

'''simple docstring'''
from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
					import tensorflow as tf

					from ..tf_utils import stable_softmax


if is_torch_available():
					import torch


_lowerCamelCase      					=     logging.get_logger(__name__)
@add_end_docstrings(
    __SCREAMING_SNAKE_CASE			,			r"\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    "			,			)
class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def 		UpperCamelCase__							(  self			,_snake_case     ):




				if self.framework == "tf":
					UpperCAmelCase_     :      Optional[int]						=  tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()
				elif self.framework == "pt":
					UpperCAmelCase_     :      List[Any]						=  torch.nonzero(input_ids == self.tokenizer.mask_token_id			,as_tuple=lowerCAmelCase__     )
				else:
					raise ValueError("Unsupported framework"     )
				return masked_index






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      int						=  self.get_masked_index(lowerCAmelCase__     )
				UpperCAmelCase_     :      List[Any]						=  np.prod(masked_index.shape     )
				if numel < 1:
					raise PipelineException(
					    "fill-mask"			,self.model.base_model_prefix			,f'''No mask_token ({self.tokenizer.mask_token}) found on the input'''			,)






			def 		UpperCamelCase__							(  self			,_snake_case     ):




				if isinstance(lowerCAmelCase__			,lowerCAmelCase__     ):
					for model_input in model_inputs:
						self._ensure_exactly_one_mask_token(model_input["input_ids"][0]     )
				else:
					for input_ids in model_inputs["input_ids"]:
						self._ensure_exactly_one_mask_token(lowerCAmelCase__     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None			,**_snake_case     ):




				if return_tensors is None:
					UpperCAmelCase_     :      Union[str, Any]						=  self.framework
				UpperCAmelCase_     :      Union[str, Any]						=  self.tokenizer(lowerCAmelCase__			,return_tensors=lowerCAmelCase__     )
				self.ensure_exactly_one_mask_token(lowerCAmelCase__     )
				return model_inputs






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Dict						=  self.model(**lowerCAmelCase__     )
				UpperCAmelCase_     :      Dict						=  model_inputs['input_ids']
				return model_outputs






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=5			,_snake_case=None     ):




				if target_ids is not None and target_ids.shape[0] < top_k:
					UpperCAmelCase_     :      Dict						=  target_ids.shape[0]
				UpperCAmelCase_     :      Optional[int]						=  model_outputs['input_ids'][0]
				UpperCAmelCase_     :      Dict						=  model_outputs['logits']

				if self.framework == "tf":
					UpperCAmelCase_     :      Any						=  tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()[:, 0]

					UpperCAmelCase_     :      Optional[int]						=  outputs.numpy()

					UpperCAmelCase_     :      str						=  outputs[0, masked_index, :]
					UpperCAmelCase_     :      Optional[Any]						=  stable_softmax(lowerCAmelCase__			,axis=-1     )
					if target_ids is not None:
						UpperCAmelCase_     :      Any						=  tf.gather_nd(tf.squeeze(lowerCAmelCase__			,0     )			,target_ids.reshape(-1			,1     )     )
						UpperCAmelCase_     :      str						=  tf.expand_dims(lowerCAmelCase__			,0     )

					UpperCAmelCase_     :      List[Any]						=  tf.math.top_k(lowerCAmelCase__			,k=lowerCAmelCase__     )
					UpperCAmelCase_     :      List[Any]						=  topk.values.numpy(), topk.indices.numpy()
				else:
					UpperCAmelCase_     :      List[Any]						=  torch.nonzero(input_ids == self.tokenizer.mask_token_id			,as_tuple=lowerCAmelCase__     ).squeeze(-1     )
					# Fill mask pipeline supports only one ${mask_token} per sample

					UpperCAmelCase_     :      int						=  outputs[0, masked_index, :]
					UpperCAmelCase_     :      Any						=  logits.softmax(dim=-1     )
					if target_ids is not None:
						UpperCAmelCase_     :      Union[str, Any]						=  probs[..., target_ids]

					UpperCAmelCase_     :      List[Any]						=  probs.topk(lowerCAmelCase__     )

				UpperCAmelCase_     :      int						=  []
				UpperCAmelCase_     :      int						=  values.shape[0] == 1
				for i, (_values, _predictions) in enumerate(zip(values.tolist()			,predictions.tolist()     )     ):
					UpperCAmelCase_     :      Dict						=  []
					for v, p in zip(_values			,_predictions     ):
						# Copy is important since we're going to modify this array in place
						UpperCAmelCase_     :      str						=  input_ids.numpy().copy()
						if target_ids is not None:
							UpperCAmelCase_     :      Tuple						=  target_ids[p].tolist()

						UpperCAmelCase_     :      Tuple						=  p
						# Filter padding out:
						UpperCAmelCase_     :      int						=  tokens[np.where(tokens != self.tokenizer.pad_token_id     )]
						# Originally we skip special tokens to give readable output.
						# For multi masks though, the other [MASK] would be removed otherwise
						# making the output look odd, so we add them back
						UpperCAmelCase_     :      List[Any]						=  self.tokenizer.decode(lowerCAmelCase__			,skip_special_tokens=lowerCAmelCase__     )
						UpperCAmelCase_     :      Any						=  {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]     ), 'sequence': sequence}
						row.append(lowerCAmelCase__     )
					result.append(lowerCAmelCase__     )
				if single_mask:
					return result[0]
				return result






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None     ):




				if isinstance(lowerCAmelCase__			,lowerCAmelCase__     ):
					UpperCAmelCase_     :      Optional[int]						=  [targets]
				try:
					UpperCAmelCase_     :      Tuple						=  self.tokenizer.get_vocab()
				except Exception:
					UpperCAmelCase_     :      str						=  {}
				UpperCAmelCase_     :      List[str]						=  []
				for target in targets:
					UpperCAmelCase_     :      Union[str, Any]						=  vocab.get(lowerCAmelCase__			,lowerCAmelCase__     )
					if id_ is None:
						UpperCAmelCase_     :      Union[str, Any]						=  self.tokenizer(
						    lowerCAmelCase__			,add_special_tokens=lowerCAmelCase__			,return_attention_mask=lowerCAmelCase__			,return_token_type_ids=lowerCAmelCase__			,max_length=1			,truncation=lowerCAmelCase__			,)['input_ids']
						if len(lowerCAmelCase__     ) == 0:
							logger.warning(
							    f'''The specified target token `{target}` does not exist in the model vocabulary. '''
							    "We cannot replace it with anything meaningful, ignoring it"     )
							continue
						UpperCAmelCase_     :      List[str]						=  input_ids[0]
						# XXX: If users encounter this pass
						# it becomes pretty slow, so let's make sure
						# The warning enables them to fix the input to
						# get faster performance.
						logger.warning(
						    f'''The specified target token `{target}` does not exist in the model vocabulary. '''
						    f'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_     )}`.'''     )
					target_ids.append(id_     )
				UpperCAmelCase_     :      int						=  list(set(lowerCAmelCase__     )     )
				if len(lowerCAmelCase__     ) == 0:
					raise ValueError("At least one target must be provided when passed."     )
				UpperCAmelCase_     :      Optional[int]						=  np.array(lowerCAmelCase__     )
				return target_ids






			def 		UpperCamelCase__							(  self			,_snake_case=None			,_snake_case=None     ):
				UpperCAmelCase_     :      List[Any]						=  {}

				if targets is not None:
					UpperCAmelCase_     :      str						=  self.get_target_ids(lowerCAmelCase__			,lowerCAmelCase__     )
					UpperCAmelCase_     :      List[str]						=  target_ids

				if top_k is not None:
					UpperCAmelCase_     :      Union[str, Any]						=  top_k

				if self.tokenizer.mask_token_id is None:
					raise PipelineException(
					    "fill-mask"			,self.model.base_model_prefix			,"The tokenizer does not define a `mask_token`."     )
				return {}, {}, postprocess_params






			def __call__(  self			,_snake_case			,*_snake_case			,**_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  super().__call__(lowerCAmelCase__			,**lowerCAmelCase__     )
				if isinstance(lowerCAmelCase__			,lowerCAmelCase__     ) and len(lowerCAmelCase__     ) == 1:
					return outputs[0]
				return outputs








import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import SwiftFormerForImageClassification, SwiftFormerModel
				from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__=1_3			,						lowerCAmelCase__=3			,						lowerCAmelCase__=True			,						lowerCAmelCase__=True			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_0_0_0			,						lowerCAmelCase__=[3, 3, 6, 4]			,						lowerCAmelCase__=[4_8, 5_6, 1_1_2, 2_2_0]			,						):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					parent
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					batch_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					num_channels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					is_training
										SCREAMING_SNAKE_CASE_		:     str					=					use_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Tuple					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     int					=					num_labels
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					image_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					layer_depths
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					embed_dims






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					None
										if self.use_labels:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					ids_tensor([self.batch_size]			,						self.num_labels       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					self.get_config()

										return config, pixel_values, labels






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return SwiftFormerConfig(
										    depths=self.layer_depths			,						embed_dims=self.embed_dims			,						mlp_ratio=4			,						downsamples=[True, True, True, True]			,						hidden_act='gelu'			,						num_labels=self.num_labels			,						down_patch_size=3			,						down_stride=2			,						down_pad=1			,						drop_rate=0.0			,						drop_path_rate=0.0			,						use_layer_scale=lowerCAmelCase__			,						layer_scale_init_value=1E-5			,						)






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerModel(config=lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     int					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.embed_dims[-1], 7, 7)       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.num_labels
										SCREAMING_SNAKE_CASE_		:     str					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					model(lowerCAmelCase__			,						labels=lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					SwiftFormerForImageClassification(lowerCAmelCase__       )
										model.to(lowerCAmelCase__       )
										model.eval()

										SCREAMING_SNAKE_CASE_		:     str					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
										SCREAMING_SNAKE_CASE_		:     str					=					model(lowerCAmelCase__       )
										self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										((SCREAMING_SNAKE_CASE_)		,      (SCREAMING_SNAKE_CASE_)		,      (SCREAMING_SNAKE_CASE_))		:     Optional[Any]					=					self.prepare_config_and_inputs()
										SCREAMING_SNAKE_CASE_		:     str					=					{'pixel_values': pixel_values}
										return config, inputs_dict







@require_torch
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      =						(
							    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False
							_UpperCAmelCase      =						False






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerModelTester(self       )
										SCREAMING_SNAKE_CASE_		:     str					=					ConfigTester(
										    self			,						config_class=lowerCAmelCase__			,						has_text_modality=lowerCAmelCase__			,						hidden_size=3_7			,						num_attention_heads=1_2			,						num_hidden_layers=1_2			,						)






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										self.config_tester.run_common_tests()






							@unittest.skip(reason='SwiftFormer does not use inputs_embeds'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Dict					=					model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__			,						nn.Linear       )       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     int					=					model_class(lowerCAmelCase__       )
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					inspect.signature(model.forward       )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													SCREAMING_SNAKE_CASE_		:     int					=					[*signature.parameters.keys()]

													SCREAMING_SNAKE_CASE_		:     Tuple					=					['pixel_values']
													self.assertListEqual(arg_names[:1]			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Any					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__       )






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					SwiftFormerModel.from_pretrained(lowerCAmelCase__       )
													self.assertIsNotNone(lowerCAmelCase__       )






							@unittest.skip(reason='SwiftFormer does not output attentions'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Tuple					=					model_class(lowerCAmelCase__       )
													model.to(lowerCAmelCase__       )
													model.eval()

													with torch.no_grad():
																SCREAMING_SNAKE_CASE_		:     str					=					model(**self._prepare_for_class(lowerCAmelCase__			,						lowerCAmelCase__       )       )

													SCREAMING_SNAKE_CASE_		:     List[str]					=					outputs.hidden_states

													SCREAMING_SNAKE_CASE_		:     Any					=					8
													self.assertEqual(len(lowerCAmelCase__       )			,						lowerCAmelCase__       )  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(lowerCAmelCase__       )       ):
																self.assertEqual(
																    hidden_states[i].shape			,						torch.Size(
																        [
																            self.model_tester.batch_size,
																            self.model_tester.embed_dims[i // 2],
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																        ]       )			,						)

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					True
													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )

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

													check_hidden_states_output(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										def _config_zero_init(lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     List[str]					=					copy.deepcopy(lowerCAmelCase__       )
													for key in configs_no_init.__dict__.keys():
																if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						1E-10       )
																if isinstance(getattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )			,						lowerCAmelCase__       ):
																			SCREAMING_SNAKE_CASE_		:     int					=					_config_zero_init(getattr(lowerCAmelCase__			,						lowerCAmelCase__       )       )
																			setattr(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       )
													return configs_no_init

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.model_tester.prepare_config_and_inputs_for_common()

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					_config_zero_init(lowerCAmelCase__       )
										for model_class in self.all_model_classes:
													SCREAMING_SNAKE_CASE_		:     str					=					model_class(config=lowerCAmelCase__       )
													for name, param in model.named_parameters():
																if param.requires_grad:
																			self.assertIn(
																			    ((param.data.mean() * 1E9) / 1E9).round().item()			,						[0.0, 1.0]			,						msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,						)






							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										pass







def 						a__			(				):
			SCREAMING_SNAKE_CASE_		:     Dict					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
			return image







@require_torch
@require_vision
class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							@cached_property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'       ) if is_vision_available() else None






							@slow
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'       ).to(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.default_image_processor
										SCREAMING_SNAKE_CASE_		:     Any					=					prepare_img()
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image_processor(images=lowerCAmelCase__			,						return_tensors='pt'       ).to(lowerCAmelCase__       )

										# forward pass
										with torch.no_grad():
													SCREAMING_SNAKE_CASE_		:     List[str]					=					model(**lowerCAmelCase__       )

										# verify the logits
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.Size((1, 1_0_0_0)       )
										self.assertEqual(outputs.logits.shape			,						lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]       ).to(lowerCAmelCase__       )
										self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCAmelCase__			,						atol=1E-4       )       )





