
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A  =	get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=XLMRobertaTokenizer
				_lowercase     						=XLMRobertaTokenizerFast
				_lowercase     						=True
				_lowercase     						=True




				def 	__a		(   self	)							-> Tuple:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase_         =      XLMRobertaTokenizer(_UpperCamelCase							,							keep_accents=_UpperCamelCase	)
							tokenizer.save_pretrained(self.tmpdirname	)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      "<pad>"
							lowerCAmelCase_         =      1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase	)							,							_UpperCamelCase	)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase	)							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> Dict:
							lowerCAmelCase_         =      list(self.get_tokenizer().get_vocab().keys()	)

							self.assertEqual(vocab_keys[0]							,							"<s>"	)
							self.assertEqual(vocab_keys[1]							,							"<pad>"	)
							self.assertEqual(vocab_keys[-1]							,							"<mask>"	)
							self.assertEqual(len(_UpperCamelCase	)							,							1_002	)




				def 	__a		(   self	)							-> int:
							self.assertEqual(self.get_tokenizer().vocab_size							,							1_002	)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      XLMRobertaTokenizer(_UpperCamelCase							,							keep_accents=_UpperCamelCase	)

							lowerCAmelCase_         =      tokenizer.tokenize("This is a test"	)
							self.assertListEqual(_UpperCamelCase							,							["▁This", "▁is", "▁a", "▁t", "est"]	)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(_UpperCamelCase	)							,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]							,							)

							lowerCAmelCase_         =      tokenizer.tokenize("I was born in 92000, and this is falsé."	)
							self.assertListEqual(
							    _UpperCamelCase							,							[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "9",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "é",
							        ".",
							    ]							,							)
							lowerCAmelCase_         =      tokenizer.convert_tokens_to_ids(_UpperCamelCase	)
							self.assertListEqual(
							    _UpperCamelCase							,							[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]							,							)

							lowerCAmelCase_         =      tokenizer.convert_ids_to_tokens(_UpperCamelCase	)
							self.assertListEqual(
							    _UpperCamelCase							,							[
							        SPIECE_UNDERLINE + "I",
							        SPIECE_UNDERLINE + "was",
							        SPIECE_UNDERLINE + "b",
							        "or",
							        "n",
							        SPIECE_UNDERLINE + "in",
							        SPIECE_UNDERLINE + "",
							        "<unk>",
							        "2",
							        "0",
							        "0",
							        "0",
							        ",",
							        SPIECE_UNDERLINE + "and",
							        SPIECE_UNDERLINE + "this",
							        SPIECE_UNDERLINE + "is",
							        SPIECE_UNDERLINE + "f",
							        "al",
							        "s",
							        "<unk>",
							        ".",
							    ]							,							)




				def 	__a		(   self	)							-> int:
							if not self.test_slow_tokenizer:
										# as we don't have a slow version, we can't compare the outputs between slow and fast versions
										return

							lowerCAmelCase_         =      (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
													lowerCAmelCase_         =      self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,							**_UpperCamelCase	)
													lowerCAmelCase_         =      self.tokenizer_class.from_pretrained(_UpperCamelCase							,							**_UpperCamelCase	)

													lowerCAmelCase_         =      tempfile.mkdtemp()

													lowerCAmelCase_         =      tokenizer_r.save_pretrained(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.save_pretrained(_UpperCamelCase	)

													# Checks it save with the same files + the tokenizer.json file for the fast one
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files	)	)
													lowerCAmelCase_         =      tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f	)
													self.assertSequenceEqual(_UpperCamelCase							,							_UpperCamelCase	)

													# Checks everything loads correctly in the same way
													lowerCAmelCase_         =      tokenizer_r.from_pretrained(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.from_pretrained(_UpperCamelCase	)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(_UpperCamelCase							,							_UpperCamelCase	)	)
																# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

													shutil.rmtree(_UpperCamelCase	)

													# Save tokenizer rust, legacy_format=True
													lowerCAmelCase_         =      tempfile.mkdtemp()

													lowerCAmelCase_         =      tokenizer_r.save_pretrained(_UpperCamelCase							,							legacy_format=_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.save_pretrained(_UpperCamelCase	)

													# Checks it save with the same files
													self.assertSequenceEqual(_UpperCamelCase							,							_UpperCamelCase	)

													# Checks everything loads correctly in the same way
													lowerCAmelCase_         =      tokenizer_r.from_pretrained(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.from_pretrained(_UpperCamelCase	)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(_UpperCamelCase							,							_UpperCamelCase	)	)

													shutil.rmtree(_UpperCamelCase	)

													# Save tokenizer rust, legacy_format=False
													lowerCAmelCase_         =      tempfile.mkdtemp()

													lowerCAmelCase_         =      tokenizer_r.save_pretrained(_UpperCamelCase							,							legacy_format=_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.save_pretrained(_UpperCamelCase	)

													# Checks it saved the tokenizer.json file
													self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files	)	)

													# Checks everything loads correctly in the same way
													lowerCAmelCase_         =      tokenizer_r.from_pretrained(_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer_p.from_pretrained(_UpperCamelCase	)

													# Check special tokens are set accordingly on Rust and Python
													for key in tokenizer_pp.special_tokens_map:
																self.assertTrue(hasattr(_UpperCamelCase							,							_UpperCamelCase	)	)

													shutil.rmtree(_UpperCamelCase	)




				@cached_property
				def 	__a		(   self	)							-> Dict:
							return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base"	)




				def 	__a		(   self	)							-> Dict:
							with tempfile.NamedTemporaryFile() as f:
										shutil.copyfile(_UpperCamelCase							,							f.name	)
										lowerCAmelCase_         =      XLMRobertaTokenizer(f.name							,							keep_accents=_UpperCamelCase	)
										lowerCAmelCase_         =      pickle.dumps(_UpperCamelCase	)
							pickle.loads(_UpperCamelCase	)




				def 	__a		(   self	)							-> Dict:
							if not self.test_rust_tokenizer:
										return

							lowerCAmelCase_         =      self.get_tokenizer()
							lowerCAmelCase_         =      self.get_rust_tokenizer()

							lowerCAmelCase_         =      "I was born in 92000, and this is falsé."

							lowerCAmelCase_         =      tokenizer.tokenize(_UpperCamelCase	)
							lowerCAmelCase_         =      rust_tokenizer.tokenize(_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

							lowerCAmelCase_         =      tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase	)
							lowerCAmelCase_         =      rust_tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

							lowerCAmelCase_         =      self.get_rust_tokenizer()
							lowerCAmelCase_         =      tokenizer.encode(_UpperCamelCase	)
							lowerCAmelCase_         =      rust_tokenizer.encode(_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)




				@slow
				def 	__a		(   self	)							-> List[str]:
							lowerCAmelCase_         =      "Hello World!"
							lowerCAmelCase_         =      [0, 35_378, 6_661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(_UpperCamelCase							,							self.big_tokenizer.encode(_UpperCamelCase	)	)




				@slow
				def 	__a		(   self	)							-> List[Any]:
							lowerCAmelCase_         =      (
							    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
							    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
							)
							lowerCAmelCase_         =      [
							    0,
							    3_293,
							    83,
							    10,
							    4_552,
							    4_989,
							    7_986,
							    678,
							    10,
							    5_915,
							    111,
							    179_459,
							    124_850,
							    4,
							    6_044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6_780,
							    705,
							    15,
							    1_388,
							    44,
							    378,
							    10_114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    22_376,
							    642,
							    1_221,
							    15_190,
							    34_153,
							    450,
							    5_608,
							    959,
							    1_119,
							    57_702,
							    136,
							    186,
							    47,
							    1_098,
							    29_367,
							    47,
							    # 4426, # What fairseq tokenizes from "<unk>": "_<"
							    # 3678, # What fairseq tokenizes from "<unk>": "unk"
							    # 2740, # What fairseq tokenizes from "<unk>": ">"
							    3,  # What we tokenize from "<unk>": "<unk>"
							    6,  # Residue from the tokenization: an extra sentencepiece underline
							    4,
							    6_044,
							    237,
							    6_284,
							    50_901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(_UpperCamelCase							,							self.big_tokenizer.encode(_UpperCamelCase	)	)




				@slow
				def 	__a		(   self	)							-> Tuple:
							# fmt: off
							lowerCAmelCase_         =      {"input_ids": [[0, 11_062, 82_772, 7, 15, 82_772, 538, 51_529, 237, 17_198, 1_290, 206, 9, 215_175, 1_314, 136, 17_198, 1_290, 206, 9, 56_359, 42, 122_009, 9, 16_466, 16, 87_344, 4_537, 9, 4_717, 78_381, 6, 159_958, 7, 15, 24_480, 618, 4, 527, 22_693, 5_428, 4, 2_777, 24_480, 9_874, 4, 43_523, 594, 4, 803, 18_392, 33_189, 18, 4, 43_523, 24_447, 12_399, 100, 24_955, 83_658, 9_626, 144_057, 15, 839, 22_335, 16, 136, 24_955, 83_658, 83_479, 15, 39_102, 724, 16, 678, 645, 2_789, 1_328, 4_589, 42, 122_009, 115_774, 23, 805, 1_328, 46_876, 7, 136, 53_894, 1_940, 42_227, 41_159, 17_721, 823, 425, 4, 27_512, 98_722, 206, 136, 5_531, 4_970, 919, 17_336, 5, 2], [0, 20_080, 618, 83, 82_775, 47, 479, 9, 1_517, 73, 53_894, 333, 80_581, 110_117, 18_811, 5_256, 1_295, 51, 152_526, 297, 7_986, 390, 124_416, 538, 35_431, 214, 98, 15_044, 25_737, 136, 7_108, 43_701, 23, 756, 135_355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63_773, 119_455, 6, 147_797, 88_203, 7, 645, 70, 21, 3_285, 10_269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCamelCase							,							model_name="xlm-roberta-base"							,							revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"							,							)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
			from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


_A  =	logging.get_logger(__name__)

_A  =	{
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''bloom'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''num_hidden_layers''': '''n_layer''',
				    '''num_attention_heads''': '''n_head''',
				}




				def __init__(   self							,							_UpperCamelCase=250_880							,							_UpperCamelCase=64							,							_UpperCamelCase=2							,							_UpperCamelCase=8							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=1							,							_UpperCamelCase=2							,							_UpperCamelCase=False							,							_UpperCamelCase=0.0							,							_UpperCamelCase=0.0							,							_UpperCamelCase=1							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> str:
							lowerCAmelCase_         =      vocab_size
							# Backward compatibility with n_embed kwarg
							lowerCAmelCase_         =      kwargs.pop("n_embed"							,							_UpperCamelCase	)
							lowerCAmelCase_         =      hidden_size if n_embed is None else n_embed
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      pretraining_tp
							lowerCAmelCase_         =      apply_residual_connection_post_layernorm
							lowerCAmelCase_         =      hidden_dropout
							lowerCAmelCase_         =      attention_dropout

							lowerCAmelCase_         =      bos_token_id
							lowerCAmelCase_         =      eos_token_id
							lowerCAmelCase_         =      slow_but_exact

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





class       _lowerCAmelCase   (      __a			):
				_lowercase     						=version.parse('''1.12'''			)




				def __init__(   self							,							_UpperCamelCase							,							_UpperCamelCase = "default"							,							_UpperCamelCase = None							,							_UpperCamelCase = False							,							)							-> int:
							super().__init__(_UpperCamelCase							,							task=_UpperCamelCase							,							patching_specs=_UpperCamelCase							,							use_past=_UpperCamelCase	)
							if not getattr(self._config							,							"pad_token_id"							,							_UpperCamelCase	):
										# TODO: how to do that better?
										lowerCAmelCase_         =      0




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							lowerCAmelCase_         =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}	)
							if self.use_past:
										# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
										self.fill_with_past_key_values_(_UpperCamelCase							,							direction="inputs"							,							inverted_values_shape=_UpperCamelCase	)
										lowerCAmelCase_         =      {0: "batch", 1: "past_sequence + sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}

							return common_inputs




				@property
				def 	__a		(   self	)							-> int:
							return self._config.n_layer




				@property
				def 	__a		(   self	)							-> int:
							return self._config.n_head




				@property
				def 	__a		(   self	)							-> float:
							return 1e-3




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = -1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      super(_UpperCamelCase							,							self	).generate_dummy_inputs(
							    _UpperCamelCase							,							batch_size=_UpperCamelCase							,							seq_length=_UpperCamelCase							,							is_pair=_UpperCamelCase							,							framework=_UpperCamelCase	)

							# We need to order the input in the way they appears in the forward()
							lowerCAmelCase_         =      OrderedDict({"input_ids": common_inputs["input_ids"]}	)

							# Need to add the past_keys
							if self.use_past:
										if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."	)
										else:
													import torch

													lowerCAmelCase_      ,    lowerCAmelCase_         =      common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													lowerCAmelCase_         =      seqlen + 2
													lowerCAmelCase_         =      self._config.hidden_size // self.num_attention_heads
													lowerCAmelCase_         =      (
													    batch * self.num_attention_heads,
													    head_dim,
													    past_key_values_length,
													)
													lowerCAmelCase_         =      (
													    batch * self.num_attention_heads,
													    past_key_values_length,
													    head_dim,
													)
													lowerCAmelCase_         =      [
													    (torch.zeros(_UpperCamelCase	), torch.zeros(_UpperCamelCase	)) for _ in range(self.num_layers	)
													]

							lowerCAmelCase_         =      common_inputs["attention_mask"]
							if self.use_past:
										lowerCAmelCase_         =      ordered_inputs["attention_mask"].dtype
										lowerCAmelCase_         =      torch.cat(
										    [ordered_inputs["attention_mask"], torch.ones(_UpperCamelCase							,							_UpperCamelCase							,							dtype=_UpperCamelCase	)]							,							dim=1	)

							return ordered_inputs




				@property
				def 	__a		(   self	)							-> int:
							return 13



