




'''simple docstring'''


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

						if is_vision_available():
												from transformers import MaskFormerImageProcessor

if is_vision_available():
						from PIL import Image




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=False ,				_lowerCAmelCase=10 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=32 * 4 ,				_lowerCAmelCase=32 * 6 ,				_lowerCAmelCase=4 ,				_lowerCAmelCase=32 ,				)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							is_training
							lowercase        =							use_auxiliary_loss
							lowercase        =							num_queries
							lowercase        =							num_channels
							lowercase        =							min_size
							lowercase        =							max_size
							lowercase        =							num_labels
							lowercase        =							mask_feature_size







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
							    _lowerCAmelCase						)

							lowercase        =							torch.ones([self.batch_size, self.min_size, self.max_size] ,				device=_lowerCAmelCase						)

							lowercase        =							(
							    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,				device=_lowerCAmelCase						) > 0.5
							).float()
							lowercase        =							(torch.rand((self.batch_size, self.num_labels) ,				device=_lowerCAmelCase						) > 0.5).long()

							lowercase        =							self.get_config()
							return config, pixel_values, pixel_mask, mask_labels, class_labels







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							return MaskFormerConfig.from_backbone_and_decoder_configs(
							    backbone_config=SwinConfig(
							        depths=[1, 1, 1, 1] ,				) ,				decoder_config=DetrConfig(
							        decoder_ffn_dim=128 ,				num_queries=self.num_queries ,				decoder_attention_heads=2 ,				d_model=self.mask_feature_size ,				) ,				mask_feature_size=self.mask_feature_size ,				fpn_feature_size=self.mask_feature_size ,				num_channels=self.num_channels ,				num_labels=self.num_labels ,				)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							self.prepare_config_and_inputs()
							lowercase        =							{"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
							return config, inputs_dict







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							output.encoder_hidden_states
							lowercase        =							output.pixel_decoder_hidden_states
							lowercase        =							output.transformer_decoder_hidden_states

							self.parent.assertTrue(len(_lowerCAmelCase						) ,				len(config.backbone_config.depths						)						)
							self.parent.assertTrue(len(_lowerCAmelCase						) ,				len(config.backbone_config.depths						)						)
							self.parent.assertTrue(len(_lowerCAmelCase						) ,				config.decoder_config.decoder_layers						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    Optional[Any]:
							'''simple docstring'''






							with torch.no_grad():
													lowercase        =							MaskFormerModel(config=_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

													lowercase        =							model(pixel_values=_lowerCAmelCase ,				pixel_mask=_lowerCAmelCase						)
													lowercase        =							model(_lowerCAmelCase ,				output_hidden_states=_lowerCAmelCase						)
							# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
							# encoder and pixel decoder
							self.parent.assertEqual(
							    output.transformer_decoder_last_hidden_state.shape ,				(self.batch_size, self.num_queries, self.mask_feature_size) ,				)
							# let's ensure the other two hidden state exists
							self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
							self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

							if output_hidden_states:
													self.check_output_hidden_state(_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							lowercase        =							MaskFormerForInstanceSegmentation(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

							def comm_check_on_output(_lowerCAmelCase						):
													# let's still check that all the required stuff is there
													self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
													self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
													self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
													# okay, now we need to check the logits shape
													# due to the encoder compression, masks have a //4 spatial size
													self.parent.assertEqual(
													    result.masks_queries_logits.shape ,				(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,				)
													# + 1 for null class
													self.parent.assertEqual(
													    result.class_queries_logits.shape ,				(self.batch_size, self.num_queries, self.num_labels + 1)						)

							with torch.no_grad():
													lowercase        =							model(pixel_values=_lowerCAmelCase ,				pixel_mask=_lowerCAmelCase						)
													lowercase        =							model(_lowerCAmelCase						)

													comm_check_on_output(_lowerCAmelCase						)

													lowercase        =							model(
													    pixel_values=_lowerCAmelCase ,				pixel_mask=_lowerCAmelCase ,				mask_labels=_lowerCAmelCase ,				class_labels=_lowerCAmelCase						)

							comm_check_on_output(_lowerCAmelCase						)

							self.parent.assertTrue(result.loss is not None						)
							self.parent.assertEqual(result.loss.shape ,				torch.Size([1]						)						)




@require_torch
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
	__A			  =					(
	    {'''feature-extraction''': MaskFormerModel, '''image-segmentation''': MaskFormerForInstanceSegmentation}
	    if is_torch_available()
	    else {}
	)

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase        =							MaskFormerModelTester(self						)
							lowercase        =							ConfigTester(self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase ,				**_lowerCAmelCase ,				output_hidden_states=_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCAmelCase						)







	@unittest.skip(reason="""MaskFormer does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method"""						)
	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							pass







	@unittest.skip(reason="""MaskFormer is not a generative model"""						)
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							pass







	@unittest.skip(reason="""MaskFormer does not use token embeddings"""						)
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							pass







	@require_torch_multi_gpu
	@unittest.skip(
	    reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`"""						)
	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							pass







	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""						)
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						)
													lowercase        =							inspect.signature(model.forward						)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase        =							[*signature.parameters.keys()]

													lowercase        =							["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,				_lowerCAmelCase						)







	@slow
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							for model_name in ["facebook/maskformer-swin-small-coco"]:
													lowercase        =							MaskFormerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							(self.model_tester.min_size,) * 2
							lowercase        =							{
							    """pixel_values""": torch.randn((2, 3, *size) ,				device=_lowerCAmelCase						),
							    """mask_labels""": torch.randn((2, 10, *size) ,				device=_lowerCAmelCase						),
							    """class_labels""": torch.zeros(2 ,				10 ,				device=_lowerCAmelCase						).long(),
							}

							lowercase        =							MaskFormerForInstanceSegmentation(MaskFormerConfig()						).to(_lowerCAmelCase						)
							lowercase        =							model(**_lowerCAmelCase						)
							self.assertTrue(outputs.loss is not None						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase ,				**_lowerCAmelCase ,				output_hidden_states=_lowerCAmelCase						)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						).to(_lowerCAmelCase						)
													lowercase        =							model(**_lowerCAmelCase ,				output_attentions=_lowerCAmelCase						)
													self.assertTrue(outputs.attentions is not None						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							if not self.model_tester.is_training:
													return
							# only MaskFormerForInstanceSegmentation has the loss
							lowercase        =							self.all_model_classes[1]
							lowercase     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs()

							lowercase        =							model_class(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.train()

							lowercase        =							model(_lowerCAmelCase ,				mask_labels=_lowerCAmelCase ,				class_labels=_lowerCAmelCase						).loss
							loss.backward()







	def   _a   (    self						)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							self.all_model_classes[1]
							lowercase     ,   lowercase     ,   lowercase     ,   lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs()
							lowercase        =							True
							lowercase        =							True

							lowercase        =							model_class(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.train()

							lowercase        =							model(_lowerCAmelCase ,				mask_labels=_lowerCAmelCase ,				class_labels=_lowerCAmelCase						)

							lowercase        =							outputs.encoder_hidden_states[0]
							encoder_hidden_states.retain_grad()

							lowercase        =							outputs.pixel_decoder_hidden_states[0]
							pixel_decoder_hidden_states.retain_grad()
							# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
							lowercase        =							outputs.transformer_decoder_hidden_states[0]
							transformer_decoder_hidden_states.retain_grad()

							lowercase        =							outputs.attentions[0]
							attentions.retain_grad()

							outputs.loss.backward(retain_graph=_lowerCAmelCase						)

							self.assertIsNotNone(encoder_hidden_states.grad						)
							self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
							self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
							self.assertIsNotNone(attentions.grad						)




lowercase_      :				Dict   					=    1e-4







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




@require_vision
@slow
class      __UpperCamelCase       (unittest.TestCase			):







	@cached_property
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							return (
							    MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
							    if is_vision_available()
							    else None
							)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco"""						).to(_lowerCAmelCase						)
							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)
							lowercase        =							inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
							# check size
							self.assertEqual(_lowerCAmelCase ,				(1, 3, 800, 1088)						)

							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)

							lowercase        =							torch.tensor(
							    [[-0.0482, 0.9228, 0.4951], [-0.2547, 0.8017, 0.8527], [-0.0069, 0.3385, -0.0089]]						).to(_lowerCAmelCase						)
							self.assertTrue(
							    torch.allclose(
							        outputs.encoder_last_hidden_state[0, 0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)

							lowercase        =							torch.tensor(
							    [[-0.8422, -0.8434, -0.9718], [-1.0144, -0.5565, -0.4195], [-1.0038, -0.4484, -0.1961]]						).to(_lowerCAmelCase						)
							self.assertTrue(
							    torch.allclose(
							        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)

							lowercase        =							torch.tensor(
							    [[0.2852, -0.0159, 0.9735], [0.6254, 0.1858, 0.8529], [-0.0680, -0.4116, 1.8413]]						).to(_lowerCAmelCase						)
							self.assertTrue(
							    torch.allclose(
							        outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
							    .to(_lowerCAmelCase						)
							    .eval()
							)
							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)
							lowercase        =							inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
							# check size
							self.assertEqual(_lowerCAmelCase ,				(1, 3, 800, 1088)						)

							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)
							# masks_queries_logits
							lowercase        =							outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,				)
							lowercase        =							[
							    [-1.373_7124, -1.772_4937, -1.936_4233],
							    [-1.597_7281, -1.986_7939, -2.152_3695],
							    [-1.579_5398, -1.926_9832, -2.09_3942],
							]
							lowercase        =							torch.tensor(_lowerCAmelCase						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)
							# class_queries_logits
							lowercase        =							outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
							lowercase        =							torch.tensor(
							    [
							        [1.65_12E00, -5.25_72E00, -3.35_19E00],
							        [3.61_69E-02, -5.90_25E00, -2.93_13E00],
							        [1.07_66E-04, -7.76_30E00, -5.12_63E00],
							    ]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)







	def   _a   (    self						)    ->    str:
							'''simple docstring'''






							lowercase        =							(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff"""						)
							    .to(_lowerCAmelCase						)
							    .eval()
							)
							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)
							lowercase        =							inputs["""pixel_values"""].shape
							# check size is divisible by 32
							self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0						)
							# check size
							self.assertEqual(_lowerCAmelCase ,				(1, 3, 800, 1088)						)

							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)
							# masks_queries_logits
							lowercase        =							outputs.masks_queries_logits
							self.assertEqual(
							    masks_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) ,				)
							lowercase        =							[[-0.9046, -2.6366, -4.6062], [-3.4179, -5.7890, -8.8057], [-4.9179, -7.6560, -10.7711]]
							lowercase        =							torch.tensor(_lowerCAmelCase						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)
							# class_queries_logits
							lowercase        =							outputs.class_queries_logits
							self.assertEqual(
							    class_queries_logits.shape ,				(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)						)
							lowercase        =							torch.tensor(
							    [[4.7188, -3.2585, -2.8857], [6.6871, -2.9181, -1.2487], [7.2449, -2.2764, -2.1874]]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,				_lowerCAmelCase ,				atol=_lowerCAmelCase						)						)







	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							lowercase        =							(
							    MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco"""						)
							    .to(_lowerCAmelCase						)
							    .eval()
							)
							lowercase        =							self.default_image_processor

							lowercase        =							image_processor(
							    [np.zeros((3, 800, 1333)						), np.zeros((3, 800, 1333)						)] ,				segmentation_maps=[np.zeros((384, 384)						).astype(np.floataa						), np.zeros((384, 384)						).astype(np.floataa						)] ,				return_tensors="""pt""" ,				)

							lowercase        =							inputs["""pixel_values"""].to(_lowerCAmelCase						)
							lowercase        =							[el.to(_lowerCAmelCase						) for el in inputs["""mask_labels"""]]
							lowercase        =							[el.to(_lowerCAmelCase						) for el in inputs["""class_labels"""]]

							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)

							self.assertTrue(outputs.loss is not None						)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_      :				Optional[Any]   					=    logging.get_logger(__name__)

lowercase_      :				int   					=    {
    '''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json''',
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''gpt_bigcode'''
	__A			  =					['''past_key_values''']
	__A			  =					{
	    '''hidden_size''': '''n_embd''',
	    '''max_position_embeddings''': '''n_positions''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}







	def __init__(    self ,				_lowerCAmelCase=5_0257 ,				_lowerCAmelCase=1024 ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=None ,				_lowerCAmelCase="gelu_pytorch_tanh" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=1E-5 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=5_0256 ,				_lowerCAmelCase=5_0256 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    Optional[int]:
							'''simple docstring'''






							lowercase        =							vocab_size
							lowercase        =							n_positions
							lowercase        =							n_embd
							lowercase        =							n_layer
							lowercase        =							n_head
							lowercase        =							n_inner
							lowercase        =							activation_function
							lowercase        =							resid_pdrop
							lowercase        =							embd_pdrop
							lowercase        =							attn_pdrop
							lowercase        =							layer_norm_epsilon
							lowercase        =							initializer_range
							lowercase        =							scale_attn_weights
							lowercase        =							use_cache
							lowercase        =							attention_softmax_in_fpaa
							lowercase        =							scale_attention_softmax_in_fpaa
							lowercase        =							multi_query

							lowercase        =							bos_token_id
							lowercase        =							eos_token_id

							super().__init__(bos_token_id=_lowerCAmelCase ,				eos_token_id=_lowerCAmelCase ,				**_lowerCAmelCase						)


