



"""simple docstring"""





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
					import tensorflow as tf

					from transformers import TFResNetForImageClassification, TFResNetModel
					from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor



class  _lowerCAmelCase     :





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase=3				,     _lowercase=3_2				,     _lowercase=3				,     _lowercase=1_0				,     _lowercase=[1_0, 2_0, 3_0, 4_0]				,     _lowercase=[1, 1, 2, 1]				,     _lowercase=True				,     _lowercase=True				,     _lowercase="relu"				,     _lowercase=3				,     _lowercase=None				,     )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      parent
								snake_case_ :			str            =      batch_size
								snake_case_ :			Union[str, Any]            =      image_size
								snake_case_ :			str            =      num_channels
								snake_case_ :			Union[str, Any]            =      embeddings_size
								snake_case_ :			Tuple            =      hidden_sizes
								snake_case_ :			int            =      depths
								snake_case_ :			int            =      is_training
								snake_case_ :			Dict            =      use_labels
								snake_case_ :			List[str]            =      hidden_act
								snake_case_ :			List[str]            =      num_labels
								snake_case_ :			Optional[Any]            =      scope
								snake_case_ :			Tuple            =      len(_lowercase )





		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

								snake_case_ :			Tuple            =      None
								if self.use_labels:
														snake_case_ :			Optional[Any]            =      ids_tensor([self.batch_size]				,     self.num_labels )

								snake_case_ :			Optional[Any]            =      self.get_config()

								return config, pixel_values, labels





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								return ResNetConfig(
								    num_channels=self.num_channels				,     embeddings_size=self.embeddings_size				,     hidden_sizes=self.hidden_sizes				,     depths=self.depths				,     hidden_act=self.hidden_act				,     num_labels=self.num_labels				,     image_size=self.image_size				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    Union[str, Any]:







								'''simple docstring'''

								snake_case_ :			Any            =      TFResNetModel(config=_lowercase )
								snake_case_ :			Tuple            =      model(_lowercase )
								# expected last hidden states: B, C, H // 32, W // 32
								self.parent.assertEqual(
								    result.last_hidden_state.shape				,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)				,     )





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase				,     _lowercase )			->    Dict:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      self.num_labels
								snake_case_ :			Optional[Any]            =      TFResNetForImageClassification(_lowercase )
								snake_case_ :			Optional[int]            =      model(_lowercase				,     labels=_lowercase )
								self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.num_labels) )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			str            =      self.prepare_config_and_inputs()
								snake_case_      ,		snake_case_      ,		snake_case_ :			Dict            =      config_and_inputs
								snake_case_ :			List[str]            =      {"""pixel_values""": pixel_values}
								return config, inputs_dict




@require_tf
class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
		_lowerCamelCase      	=							(
		    {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
		    if is_tf_available()
		    else {}
		)

		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False
		_lowerCamelCase      	=							False





		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								snake_case_ :			List[str]            =      TFResNetModelTester(self )
								snake_case_ :			List[Any]            =      ConfigTester(self				,     config_class=_lowercase				,     has_text_modality=_lowercase )





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()





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







								'''simple docstring'''

								return





		@unittest.skip(reason="""ResNet does not use inputs_embeds""" )
		def  UpperCAmelCase__				(       self )			->    Optional[Any]:







								'''simple docstring'''

								pass





		@unittest.skip(reason="""ResNet does not support input and output embeddings""" )
		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								pass





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_      ,		snake_case_ :			List[Any]            =      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														snake_case_ :			Optional[Any]            =      model_class(_lowercase )
														snake_case_ :			List[Any]            =      inspect.signature(model.call )
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														snake_case_ :			Any            =      [*signature.parameters.keys()]

														snake_case_ :			str            =      ["""pixel_values"""]
														self.assertListEqual(arg_names[:1]				,     _lowercase )





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







								'''simple docstring'''

								snake_case_ :			List[Any]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowercase )





		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

								def check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase ):
														snake_case_ :			str            =      model_class(_lowercase )
														snake_case_ :			Tuple            =      model(**self._prepare_for_class(_lowercase				,     _lowercase ) )

														snake_case_ :			Optional[Any]            =      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

														snake_case_ :			Any            =      self.model_tester.num_stages
														self.assertEqual(len(_lowercase )				,     expected_num_stages + 1 )

														# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
														self.assertListEqual(
														    list(hidden_states[0].shape[-2:] )				,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]				,     )

								snake_case_      ,		snake_case_ :			Union[str, Any]            =      self.model_tester.prepare_config_and_inputs_for_common()
								snake_case_ :			Optional[Any]            =      ["""basic""", """bottleneck"""]
								for model_class in self.all_model_classes:
														for layer_type in layers_type:
																				snake_case_ :			List[str]            =      layer_type
																				snake_case_ :			Any            =      True
																				check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase )

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				snake_case_ :			Tuple            =      True

																				check_hidden_states_output(_lowercase				,     _lowercase				,     _lowercase )





		def  UpperCAmelCase__				(       self )			->    List[str]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowercase )





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







								'''simple docstring'''

								for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_ :			str            =      TFResNetModel.from_pretrained(_lowercase )
														self.assertIsNotNone(_lowercase )




def 						__lowerCAmelCase    (		):


						'''simple docstring'''






						snake_case_ :			str            =      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
						return image






@require_tf
@require_vision
class  _lowerCAmelCase     (					unittest.TestCase		):





		"""simple docstring"""






		@cached_property
		def  UpperCAmelCase__				(       self )			->    Tuple:







								'''simple docstring'''

								return (
								    AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
								    if is_vision_available()
								    else None
								)





		@slow
		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			Optional[int]            =      TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

								snake_case_ :			str            =      self.default_image_processor
								snake_case_ :			Optional[Any]            =      prepare_img()
								snake_case_ :			Dict            =      image_processor(images=_lowercase				,     return_tensors="""tf""" )

								# forward pass
								snake_case_ :			Dict            =      model(**_lowercase )

								# verify the logits
								snake_case_ :			str            =      tf.TensorShape((1, 1_0_0_0) )
								self.assertEqual(outputs.logits.shape				,     _lowercase )

								snake_case_ :			Optional[Any]            =      tf.constant([-11.1069, -9.7877, -8.3777] )

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







import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Dict			,      __lowercase				:	Any			,      __lowercase				:	str=13			,      __lowercase				:	Optional[Any]=7			,      __lowercase				:	Tuple=True			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	List[str]=True			,      __lowercase				:	Dict=True			,      __lowercase				:	Any=99			,      __lowercase				:	Any=32			,      __lowercase				:	List[Any]=5			,      __lowercase				:	Any=4			,      __lowercase				:	List[str]=37			,      __lowercase				:	Union[str, Any]="gelu"			,      __lowercase				:	Union[str, Any]=0.1			,      __lowercase				:	str=0.1			,      __lowercase				:	List[str]=5_12			,      __lowercase				:	Tuple=16			,      __lowercase				:	List[str]=2			,      __lowercase				:	Tuple=0.02			,      __lowercase				:	Optional[Any]=False			,      __lowercase				:	Optional[int]=True			,      __lowercase				:	Union[str, Any]="None"			,      __lowercase				:	Union[str, Any]=3			,      __lowercase				:	int=4			,      __lowercase				:	Tuple=None			,      ):






							"""simple docstring"""






							snake_case_						       =	parent
							snake_case_						       =	batch_size
							snake_case_						       =	seq_length
							snake_case_						       =	is_training
							snake_case_						       =	use_input_mask
							snake_case_						       =	use_token_type_ids
							snake_case_						       =	use_labels
							snake_case_						       =	vocab_size
							snake_case_						       =	hidden_size
							snake_case_						       =	num_hidden_layers
							snake_case_						       =	num_attention_heads
							snake_case_						       =	intermediate_size
							snake_case_						       =	hidden_act
							snake_case_						       =	hidden_dropout_prob
							snake_case_						       =	attention_probs_dropout_prob
							snake_case_						       =	max_position_embeddings
							snake_case_						       =	type_vocab_size
							snake_case_						       =	type_sequence_label_size
							snake_case_						       =	initializer_range
							snake_case_						       =	num_labels
							snake_case_						       =	num_choices
							snake_case_						       =	relative_attention
							snake_case_						       =	position_biased_input
							snake_case_						       =	pos_att_type
							snake_case_						       =	scope






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size		)

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

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

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

							snake_case_						       =	self.get_config()

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






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return DebertaConfig(
							    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			,      relative_attention=self.relative_attention			,      position_biased_input=self.position_biased_input			,      pos_att_type=self.pos_att_type			,      )






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	self.get_config()
							snake_case_						       =	3_00
							return config






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






							self.parent.assertListEqual(list(result.loss.size()		)			,      []		)






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






							"""simple docstring"""






							snake_case_						       =	DebertaModel(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase			,      token_type_ids=__lowercase		)[0]
							snake_case_						       =	model(__lowercase		)[0]

							self.parent.assertListEqual(list(sequence_output.size()		)			,      [self.batch_size, self.seq_length, self.hidden_size]		)






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






							"""simple docstring"""






							snake_case_						       =	DebertaForMaskedLM(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)

							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size)		)






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






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForSequenceClassification(__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertListEqual(list(result.logits.size()		)			,      [self.batch_size, self.num_labels]		)
							self.check_loss_output(__lowercase		)






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






							"""simple docstring"""






							snake_case_						       =	self.num_labels
							snake_case_						       =	DebertaForTokenClassification(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      labels=__lowercase		)
							self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)		)






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






							"""simple docstring"""






							snake_case_						       =	DebertaForQuestionAnswering(config=__lowercase		)
							model.to(__lowercase		)
							model.eval()
							snake_case_						       =	model(
							    __lowercase			,      attention_mask=__lowercase			,      token_type_ids=__lowercase			,      start_positions=__lowercase			,      end_positions=__lowercase			,      )
							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				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , (
							    snake_case_
							)       , 
							)						       =	config_and_inputs
							snake_case_						       =	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
							return config, inputs_dict







@require_torch
class 						UpperCAmelCase  (							UpperCAmelCase__			,  UpperCAmelCase__			,  unittest.TestCase     ):







						'''simple docstring'''





						lowerCAmelCase_							=  (
						    (
						        DebertaModel,
						        DebertaForMaskedLM,
						        DebertaForSequenceClassification,
						        DebertaForTokenClassification,
						        DebertaForQuestionAnswering,
						    )
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_							=  (
						    {
						        '''feature-extraction''': DebertaModel,
						        '''fill-mask''': DebertaForMaskedLM,
						        '''question-answering''': DebertaForQuestionAnswering,
						        '''text-classification''': DebertaForSequenceClassification,
						        '''token-classification''': DebertaForTokenClassification,
						        '''zero-shot''': DebertaForSequenceClassification,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase_							=  True
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False
						lowerCAmelCase_							=  False






						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	DebertaModelTester(self		)
							snake_case_						       =	ConfigTester(self			,      config_class=__lowercase			,      hidden_size=37		)






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						def 			snake_case__		(		self				:	Dict		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_model(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowercase		)






						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_masked_lm(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_question_answering(*__lowercase		)






						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							snake_case_						       =	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_deberta_for_token_classification(*__lowercase		)






						@slow
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_						       =	DebertaModel.from_pretrained(__lowercase		)
								self.assertIsNotNone(__lowercase		)







@require_torch
@require_sentencepiece
@require_tokenizers
class 						UpperCAmelCase  (							unittest.TestCase     ):







						'''simple docstring'''





						@unittest.skip(reason="Model not available yet"		)
						def 			snake_case__		(		self				:	str		):






							"""simple docstring"""






							pass






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






							"""simple docstring"""






							snake_case_						       =	DebertaModel.from_pretrained("microsoft/deberta-base"		)

							snake_case_						       =	torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]		)
							snake_case_						       =	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
							with torch.no_grad():
								snake_case_						       =	model(__lowercase			,      attention_mask=__lowercase		)[0]
							# compare the actual values for a slice.
							snake_case_						       =	torch.tensor(
							    [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]		)
							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,      __lowercase			,      atol=1E-4		)			,      f"{output[:, 1:4, 1:4]}"		)
