
"""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  _snake_case	:






							def __init__(       self    :      List[Any]    ,			snake_case__    :      Tuple    ,			snake_case__    :      Optional[Any]=1_3    ,			snake_case__    :      int=7    ,			snake_case__    :      Union[str, Any]=True    ,			snake_case__    :      Tuple=True    ,			snake_case__    :      int=True    ,			snake_case__    :      str=True    ,			snake_case__    :      List[Any]=9_9    ,			snake_case__    :      Optional[int]=3_2    ,			snake_case__    :      Any=5    ,			snake_case__    :      Any=4    ,			snake_case__    :      Optional[int]=3_7    ,			snake_case__    :      str="gelu"    ,			snake_case__    :      Any=0.1    ,			snake_case__    :      int=0.1    ,			snake_case__    :      List[str]=5_1_2    ,			snake_case__    :      Dict=1_6    ,			snake_case__    :      str=2    ,			snake_case__    :      Union[str, Any]=0.02    ,			snake_case__    :      str=3    ,			snake_case__    :      List[Any]=4    ,			snake_case__    :      Optional[Any]=None    ,			snake_case__    :      List[str]=1_0_0_0    ,			):






												"""simple docstring"""



												__a:    List[str]		     =       parent
												__a:    List[str]		     =       batch_size
												__a:    str		     =       seq_length
												__a:    Union[str, Any]		     =       is_training
												__a:    int		     =       use_input_mask
												__a:    List[str]		     =       use_token_type_ids
												__a:    List[str]		     =       use_labels
												__a:    int		     =       vocab_size
												__a:    Optional[Any]		     =       hidden_size
												__a:    Dict		     =       num_hidden_layers
												__a:    int		     =       num_attention_heads
												__a:    int		     =       intermediate_size
												__a:    Optional[int]		     =       hidden_act
												__a:    Optional[int]		     =       hidden_dropout_prob
												__a:    int		     =       attention_probs_dropout_prob
												__a:    Union[str, Any]		     =       max_position_embeddings
												__a:    Union[str, Any]		     =       type_vocab_size
												__a:    Optional[int]		     =       type_sequence_label_size
												__a:    Optional[Any]		     =       initializer_range
												__a:    str		     =       num_labels
												__a:    int		     =       num_choices
												__a:    str		     =       scope
												__a:    Dict		     =       range_bbox






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    Any		     =       ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size     )

												__a:    List[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]:
																											__a:    List[Any]		     =       bbox[i, j, 3]
																											__a:    List[Any]		     =       bbox[i, j, 1]
																											__a:    Optional[int]		     =       t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											__a:    Tuple		     =       bbox[i, j, 2]
																											__a:    Union[str, Any]		     =       bbox[i, j, 0]
																											__a:    Optional[int]		     =       t

												__a:    Optional[int]		     =       None
												if self.use_input_mask:
																	__a:    List[Any]		     =       ids_tensor([self.batch_size, self.seq_length]    ,			vocab_size=2     )

												__a:    int		     =       None
												if self.use_token_type_ids:
																	__a:    Union[str, Any]		     =       ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size     )

												__a:    int		     =       None
												__a:    Tuple		     =       None
												__a:    List[str]		     =       None
												if self.use_labels:
																	__a:    str		     =       ids_tensor([self.batch_size]    ,			self.type_sequence_label_size     )
																	__a:    List[str]		     =       ids_tensor([self.batch_size, self.seq_length]    ,			self.num_labels     )
																	__a:    str		     =       ids_tensor([self.batch_size]    ,			self.num_choices     )

												__a:    Optional[int]		     =       self.get_config()

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






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												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 				__lowercase	(       self    :      Tuple    ,			snake_case__    :      List[str]    ,			snake_case__    :      Dict    ,			snake_case__    :      List[str]    ,			snake_case__    :      Dict    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Dict    ,			snake_case__    :      List[str]     ):






												"""simple docstring"""



												__a:    Optional[int]		     =       LayoutLMModel(config=snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    int		     =       model(snake_case__    ,			snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__     )
												__a:    Any		     =       model(snake_case__    ,			snake_case__    ,			token_type_ids=snake_case__     )
												__a:    List[str]		     =       model(snake_case__    ,			snake_case__     )
												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 				__lowercase	(       self    :      int    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      List[Any]    ,			snake_case__    :      str    ,			snake_case__    :      Tuple    ,			snake_case__    :      List[Any]    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      Any     ):






												"""simple docstring"""



												__a:    int		     =       LayoutLMForMaskedLM(config=snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    int		     =       model(snake_case__    ,			snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=snake_case__     )
												self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size)     )






							def 				__lowercase	(       self    :      Optional[int]    ,			snake_case__    :      Dict    ,			snake_case__    :      List[str]    ,			snake_case__    :      Any    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      Dict    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      int    ,			snake_case__    :      List[str]     ):






												"""simple docstring"""



												__a:    Dict		     =       self.num_labels
												__a:    int		     =       LayoutLMForSequenceClassification(snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    str		     =       model(
												    snake_case__    ,			snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=snake_case__     )
												self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)     )






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      List[Any]    ,			snake_case__    :      Dict    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      int    ,			snake_case__    :      List[str]    ,			snake_case__    :      Tuple    ,			snake_case__    :      str    ,			snake_case__    :      Any     ):






												"""simple docstring"""



												__a:    List[str]		     =       self.num_labels
												__a:    Optional[int]		     =       LayoutLMForTokenClassification(config=snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    Dict		     =       model(snake_case__    ,			snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=snake_case__     )
												self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels)     )






							def 				__lowercase	(       self    :      List[str]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      str    ,			snake_case__    :      Any    ,			snake_case__    :      str    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      List[Any]    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      int     ):






												"""simple docstring"""



												__a:    Tuple		     =       LayoutLMForQuestionAnswering(config=snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    Optional[Any]		     =       model(
												    snake_case__    ,			bbox=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			start_positions=snake_case__    ,			end_positions=snake_case__    ,			)
												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 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       self.prepare_config_and_inputs()
												(
												    (
												    __a
												)     ,					(
												    __a
												)     ,					(
												    __a
												)     ,					(
												    __a
												)     ,					(
												    __a
												)     ,					(
												    __a
												)     ,					(
												    __a
												)     ,					(
												    __a
												)     ,					
												):    Tuple		     =       config_and_inputs
												__a:    List[str]		     =       {
												    """input_ids""": input_ids,
												    """bbox""": bbox,
												    """token_type_ids""": token_type_ids,
												    """attention_mask""": input_mask,
												}
												return config, inputs_dict






@require_torch
class  _snake_case	(    _lowercase			,					_lowercase			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    (
							    (
							        LayoutLMModel,
							        LayoutLMForMaskedLM,
							        LayoutLMForSequenceClassification,
							        LayoutLMForTokenClassification,
							        LayoutLMForQuestionAnswering,
							    )
							    if is_torch_available()
							    else None
							)
							SCREAMING_SNAKE_CASE		  =    (
							    {
							        '''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		  =    True






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       LayoutLMModelTester(self     )
												__a:    Optional[Any]		     =       ConfigTester(self    ,			config_class=snake_case__    ,			hidden_size=3_7     )






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												self.config_tester.run_common_tests()






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    int		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__     )






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






												"""simple docstring"""



												__a:    Tuple		     =       self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	__a:    Optional[int]		     =       type
																	self.model_tester.create_and_check_model(*snake_case__     )






							def 				__lowercase	(       self    :      Tuple     ):






												"""simple docstring"""



												__a:    int		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*snake_case__     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*snake_case__     )






							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    Tuple		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*snake_case__     )






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    List[str]		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*snake_case__     )






							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""     )
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												pass






def      lowerCamelCase_     (						)  ->      Any:


					'''simple docstring'''
					# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
					# fmt: off
					__a:    int		     =       torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]						,					device=lowerCamelCase_			)  # noqa: E231
					__a:    str		     =       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=lowerCamelCase_			)  # noqa: E231
					__a:    Union[str, Any]		     =       torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]						,					device=lowerCamelCase_			)  # noqa: E231
					__a:    Optional[int]		     =       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=lowerCamelCase_			)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					__a:    int		     =       torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]						,					device=lowerCamelCase_			)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class  _snake_case	(    unittest.TestCase							):






							@slow
							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												__a:    List[str]		     =       LayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""     ).to(snake_case__     )

												__a     ,					__a     ,					__a     ,					__a     ,					__a:    Dict		     =       prepare_layoutlm_batch_inputs()

												# forward pass
												__a:    int		     =       model(input_ids=snake_case__    ,			bbox=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__     )

												# test the sequence output on [0, :3, :3]
												__a:    Optional[Any]		     =       torch.tensor(
												    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]    ,			device=snake_case__    ,			)

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

												# test the pooled output on [1, :3]
												__a:    Optional[Any]		     =       torch.tensor([-0.6_580, -0.0_214, 0.8_552]    ,			device=snake_case__     )

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






							@slow
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       LayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,			num_labels=2     ).to(
												    snake_case__     )

												__a     ,					__a     ,					__a     ,					__a     ,					__a:    Optional[Any]		     =       prepare_layoutlm_batch_inputs()

												# forward pass
												__a:    Tuple		     =       model(
												    input_ids=snake_case__    ,			bbox=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=torch.tensor([1, 1]    ,			device=snake_case__     )    ,			)

												# test whether we get a loss as a scalar
												__a:    Union[str, Any]		     =       outputs.loss
												__a:    Union[str, Any]		     =       torch.Size([]     )
												self.assertEqual(loss.shape    ,			snake_case__     )

												# test the shape of the logits
												__a:    Optional[Any]		     =       outputs.logits
												__a:    List[str]		     =       torch.Size((2, 2)     )
												self.assertEqual(logits.shape    ,			snake_case__     )






							@slow
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    Union[str, Any]		     =       LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,			num_labels=1_3     ).to(
												    snake_case__     )

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

												# forward pass
												__a:    Any		     =       model(
												    input_ids=snake_case__    ,			bbox=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__    ,			labels=snake_case__     )

												# 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
												__a:    Optional[Any]		     =       outputs.logits
												__a:    Dict		     =       torch.Size((2, 2_5, 1_3)     )
												self.assertEqual(logits.shape    ,			snake_case__     )






							@slow
							def 				__lowercase	(       self    :      Optional[int]     ):






												"""simple docstring"""



												__a:    List[str]		     =       LayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""     ).to(snake_case__     )

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

												# forward pass
												__a:    Tuple		     =       model(input_ids=snake_case__    ,			bbox=snake_case__    ,			attention_mask=snake_case__    ,			token_type_ids=snake_case__     )

												# test the shape of the logits
												__a:    Any		     =       torch.Size((2, 2_5)     )
												self.assertEqual(outputs.start_logits.shape    ,			snake_case__     )
												self.assertEqual(outputs.end_logits.shape    ,			snake_case__     )


"""simple docstring"""
def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 9 / 5) + 32						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) + 273.15						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 9 / 5) + 491.67						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) - 32) * 5 / 9						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(((float(lowerCamelCase_			) - 32) * 5 / 9) + 273.15						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) + 459.67						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) - 273.15						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(((float(lowerCamelCase_			) - 273.15) * 9 / 5) + 32						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 9 / 5)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) - 491.67) * 5 / 9						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round(float(lowerCamelCase_			) - 459.67						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 5 / 9)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 1.25 + 273.15)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 2.25 + 32)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 1.25)						,					lowerCamelCase_			)


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_ = 2			)  ->      float:


					'''simple docstring'''
					return round((float(lowerCamelCase_			) * 2.25 + 32 + 459.67)						,					lowerCamelCase_			)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
