


import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowerCAmelCase__  :str   			=							logging.get_logger(__name__)

lowerCAmelCase__  :Dict   			=							{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

lowerCAmelCase__  :Union[str, Any]   			=							{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

lowerCAmelCase__  :List[str]   			=							{
    '''abeja/gpt-neox-japanese-2.7b''': 2_0_4_8,
}

def        lowerCAmelCase__ (			a__:		Optional[Any]			,  a__:		Optional[Any]       )  ->		Optional[int]:


							'''simple docstring'''





							with open(_A			,  'r'			,  encoding='utf-8'       ) as f:
														_UpperCAmelCase 		=			json.loads(f.read()       )

							_UpperCAmelCase 		=			collections.OrderedDict()
							_UpperCAmelCase 		=			collections.OrderedDict()
							_UpperCAmelCase 		=			collections.OrderedDict()
							with open(_A			,  'r'			,  encoding='utf-8'       ) as f:
														_UpperCAmelCase 		=			f.readlines()
							_UpperCAmelCase 		=			[[t.rstrip('\n'       )] if (t == ',' or ',' not in t) else t.rstrip('\n'       ).split(','       ) for t in token]
							for idx, b in enumerate(_A       ):
														_UpperCAmelCase 		=			b
														_UpperCAmelCase 		=			idx
														for wd in b:
																					_UpperCAmelCase 		=			idx

							return vocab, raw_vocab, ids_to_tokens, emoji

class 		__a      (				lowerCAmelCase__			):
		_a	:  List[str]				=	VOCAB_FILES_NAMES
		_a	:  int				=	PRETRAINED_VOCAB_FILES_MAP
		_a	:  int				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a	:  str				=	['''input_ids''', '''attention_mask''']
		def __init__(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE="<|endoftext|>"			,    _SCREAMING_SNAKE_CASE="<|endoftext|>"			,    _SCREAMING_SNAKE_CASE="<|startoftext|>"			,    _SCREAMING_SNAKE_CASE="<|endoftext|>"			,    _SCREAMING_SNAKE_CASE=False			,    **_SCREAMING_SNAKE_CASE			,    ) ->       int:




									"""simple docstring"""





									super().__init__(
									    unk_token=__lowerCAmelCase			,    pad_token=__lowerCAmelCase			,    bos_token=__lowerCAmelCase			,    eos_token=__lowerCAmelCase			,    do_clean_text=__lowerCAmelCase			,    **__lowerCAmelCase			,    )
									if not os.path.isfile(__lowerCAmelCase						):
																raise ValueError(
																    f'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
																    ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'						)
									if not os.path.isfile(__lowerCAmelCase						):
																raise ValueError(
																    f'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
																    ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'						)
									_UpperCAmelCase 		=			do_clean_text
									_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase 		=			load_vocab_and_emoji(__lowerCAmelCase			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			SubWordJapaneseTokenizer(
									    vocab=self.vocab			,    ids_to_tokens=self.ids_to_tokens			,    emoji=self.emoji						)
		@property
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





									return len(self.raw_vocab						)
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									return dict(self.raw_vocab			,    **self.added_tokens_encoder						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       Tuple:




									"""simple docstring"""





									return self.subword_tokenizer.tokenize(__lowerCAmelCase			,    clean=self.do_clean_text						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									return self.vocab.get(__lowerCAmelCase			,    self.vocab.get(self.unk_token						)						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       Dict:




									"""simple docstring"""





									return self.subword_tokenizer.convert_id_to_token(__lowerCAmelCase						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									_UpperCAmelCase 		=			''.join(__lowerCAmelCase						).strip()
									return out_string
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			[]
									for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(__lowerCAmelCase			,    add_special_tokens=__lowerCAmelCase						) + [self.eos_token_id]						)

									if len(__lowerCAmelCase						) > self.model_max_length:
																_UpperCAmelCase 		=			input_ids[-self.model_max_length :]
									return input_ids






		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE = None						) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase 		=			0
									if os.path.isdir(__lowerCAmelCase						):
																_UpperCAmelCase 		=			os.path.join(
																    __lowerCAmelCase			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)
																_UpperCAmelCase 		=			os.path.join(
																    __lowerCAmelCase			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']						)
									else:
																_UpperCAmelCase 		=			(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
																)
																_UpperCAmelCase 		=			(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
																)
									with open(__lowerCAmelCase			,    'w'			,    encoding='utf-8'						) as writer:
																for token_index, token in self.ids_to_tokens.items():
																							if index != token_index:
																														logger.warning(
																														    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																														    ' Please check that the vocabulary is not corrupted!'						)
																														_UpperCAmelCase 		=			token_index
																							writer.write(','.join(__lowerCAmelCase						) + '\n'						)
																							index += 1
									with open(__lowerCAmelCase			,    'w'			,    encoding='utf-8'						) as writer:
																json.dump(self.emoji			,    __lowerCAmelCase						)
									return vocab_file, emoji_file



class 		__a      (				lowerCAmelCase__			):
		def __init__(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) ->       Tuple:




									"""simple docstring"""





									_UpperCAmelCase 		=			vocab  # same as swe
									_UpperCAmelCase 		=			ids_to_tokens  # same as bpe
									_UpperCAmelCase 		=			emoji
									_UpperCAmelCase 		=			np.max([len(__lowerCAmelCase						) for w in self.vocab.keys()]						)
									_UpperCAmelCase 		=			re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'						)
									_UpperCAmelCase 		=			re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'						)
									_UpperCAmelCase 		=			re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'						)
									_UpperCAmelCase 		=			re.compile(
									    R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'						)
									_UpperCAmelCase 		=			re.compile(
									    R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'						)
									_UpperCAmelCase 		=			re.compile(
									    R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'						)
									_UpperCAmelCase 		=			'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
									_UpperCAmelCase 		=			'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
									_UpperCAmelCase 		=			str.maketrans({k: '<BLOCK>' for k in keisen + blocks}						)
		def __len__(			self						) ->       Optional[int]:




									"""simple docstring"""





									return len(self.ids_to_tokens						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.content_repattera.sub('<URL>'			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			self.content_repattera.sub('<EMAIL>'			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			self.content_repattera.sub('<TEL>'			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			self.content_repattera.sub('<DATE>'			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			self.content_repattera.sub('<DATE>'			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			self.content_repattera.sub('<PRICE>'			,    __lowerCAmelCase						)
									_UpperCAmelCase 		=			content.translate(self.content_transa						)
									while "<BLOCK><BLOCK>" in content:
																_UpperCAmelCase 		=			content.replace('<BLOCK><BLOCK>'			,    '<BLOCK>'						)
									return content
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=False						) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase 		=			text.replace(' '			,    '<SP>'						)
									_UpperCAmelCase 		=			text.replace('　'			,    '<SP>'						)
									_UpperCAmelCase 		=			text.replace('\r\n'			,    '<BR>'						)
									_UpperCAmelCase 		=			text.replace('\n'			,    '<BR>'						)
									_UpperCAmelCase 		=			text.replace('\r'			,    '<BR>'						)
									_UpperCAmelCase 		=			text.replace('\t'			,    '<TAB>'						)
									_UpperCAmelCase 		=			text.replace('—'			,    'ー'						)
									_UpperCAmelCase 		=			text.replace('−'			,    'ー'						)
									for k, v in self.emoji["emoji"].items():
																if k in text:
																							_UpperCAmelCase 		=			text.replace(__lowerCAmelCase			,    __lowerCAmelCase						)
									if clean:
																_UpperCAmelCase 		=			self.clean_text(__lowerCAmelCase						)

									def check_simbol(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase 		=			x.encode()
																if len(__lowerCAmelCase						) == 1 and len(__lowerCAmelCase						) == 2:
																							_UpperCAmelCase 		=			(int(e[0]						) << 8) + int(e[1]						)
																							if (
																							    (c >= 0XC_2A1 and c <= 0XC_2BF)
																							    or (c >= 0XC_780 and c <= 0XC_783)
																							    or (c >= 0XC_AB9 and c <= 0XC_BBF)
																							    or (c >= 0XC_C80 and c <= 0XC_DA2)
																							):
																														return True
																return False

									def checkuae(_SCREAMING_SNAKE_CASE						):
																_UpperCAmelCase 		=			x.encode()
																if len(__lowerCAmelCase						) == 1 and len(__lowerCAmelCase						) == 3:
																							_UpperCAmelCase 		=			(int(e[0]						) << 16) + (int(e[1]						) << 8) + int(e[2]						)
																							if c >= 0XE28_080 and c <= 0XE2B_07F:
																														return True
																return False

									_UpperCAmelCase 		=			0
									_UpperCAmelCase 		=			[]
									while pos < len(__lowerCAmelCase						):
																_UpperCAmelCase 		=			min(len(__lowerCAmelCase						)			,    pos + self.maxlen + 1						) if text[pos] == '<' else pos + 3
																_UpperCAmelCase 		=			[]  # (token_id, token, pos)
																for e in range(__lowerCAmelCase			,    __lowerCAmelCase			,    -1						):
																							_UpperCAmelCase 		=			text[pos:e]
																							if wd in self.vocab:
																														if wd[0] == "<" and len(__lowerCAmelCase						) > 2:
																																					_UpperCAmelCase 		=			[(self.vocab[wd], wd, e)]
																																					break
																														else:
																																					candidates.append((self.vocab[wd], wd, e)						)
																if len(__lowerCAmelCase						) > 0:
																							# the smallest token_id is adopted
																							_UpperCAmelCase		,       _UpperCAmelCase		,       _UpperCAmelCase 		=			sorted(__lowerCAmelCase			,    key=lambda _SCREAMING_SNAKE_CASE						: x[0]						)[0]
																							result.append(__lowerCAmelCase						)
																							_UpperCAmelCase 		=			e
																else:
																							_UpperCAmelCase 		=			pos + 1
																							_UpperCAmelCase 		=			text[pos:end]
																							if check_simbol(__lowerCAmelCase						):
																														result.append('<KIGOU>'						)
																							elif checkuae(__lowerCAmelCase						):
																														result.append('<U2000U2BFF>'						)
																							else:
																														for i in wd.encode('utf-8'						):
																																					result.append('<|byte%d|>' % i						)
																							_UpperCAmelCase 		=			end
									return result






		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE="\n"						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			[]
									_UpperCAmelCase 		=			[]
									_UpperCAmelCase 		=			self.ids_to_tokens[index][0]
									if word[:6] == "<|byte" and word[-2:] == "|>":
																byte_tokens.append(int(word[6:-2]						)						)
									else:
																if len(__lowerCAmelCase						) > 0:
																							words.append(bytearray(__lowerCAmelCase						).decode('utf-8'			,    errors='replace'						)						)
																							_UpperCAmelCase 		=			[]
																if word[:7] == "<|emoji" and word[-2:] == "|>":
																							words.append(self.emoji['emoji_inv'][word]						)
																elif word == "<SP>":
																							words.append(' '						)
																elif word == "<BR>":
																							words.append(__lowerCAmelCase						)
																elif word == "<TAB>":
																							words.append('\t'						)
																elif word == "<BLOCK>":
																							words.append('▀'						)
																elif word == "<KIGOU>":
																							words.append('ǀ'						)
																elif word == "<U2000U2BFF>":
																							words.append('‖'						)
																else:
																							words.append(__lowerCAmelCase						)
									if len(__lowerCAmelCase						) > 0:
																words.append(bytearray(__lowerCAmelCase						).decode('utf-8'			,    errors='replace'						)						)
									_UpperCAmelCase 		=			''.join(__lowerCAmelCase						)
									return text

'''simple docstring'''




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors






@require_tokenizers
class 				a__(					lowerCAmelCase__ ,						unittest.TestCase	):


			'''simple docstring'''






			UpperCAmelCase_			:     Dict      =       MvpTokenizer
			UpperCAmelCase_			:     Optional[Any]      =       MvpTokenizerFast
			UpperCAmelCase_			:     str      =       True
			UpperCAmelCase_			:     List[Any]      =       filter_roberta_detectors



			def 							a_       (   self):




					"""simple docstring"""


					super().setUp()
					lowerCAmelCase   					=  [
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """\u0120""",
					    """\u0120l""",
					    """\u0120n""",
					    """\u0120lo""",
					    """\u0120low""",
					    """er""",
					    """\u0120lowest""",
					    """\u0120newer""",
					    """\u0120wider""",
					    """<unk>""",
					]
					lowerCAmelCase   					=  dict(zip(__lowerCAmelCase       ,    range(len(__lowerCAmelCase))))
					lowerCAmelCase   					=  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
					lowerCAmelCase   					=  {"""unk_token""": """<unk>"""}

					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase   					=  os.path.join(self.tmpdirname       ,    VOCAB_FILES_NAMES["""merges_file"""])
					with open(self.vocab_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write(json.dumps(__lowerCAmelCase) + """\n""")
					with open(self.merges_file       ,    """w"""       ,    encoding="""utf-8""") as fp:
							fp.write("""\n""".join(__lowerCAmelCase))



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    **__lowerCAmelCase):




					"""simple docstring"""


					kwargs.update(self.special_tokens_map)
					return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return "lower newer", "lower newer"



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""")



			@cached_property
			def 							a_       (   self):




					"""simple docstring"""


					return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""")



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					lowerCAmelCase   					=  [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    max_length=len(__lowerCAmelCase)       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)

							self.assertEqual((2, 9)       ,    batch.input_ids.shape)
							self.assertEqual((2, 9)       ,    batch.attention_mask.shape)
							lowerCAmelCase   					=  batch.input_ids.tolist()[0]
							self.assertListEqual(__lowerCAmelCase       ,    __lowerCAmelCase)
							# Test that special tokens are reset



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    padding=__lowerCAmelCase       ,    return_tensors="""pt""")
							# check if input_ids are returned and no labels
							self.assertIn("""input_ids"""       ,    __lowerCAmelCase)
							self.assertIn("""attention_mask"""       ,    __lowerCAmelCase)
							self.assertNotIn("""labels"""       ,    __lowerCAmelCase)
							self.assertNotIn("""decoder_attention_mask"""       ,    __lowerCAmelCase)



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(text_target=__lowerCAmelCase       ,    max_length=32       ,    padding="""max_length"""       ,    return_tensors="""pt""")
							self.assertEqual(32       ,    targets["""input_ids"""].shape[1])



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(
							    ["""I am a small frog""" * 1024, """I am a small frog"""]       ,    padding=__lowerCAmelCase       ,    truncation=__lowerCAmelCase       ,    return_tensors="""pt""")
							self.assertIsInstance(__lowerCAmelCase       ,    __lowerCAmelCase)
							self.assertEqual(batch.input_ids.shape       ,    (2, 1024))



			@require_torch
			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  ["""A long paragraph for summarization."""]
					lowerCAmelCase   					=  [
					    """Summary of the text.""",
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCAmelCase   					=  tokenizer(__lowerCAmelCase       ,    text_target=__lowerCAmelCase       ,    return_tensors="""pt""")
							lowerCAmelCase   					=  inputs["""input_ids"""]
							lowerCAmelCase   					=  inputs["""labels"""]
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



			def 							a_       (   self):




					"""simple docstring"""


					pass







			def 							a_       (   self):




					"""simple docstring"""


					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(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  self.tokenizer_class.from_pretrained(__lowerCAmelCase       ,    **__lowerCAmelCase)
									lowerCAmelCase   					=  """A, <mask> AllenNLP sentence."""
									lowerCAmelCase   					=  tokenizer_r.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)
									lowerCAmelCase   					=  tokenizer_p.encode_plus(__lowerCAmelCase       ,    add_special_tokens=__lowerCAmelCase       ,    return_token_type_ids=__lowerCAmelCase)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r["""token_type_ids"""])       ,    sum(tokens_p["""token_type_ids"""]))

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])       ,    sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])       ,    )

									lowerCAmelCase   					=  tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
									lowerCAmelCase   					=  tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])
									self.assertSequenceEqual(tokens_r["""input_ids"""]       ,    [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2])

									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
									self.assertSequenceEqual(
									    __lowerCAmelCase       ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])




