
"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerModel,
				)
				from transformers.models.roformer.modeling_tf_roformer import (
				    TFRoFormerSelfAttention,
				    TFRoFormerSinusoidalPositionalEmbedding,
				)






class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=13							,						__a=7							,						__a=True							,						__a=True							,						__a=True							,						__a=True							,						__a=99							,						__a=32							,						__a=2							,						__a=4							,						__a=37							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=5_12							,						__a=16							,						__a=2							,						__a=0.02							,						__a=3							,						__a=4							,						__a=None							,						)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    13
							_UpperCamelCase									=    7
							_UpperCamelCase									=    True
							_UpperCamelCase									=    True
							_UpperCamelCase									=    True
							_UpperCamelCase									=    True
							_UpperCamelCase									=    99
							_UpperCamelCase									=    32
							_UpperCamelCase									=    2
							_UpperCamelCase									=    4
							_UpperCamelCase									=    37
							_UpperCamelCase									=    '''gelu'''
							_UpperCamelCase									=    0.1
							_UpperCamelCase									=    0.1
							_UpperCamelCase									=    5_12
							_UpperCamelCase									=    16
							_UpperCamelCase									=    2
							_UpperCamelCase									=    0.02
							_UpperCamelCase									=    3
							_UpperCamelCase									=    4
							_UpperCamelCase									=    None



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size)

							_UpperCamelCase									=    None
							if self.use_input_mask:
												_UpperCamelCase									=    random_attention_mask([self.batch_size, self.seq_length])

							_UpperCamelCase									=    None
							if self.use_token_type_ids:
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.type_vocab_size)

							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							_UpperCamelCase									=    None
							if self.use_labels:
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.type_sequence_label_size)
												_UpperCamelCase									=    ids_tensor([self.batch_size, self.seq_length]							,						self.num_labels)
												_UpperCamelCase									=    ids_tensor([self.batch_size]							,						self.num_choices)

							_UpperCamelCase									=    RoFormerConfig(
							    vocab_size=self.vocab_size							,						hidden_size=self.hidden_size							,						num_hidden_layers=self.num_hidden_layers							,						num_attention_heads=self.num_attention_heads							,						intermediate_size=self.intermediate_size							,						hidden_act=self.hidden_act							,						hidden_dropout_prob=self.hidden_dropout_prob							,						attention_probs_dropout_prob=self.attention_probs_dropout_prob							,						max_position_embeddings=self.max_position_embeddings							,						type_vocab_size=self.type_vocab_size							,						initializer_range=self.initializer_range							,						return_dict=__a							,						)

							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerModel(config=__a)
							_UpperCamelCase									=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

							_UpperCamelCase									=    [input_ids, input_mask]
							_UpperCamelCase									=    model(__a)

							_UpperCamelCase									=    model(__a)

							self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.seq_length, self.hidden_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    True
							_UpperCamelCase									=    TFRoFormerForCausalLM(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							_UpperCamelCase									=    model(__a)['''logits''']
							self.parent.assertListEqual(
							    list(prediction_scores.numpy().shape)							,						[self.batch_size, self.seq_length, self.vocab_size])



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerForMaskedLM(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.vocab_size))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    TFRoFormerForSequenceClassification(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_choices
							_UpperCamelCase									=    TFRoFormerForMultipleChoice(config=__a)
							_UpperCamelCase									=    tf.tile(tf.expand_dims(__a							,						1)							,						(1, self.num_choices, 1))
							_UpperCamelCase									=    tf.tile(tf.expand_dims(__a							,						1)							,						(1, self.num_choices, 1))
							_UpperCamelCase									=    tf.tile(tf.expand_dims(__a							,						1)							,						(1, self.num_choices, 1))
							_UpperCamelCase									=    {
							    '''input_ids''': multiple_choice_inputs_ids,
							    '''attention_mask''': multiple_choice_input_mask,
							    '''token_type_ids''': multiple_choice_token_type_ids,
							}
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_choices))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.num_labels
							_UpperCamelCase									=    TFRoFormerForTokenClassification(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}
							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.seq_length, self.num_labels))



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a							,						__a)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerForQuestionAnswering(config=__a)
							_UpperCamelCase									=    {
							    '''input_ids''': input_ids,
							    '''attention_mask''': input_mask,
							    '''token_type_ids''': token_type_ids,
							}

							_UpperCamelCase									=    model(__a)
							self.parent.assertEqual(result.start_logits.shape							,						(self.batch_size, self.seq_length))
							self.parent.assertEqual(result.end_logits.shape							,						(self.batch_size, self.seq_length))




		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.prepare_config_and_inputs()
							(
							    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    (
							    _UpperCamelCase
							)					,    
							)									=    config_and_inputs
							_UpperCamelCase									=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
							return config, inputs_dict






@require_tf
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (
		    (
		        TFRoFormerModel,
		        TFRoFormerForCausalLM,
		        TFRoFormerForMaskedLM,
		        TFRoFormerForQuestionAnswering,
		        TFRoFormerForSequenceClassification,
		        TFRoFormerForTokenClassification,
		        TFRoFormerForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		lowercase__         = (
		    {
		        'feature-extraction': TFRoFormerModel,
		        'fill-mask': TFRoFormerForMaskedLM,
		        'question-answering': TFRoFormerForQuestionAnswering,
		        'text-classification': TFRoFormerForSequenceClassification,
		        'text-generation': TFRoFormerForCausalLM,
		        'token-classification': TFRoFormerForTokenClassification,
		        'zero-shot': TFRoFormerForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)

		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							if pipeline_test_casse_name == "TextGenerationPipelineTests":
												return True

							return False



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						hidden_size=37)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__a)



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__a)




		@slow
		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base''')
							self.assertIsNotNone(__a)






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):



		@slow
		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''')
							_UpperCamelCase									=    tf.constant([[0, 1, 2, 3, 4, 5]])
							_UpperCamelCase									=    model(__a)[0]

							# TODO Replace vocab size
							_UpperCamelCase									=    5_00_00

							_UpperCamelCase									=    [1, 6, vocab_size]
							self.assertEqual(output.shape							,						__a)

							print(output[:, :3, :3])

							# TODO Replace values below with what was printed above.
							_UpperCamelCase									=    tf.constant(
							    [
							        [
							            [-0.1205_3341, -1.026_4901, 0.2922_1946],
							            [-1.513_3783, 0.19_7433, 0.1519_0607],
							            [-5.013_5403, -3.90_0256, -0.8403_8764],
							        ]
							    ])
							tf.debugging.assert_near(output[:, :3, :3]							,						__a							,						atol=1e-4)






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):
		lowercase__         = 1E-4



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    tf.constant([[4, 10]])
							_UpperCamelCase									=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6							,						embedding_dim=6)

							_UpperCamelCase									=    emba(input_ids.shape)
							_UpperCamelCase									=    tf.constant(
							    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]])

							tf.debugging.assert_near(__a							,						__a							,						atol=self.tolerance)




		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    tf.constant(
							    [
							        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
							        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
							        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
							    ])
							_UpperCamelCase									=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12							,						embedding_dim=5_12)
							emba([2, 16, 5_12])
							_UpperCamelCase									=    emba.weight[:3, :5]

							tf.debugging.assert_near(__a							,						__a							,						atol=self.tolerance)






@require_tf
class        _UpperCAmelCase(       unittest.TestCase      ):
		lowercase__         = 1E-4



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							# 2,12,16,64
							_UpperCamelCase									=    tf.reshape(tf.range(2 * 12 * 16 * 64							,						dtype=tf.floataa)							,						shape=(2, 12, 16, 64)) / 1_00

							_UpperCamelCase									=    -tf.reshape(tf.range(2 * 12 * 16 * 64							,						dtype=tf.floataa)							,						shape=(2, 12, 16, 64)) / 1_00

							_UpperCamelCase									=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=32							,						embedding_dim=64)
							_UpperCamelCase									=    embed_positions([2, 16, 7_68])[None, None, :, :]

							_UpperCamelCase					,    _UpperCamelCase									=    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
							    __a							,						__a							,						__a)

							_UpperCamelCase									=    tf.constant(
							    [
							        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
							        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
							        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
							        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
							        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
							        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
							    ])
							_UpperCamelCase									=    tf.constant(
							    [
							        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
							        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
							        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
							        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
							        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
							        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
							    ])

							tf.debugging.assert_near(query_layer[0, 0, :6, :8]							,						__a							,						atol=self.tolerance)
							tf.debugging.assert_near(key_layer[0, 0, :6, :8]							,						__a							,						atol=self.tolerance)








"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					if (
					    (cp >= 0x4E00 and cp <= 0x9FFF)
					    or (cp >= 0x3400 and cp <= 0x4DBF)  #
					    or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
					    or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
					    or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
					    or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
					    or (cp >= 0xF900 and cp <= 0xFAFF)
					    or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
					):  #
										return True

					return False



def  lowerCamelCase__						(     __snake_case      )				->						Optional[Any]:


					"""simple docstring"""





					for char in word:
										_UpperCamelCase									=    ord(__snake_case      )
										if not _is_chinese_char(__snake_case      ):
															return 0
					return 1



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    set()

					for token in tokens:
										_UpperCamelCase									=    len(__snake_case      ) > 1 and is_chinese(__snake_case      )
										if chinese_word:
															word_set.add(__snake_case      )
					_UpperCamelCase									=    list(__snake_case      )
					return word_list



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					if not chinese_word_set:
										return bert_tokens
					_UpperCamelCase									=    max([len(__snake_case      ) for w in chinese_word_set]      )

					_UpperCamelCase									=    bert_tokens
					_UpperCamelCase					,    _UpperCamelCase									=    0, len(__snake_case      )
					while start < end:
										_UpperCamelCase									=    True
										if is_chinese(bert_word[start]      ):
															_UpperCamelCase									=    min(end - start,	__snake_case      )
															for i in range(__snake_case,	1,	-1      ):
																				_UpperCamelCase									=    ''''''.join(bert_word[start : start + i]      )
																				if whole_word in chinese_word_set:
																									for j in range(start + 1,	start + i      ):
																														_UpperCamelCase									=    '''##''' + bert_word[j]
																									_UpperCamelCase									=    start + i
																									_UpperCamelCase									=    False
																									break
										if single_word:
															start += 1
					return bert_word



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    ltp_tokenizer.pipeline(lines[i : i + 1_00],	tasks=['''cws''']      ).cws
										_UpperCamelCase									=    [get_chinese_word(__snake_case      ) for r in res]
										ltp_res.extend(__snake_case      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for i in range(0,	len(__snake_case      ),	1_00      ):
										_UpperCamelCase									=    bert_tokenizer(lines[i : i + 1_00],	add_special_tokens=__snake_case,	truncation=__snake_case,	max_length=5_12      )
										bert_res.extend(res['''input_ids''']      )
					assert len(__snake_case      ) == len(__snake_case      )

					_UpperCamelCase									=    []
					for input_ids, chinese_word in zip(__snake_case,	__snake_case      ):
										_UpperCamelCase									=    []
										for id in input_ids:
															_UpperCamelCase									=    bert_tokenizer._convert_id_to_token(__snake_case      )
															input_tokens.append(__snake_case      )
										_UpperCamelCase									=    add_sub_symbol(__snake_case,	__snake_case      )
										_UpperCamelCase									=    []
										# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
										for i, token in enumerate(__snake_case      ):
															if token[:2] == "##":
																				_UpperCamelCase									=    token[2:]
																				# save chinese tokens' pos
																				if len(__snake_case      ) == 1 and _is_chinese_char(ord(__snake_case      )      ):
																									ref_id.append(__snake_case      )
										ref_ids.append(__snake_case      )

					assert len(__snake_case      ) == len(__snake_case      )

					return ref_ids



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					with open(args.file_name,	'''r''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    f.readlines()
					_UpperCamelCase									=    [line.strip() for line in data if len(__snake_case      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
					_UpperCamelCase									=    LTP(args.ltp      )  # faster in GPU device
					_UpperCamelCase									=    BertTokenizer.from_pretrained(args.bert      )

					_UpperCamelCase									=    prepare_ref(__snake_case,	__snake_case,	__snake_case      )

					with open(args.save_path,	'''w''',	encoding='''utf-8'''      ) as f:
										_UpperCamelCase									=    [json.dumps(__snake_case      ) + '''\n''' for ref in ref_ids]
										f.writelines(__snake_case      )


if __name__ == "__main__":
				_a								=						argparse.ArgumentParser(description="""prepare_chinese_ref""")
				parser.add_argument(
				    """--file_name""",
				    required=False,
				    type=str,
				    default="""./resources/chinese-demo.txt""",
				    help="""file need process, same as training data in lm""",
				)
				parser.add_argument(
				    """--ltp""",
				    required=False,
				    type=str,
				    default="""./resources/ltp""",
				    help="""resources for LTP tokenizer, usually a path""",
				)
				parser.add_argument(
				    """--bert""",
				    required=False,
				    type=str,
				    default="""./resources/robert""",
				    help="""resources for Bert tokenizer""",
				)
				parser.add_argument(
				    """--save_path""",
				    required=False,
				    type=str,
				    default="""./resources/ref.txt""",
				    help="""path to save res""",
				)

				_a								=						parser.parse_args()
				main(args)






