



"""simple docstring"""
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 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__=2				,       lowerCamelCase__=3				,       lowerCamelCase__=4				,       lowerCamelCase__=2				,       lowerCamelCase__=7				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=True				,       lowerCamelCase__=9_9				,       lowerCamelCase__=3_6				,       lowerCamelCase__=2				,       lowerCamelCase__=4				,       lowerCamelCase__=3_7				,       lowerCamelCase__="gelu"				,       lowerCamelCase__=0.1				,       lowerCamelCase__=0.1				,       lowerCamelCase__=5_1_2				,       lowerCamelCase__=1_6				,       lowerCamelCase__=2				,       lowerCamelCase__=0.0_2				,       lowerCamelCase__=6				,       lowerCamelCase__=6				,       lowerCamelCase__=3				,       lowerCamelCase__=4				,       lowerCamelCase__=None				,       lowerCamelCase__=1_0_0_0				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				image_size
									_lowerCamelCase        =				patch_size
									_lowerCamelCase        =				is_training
									_lowerCamelCase        =				use_input_mask
									_lowerCamelCase        =				use_token_type_ids
									_lowerCamelCase        =				use_labels
									_lowerCamelCase        =				vocab_size
									_lowerCamelCase        =				hidden_size
									_lowerCamelCase        =				num_hidden_layers
									_lowerCamelCase        =				num_attention_heads
									_lowerCamelCase        =				intermediate_size
									_lowerCamelCase        =				hidden_act
									_lowerCamelCase        =				hidden_dropout_prob
									_lowerCamelCase        =				attention_probs_dropout_prob
									_lowerCamelCase        =				max_position_embeddings
									_lowerCamelCase        =				type_vocab_size
									_lowerCamelCase        =				type_sequence_label_size
									_lowerCamelCase        =				initializer_range
									_lowerCamelCase        =				coordinate_size
									_lowerCamelCase        =				shape_size
									_lowerCamelCase        =				num_labels
									_lowerCamelCase        =				num_choices
									_lowerCamelCase        =				scope
									_lowerCamelCase        =				range_bbox

									# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
									_lowerCamelCase        =				text_seq_length
									_lowerCamelCase        =				(image_size // patch_size) ** 2 + 1
									_lowerCamelCase        =				self.text_seq_length + self.image_seq_length

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				ids_tensor([self.batch_size, self.text_seq_length]				,       self.vocab_size   )

									_lowerCamelCase        =				ids_tensor([self.batch_size, self.text_seq_length, 4]				,       self.range_bbox   )
									_lowerCamelCase        =				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]:
																					_lowerCamelCase        =				bbox[i, j, 3]
																					_lowerCamelCase        =				bbox[i, j, 1]
																					_lowerCamelCase        =				tmp_coordinate
																	if bbox[i, j, 2] < bbox[i, j, 0]:
																					_lowerCamelCase        =				bbox[i, j, 2]
																					_lowerCamelCase        =				bbox[i, j, 0]
																					_lowerCamelCase        =				tmp_coordinate
									_lowerCamelCase        =				tf.constant(lowerCamelCase__   )

									_lowerCamelCase        =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

									_lowerCamelCase        =				None
									if self.use_input_mask:
													_lowerCamelCase        =				random_attention_mask([self.batch_size, self.text_seq_length]   )

									_lowerCamelCase        =				None
									if self.use_token_type_ids:
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.text_seq_length]				,       self.type_vocab_size   )

									_lowerCamelCase        =				None
									_lowerCamelCase        =				None
									if self.use_labels:
													_lowerCamelCase        =				ids_tensor([self.batch_size]				,       self.type_sequence_label_size   )
													_lowerCamelCase        =				ids_tensor([self.batch_size, self.text_seq_length]				,       self.num_labels   )

									_lowerCamelCase        =				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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				TFLayoutLMvaModel(config=lowerCamelCase__   )

									# text + image
									_lowerCamelCase        =				model(lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       training=lowerCamelCase__   )
									_lowerCamelCase        =				model(
									    lowerCamelCase__				,       bbox=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       token_type_ids=lowerCamelCase__				,       training=lowerCamelCase__				,       )
									_lowerCamelCase        =				model(lowerCamelCase__				,       bbox=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       training=lowerCamelCase__   )

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

									# text only
									_lowerCamelCase        =				model(lowerCamelCase__				,       training=lowerCamelCase__   )
									self.parent.assertEqual(
									    result.last_hidden_state.shape				,       (self.batch_size, self.text_seq_length, self.hidden_size)   )

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

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.num_labels
									_lowerCamelCase        =				TFLayoutLMvaForSequenceClassification(config=lowerCamelCase__   )
									_lowerCamelCase        =				model(
									    lowerCamelCase__				,       bbox=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       token_type_ids=lowerCamelCase__				,       labels=lowerCamelCase__				,       training=lowerCamelCase__				,       )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.num_labels
									_lowerCamelCase        =				TFLayoutLMvaForTokenClassification(config=lowerCamelCase__   )
									_lowerCamelCase        =				model(
									    lowerCamelCase__				,       bbox=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       token_type_ids=lowerCamelCase__				,       labels=lowerCamelCase__				,       training=lowerCamelCase__				,       )
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.text_seq_length, self.num_labels)   )

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									_lowerCamelCase        =				2
									_lowerCamelCase        =				TFLayoutLMvaForQuestionAnswering(config=lowerCamelCase__   )
									_lowerCamelCase        =				model(
									    lowerCamelCase__				,       bbox=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       attention_mask=lowerCamelCase__				,       token_type_ids=lowerCamelCase__				,       start_positions=lowerCamelCase__				,       end_positions=lowerCamelCase__				,       training=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   ):
									_lowerCamelCase        =				self.prepare_config_and_inputs()
									((_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase)  ,     (_lowerCamelCase))        =				config_and_inputs
									_lowerCamelCase        =				{
									    '''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 							lowerCamelCase_(   snake_case_,				snake_case_,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       Union[str, Any]										= (
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					lowercase__   :       List[str]										= (
					    {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					lowercase__   :       Optional[int]										= False
					lowercase__   :       Tuple										= False
					lowercase__   :       Union[str, Any]										= False

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ):
									return True

					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=False   ):
									_lowerCamelCase        =				copy.deepcopy(lowerCamelCase__   )

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

									if return_labels:
													if model_class in get_values(lowerCamelCase__   ):
																	_lowerCamelCase        =				tf.ones(self.model_tester.batch_size				,       dtype=tf.intaa   )
													elif model_class in get_values(lowerCamelCase__   ):
																	_lowerCamelCase        =				tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa   )
																	_lowerCamelCase        =				tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa   )
													elif model_class in get_values(lowerCamelCase__   ):
																	_lowerCamelCase        =				tf.zeros(self.model_tester.batch_size				,       dtype=tf.intaa   )
													elif model_class in get_values(lowerCamelCase__   ):
																	_lowerCamelCase        =				tf.zeros(
																	    (self.model_tester.batch_size, self.model_tester.text_seq_length)				,       dtype=tf.intaa   )

									return inputs_dict

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFLayoutLMvaModelTester(self   )
									_lowerCamelCase        =				ConfigTester(self				,       config_class=lowerCamelCase__				,       hidden_size=3_7   )

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

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

																	# Test that model correctly compute the loss with kwargs
																	_lowerCamelCase        =				self._prepare_for_class(inputs_dict.copy()				,       lowerCamelCase__				,       return_labels=lowerCamelCase__   )
																	_lowerCamelCase        =				prepared_for_class.pop('''input_ids'''   )

																	_lowerCamelCase        =				model(lowerCamelCase__				,       **lowerCamelCase__   )[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
																	_lowerCamelCase        =				self._prepare_for_class(inputs_dict.copy()				,       lowerCamelCase__				,       return_labels=lowerCamelCase__   )
																	_lowerCamelCase        =				prepared_for_class.pop('''input_ids'''   )
																	if "labels" in prepared_for_class:
																					_lowerCamelCase        =				prepared_for_class['''labels'''].numpy()
																					if len(labels.shape   ) > 1 and labels.shape[1] != 1:
																									_lowerCamelCase        =				-1_0_0
																									_lowerCamelCase        =				tf.convert_to_tensor(lowerCamelCase__   )
																									_lowerCamelCase        =				model(lowerCamelCase__				,       **lowerCamelCase__   )[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
																	_lowerCamelCase        =				self._prepare_for_class(inputs_dict.copy()				,       lowerCamelCase__				,       return_labels=lowerCamelCase__   )
																	_lowerCamelCase        =				model(lowerCamelCase__   )[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
																	_lowerCamelCase        =				self._prepare_for_class(inputs_dict.copy()				,       lowerCamelCase__				,       return_labels=lowerCamelCase__   )

																	# Get keys that were added with the _prepare_for_class function
																	_lowerCamelCase        =				prepared_for_class.keys() - inputs_dict.keys()
																	_lowerCamelCase        =				inspect.signature(model.call   ).parameters
																	_lowerCamelCase        =				list(signature.keys()   )

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

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

																	for index, value in sorted_tuple_index_mapping:
																					_lowerCamelCase        =				prepared_for_class[value]

																	_lowerCamelCase        =				tuple(lowerCamelCase__   )

																	# Send to model
																	_lowerCamelCase        =				model(tuple_input[:-1]   )[0]

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

					def 			snake_case__      (    self   ):
									(
									    (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,           
									)        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									(
									    (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,           
									)        =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													_lowerCamelCase        =				type
													self.model_tester.create_and_check_model(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									(
									    (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,           
									)        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(
									    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									(
									    (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,           
									)        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(
									    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )

					def 			snake_case__      (    self   ):
									(
									    (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,     (
									    _lowerCamelCase      
									)  ,           
									)        =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(
									    lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   )





					@slow
					def 			snake_case__      (    self   ):
									for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase        =				TFLayoutLMvaModel.from_pretrained(lowerCamelCase__   )
													self.assertIsNotNone(lowerCamelCase__   )



def        lowerCAmelCase_(   )	->       Optional[int]:
				_lowerCamelCase        =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
				return image




@require_tf
class 							lowerCamelCase_(   unittest.TestCase    ):





					'''simple docstring'''



					@cached_property
					def 			snake_case__      (    self   ):
									return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase__   ) if is_vision_available() else None





					@slow
					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''   )

									_lowerCamelCase        =				self.default_image_processor
									_lowerCamelCase        =				prepare_img()
									_lowerCamelCase        =				image_processor(images=lowerCamelCase__				,       return_tensors='''tf'''   ).pixel_values

									_lowerCamelCase        =				tf.constant([[1, 2]]   )
									_lowerCamelCase        =				tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]   )				,       axis=0   )

									# forward pass
									_lowerCamelCase        =				model(input_ids=lowerCamelCase__				,       bbox=lowerCamelCase__				,       pixel_values=lowerCamelCase__				,       training=lowerCamelCase__   )

									# verify the logits
									_lowerCamelCase        =				(1, 1_9_9, 7_6_8)
									self.assertEqual(outputs.last_hidden_state.shape				,       lowerCamelCase__   )

									_lowerCamelCase        =				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]				,       lowerCamelCase__				,       atol=1e-4   )   )









"""simple docstring"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
