
"""simple docstring"""




import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('0.8.3'):
					raise Exception('requires gluonnlp == 0.8.3')

if version.parse(mx.__version__) != version.parse('1.5.0'):
					raise Exception('requires mxnet == 1.5.0')

logging.set_verbosity_info()
UpperCAmelCase__   :					Optional[int]												=				logging.get_logger(__name__)

UpperCAmelCase__   :					int												=				"""The Nymphenburg Palace is a beautiful palace in Munich!"""


def lowercase_						(     _snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     {
							    "attention_cell": "multi_head",
							    "num_layers": 4,
							    "units": 1_024,
							    "hidden_size": 768,
							    "max_length": 512,
							    "num_heads": 8,
							    "scaled": True,
							    "dropout": 0.1,
							    "use_residual": True,
							    "embed_size": 1_024,
							    "embed_dropout": 0.1,
							    "word_embed": None,
							    "layer_norm_eps": 1E-5,
							    "token_type_vocab_size": 2,
							}

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     bort_4_8_768_1024_hparams

							# Let's construct the original Bort model here
							# Taken from official BERT implementation, see:
							# https://github.com/alexa/bort/blob/master/bort/bort.py
							SCREAMING_SNAKE_CASE__		: int     =     BERTEncoder(
							    attention_cell=predefined_args["""attention_cell"""]     ,num_layers=predefined_args["""num_layers"""]     ,units=predefined_args["""units"""]     ,hidden_size=predefined_args["""hidden_size"""]     ,max_length=predefined_args["""max_length"""]     ,num_heads=predefined_args["""num_heads"""]     ,scaled=predefined_args["""scaled"""]     ,dropout=predefined_args["""dropout"""]     ,output_attention=_snake_case     ,output_all_encodings=_snake_case     ,use_residual=predefined_args["""use_residual"""]     ,activation=predefined_args.get("""activation"""     ,"""gelu"""		)     ,layer_norm_eps=predefined_args.get("""layer_norm_eps"""     ,_snake_case		)     ,)

							# Vocab information needs to be fetched first
							# It's the same as RoBERTa, so RobertaTokenizer can be used later
							SCREAMING_SNAKE_CASE__		: str     =     "openwebtext_ccnews_stories_books_cased"

							# Specify download folder to Gluonnlp's vocab
							SCREAMING_SNAKE_CASE__		: Tuple     =     os.path.join(get_home_dir()     ,"""models"""		)
							SCREAMING_SNAKE_CASE__		: str     =     _load_vocab(_snake_case     ,_snake_case     ,_snake_case     ,cls=_snake_case		)

							SCREAMING_SNAKE_CASE__		: Tuple     =     nlp.model.BERTModel(
							    _snake_case     ,len(_snake_case		)     ,units=predefined_args["""units"""]     ,embed_size=predefined_args["""embed_size"""]     ,embed_dropout=predefined_args["""embed_dropout"""]     ,word_embed=predefined_args["""word_embed"""]     ,use_pooler=_snake_case     ,use_token_type_embed=_snake_case     ,token_type_vocab_size=predefined_args["""token_type_vocab_size"""]     ,use_classifier=_snake_case     ,use_decoder=_snake_case     ,)

							original_bort.load_parameters(_snake_case     ,cast_dtype=_snake_case     ,ignore_extra=_snake_case		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     original_bort._collect_params_with_prefix()

							# Build our config 🤗
							SCREAMING_SNAKE_CASE__		: str     =     {
							    "architectures": ["BertForMaskedLM"],
							    "attention_probs_dropout_prob": predefined_args["dropout"],
							    "hidden_act": "gelu",
							    "hidden_dropout_prob": predefined_args["dropout"],
							    "hidden_size": predefined_args["embed_size"],
							    "initializer_range": 0.02,
							    "intermediate_size": predefined_args["hidden_size"],
							    "layer_norm_eps": predefined_args["layer_norm_eps"],
							    "max_position_embeddings": predefined_args["max_length"],
							    "model_type": "bort",
							    "num_attention_heads": predefined_args["num_heads"],
							    "num_hidden_layers": predefined_args["num_layers"],
							    "pad_token_id": 1,  # 2 = BERT, 1 = RoBERTa
							    "type_vocab_size": 1,  # 2 = BERT, 1 = RoBERTa
							    "vocab_size": len(_snake_case		),
							}

							SCREAMING_SNAKE_CASE__		: Any     =     BertConfig.from_dict(_snake_case		)
							SCREAMING_SNAKE_CASE__		: Dict     =     BertForMaskedLM(_snake_case		)
							hf_bort_model.eval()

							# Parameter mapping table (Gluonnlp to Transformers)
							# * denotes layer index
							#
							# | Gluon Parameter                                                | Transformers Parameter
							# | -------------------------------------------------------------- | ----------------------
							# | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
							# | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
							# | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
							# | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
							# | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
							# | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
							# | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
							# | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
							# | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
							# | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
							# | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
							# | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
							# | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
							# | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
							# | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
							# | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

							# Helper function to convert MXNET Arrays to PyTorch
							def to_torch(_snake_case		) -> nn.Parameter:
														return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()		)		)

							# Check param shapes and map new HF param back
							def check_and_map_params(_snake_case     ,_snake_case		):
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     hf_param.shape

														SCREAMING_SNAKE_CASE__		: str     =     to_torch(params[gluon_param]		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     gluon_param.shape

														assert (
														    shape_hf == shape_gluon
														), f'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

														return gluon_param

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     check_and_map_params(
							    hf_bort_model.bert.embeddings.word_embeddings.weight     ,"""word_embed.0.weight"""		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     check_and_map_params(
							    hf_bort_model.bert.embeddings.position_embeddings.weight     ,"""encoder.position_weight"""		)
							SCREAMING_SNAKE_CASE__		: List[str]     =     check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.bias     ,"""encoder.layer_norm.beta"""		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     check_and_map_params(
							    hf_bort_model.bert.embeddings.LayerNorm.weight     ,"""encoder.layer_norm.gamma"""		)

							# Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
							SCREAMING_SNAKE_CASE__		: Dict     =     torch.zeros_like(
							    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data		)

							for i in range(hf_bort_config.num_hidden_layers		):
														SCREAMING_SNAKE_CASE__		: BertLayer     =     hf_bort_model.bert.encoder.layer[i]

														# self attention
														SCREAMING_SNAKE_CASE__		: BertSelfAttention     =     layer.attention.self

														SCREAMING_SNAKE_CASE__		: int     =     check_and_map_params(
														    self_attn.key.bias.data     ,f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''		)

														SCREAMING_SNAKE_CASE__		: Dict     =     check_and_map_params(
														    self_attn.key.weight.data     ,f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''		)
														SCREAMING_SNAKE_CASE__		: Dict     =     check_and_map_params(
														    self_attn.query.bias.data     ,f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''		)
														SCREAMING_SNAKE_CASE__		: str     =     check_and_map_params(
														    self_attn.query.weight.data     ,f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''		)
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     check_and_map_params(
														    self_attn.value.bias.data     ,f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''		)
														SCREAMING_SNAKE_CASE__		: List[str]     =     check_and_map_params(
														    self_attn.value.weight.data     ,f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''		)

														# self attention output
														SCREAMING_SNAKE_CASE__		: BertSelfOutput     =     layer.attention.output

														SCREAMING_SNAKE_CASE__		: Any     =     check_and_map_params(
														    self_output.dense.bias     ,f'''encoder.transformer_cells.{i}.proj.bias'''		)
														SCREAMING_SNAKE_CASE__		: Dict     =     check_and_map_params(
														    self_output.dense.weight     ,f'''encoder.transformer_cells.{i}.proj.weight'''		)
														SCREAMING_SNAKE_CASE__		: int     =     check_and_map_params(
														    self_output.LayerNorm.bias     ,f'''encoder.transformer_cells.{i}.layer_norm.beta'''		)
														SCREAMING_SNAKE_CASE__		: Any     =     check_and_map_params(
														    self_output.LayerNorm.weight     ,f'''encoder.transformer_cells.{i}.layer_norm.gamma'''		)

														# intermediate
														SCREAMING_SNAKE_CASE__		: BertIntermediate     =     layer.intermediate

														SCREAMING_SNAKE_CASE__		: int     =     check_and_map_params(
														    intermediate.dense.bias     ,f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     check_and_map_params(
														    intermediate.dense.weight     ,f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''		)

														# output
														SCREAMING_SNAKE_CASE__		: BertOutput     =     layer.output

														SCREAMING_SNAKE_CASE__		: int     =     check_and_map_params(
														    bert_output.dense.bias     ,f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''		)
														SCREAMING_SNAKE_CASE__		: Dict     =     check_and_map_params(
														    bert_output.dense.weight     ,f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''		)
														SCREAMING_SNAKE_CASE__		: Any     =     check_and_map_params(
														    bert_output.LayerNorm.bias     ,f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     check_and_map_params(
														    bert_output.LayerNorm.weight     ,f'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''		)

							# Save space and energy 🎄
							hf_bort_model.half()

							# Compare output of both models
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     RobertaTokenizer.from_pretrained("""roberta-base"""		)

							SCREAMING_SNAKE_CASE__		: Any     =     tokenizer.encode_plus(_snake_case		)["input_ids"]

							# Get gluon output
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     mx.nd.array([input_ids]		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     original_bort(inputs=_snake_case     ,token_types=[]		)

							# Get Transformer output (save and reload model again)
							hf_bort_model.save_pretrained(_snake_case		)
							SCREAMING_SNAKE_CASE__		: str     =     BertModel.from_pretrained(_snake_case		)
							hf_bort_model.eval()

							SCREAMING_SNAKE_CASE__		: str     =     tokenizer.encode_plus(_snake_case     ,return_tensors="""pt"""		)
							SCREAMING_SNAKE_CASE__		: Tuple     =     hf_bort_model(**_snake_case		)[0]

							SCREAMING_SNAKE_CASE__		: Any     =     output_gluon[0].asnumpy()
							SCREAMING_SNAKE_CASE__		: Tuple     =     output_hf[0].detach().numpy()

							SCREAMING_SNAKE_CASE__		: Optional[int]     =     np.max(np.abs(hf_layer - gluon_layer		)		).item()
							SCREAMING_SNAKE_CASE__		: List[Any]     =     np.allclose(_snake_case     ,_snake_case     ,atol=1E-3		)

							if success:
														print("""✔️ Both model do output the same tensors"""		)
							else:
														print("""❌ Both model do **NOT** output the same tensors"""		)
														print("""Absolute difference is:"""     ,_snake_case		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Tuple												=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--bort_checkpoint_path', default=None, type=str, required=True, help='Path the official Bort params file.'
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					UpperCAmelCase__   :					Tuple												=				parser.parse_args()
					convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""




import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

UpperCAmelCase__   :					str												=				{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		List[Any]       =     '''detr'''
				__UpperCamelCase   :		List[Any]       =     ['''past_key_values''']
				__UpperCamelCase   :		List[Any]       =     {
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}



				def __init__(self     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=1_00     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=6     , SCREAMING_SNAKE_CASE__=20_48     , SCREAMING_SNAKE_CASE__=8     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__="relu"     , SCREAMING_SNAKE_CASE__=2_56     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.0     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=1.0     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__="sine"     , SCREAMING_SNAKE_CASE__="resnet50"     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=1     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.1     , **SCREAMING_SNAKE_CASE__     , )      ->	Optional[Any]:



											"""simple docstring"""




											if backbone_config is not None and use_timm_backbone:
																		raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""					)

											if not use_timm_backbone:
																		if backbone_config is None:
																									logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""					)
																									SCREAMING_SNAKE_CASE__		: Any     =     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]					)
																		elif isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     backbone_config.get("""model_type"""					)
																									SCREAMING_SNAKE_CASE__		: int     =     CONFIG_MAPPING[backbone_model_type]
																									SCREAMING_SNAKE_CASE__		: str     =     config_class.from_dict(SCREAMING_SNAKE_CASE__					)
																		# set timm attributes to None
																		SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     None, None, None

											SCREAMING_SNAKE_CASE__		: Optional[int]     =     use_timm_backbone
											SCREAMING_SNAKE_CASE__		: Tuple     =     backbone_config
											SCREAMING_SNAKE_CASE__		: List[Any]     =     num_channels
											SCREAMING_SNAKE_CASE__		: Tuple     =     num_queries
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     d_model
											SCREAMING_SNAKE_CASE__		: str     =     encoder_ffn_dim
											SCREAMING_SNAKE_CASE__		: str     =     encoder_layers
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     encoder_attention_heads
											SCREAMING_SNAKE_CASE__		: Any     =     decoder_ffn_dim
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     decoder_layers
											SCREAMING_SNAKE_CASE__		: Dict     =     decoder_attention_heads
											SCREAMING_SNAKE_CASE__		: List[str]     =     dropout
											SCREAMING_SNAKE_CASE__		: Tuple     =     attention_dropout
											SCREAMING_SNAKE_CASE__		: Tuple     =     activation_dropout
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     activation_function
											SCREAMING_SNAKE_CASE__		: Any     =     init_std
											SCREAMING_SNAKE_CASE__		: Dict     =     init_xavier_std
											SCREAMING_SNAKE_CASE__		: Any     =     encoder_layerdrop
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     decoder_layerdrop
											SCREAMING_SNAKE_CASE__		: Dict     =     encoder_layers
											SCREAMING_SNAKE_CASE__		: List[str]     =     auxiliary_loss
											SCREAMING_SNAKE_CASE__		: List[Any]     =     position_embedding_type
											SCREAMING_SNAKE_CASE__		: List[str]     =     backbone
											SCREAMING_SNAKE_CASE__		: Dict     =     use_pretrained_backbone
											SCREAMING_SNAKE_CASE__		: Any     =     dilation
											# Hungarian matcher
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     class_cost
											SCREAMING_SNAKE_CASE__		: Tuple     =     bbox_cost
											SCREAMING_SNAKE_CASE__		: List[Any]     =     giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     mask_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     dice_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     bbox_loss_coefficient
											SCREAMING_SNAKE_CASE__		: Any     =     giou_loss_coefficient
											SCREAMING_SNAKE_CASE__		: List[str]     =     eos_coefficient
											super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return self.encoder_attention_heads



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return self.d_model



				@classmethod
				def 			__magic_name__					(cls     , SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)      ->	Tuple:



											"""simple docstring"""




											return cls(backbone_config=SCREAMING_SNAKE_CASE__     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict[str, any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     copy.deepcopy(self.__dict__					)
											if output["backbone_config"] is not None:
																		SCREAMING_SNAKE_CASE__		: Any     =     self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE__		: Dict     =     self.__class__.model_type
											return output






class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				__UpperCamelCase   :		Any       =     version.parse('''1.11'''						)



				@property
				def 			__magic_name__					(self					)      ->	Mapping[str, Mapping[int, str]]:



											"""simple docstring"""




											return OrderedDict(
											    [
											        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
											        ("""pixel_mask""", {0: """batch"""}),
											    ]					)



				@property
				def 			__magic_name__					(self					)      ->	float:



											"""simple docstring"""




											return 1E-5



				@property
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return 12




