





from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
						    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
						    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
						    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
						    LayoutLMvaConfig,
						    TFLayoutLMvaForQuestionAnswering,
						    TFLayoutLMvaForSequenceClassification,
						    TFLayoutLMvaForTokenClassification,
						    TFLayoutLMvaModel,
						)

if is_vision_available():
						from PIL import Image

						from transformers import LayoutLMvaImageProcessor
class SCREAMING_SNAKE_CASE_						:






						def __init__(      self      :      Any		,   _A      :      Union[str, Any]		,   _A      :      Any=2		,   _A      :      Union[str, Any]=3		,   _A      :      int=4		,   _A      :      Tuple=2		,   _A      :      Optional[int]=7		,   _A      :      int=True		,   _A      :      List[str]=True		,   _A      :      int=True		,   _A      :      Tuple=True		,   _A      :      Dict=99		,   _A      :      Tuple=36		,   _A      :      List[Any]=2		,   _A      :      List[Any]=4		,   _A      :      List[Any]=37		,   _A      :      Union[str, Any]="gelu"		,   _A      :      Tuple=0.1		,   _A      :      List[str]=0.1		,   _A      :      str=512		,   _A      :      List[str]=16		,   _A      :      Optional[Any]=2		,   _A      :      Any=0.0_2		,   _A      :      List[str]=6		,   _A      :      Union[str, Any]=6		,   _A      :      Any=3		,   _A      :      Any=4		,   _A      :      Tuple=None		,   _A      :      Union[str, Any]=1000		,   )							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	parent
							snake_case_		:			Dict											=	batch_size
							snake_case_		:			List[str]											=	num_channels
							snake_case_		:			Dict											=	image_size
							snake_case_		:			Dict											=	patch_size
							snake_case_		:			int											=	is_training
							snake_case_		:			Union[str, Any]											=	use_input_mask
							snake_case_		:			Optional[int]											=	use_token_type_ids
							snake_case_		:			Union[str, Any]											=	use_labels
							snake_case_		:			List[str]											=	vocab_size
							snake_case_		:			int											=	hidden_size
							snake_case_		:			List[Any]											=	num_hidden_layers
							snake_case_		:			Tuple											=	num_attention_heads
							snake_case_		:			str											=	intermediate_size
							snake_case_		:			Any											=	hidden_act
							snake_case_		:			Any											=	hidden_dropout_prob
							snake_case_		:			str											=	attention_probs_dropout_prob
							snake_case_		:			Union[str, Any]											=	max_position_embeddings
							snake_case_		:			List[str]											=	type_vocab_size
							snake_case_		:			str											=	type_sequence_label_size
							snake_case_		:			List[str]											=	initializer_range
							snake_case_		:			Optional[int]											=	coordinate_size
							snake_case_		:			Tuple											=	shape_size
							snake_case_		:			Dict											=	num_labels
							snake_case_		:			Union[str, Any]											=	num_choices
							snake_case_		:			str											=	scope
							snake_case_		:			Any											=	range_bbox

							# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
							snake_case_		:			Union[str, Any]											=	text_seq_length
							snake_case_		:			int											=	(image_size // patch_size) ** 2 + 1
							snake_case_		:			Tuple											=	self.text_seq_length + self.image_seq_length






						def UpperCAmelCase_				(      self      :      str	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			Any											=	ids_tensor([self.batch_size, self.text_seq_length]		,   self.vocab_size	)

							snake_case_		:			Optional[Any]											=	ids_tensor([self.batch_size, self.text_seq_length, 4]		,   self.range_bbox	)
							snake_case_		:			Tuple											=	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]:
										snake_case_		:			Union[str, Any]											=	bbox[i, j, 3]
										snake_case_		:			str											=	bbox[i, j, 1]
										snake_case_		:			Optional[Any]											=	tmp_coordinate
									if bbox[i, j, 2] < bbox[i, j, 0]:
										snake_case_		:			Optional[int]											=	bbox[i, j, 2]
										snake_case_		:			Any											=	bbox[i, j, 0]
										snake_case_		:			Any											=	tmp_coordinate
							snake_case_		:			List[Any]											=	tf.constant(_A	)

							snake_case_		:			Tuple											=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

							snake_case_		:			Optional[int]											=	None
							if self.use_input_mask:
								snake_case_		:			Optional[Any]											=	random_attention_mask([self.batch_size, self.text_seq_length]	)

							snake_case_		:			Tuple											=	None
							if self.use_token_type_ids:
								snake_case_		:			List[str]											=	ids_tensor([self.batch_size, self.text_seq_length]		,   self.type_vocab_size	)

							snake_case_		:			int											=	None
							snake_case_		:			List[Any]											=	None
							if self.use_labels:
								snake_case_		:			Any											=	ids_tensor([self.batch_size]		,   self.type_sequence_label_size	)
								snake_case_		:			Union[str, Any]											=	ids_tensor([self.batch_size, self.text_seq_length]		,   self.num_labels	)

							snake_case_		:			List[Any]											=	LayoutLMvaConfig(
							    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		,   coordinate_size=self.coordinate_size		,   shape_size=self.shape_size		,   input_size=self.image_size		,   patch_size=self.patch_size		,   )

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






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      List[str]		,   _A      :      str		,   _A      :      Union[str, Any]		,   _A      :      Dict		,   _A      :      Union[str, Any]		,   _A      :      List[Any]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	TFLayoutLMvaModel(config=_A	)

							# text + image
							snake_case_		:			int											=	model(_A		,   pixel_values=_A		,   training=_A	)
							snake_case_		:			int											=	model(
							    _A		,   bbox=_A		,   pixel_values=_A		,   attention_mask=_A		,   token_type_ids=_A		,   training=_A		,   )
							snake_case_		:			Dict											=	model(_A		,   bbox=_A		,   pixel_values=_A		,   training=_A	)

							self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, self.seq_length, self.hidden_size)	)

							# text only
							snake_case_		:			List[str]											=	model(_A		,   training=_A	)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,   (self.batch_size, self.text_seq_length, self.hidden_size)	)

							# image only
							snake_case_		:			str											=	model({'pixel_values': pixel_values}		,   training=_A	)
							self.parent.assertEqual(
							    result.last_hidden_state.shape		,   (self.batch_size, self.image_seq_length, self.hidden_size)	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      int		,   _A      :      int		,   _A      :      Optional[Any]		,   _A      :      List[str]		,   _A      :      List[Any]		,   _A      :      str		,   _A      :      Any	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			str											=	self.num_labels
							snake_case_		:			Tuple											=	TFLayoutLMvaForSequenceClassification(config=_A	)
							snake_case_		:			List[str]											=	model(
							    _A		,   bbox=_A		,   pixel_values=_A		,   attention_mask=_A		,   token_type_ids=_A		,   labels=_A		,   training=_A		,   )
							self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.num_labels)	)






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      List[str]		,   _A      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      str		,   _A      :      Union[str, Any]		,   _A      :      Optional[int]		,   _A      :      Tuple	)							->    str:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.num_labels
							snake_case_		:			Optional[Any]											=	TFLayoutLMvaForTokenClassification(config=_A	)
							snake_case_		:			Union[str, Any]											=	model(
							    _A		,   bbox=_A		,   pixel_values=_A		,   attention_mask=_A		,   token_type_ids=_A		,   labels=_A		,   training=_A		,   )
							self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.text_seq_length, self.num_labels)	)






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[int]		,   _A      :      Any		,   _A      :      List[str]		,   _A      :      Dict		,   _A      :      Any		,   _A      :      Any		,   _A      :      int	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			int											=	2
							snake_case_		:			List[str]											=	TFLayoutLMvaForQuestionAnswering(config=_A	)
							snake_case_		:			List[Any]											=	model(
							    _A		,   bbox=_A		,   pixel_values=_A		,   attention_mask=_A		,   token_type_ids=_A		,   start_positions=_A		,   end_positions=_A		,   training=_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 UpperCAmelCase_				(      self      :      int	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.prepare_config_and_inputs()
							((snake_case_)						,(snake_case_)						,(snake_case_)						,(snake_case_)						,(snake_case_)						,(snake_case_)						,(snake_case_)						,(snake_case_))		:			str											=	config_and_inputs
							snake_case_		:			Dict											=	{
							    'input_ids': input_ids,
							    'bbox': bbox,
							    'pixel_values': pixel_values,
							    'token_type_ids': token_type_ids,
							    'attention_mask': input_mask,
							}
							return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Tuple       =       (
						    (
						        TFLayoutLMvaModel,
						        TFLayoutLMvaForQuestionAnswering,
						        TFLayoutLMvaForSequenceClassification,
						        TFLayoutLMvaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						__magic_name__:			List[Any]       =       (
						    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
						    if is_tf_available()
						    else {}
						)

						__magic_name__:			Optional[Any]       =       False
						__magic_name__:			Dict       =       False
						__magic_name__:			List[str]       =       False






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Dict		,   _A      :      Dict		,   _A      :      Optional[Any]		,   _A      :      Union[str, Any]		,   _A      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


							return True






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      List[str]		,   _A      :      List[str]		,   _A      :      Optional[int]=False	)							->    dict:







							"""simple docstring"""


							snake_case_		:			str											=	copy.deepcopy(_A	)

							if model_class in get_values(_A	):
								snake_case_		:			Dict											=	{
								    k: tf.tile(tf.expand_dims(_A		,   1	)		,   (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)	)
								    if isinstance(_A		,   tf.Tensor	) and v.ndim > 0
								    else v
								    for k, v in inputs_dict.items()
								}

							if return_labels:
								if model_class in get_values(_A	):
									snake_case_		:			str											=	tf.ones(self.model_tester.batch_size		,   dtype=tf.intaa	)
								elif model_class in get_values(_A	):
									snake_case_		:			Optional[Any]											=	tf.zeros(self.model_tester.batch_size		,   dtype=tf.intaa	)
									snake_case_		:			List[str]											=	tf.zeros(self.model_tester.batch_size		,   dtype=tf.intaa	)
								elif model_class in get_values(_A	):
									snake_case_		:			Optional[int]											=	tf.zeros(self.model_tester.batch_size		,   dtype=tf.intaa	)
								elif model_class in get_values(_A	):
									snake_case_		:			Tuple											=	tf.zeros(
									    (self.model_tester.batch_size, self.model_tester.text_seq_length)		,   dtype=tf.intaa	)

							return inputs_dict






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    int:







							"""simple docstring"""


							snake_case_		:			Any											=	TFLayoutLMvaModelTester(self	)
							snake_case_		:			Dict											=	ConfigTester(self		,   config_class=_A		,   hidden_size=37	)






						def UpperCAmelCase_				(      self      :      Union[str, Any]	)							->    Optional[int]:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    List[str]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[Any]											=	self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								snake_case_		:			Dict											=	model_class(_A	)
								if getattr(_A		,   'hf_compute_loss'		,   _A	):
									# The number of elements in the loss should be the same as the number of elements in the label
									snake_case_		:			List[str]											=	self._prepare_for_class(inputs_dict.copy()		,   _A		,   return_labels=_A	)
									snake_case_		:			Dict											=	prepared_for_class[
									    sorted(prepared_for_class.keys() - inputs_dict.keys()		,   reverse=_A	)[0]
									]
									snake_case_		:			str											=	added_label.shape.as_list()[:1]

									# Test that model correctly compute the loss with kwargs
									snake_case_		:			List[Any]											=	self._prepare_for_class(inputs_dict.copy()		,   _A		,   return_labels=_A	)
									snake_case_		:			int											=	prepared_for_class.pop('input_ids'	)

									snake_case_		:			Dict											=	model(_A		,   **_A	)[0]
									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

									# Test that model correctly compute the loss when we mask some positions
									snake_case_		:			Optional[int]											=	self._prepare_for_class(inputs_dict.copy()		,   _A		,   return_labels=_A	)
									snake_case_		:			List[Any]											=	prepared_for_class.pop('input_ids'	)
									if "labels" in prepared_for_class:
										snake_case_		:			List[Any]											=	prepared_for_class['labels'].numpy()
										if len(labels.shape	) > 1 and labels.shape[1] != 1:
											snake_case_		:			Any											=	-100
											snake_case_		:			Dict											=	tf.convert_to_tensor(_A	)
											snake_case_		:			Tuple											=	model(_A		,   **_A	)[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)
											self.assertTrue(not np.any(np.isnan(loss.numpy()	)	)	)

                # Test that model correctly compute the loss with a dict
									snake_case_		:			Tuple											=	self._prepare_for_class(inputs_dict.copy()		,   _A		,   return_labels=_A	)
									snake_case_		:			Optional[int]											=	model(_A	)[0]
									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)

									# Test that model correctly compute the loss with a tuple
									snake_case_		:			int											=	self._prepare_for_class(inputs_dict.copy()		,   _A		,   return_labels=_A	)

									# Get keys that were added with the _prepare_for_class function
									snake_case_		:			Tuple											=	prepared_for_class.keys() - inputs_dict.keys()
									snake_case_		:			List[str]											=	inspect.signature(model.call	).parameters
									snake_case_		:			List[Any]											=	list(signature.keys()	)

									# Create a dictionary holding the location of the tensors in the tuple
									snake_case_		:			Optional[int]											=	{0: 'input_ids'}
									for label_key in label_keys:
										snake_case_		:			Dict											=	signature_names.index(_A	)
										snake_case_		:			List[str]											=	label_key
									snake_case_		:			Tuple											=	sorted(tuple_index_mapping.items()	)
									# Initialize a list with their default values, update the values and convert to a tuple
									snake_case_		:			Dict											=	[]

									for name in signature_names:
										if name != "kwargs":
											list_input.append(signature[name].default	)

									for index, value in sorted_tuple_index_mapping:
										snake_case_		:			Dict											=	prepared_for_class[value]

									snake_case_		:			List[str]											=	tuple(_A	)

									# Send to model
									snake_case_		:			Optional[Any]											=	model(tuple_input[:-1]	)[0]

									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]	)






						def UpperCAmelCase_				(      self      :      str	)							->    Tuple:







							"""simple docstring"""


							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			int											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(_A		,   _A		,   _A		,   _A		,   _A		,   _A	)






						def UpperCAmelCase_				(      self      :      int	)							->    Any:







							"""simple docstring"""


							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			Any											=	self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								snake_case_		:			Optional[int]											=	type
								self.model_tester.create_and_check_model(_A		,   _A		,   _A		,   _A		,   _A		,   _A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Union[str, Any]:







							"""simple docstring"""


							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			str											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[int]	)							->    int:







							"""simple docstring"""


							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			int											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A	)






						def UpperCAmelCase_				(      self      :      Any	)							->    str:







							"""simple docstring"""


							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			str											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A	)






						@slow
						def UpperCAmelCase_				(      self      :      Dict	)							->    str:







							"""simple docstring"""


							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		:			int											=	TFLayoutLMvaModel.from_pretrained(_A	)
								self.assertIsNotNone(_A	)


def 							SCREAMING_SNAKE_CASE__						(    ):
	snake_case_		:			Union[str, Any]											=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
	return image


@require_tf
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						@cached_property
						def UpperCAmelCase_				(      self      :      str	)							->    Union[str, Any]:







							"""simple docstring"""


							return LayoutLMvaImageProcessor(apply_ocr=_A	) if is_vision_available() else None






						@slow
						def UpperCAmelCase_				(      self      :      List[str]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'	)

							snake_case_		:			Any											=	self.default_image_processor
							snake_case_		:			List[str]											=	prepare_img()
							snake_case_		:			Tuple											=	image_processor(images=_A		,   return_tensors='tf'	).pixel_values

							snake_case_		:			List[Any]											=	tf.constant([[1, 2]]	)
							snake_case_		:			Tuple											=	tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]	)		,   axis=0	)

							# forward pass
							snake_case_		:			Tuple											=	model(input_ids=_A		,   bbox=_A		,   pixel_values=_A		,   training=_A	)

							# verify the logits
							snake_case_		:			List[Any]											=	(1, 199, 768)
							self.assertEqual(outputs.last_hidden_state.shape		,   _A	)

							snake_case_		:			str											=	tf.constant(
							    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]	)

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]		,   _A		,   atol=1E-4	)	)








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		snake_case_		:			int											=	0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			snake_case_		:			str											=	arrival_time[i]

		snake_case_		:			Optional[Any]											=	0
		# Index showing the location of the process being performed
		snake_case_		:			Tuple											=	0
		# Saves the current response ratio.
		snake_case_		:			List[Any]											=	0
		for i in range(0   , __a   ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				snake_case_		:			Optional[Any]											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				snake_case_		:			Optional[int]											=	temp
				snake_case_		:			Optional[Any]											=	i

        # Calculate the turn around time
		snake_case_		:			Any											=	current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		snake_case_		:			Optional[Any]											=	1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
						for i in range(0, no_of_process):
												print(
												    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
												    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
												)

						print(F'''average waiting time : {mean(waiting_time):.5f}''')
						print(F'''average turn around time : {mean(turn_around_time):.5f}''')

