


'''simple docstring'''


from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers.models.layoutlm.modeling_tf_layoutlm import (
				    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMForMaskedLM,
				    TFLayoutLMForQuestionAnswering,
				    TFLayoutLMForSequenceClassification,
				    TFLayoutLMForTokenClassification,
				    TFLayoutLMModel,
				)





class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case=13    ,					__snake_case=7    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=True    ,					__snake_case=99    ,					__snake_case=32    ,					__snake_case=2    ,					__snake_case=4    ,					__snake_case=37    ,					__snake_case="gelu"    ,					__snake_case=0.1    ,					__snake_case=0.1    ,					__snake_case=512    ,					__snake_case=16    ,					__snake_case=2    ,					__snake_case=0.02    ,					__snake_case=3    ,					__snake_case=4    ,					__snake_case=None    ,					__snake_case=1000    ,					):
											_SCREAMING_SNAKE_CASE :       Any     		=      parent
											_SCREAMING_SNAKE_CASE :       Dict     		=      batch_size
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      seq_length
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      is_training
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      use_input_mask
											_SCREAMING_SNAKE_CASE :       Any     		=      use_token_type_ids
											_SCREAMING_SNAKE_CASE :       List[str]     		=      use_labels
											_SCREAMING_SNAKE_CASE :       Any     		=      vocab_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      hidden_size
											_SCREAMING_SNAKE_CASE :       Tuple     		=      num_hidden_layers
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_attention_heads
											_SCREAMING_SNAKE_CASE :       Tuple     		=      intermediate_size
											_SCREAMING_SNAKE_CASE :       Dict     		=      hidden_act
											_SCREAMING_SNAKE_CASE :       Tuple     		=      hidden_dropout_prob
											_SCREAMING_SNAKE_CASE :       Any     		=      attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE :       str     		=      max_position_embeddings
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      type_vocab_size
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      type_sequence_label_size
											_SCREAMING_SNAKE_CASE :       int     		=      initializer_range
											_SCREAMING_SNAKE_CASE :       Dict     		=      num_labels
											_SCREAMING_SNAKE_CASE :       int     		=      num_choices
											_SCREAMING_SNAKE_CASE :       Any     		=      scope
											_SCREAMING_SNAKE_CASE :       str     		=      range_bbox






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size  )

											# convert bbox to numpy since TF does not support item assignment
											_SCREAMING_SNAKE_CASE :       str     		=      ids_tensor([self.batch_size, self.seq_length, 4]    ,					self.range_bbox  ).numpy()
											# 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]:
																							_SCREAMING_SNAKE_CASE :       str     		=      bbox[i, j, 3]
																							_SCREAMING_SNAKE_CASE :       List[Any]     		=      bbox[i, j, 1]
																							_SCREAMING_SNAKE_CASE :       str     		=      t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																							_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      bbox[i, j, 2]
																							_SCREAMING_SNAKE_CASE :       Optional[int]     		=      bbox[i, j, 0]
																							_SCREAMING_SNAKE_CASE :       Dict     		=      t
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      tf.convert_to_tensor(__snake_case  )

											_SCREAMING_SNAKE_CASE :       str     		=      None
											if self.use_input_mask:
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      random_attention_mask([self.batch_size, self.seq_length]  )

											_SCREAMING_SNAKE_CASE :       str     		=      None
											if self.use_token_type_ids:
															_SCREAMING_SNAKE_CASE :       str     		=      ids_tensor([self.batch_size, self.seq_length]    ,					self.type_vocab_size  )

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

											_SCREAMING_SNAKE_CASE :       Dict     		=      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    ,					)

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






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      TFLayoutLMModel(config=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      model(__snake_case    ,					__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(__snake_case    ,					__snake_case    ,					token_type_ids=__snake_case  )
											_SCREAMING_SNAKE_CASE :       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 UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      TFLayoutLMForMaskedLM(config=__snake_case  )

											_SCREAMING_SNAKE_CASE :       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.seq_length, self.vocab_size)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.num_labels
											_SCREAMING_SNAKE_CASE :       Dict     		=      TFLayoutLMForSequenceClassification(config=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      model(__snake_case    ,					__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case  )
											self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.num_labels
											_SCREAMING_SNAKE_CASE :       List[str]     		=      TFLayoutLMForTokenClassification(config=__snake_case  )

											_SCREAMING_SNAKE_CASE :       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 UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      TFLayoutLMForQuestionAnswering(config=__snake_case  )

											_SCREAMING_SNAKE_CASE :       Any     		=      model(__snake_case    ,					__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__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 UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.prepare_config_and_inputs()
											(
											    (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       (
											    _SCREAMING_SNAKE_CASE
											)		,       
											) :       List[Any]     		=      config_and_inputs
											_SCREAMING_SNAKE_CASE :       Any     		=      {
											    """input_ids""": input_ids,
											    """bbox""": bbox,
											    """token_type_ids""": token_type_ids,
											    """attention_mask""": input_mask,
											}
											return config, inputs_dict





@require_tf
class     lowercase__  (						_snake_case     ,     _snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      Any      	=  (
							    (
							        TFLayoutLMModel,
							        TFLayoutLMForMaskedLM,
							        TFLayoutLMForTokenClassification,
							        TFLayoutLMForSequenceClassification,
							        TFLayoutLMForQuestionAnswering,
							    )
							    if is_tf_available()
							    else ()
							)
							A_							:      Union[str, Any]      	=  (
							    {
							        """feature-extraction""": TFLayoutLMModel,
							        """fill-mask""": TFLayoutLMForMaskedLM,
							        """text-classification""": TFLayoutLMForSequenceClassification,
							        """token-classification""": TFLayoutLMForTokenClassification,
							        """zero-shot""": TFLayoutLMForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							A_							:      Union[str, Any]      	=  False
							A_							:      int      	=  True
							A_							:      Optional[int]      	=  10






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      TFLayoutLMModelTester(self  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      ConfigTester(self    ,					config_class=__snake_case    ,					hidden_size=37  )






							def UpperCAmelCase_	(    self  ):
											self.config_tester.run_common_tests()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       int     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Any     		=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*__snake_case  )






							@slow
							def UpperCAmelCase_	(    self  ):
											for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_SCREAMING_SNAKE_CASE :       int     		=      TFLayoutLMModel.from_pretrained(__snake_case  )
															self.assertIsNotNone(__snake_case  )






							@unittest.skip("""Onnx compliancy broke with TF 2.10"""  )
							def UpperCAmelCase_	(    self  ):
											pass







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]     )  # noqa: E231
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      tf.convert_to_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],]     )  # noqa: E231
				_SCREAMING_SNAKE_CASE :       List[str]     		=      tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]     )  # noqa: E231
				_SCREAMING_SNAKE_CASE :       Any     		=      tf.convert_to_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]]     )  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]     )  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""  )

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Any     		=      prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE :       List[str]     		=      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]
											_SCREAMING_SNAKE_CASE :       Any     		=      tf.convert_to_tensor(
											    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]    ,					)

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

											# test the pooled output on [1, :3]
											_SCREAMING_SNAKE_CASE :       Any     		=      tf.convert_to_tensor([-0.6580, -0.0214, 0.8552]  )

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






							@slow
							def UpperCAmelCase_	(    self  ):
											# initialize model with randomly initialized sequence classification head
											_SCREAMING_SNAKE_CASE :       int     		=      TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,					num_labels=2  )

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE :       List[str]     		=      model(
											    input_ids=__snake_case    ,					bbox=__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=tf.convert_to_tensor([1, 1]  )    ,					)

											# test whether we get a loss as a scalar
											_SCREAMING_SNAKE_CASE :       Tuple     		=      outputs.loss
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      (2,)
											self.assertEqual(loss.shape    ,					__snake_case  )

											# test the shape of the logits
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      outputs.logits
											_SCREAMING_SNAKE_CASE :       Tuple     		=      (2, 2)
											self.assertEqual(logits.shape    ,					__snake_case  )






							@slow
							def UpperCAmelCase_	(    self  ):
											# initialize model with randomly initialized token classification head
											_SCREAMING_SNAKE_CASE :       List[str]     		=      TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""    ,					num_labels=13  )

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Dict     		=      prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE :       Tuple     		=      model(
											    input_ids=__snake_case    ,					bbox=__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case    ,					labels=__snake_case  )

											# test the shape of the logits
											_SCREAMING_SNAKE_CASE :       int     		=      outputs.logits
											_SCREAMING_SNAKE_CASE :       Tuple     		=      tf.convert_to_tensor((2, 25, 13)  )
											self.assertEqual(logits.shape    ,					__snake_case  )






							@slow
							def UpperCAmelCase_	(    self  ):
											# initialize model with randomly initialized token classification head
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""  )

											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE :       Any     		=      model(input_ids=__snake_case    ,					bbox=__snake_case    ,					attention_mask=__snake_case    ,					token_type_ids=__snake_case  )

											# test the shape of the logits
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      tf.convert_to_tensor((2, 25)  )
											self.assertEqual(outputs.start_logits.shape    ,					__snake_case  )
											self.assertEqual(outputs.end_logits.shape    ,					__snake_case  )








'''simple docstring'''


import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
				import ctypes
				import msvcrt  # noqa





				class     lowercase__  (						ctypes.Structure					):





											'''simple docstring'''


											A_							:      Optional[Any]      	=  [("""size""", ctypes.c_int), ("""visible""", ctypes.c_byte)]







def 			snake_case_   (  ):







				"""simple docstring"""





				if os.name == "nt":
								_SCREAMING_SNAKE_CASE :       Tuple     		=      CursorInfo()
								_SCREAMING_SNAKE_CASE :       Tuple     		=      ctypes.windll.kernelaa.GetStdHandle(-11     )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
				elif os.name == "posix":
								sys.stdout.write("""\033[?25l"""     )
								sys.stdout.flush()







def 			snake_case_   (  ):







				"""simple docstring"""





				if os.name == "nt":
								_SCREAMING_SNAKE_CASE :       int     		=      CursorInfo()
								_SCREAMING_SNAKE_CASE :       List[str]     		=      ctypes.windll.kernelaa.GetStdHandle(-11     )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
								_SCREAMING_SNAKE_CASE :       Tuple     		=      True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(SCREAMING_SNAKE_CASE__							,      ctypes.byref(SCREAMING_SNAKE_CASE__     )     )
				elif os.name == "posix":
								sys.stdout.write("""\033[?25h"""     )
								sys.stdout.flush()







@contextmanager
def 			snake_case_   (  ):







				"""simple docstring"""





				try:
								hide_cursor()
								yield
				finally:
								show_cursor()




