






"""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     A_				:







				def __init__(				self				:    Optional[int]    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Dict=1_3    ,     __lowerCamelCase				:    List[str]=7    ,     __lowerCamelCase				:    int=True    ,     __lowerCamelCase				:    int=True    ,     __lowerCamelCase				:    List[str]=True    ,     __lowerCamelCase				:    Optional[int]=True    ,     __lowerCamelCase				:    List[Any]=9_9    ,     __lowerCamelCase				:    Optional[Any]=3_2    ,     __lowerCamelCase				:    Any=5    ,     __lowerCamelCase				:    Optional[int]=4    ,     __lowerCamelCase				:    List[str]=3_7    ,     __lowerCamelCase				:    Optional[Any]="gelu"    ,     __lowerCamelCase				:    Union[str, Any]=0.1    ,     __lowerCamelCase				:    Any=0.1    ,     __lowerCamelCase				:    Any=5_1_2    ,     __lowerCamelCase				:    Any=1_6    ,     __lowerCamelCase				:    List[Any]=2    ,     __lowerCamelCase				:    int=0.02    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Tuple=4    ,     __lowerCamelCase				:    Optional[Any]=None    ,     __lowerCamelCase				:    Any=1_0_0_0    ,     )					->       int:
										__magic_name__      =  parent
										__magic_name__      =  batch_size
										__magic_name__      =  seq_length
										__magic_name__      =  is_training
										__magic_name__      =  use_input_mask
										__magic_name__      =  use_token_type_ids
										__magic_name__      =  use_labels
										__magic_name__      =  vocab_size
										__magic_name__      =  hidden_size
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  intermediate_size
										__magic_name__      =  hidden_act
										__magic_name__      =  hidden_dropout_prob
										__magic_name__      =  attention_probs_dropout_prob
										__magic_name__      =  max_position_embeddings
										__magic_name__      =  type_vocab_size
										__magic_name__      =  type_sequence_label_size
										__magic_name__      =  initializer_range
										__magic_name__      =  num_labels
										__magic_name__      =  num_choices
										__magic_name__      =  scope
										__magic_name__      =  range_bbox







				def      _snake_case       (				self				:    int						)					->       int:
										__magic_name__      =  ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size						)

										__magic_name__      =  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]:
																												__magic_name__      =  bbox[i, j, 3]
																												__magic_name__      =  bbox[i, j, 1]
																												__magic_name__      =  t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																												__magic_name__      =  bbox[i, j, 2]
																												__magic_name__      =  bbox[i, j, 0]
																												__magic_name__      =  t

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

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

										__magic_name__      =  None
										__magic_name__      =  None
										__magic_name__      =  None
										if self.use_labels:
																__magic_name__      =  ids_tensor([self.batch_size]    ,     self.type_sequence_label_size						)
																__magic_name__      =  ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels						)
																__magic_name__      =  ids_tensor([self.batch_size]    ,     self.num_choices						)

										__magic_name__      =  self.get_config()

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







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







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







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







				def      _snake_case       (				self				:    Dict    ,     __lowerCamelCase				:    str    ,     __lowerCamelCase				:    Union[str, Any]    ,     __lowerCamelCase				:    Any    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    Dict    ,     __lowerCamelCase				:    int    ,     __lowerCamelCase				:    Optional[Any]    ,     __lowerCamelCase				:    Union[str, Any]						)					->       Optional[int]:
										__magic_name__      =  LayoutLMForQuestionAnswering(config=__lowerCamelCase						)
										model.to(__lowerCamelCase						)
										model.eval()
										__magic_name__      =  model(
										    __lowerCamelCase    ,     bbox=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase    ,     start_positions=__lowerCamelCase    ,     end_positions=__lowerCamelCase    ,     )
										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      _snake_case       (				self				:    Dict						)					->       Dict:
										__magic_name__      =  self.prepare_config_and_inputs()
										(
										    (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     (
										    __magic_name__
										)   ,     
										)      =  config_and_inputs
										__magic_name__      =  {
										    "input_ids": input_ids,
										    "bbox": bbox,
										    "token_type_ids": token_type_ids,
										    "attention_mask": input_mask,
										}
										return config, inputs_dict





@require_torch
class     A_				(   snake_case_       ,				snake_case_       ,				unittest.TestCase	):
				UpperCAmelCase__										=					(
				    (
				        LayoutLMModel,
				        LayoutLMForMaskedLM,
				        LayoutLMForSequenceClassification,
				        LayoutLMForTokenClassification,
				        LayoutLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				UpperCAmelCase__										=					(
				    {
				        '''document-question-answering''': LayoutLMForQuestionAnswering,
				        '''feature-extraction''': LayoutLMModel,
				        '''fill-mask''': LayoutLMForMaskedLM,
				        '''text-classification''': LayoutLMForSequenceClassification,
				        '''token-classification''': LayoutLMForTokenClassification,
				        '''zero-shot''': LayoutLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase__										=					True







				def      _snake_case       (				self				:    str						)					->       Dict:
										__magic_name__      =  LayoutLMModelTester(self						)
										__magic_name__      =  ConfigTester(self    ,     config_class=__lowerCamelCase    ,     hidden_size=3_7						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Dict:
										self.config_tester.run_common_tests()







				def      _snake_case       (				self				:    Union[str, Any]						)					->       Union[str, Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Optional[int]						)					->       str:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__magic_name__      =  type
																self.model_tester.create_and_check_model(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       str:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase						)







				def      _snake_case       (				self				:    str						)					->       List[Any]:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase						)







				def      _snake_case       (				self				:    Union[str, Any]						)					->       int:
										__magic_name__      =  self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase						)







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







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






def _lowerCAmelCase						(	):

						'''simple docstring'''






						__magic_name__      =  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=__lowerCamelCase )  # noqa: E231
						__magic_name__      =  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
						__magic_name__      =  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=__lowerCamelCase )  # noqa: E231
						__magic_name__      =  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)
						__magic_name__      =  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=__lowerCamelCase )  # noqa: E231
						# fmt: on

						return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Optional[Any]						)					->       Any:
										__magic_name__      =  LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"						).to(__lowerCamelCase						)

										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  prepare_layoutlm_batch_inputs()

										# forward pass
										__magic_name__      =  model(input_ids=__lowerCamelCase    ,     bbox=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase						)

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

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

										# test the pooled output on [1, :3]
										__magic_name__      =  torch.tensor([-0.6580, -0.0214, 0.8552]    ,     device=__lowerCamelCase						)

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







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

										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  prepare_layoutlm_batch_inputs()

										# forward pass
										__magic_name__      =  model(
										    input_ids=__lowerCamelCase    ,     bbox=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase    ,     labels=torch.tensor([1, 1]    ,     device=__lowerCamelCase						)    ,     )

										# test whether we get a loss as a scalar
										__magic_name__      =  outputs.loss
										__magic_name__      =  torch.Size([]						)
										self.assertEqual(loss.shape    ,     __lowerCamelCase						)

										# test the shape of the logits
										__magic_name__      =  outputs.logits
										__magic_name__      =  torch.Size((2, 2)						)
										self.assertEqual(logits.shape    ,     __lowerCamelCase						)







				@slow
				def      _snake_case       (				self				:    Union[str, Any]						)					->       Union[str, Any]:
										# initialize model with randomly initialized token classification head
										__magic_name__      =  LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"    ,     num_labels=1_3						).to(
										    __lowerCamelCase						)

										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  prepare_layoutlm_batch_inputs()

										# forward pass
										__magic_name__      =  model(
										    input_ids=__lowerCamelCase    ,     bbox=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase    ,     labels=__lowerCamelCase						)

										# 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
										__magic_name__      =  outputs.logits
										__magic_name__      =  torch.Size((2, 2_5, 1_3)						)
										self.assertEqual(logits.shape    ,     __lowerCamelCase						)







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

										__magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__   ,     __magic_name__      =  prepare_layoutlm_batch_inputs()

										# forward pass
										__magic_name__      =  model(input_ids=__lowerCamelCase    ,     bbox=__lowerCamelCase    ,     attention_mask=__lowerCamelCase    ,     token_type_ids=__lowerCamelCase						)

										# test the shape of the logits
										__magic_name__      =  torch.Size((2, 2_5)						)
										self.assertEqual(outputs.start_logits.shape    ,     __lowerCamelCase						)
										self.assertEqual(outputs.end_logits.shape    ,     __lowerCamelCase						)











"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey






def _lowerCAmelCase						(	__lowerCamelCase:Tuple			,    __lowerCamelCase:int ):

						'''simple docstring'''






						__magic_name__   ,     __magic_name__      =  img.shape[0], img.shape[1]

						def to_grayscale(__lowerCamelCase:List[str]			,    __lowerCamelCase:Optional[Any]			,    __lowerCamelCase:int ):
												return 0.2_1_2_6 * red + 0.5_8_7 * green + 0.1_1_4 * blue

						def normalize(__lowerCamelCase:Union[str, Any] ):
												return min(__lowerCamelCase			,    2_5_5 )

						for i in range(__lowerCamelCase ):
												for j in range(__lowerCamelCase ):
																		__magic_name__      =  int(to_grayscale(*img[i][j] ) )
																		__magic_name__      =  [
																		    normalize(__lowerCamelCase ),
																		    normalize(greyscale + factor ),
																		    normalize(greyscale + 2 * factor ),
																		]

						return img


if __name__ == "__main__":
			# read original image
			lowercase           =							{
			    percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
			}

			for percentage, img in images.items():
						make_sepia(img, percentage)

			for percentage, img in images.items():
						imshow(f'''Original image with sepia (factor: {percentage})''', img)

			waitKey(0)
			destroyAllWindows()



