





import os
import pickle
import unittest

from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
    VOCAB_FILES_NAMES,
    BertJapaneseTokenizer,
    CharacterTokenizer,
    JumanppTokenizer,
    MecabTokenizer,
    SudachiTokenizer,
    WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi

from ...test_tokenization_common import TokenizerTesterMixin

@custom_tokenizers
class  __A       (  lowercase__      ,   unittest.TestCase      ):
				__A								=			BertJapaneseTokenizer
				__A								=			False
				__A								=			True


				def _snake_case    (						self							):
								super().setUp()

								lowerCamelCase			=[
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """こんにちは""",
								    """こん""",
								    """にちは""",
								    """ばんは""",
								    """##こん""",
								    """##にちは""",
								    """##ばんは""",
								    """世界""",
								    """##世界""",
								    """、""",
								    """##、""",
								    """。""",
								    """##。""",
								]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								with open(self.vocab_file				,	"""w"""				,	encoding="""utf-8"""							) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""こんにちは、世界。 \nこんばんは、世界。"""
								lowerCamelCase			="""こんにちは 、 世界 。 こんばんは 、 世界 。"""
								return input_text, output_text


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=self.get_input_output_texts(_UpperCamelCase							)
								lowerCamelCase			=tokenizer.encode(_UpperCamelCase				,	add_special_tokens=_UpperCamelCase							)
								lowerCamelCase			=tokenizer.decode(_UpperCamelCase				,	clean_up_tokenization_spaces=_UpperCamelCase							)
								return text, ids


				def _snake_case    (						self							):
								pass  # TODO add if relevant


				def _snake_case    (						self							):
								pass  # TODO add if relevant


				def _snake_case    (						self							):
								pass  # TODO add if relevant


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class(self.vocab_file							)

								lowerCamelCase			=tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。"""							)
								self.assertListEqual(_UpperCamelCase				,	["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)				,	[3, 12, 10, 14, 4, 9, 12, 10, 14]							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class(self.vocab_file				,	word_tokenizer_type="""mecab"""							)
								self.assertIsNotNone(_UpperCamelCase							)

								lowerCamelCase			="""こんにちは、世界。\nこんばんは、世界。"""
								lowerCamelCase			=tokenizer.tokenize(_UpperCamelCase							)
								self.assertListEqual(_UpperCamelCase				,	["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)				,	[3, 12, 10, 14, 4, 9, 12, 10, 14]							)

								lowerCamelCase			=os.path.join(self.tmpdirname				,	"""tokenizer.bin"""							)
								with open(_UpperCamelCase				,	"""wb"""							) as handle:
												pickle.dump(_UpperCamelCase				,	_UpperCamelCase							)

								with open(_UpperCamelCase				,	"""rb"""							) as handle:
												lowerCamelCase			=pickle.load(_UpperCamelCase							)

								lowerCamelCase			=tokenizer_new.tokenize(_UpperCamelCase							)

								self.assertListEqual(_UpperCamelCase				,	_UpperCamelCase							)


				def _snake_case    (						self							):
								lowerCamelCase			=MecabTokenizer(mecab_dic="""ipadic"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]				,	)


				def _snake_case    (						self							):
								try:
												lowerCamelCase			=MecabTokenizer(mecab_dic="""unidic_lite"""							)
								except ModuleNotFoundError:
												return

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]				,	)


				def _snake_case    (						self							):
								try:
												lowerCamelCase			=MecabTokenizer(mecab_dic="""unidic"""							)
								except ModuleNotFoundError:
												return

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]				,	)


				def _snake_case    (						self							):
								lowerCamelCase			=MecabTokenizer(do_lower_case=_UpperCamelCase				,	mecab_dic="""ipadic"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]				,	)


				def _snake_case    (						self							):
								try:
												lowerCamelCase			=MecabTokenizer(
												    do_lower_case=_UpperCamelCase				,	normalize_text=_UpperCamelCase				,	mecab_option="""-d /usr/local/lib/mecab/dic/jumandic"""							)
								except RuntimeError:
												# if dict doesn't exist in the system, previous code raises this error.
												return

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""]				,	)


				def _snake_case    (						self							):
								lowerCamelCase			=MecabTokenizer(normalize_text=_UpperCamelCase				,	mecab_dic="""ipadic"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """　""", """。"""]				,	)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class(self.vocab_file				,	word_tokenizer_type="""sudachi"""							)
								self.assertIsNotNone(_UpperCamelCase							)

								lowerCamelCase			="""こんにちは、世界。\nこんばんは、世界。"""
								lowerCamelCase			=tokenizer.tokenize(_UpperCamelCase							)
								self.assertListEqual(_UpperCamelCase				,	["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)				,	[3, 12, 10, 14, 4, 9, 12, 10, 14]							)

								lowerCamelCase			=os.path.join(self.tmpdirname				,	"""tokenizer.bin"""							)
								with open(_UpperCamelCase				,	"""wb"""							) as handle:
												pickle.dump(_UpperCamelCase				,	_UpperCamelCase							)

								with open(_UpperCamelCase				,	"""rb"""							) as handle:
												lowerCamelCase			=pickle.load(_UpperCamelCase							)

								lowerCamelCase			=tokenizer_new.tokenize(_UpperCamelCase							)

								self.assertListEqual(_UpperCamelCase				,	_UpperCamelCase							)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(sudachi_dict_type="""core"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	[""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """]				,	)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(sudachi_dict_type="""core"""				,	sudachi_split_mode="""A"""							)

								self.assertListEqual(tokenizer.tokenize("""外国人参政権"""							)				,	["""外国""", """人""", """参政""", """権"""]							)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(sudachi_dict_type="""core"""				,	sudachi_split_mode="""B"""							)

								self.assertListEqual(tokenizer.tokenize("""外国人参政権"""							)				,	["""外国人""", """参政権"""]							)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(sudachi_dict_type="""core"""				,	sudachi_split_mode="""C"""							)

								self.assertListEqual(tokenizer.tokenize("""外国人参政権"""							)				,	["""外国人参政権"""]							)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(do_lower_case=_UpperCamelCase				,	sudachi_dict_type="""core"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	[""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """]				,	)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(normalize_text=_UpperCamelCase				,	sudachi_dict_type="""core"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	[""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """]				,	)


				@require_sudachi
				def _snake_case    (						self							):
								lowerCamelCase			=SudachiTokenizer(trim_whitespace=_UpperCamelCase				,	sudachi_dict_type="""core"""							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""]				,	)


				@require_jumanpp
				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class(self.vocab_file				,	word_tokenizer_type="""jumanpp"""							)
								self.assertIsNotNone(_UpperCamelCase							)

								lowerCamelCase			="""こんにちは、世界。\nこんばんは、世界。"""
								lowerCamelCase			=tokenizer.tokenize(_UpperCamelCase							)
								self.assertListEqual(_UpperCamelCase				,	["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""]							)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)				,	[3, 12, 10, 14, 4, 9, 12, 10, 14]							)

								lowerCamelCase			=os.path.join(self.tmpdirname				,	"""tokenizer.bin"""							)
								with open(_UpperCamelCase				,	"""wb"""							) as handle:
												pickle.dump(_UpperCamelCase				,	_UpperCamelCase							)

								with open(_UpperCamelCase				,	"""rb"""							) as handle:
												lowerCamelCase			=pickle.load(_UpperCamelCase							)

								lowerCamelCase			=tokenizer_new.tokenize(_UpperCamelCase							)

								self.assertListEqual(_UpperCamelCase				,	_UpperCamelCase							)


				@require_jumanpp
				def _snake_case    (						self							):
								lowerCamelCase			=JumanppTokenizer()

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]				,	)


				@require_jumanpp
				def _snake_case    (						self							):
								lowerCamelCase			=JumanppTokenizer(do_lower_case=_UpperCamelCase							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]				,	)


				@require_jumanpp
				def _snake_case    (						self							):
								lowerCamelCase			=JumanppTokenizer(normalize_text=_UpperCamelCase							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""]				,	)


				@require_jumanpp
				def _snake_case    (						self							):
								lowerCamelCase			=JumanppTokenizer(trim_whitespace=_UpperCamelCase							)

								self.assertListEqual(
								    tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  """							)				,	["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""]				,	)


				@require_jumanpp
				def _snake_case    (						self							):
								lowerCamelCase			=JumanppTokenizer()

								self.assertListEqual(
								    tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。"""							)				,	["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""]				,	)


				def _snake_case    (						self							):
								lowerCamelCase			=["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]

								lowerCamelCase			={}
								for i, token in enumerate(_UpperCamelCase							):
												lowerCamelCase			=i
								lowerCamelCase			=WordpieceTokenizer(vocab=_UpperCamelCase				,	unk_token="""[UNK]"""							)

								self.assertListEqual(tokenizer.tokenize(""""""							)				,	[]							)

								self.assertListEqual(tokenizer.tokenize("""こんにちは"""							)				,	["""こんにちは"""]							)

								self.assertListEqual(tokenizer.tokenize("""こんばんは"""							)				,	["""こん""", """##ばんは"""]							)

								self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは"""							)				,	["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""]							)


				def _snake_case    (						self							):
								lowerCamelCase			=BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp"""							)
								lowerCamelCase			=tokenizer.subword_tokenizer

								lowerCamelCase			=subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。"""							)
								self.assertListEqual(_UpperCamelCase				,	["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""]							)

								lowerCamelCase			=subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは"""							)
								self.assertListEqual(_UpperCamelCase				,	["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""]							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese"""							)

								lowerCamelCase			=tokenizer.encode("""ありがとう。"""				,	add_special_tokens=_UpperCamelCase							)
								lowerCamelCase			=tokenizer.encode("""どういたしまして。"""				,	add_special_tokens=_UpperCamelCase							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(_UpperCamelCase				,	_UpperCamelCase							)

								# 2 is for "[CLS]", 3 is for "[SEP]"
								assert encoded_sentence == [2] + text + [3]
								assert encoded_pair == [2] + text + [3] + text_a + [3]




@custom_tokenizers
class  __A       (  lowercase__      ,   unittest.TestCase      ):
				__A								=			BertJapaneseTokenizer
				__A								=			False


				def _snake_case    (						self							):
								super().setUp()

								lowerCamelCase			=["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]

								lowerCamelCase			=os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""]							)
								with open(self.vocab_file				,	"""w"""				,	encoding="""utf-8"""							) as vocab_writer:
												vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]							)							)


				def _snake_case    (						self				,	**UpperCAmelCase_							):
								return BertJapaneseTokenizer.from_pretrained(self.tmpdirname				,	subword_tokenizer_type="""character"""				,	**_UpperCamelCase							)


				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			="""こんにちは、世界。 \nこんばんは、世界。"""
								lowerCamelCase			="""こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
								return input_text, output_text


				def _snake_case    (						self							):
								pass  # TODO add if relevant


				def _snake_case    (						self							):
								pass  # TODO add if relevant


				def _snake_case    (						self							):
								pass  # TODO add if relevant


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class(self.vocab_file				,	subword_tokenizer_type="""character"""							)

								lowerCamelCase			=tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。"""							)
								self.assertListEqual(
								    _UpperCamelCase				,	["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""]							)
								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(_UpperCamelCase							)				,	[3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12]							)


				def _snake_case    (						self							):
								lowerCamelCase			=["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]

								lowerCamelCase			={}
								for i, token in enumerate(_UpperCamelCase							):
												lowerCamelCase			=i
								lowerCamelCase			=CharacterTokenizer(vocab=_UpperCamelCase				,	unk_token="""[UNK]"""							)

								self.assertListEqual(tokenizer.tokenize(""""""							)				,	[]							)

								self.assertListEqual(tokenizer.tokenize("""こんにちは"""							)				,	["""こ""", """ん""", """に""", """ち""", """は"""]							)

								self.assertListEqual(tokenizer.tokenize("""こんにちほ"""							)				,	["""こ""", """ん""", """に""", """ち""", """[UNK]"""]							)


				def _snake_case    (						self							):
								lowerCamelCase			=self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char"""							)

								lowerCamelCase			=tokenizer.encode("""ありがとう。"""				,	add_special_tokens=_UpperCamelCase							)
								lowerCamelCase			=tokenizer.encode("""どういたしまして。"""				,	add_special_tokens=_UpperCamelCase							)

								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(_UpperCamelCase							)
								lowerCamelCase			=tokenizer.build_inputs_with_special_tokens(_UpperCamelCase				,	_UpperCamelCase							)

								# 2 is for "[CLS]", 3 is for "[SEP]"
								assert encoded_sentence == [2] + text + [3]
								assert encoded_pair == [2] + text + [3] + text_a + [3]




@custom_tokenizers
class  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								lowerCamelCase			="""cl-tohoku/bert-base-japanese"""
								lowerCamelCase			=AutoTokenizer.from_pretrained(_UpperCamelCase							)
								self.assertIsInstance(_UpperCamelCase				,	_UpperCamelCase							)




class  __A       (  unittest.TestCase      ):


				def _snake_case    (						self							):
								lowerCamelCase			="""cl-tohoku/bert-base-japanese"""
								with self.assertLogs("""transformers"""				,	level="""WARNING"""							) as cm:
												BertTokenizer.from_pretrained(_UpperCamelCase							)
												self.assertTrue(
												    cm.records[0].message.startswith(
												        """The tokenizer class you load from this checkpoint is not the same type as the class this function"""
												        """ is called from."""							)							)
								lowerCamelCase			="""bert-base-cased"""
								with self.assertLogs("""transformers"""				,	level="""WARNING"""							) as cm:
												BertJapaneseTokenizer.from_pretrained(_UpperCamelCase							)
												self.assertTrue(
												    cm.records[0].message.startswith(
												        """The tokenizer class you load from this checkpoint is not the same type as the class this function"""
												        """ is called from."""							)							)












import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__  :     Union[str, Any]   			=logging.getLogger(__name__)





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					int:
				return (preds == labels).mean()

@dataclass
class  __A       :
				__A								=			field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,   )




@dataclass
class  __A       :
				__A								=			field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()      )}      )
				__A								=			field(metadata={"""help""": """Should contain the data files for the task."""}      )
				__A								=			field(
				    default=1_28      ,   metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }      ,   )
				__A								=			field(
				    default=a      ,   metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )





def        _lowercase   (  )  ->					Optional[int]:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowerCamelCase			=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
				lowerCamelCase	,					lowerCamelCase	,					lowerCamelCase			=parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir			)
				    and os.listdir(training_args.output_dir			)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    """ --overwrite_output_dir to overcome."""			)

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""     , datefmt="""%m/%d/%Y %H:%M:%S"""     , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     , )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""     , training_args.local_rank     , training_args.device     , training_args.n_gpu     , bool(training_args.local_rank != -1			)     , training_args.fpaa     , )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank			):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""     , _UpperCAmelCase			)

				# Set seed
				set_seed(training_args.seed			)

				try:
								lowerCamelCase			=processors[data_args.task_name]()
								lowerCamelCase			=processor.get_labels()
								lowerCamelCase			=len(_UpperCAmelCase			)
				except KeyError:
								raise ValueError("""Task not found: %s""" % (data_args.task_name)			)

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowerCamelCase			=AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path     , num_labels=_UpperCAmelCase     , finetuning_task=data_args.task_name     , cache_dir=model_args.cache_dir     , )
				lowerCamelCase			=AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     , cache_dir=model_args.cache_dir     , )
				lowerCamelCase			=AutoModelForMultipleChoice.from_pretrained(
				    model_args.model_name_or_path     , from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)     , config=_UpperCAmelCase     , cache_dir=model_args.cache_dir     , )

				# Get datasets
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , task=data_args.task_name     , max_seq_length=data_args.max_seq_length     , overwrite_cache=data_args.overwrite_cache     , mode=Split.train     , )
				    if training_args.do_train
				    else None
				)
				lowerCamelCase			=(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir     , tokenizer=_UpperCAmelCase     , task=data_args.task_name     , max_seq_length=data_args.max_seq_length     , overwrite_cache=data_args.overwrite_cache     , mode=Split.dev     , )
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(_UpperCAmelCase			) -> Dict:
								lowerCamelCase			=np.argmax(p.predictions     , axis=1			)
								return {"acc": simple_accuracy(_UpperCAmelCase     , p.label_ids			)}

				# Data collator
				lowerCamelCase			=DataCollatorWithPadding(_UpperCAmelCase     , pad_to_multiple_of=8			) if training_args.fpaa else None

				# Initialize our Trainer
				lowerCamelCase			=Trainer(
				    model=_UpperCAmelCase     , args=_UpperCAmelCase     , train_dataset=_UpperCAmelCase     , eval_dataset=_UpperCAmelCase     , compute_metrics=_UpperCAmelCase     , data_collator=_UpperCAmelCase     , )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path			) else None			)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir			)

    # Evaluation
				lowerCamelCase			={}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***"""			)

								lowerCamelCase			=trainer.evaluate()

								lowerCamelCase			=os.path.join(training_args.output_dir     , """eval_results.txt"""			)
								if trainer.is_world_master():
												with open(_UpperCAmelCase     , """w"""			) as writer:
																logger.info("""***** Eval results *****"""			)
																for key, value in result.items():
																				logger.info("""  %s = %s"""     , _UpperCAmelCase     , _UpperCAmelCase			)
																				writer.write("""%s = %s\n""" % (key, value)			)

																results.update(_UpperCAmelCase			)

				return results





def        _lowercase   (  _UpperCAmelCase			)  ->					Union[str, Any]:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
							main()





