



'''simple docstring'''
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


snake_case_ :	List[Any]         =  get_tests_dir('fixtures/test_sentencepiece.model')






@require_sentencepiece
@require_tokenizers
class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			XLMRobertaTokenizer
	lowercase__  				=			XLMRobertaTokenizerFast
	lowercase__  				=			True
	lowercase__  				=			True





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							super().setUp()

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





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[int] 				=   '<pad>'
							_UpperCamelCase					:		str 				=   1

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





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   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(lowerCamelCase__ ) ,1002 )





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							self.assertEqual(self.get_tokenizer().vocab_size ,1002 )





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   XLMRobertaTokenizer(lowerCamelCase__ ,keep_accents=lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   tokenizer.tokenize('This is a test' )
							self.assertListEqual(lowerCamelCase__ ,['▁This', '▁is', '▁a', '▁t', 'est'] )

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

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.tokenize('I was born in 92000, and this is falsé.' )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        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',
							        '.',
							    ] ,)
							_UpperCamelCase					:		int 				=   tokenizer.convert_tokens_to_ids(lowerCamelCase__ )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        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 ^
							    ] ,)

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.convert_ids_to_tokens(lowerCamelCase__ )
							self.assertListEqual(
							    lowerCamelCase__ ,[
							        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 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							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

							_UpperCamelCase					:		Tuple 				=   (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})' ):
																			_UpperCamelCase					:		List[Any] 				=   self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
																			_UpperCamelCase					:		Tuple 				=   self.tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )

																			_UpperCamelCase					:		Optional[int] 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		str 				=   tokenizer_r.save_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		Union[str, Any] 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# 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 ) )
																			_UpperCamelCase					:		str 				=   tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
																			self.assertSequenceEqual(lowerCamelCase__ ,lowerCamelCase__ )

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		int 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

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

																			shutil.rmtree(lowerCamelCase__ )

																			# Save tokenizer rust, legacy_format=True
																			_UpperCamelCase					:		str 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		Any 				=   tokenizer_r.save_pretrained(lowerCamelCase__ ,legacy_format=lowerCamelCase__ )
																			_UpperCamelCase					:		Optional[Any] 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

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

																			# Checks everything loads correctly in the same way
																			_UpperCamelCase					:		Tuple 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		str 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

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

																			shutil.rmtree(lowerCamelCase__ )

																			# Save tokenizer rust, legacy_format=False
																			_UpperCamelCase					:		Tuple 				=   tempfile.mkdtemp()

																			_UpperCamelCase					:		Dict 				=   tokenizer_r.save_pretrained(lowerCamelCase__ ,legacy_format=lowerCamelCase__ )
																			_UpperCamelCase					:		int 				=   tokenizer_p.save_pretrained(lowerCamelCase__ )

																			# 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
																			_UpperCamelCase					:		List[Any] 				=   tokenizer_r.from_pretrained(lowerCamelCase__ )
																			_UpperCamelCase					:		Any 				=   tokenizer_p.from_pretrained(lowerCamelCase__ )

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

																			shutil.rmtree(lowerCamelCase__ )





	@cached_property
	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							with tempfile.NamedTemporaryFile() as f:
													shutil.copyfile(lowerCamelCase__ ,f.name )
													_UpperCamelCase					:		Optional[Any] 				=   XLMRobertaTokenizer(f.name ,keep_accents=lowerCamelCase__ )
													_UpperCamelCase					:		str 				=   pickle.dumps(lowerCamelCase__ )
							pickle.loads(lowerCamelCase__ )





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							if not self.test_rust_tokenizer:
													return

							_UpperCamelCase					:		int 				=   self.get_tokenizer()
							_UpperCamelCase					:		List[str] 				=   self.get_rust_tokenizer()

							_UpperCamelCase					:		Any 				=   'I was born in 92000, and this is falsé.'

							_UpperCamelCase					:		str 				=   tokenizer.tokenize(lowerCamelCase__ )
							_UpperCamelCase					:		Any 				=   rust_tokenizer.tokenize(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
							_UpperCamelCase					:		Tuple 				=   rust_tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   self.get_rust_tokenizer()
							_UpperCamelCase					:		Union[str, Any] 				=   tokenizer.encode(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[int] 				=   rust_tokenizer.encode(lowerCamelCase__ )
							self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )





	@slow
	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		Union[str, Any] 				=   'Hello World!'
							_UpperCamelCase					:		str 				=   [0, 35378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

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





	@slow
	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		Optional[Any] 				=   (
							    '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'
							)
							_UpperCamelCase					:		str 				=   [
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    179459,
							    124850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    10114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    22376,
							    642,
							    1221,
							    15190,
							    34153,
							    450,
							    5608,
							    959,
							    1119,
							    57702,
							    136,
							    186,
							    47,
							    1098,
							    29367,
							    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,
							    6044,
							    237,
							    6284,
							    50901,
							    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(lowerCamelCase__ ,self.big_tokenizer.encode(lowerCamelCase__ ) )





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







							'''simple docstring'''
							# fmt: off
							_UpperCamelCase					:		Union[str, Any] 				=   {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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=lowerCamelCase__ ,model_name='xlm-roberta-base' ,revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' ,)




from collections import OrderedDict
from typing import Mapping

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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   int				       ="""xmod"""
	def __init__(			self							:   Optional[Any]       ,   UpperCAmelCase_							:   Optional[int]=30522       ,   UpperCAmelCase_							:   Union[str, Any]=768       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=12       ,   UpperCAmelCase_							:   List[str]=3072       ,   UpperCAmelCase_							:   Dict="gelu"       ,   UpperCAmelCase_							:   str=0.1       ,   UpperCAmelCase_							:   Dict=0.1       ,   UpperCAmelCase_							:   Optional[Any]=512       ,   UpperCAmelCase_							:   Any=2       ,   UpperCAmelCase_							:   Optional[Any]=0.02       ,   UpperCAmelCase_							:   Optional[Any]=1e-1_2       ,   UpperCAmelCase_							:   Any=1       ,   UpperCAmelCase_							:   Dict=0       ,   UpperCAmelCase_							:   Optional[int]=2       ,   UpperCAmelCase_							:   Tuple="absolute"       ,   UpperCAmelCase_							:   Union[str, Any]=True       ,   UpperCAmelCase_							:   Dict=None       ,   UpperCAmelCase_							:   Union[str, Any]=False       ,   UpperCAmelCase_							:   int=2       ,   UpperCAmelCase_							:   int=False       ,   UpperCAmelCase_							:   Optional[int]=True       ,   UpperCAmelCase_							:   List[Any]=True       ,   UpperCAmelCase_							:   Union[str, Any]=("en_XX",)       ,   UpperCAmelCase_							:   int=None       ,   **UpperCAmelCase_							:   str       ,   ):
						super().__init__(pad_token_id=UpperCAmelCase_       ,   bos_token_id=UpperCAmelCase_       ,   eos_token_id=UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= vocab_size
						SCREAMING_SNAKE_CASE__													= hidden_size
						SCREAMING_SNAKE_CASE__													= num_hidden_layers
						SCREAMING_SNAKE_CASE__													= num_attention_heads
						SCREAMING_SNAKE_CASE__													= hidden_act
						SCREAMING_SNAKE_CASE__													= intermediate_size
						SCREAMING_SNAKE_CASE__													= hidden_dropout_prob
						SCREAMING_SNAKE_CASE__													= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__													= max_position_embeddings
						SCREAMING_SNAKE_CASE__													= type_vocab_size
						SCREAMING_SNAKE_CASE__													= initializer_range
						SCREAMING_SNAKE_CASE__													= layer_norm_eps
						SCREAMING_SNAKE_CASE__													= position_embedding_type
						SCREAMING_SNAKE_CASE__													= use_cache
						SCREAMING_SNAKE_CASE__													= classifier_dropout
						SCREAMING_SNAKE_CASE__													= pre_norm
						SCREAMING_SNAKE_CASE__													= adapter_reduction_factor
						SCREAMING_SNAKE_CASE__													= adapter_layer_norm
						SCREAMING_SNAKE_CASE__													= adapter_reuse_layer_norm
						SCREAMING_SNAKE_CASE__													= ln_before_adapter
						SCREAMING_SNAKE_CASE__													= list(UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= default_language





class 						lowercase__  (						_UpperCAmelCase  ):
	@property
	def      A_				(			self							:   List[Any]							):
						if self.task == "multiple-choice":
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
											SCREAMING_SNAKE_CASE__													= {0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]							)





