			:			List[Any]								=       outputs.loss
							lowercase_			:			Dict								=       torch.Size([]       )
							self.assertEqual(loss.shape   ,						A       )

							# test the shape of the logits
							lowercase_			:			Tuple								=       outputs.logits
							lowercase_			:			Tuple								=       torch.Size((2, 2)       )
							self.assertEqual(logits.shape   ,						A       )




					@slow
					def   A  (    self					:						Dict       )  ->  int:
							# initialize model with randomly initialized token classification head
							lowercase_			:			List[str]								=       LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''   ,						num_labels=13       ).to(
							    A       )

							lowercase_	, lowercase_	, lowercase_	, lowercase_	, lowercase_			:			Union[str, Any]								=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_			:			Union[str, Any]								=       model(
							    input_ids=A   ,						bbox=A   ,						attention_mask=A   ,						token_type_ids=A   ,						labels=A       )

							# test the loss calculation to be around 2.65
							# expected_loss = torch.tensor(2.65, device=torch_device)

							# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
							# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

							# test the shape of the logits
							lowercase_			:			Tuple								=       outputs.logits
							lowercase_			:			int								=       torch.Size((2, 25, 13)       )
							self.assertEqual(logits.shape   ,						A       )






					@slow
					def   A  (    self					:						Any       )  ->  int:
							# initialize model with randomly initialized token classification head
							lowercase_			:			Optional[Any]								=       LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''       ).to(A       )

							lowercase_	, lowercase_	, lowercase_	, lowercase_	, lowercase_			:			Optional[int]								=       prepare_layoutlm_batch_inputs()

							# forward pass
							lowercase_			:			Optional[int]								=       model(input_ids=A   ,						bbox=A   ,						attention_mask=A   ,						token_type_ids=A       )

							# test the shape of the logits
							lowercase_			:			List[Any]								=       torch.Size((2, 25)       )
							self.assertEqual(outputs.start_logits.shape   ,						A       )
							self.assertEqual(outputs.end_logits.shape   ,						A       )











"""simple docstring"""



import math
import os

import cva
import numpy as np

__A		:	int      =		255.0



def 						lowercase   (    __snake_case    :			float		, __snake_case    :			float     ):
		lowercase_			:			Any								=       np.mean((original - contrast) ** 2     )
		if mse == 0:
				return 1_0_0

		return 2_0 * math.logaa(PIXEL_MAX / math.sqrt(__snake_case     )     )



def 						lowercase   (    ):
		lowercase_			:			Any								=       os.path.dirname(os.path.realpath(__snake_case     )     )
		# Loading images (original image and compressed image)
		lowercase_			:			Union[str, Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/original_image.png'''     )     )
		lowercase_			:			List[Any]								=       cva.imread(os.path.join(__snake_case		, '''image_data/compressed_image.png'''     )		, 1     )

		lowercase_			:			Tuple								=       cva.imread(os.path.join(__snake_case		, '''image_data/PSNR-example-base.png'''     )     )
		lowercase_			:			List[str]								=       cva.imread(
		    os.path.join(__snake_case		, '''image_data/PSNR-example-comp-10.jpg'''     )		, 1     )

		# Value expected: 29.73dB
		print('''-- First Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )

		# # Value expected: 31.53dB (Wikipedia Example)
		print('''\n-- Second Test --'''     )
		print(F'''PSNR value is {peak_signal_to_noise_ratio(__snake_case		, __snake_case     )} dB'''     )


if __name__ == "__main__":
						main()



