





'''simple docstring'''




import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class  SCREAMING_SNAKE_CASE    (a__					,		unittest.TestCase							):
     lowerCAmelCase					=       GPTSanJapaneseTokenizer
     lowerCAmelCase					=       False
     lowerCAmelCase					=       {'''do_clean_text''': False, '''add_prefix_space''': False}


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().setUp()

          # fmt: off
          __A						: int      =       ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
          # fmt: on
          __A						: Union[str, Any]      =       {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
          __A						: Any      =       {'unk_token': '<unk>'}

          __A						: Any      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['vocab_file'])
          __A						: Dict      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['emoji_file'])
          with open(self.vocab_file						,	'w'						,	encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
          with open(self.emoji_file						,	'w') as emoji_writer:
               emoji_writer.write(json.dumps(_UpperCAmelCase))


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          kwargs.update(self.special_tokens_map)
          return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       'こんにちは、世界。 \nこんばんは、㔺界。😀'
          __A						: Any      =       'こんにちは、世界。 \nこんばんは、世界。😀'
          return input_text, output_text


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase):

          '''simple docstring'''






          __A ,__A						: Union[str, Any]      =       self.get_input_output_texts(_UpperCAmelCase)
          __A						: List[Any]      =       tokenizer.encode(_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase)
          __A						: Union[str, Any]      =       tokenizer.decode(_UpperCAmelCase						,	clean_up_tokenization_spaces=_UpperCAmelCase)
          return text, ids


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass  # TODO add if relevant


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass  # TODO add if relevant


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass  # TODO add if relevant


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_tokenizer()

          # Testing tokenization
          __A						: List[str]      =       'こんにちは、世界。　こんばんは、㔺界。'
          __A						: List[str]      =       ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
          __A						: Optional[Any]      =       tokenizer.tokenize(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)

          # Testing conversion to ids without special tokens
          __A						: int      =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
          __A						: List[Any]      =       tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)

          # Testing conversion to ids with special tokens
          __A						: int      =       tokens + [tokenizer.unk_token]
          __A						: Dict      =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
          __A						: Dict      =       tokenizer.convert_tokens_to_ids(_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.get_tokenizer()

          # Testing tokenization
          __A						: Dict      =       'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
          __A						: Optional[Any]      =       'こんにちは、、、、世界。こんばんは、、、、世界。'
          __A						: int      =       tokenizer.encode(_UpperCAmelCase)
          __A						: Tuple      =       tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          # Testing tokenization
          __A						: int      =       'こんにちは、世界。'
          __A						: Optional[Any]      =       'こんばんは、㔺界。😀'
          __A						: List[str]      =       'こんにちは、世界。こんばんは、世界。😀'
          __A						: Tuple      =       tokenizer.encode(prefix_text + input_text)
          __A						: Union[str, Any]      =       tokenizer.encode(''						,	prefix_text=prefix_text + input_text)
          __A						: Union[str, Any]      =       tokenizer.encode(_UpperCAmelCase						,	prefix_text=_UpperCAmelCase)
          __A						: List[Any]      =       tokenizer.decode(_UpperCAmelCase)
          __A						: Dict      =       tokenizer.decode(_UpperCAmelCase)
          __A						: Any      =       tokenizer.decode(_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)
          self.assertEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          # Testing tokenization
          __A						: Optional[Any]      =       'こんにちは、世界。'
          __A						: List[Any]      =       'こんばんは、㔺界。😀'

          __A						: Tuple      =       len(tokenizer.encode(_UpperCAmelCase)) - 2
          __A						: Optional[int]      =       len(tokenizer.encode(_UpperCAmelCase)) - 2

          __A						: Tuple      =       [1] + [0] * (len_prefix + len_text + 1)
          __A						: Union[str, Any]      =       [1] * (len_prefix + len_text + 1) + [0]
          __A						: str      =       [1] + [1] * (len_prefix) + [0] * (len_text + 1)

          __A						: Dict      =       tokenizer(prefix_text + input_text).token_type_ids
          __A						: str      =       tokenizer(''						,	prefix_text=prefix_text + input_text).token_type_ids
          __A						: Any      =       tokenizer(_UpperCAmelCase						,	prefix_text=_UpperCAmelCase).token_type_ids
          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)
          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          __A						: List[Any]      =       tokenizer.encode('あンいワ')
          __A						: Tuple      =       tokenizer.encode(''						,	prefix_text='あンいワ')
          __A						: int      =       tokenizer.encode('いワ'						,	prefix_text='あン')

          self.assertEqual(tokenizer.decode(_UpperCAmelCase)						,	tokenizer.decode(_UpperCAmelCase))
          self.assertEqual(tokenizer.decode(_UpperCAmelCase)						,	tokenizer.decode(_UpperCAmelCase))
          self.assertNotEqual(_UpperCAmelCase						,	_UpperCAmelCase)
          self.assertNotEqual(_UpperCAmelCase						,	_UpperCAmelCase)
          self.assertEqual(x_token_a[1]						,	x_token_a[-1])  # SEG token
          self.assertEqual(x_token_a[1]						,	x_token_a[3])  # SEG token


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese')

          __A						: Any      =       [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
          __A						: List[Any]      =       tokenizer(_UpperCAmelCase						,	padding=_UpperCAmelCase)
          __A						: str      =       tokenizer.batch_encode_plus(_UpperCAmelCase						,	padding=_UpperCAmelCase)

          # fmt: off
          __A						: Optional[Any]      =       [[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
          __A						: Tuple      =       [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
          __A						: Tuple      =       [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
          # fmt: on
          self.assertListEqual(x_token.input_ids						,	_UpperCAmelCase)
          self.assertListEqual(x_token.token_type_ids						,	_UpperCAmelCase)
          self.assertListEqual(x_token.attention_mask						,	_UpperCAmelCase)
          self.assertListEqual(x_token_a.input_ids						,	_UpperCAmelCase)
          self.assertListEqual(x_token_a.token_type_ids						,	_UpperCAmelCase)
          self.assertListEqual(x_token_a.attention_mask						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])