






import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class   SCREAMING_SNAKE_CASE (       snake_case     ,		unittest.TestCase    ):





							"""simple docstring"""

							A_       =    GPTNeoXJapaneseTokenizer
							A_       =    False
							A_       =    {"do_clean_text": False, "add_prefix_space": False}

							def 					__A       (     self:      int      )			-> int:
												super().setUp()

												_A									=       [
												    '''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>''',
												    '''<|startoftext|>''',
												    '''<|endoftext|>''',
												]
												_A									=       {'''emoji''': {'''\ud83d\ude00''': '''<|emoji1|>'''}, '''emoji_inv''': {'''<|emoji1|>''': '''\ud83d\ude00'''}}  # 😀
												_A									=       {'''unk_token''': '''<unk>'''}

												_A									=       os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['''vocab_file''']      )
												_A									=       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(__A      )      )

							def 					__A       (     self:      Dict						,   **__A:      Dict      )			-> List[str]:
												kwargs.update(self.special_tokens_map      )
												return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname						,   **__A      )

							def 					__A       (     self:      Tuple						,   __A:      Any      )			-> Union[str, Any]:
												_A									=       '''こんにちは、世界。 \nこんばんは、㔺界。😀'''
												_A									=       '''こんにちは、世界。 \nこんばんは、世界。😀'''
												return input_text, output_text

							def 					__A       (     self:      int						,   __A:      Optional[int]      )			-> Union[str, Any]:
												_A							,_A									=       self.get_input_output_texts(__A      )
												_A									=       tokenizer.encode(__A						,   add_special_tokens=__A      )
												_A									=       tokenizer.decode(__A						,   clean_up_tokenization_spaces=__A      )
												return text, ids

							def 					__A       (     self:      List[str]      )			-> str:
												pass  # TODO add if relevant

							def 					__A       (     self:      Dict      )			-> Any:
												pass  # TODO add if relevant

							def 					__A       (     self:      Tuple      )			-> str:
												pass  # TODO add if relevant

							def 					__A       (     self:      Any      )			-> str:
												_A									=       self.get_tokenizer()

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

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

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

							@slow
							def 					__A       (     self:      Optional[int]      )			-> Optional[Any]:
												_A									=       self.tokenizer_class.from_pretrained('''abeja/gpt-neox-japanese-2.7b'''      )

												_A									=       tokenizer.encode('''ありがとう。'''						,   add_special_tokens=__A      )
												_A									=       tokenizer.encode('''どういたしまして。'''						,   add_special_tokens=__A      )

												_A									=       tokenizer.build_inputs_with_special_tokens(__A      )
												_A									=       tokenizer.build_inputs_with_special_tokens(__A						,   __A      )

												assert encoded_sentence == ids_a
												assert encoded_pair == ids_a + ids_a

							def 					__A       (     self:      Optional[int]      )			-> Optional[Any]:
												# Intentionally convert some words to accommodate character fluctuations unique to Japanese
												pass



							def 					__A       (     self:      Dict      )			-> str:
												# tokenizer has no padding token
												pass











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__A          = logging.get_logger(__name__)

__A          = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A          = {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

__A          = {
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}





class   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    VOCAB_FILES_NAMES
							A_       =    PRETRAINED_VOCAB_FILES_MAP
							A_       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							A_       =    ["input_ids", "attention_mask"]
							A_       =    LongformerTokenizer

							def __init__(     self:      List[str]						,   __A:      Dict=None						,   __A:      int=None						,   __A:      Tuple=None						,   __A:      Any="replace"						,   __A:      List[str]="<s>"						,   __A:      int="</s>"						,   __A:      Dict="</s>"						,   __A:      Tuple="<s>"						,   __A:      Optional[int]="<unk>"						,   __A:      Optional[int]="<pad>"						,   __A:      List[Any]="<mask>"						,   __A:      Tuple=False						,   __A:      Dict=True						,   **__A:      List[Any]						,   )			-> List[Any]:
												super().__init__(
												    __A						,   __A						,   tokenizer_file=__A						,   errors=__A						,   bos_token=__A						,   eos_token=__A						,   sep_token=__A						,   cls_token=__A						,   unk_token=__A						,   pad_token=__A						,   mask_token=__A						,   add_prefix_space=__A						,   trim_offsets=__A						,   **__A						,   )

												_A									=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
												if pre_tok_state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																	_A									=       getattr(__A						,   pre_tok_state.pop('''type'''      )      )
																	_A									=       add_prefix_space
																	_A									=       pre_tok_class(**__A      )

												_A									=       add_prefix_space

												_A									=       '''post_processor'''
												_A									=       getattr(self.backend_tokenizer						,   __A						,   __A      )
												if tokenizer_component_instance:
																	_A									=       json.loads(tokenizer_component_instance.__getstate__()      )

																	# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																	if "sep" in state:
																						_A									=       tuple(state['''sep''']      )
																	if "cls" in state:
																						_A									=       tuple(state['''cls''']      )

																	_A									=       False

																	if state.get('''add_prefix_space'''						,   __A      ) != add_prefix_space:
																						_A									=       add_prefix_space
																						_A									=       True

																	if state.get('''trim_offsets'''						,   __A      ) != trim_offsets:
																						_A									=       trim_offsets
																						_A									=       True

																	if changes_to_apply:
																						_A									=       getattr(__A						,   state.pop('''type'''      )      )
																						_A									=       component_class(**__A      )
																						setattr(self.backend_tokenizer						,   __A						,   __A      )

							@property
							def 					__A       (     self:      Union[str, Any]      )			-> str:
												if self._mask_token is None:
																	if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.'''      )
																	return None
												return str(self._mask_token      )

							@mask_token.setter
							def 					__A       (     self:      Optional[int]						,   __A:      str      )			-> Union[str, Any]:
												_A									=       AddedToken(__A						,   lstrip=__A						,   rstrip=__A      ) if isinstance(__A						,   __A      ) else value
												_A									=       value

							def 					__A       (     self:      Optional[Any]						,   *__A:      Any						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )
												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._batch_encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      Optional[Any]						,   *__A:      List[str]						,   **__A:      List[Any]      )			-> BatchEncoding:
												_A									=       kwargs.get('''is_split_into_words'''						,   __A      )

												assert self.add_prefix_space or not is_split_into_words, (
												    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs."
												)

												return super()._encode_plus(*__A						,   **__A      )

							def 					__A       (     self:      List[str]						,   __A:      str						,   __A:      Optional[str] = None      )			-> Tuple[str]:
												_A									=       self._tokenizer.model.save(__A						,   name=__A      )
												return tuple(__A      )

							def 					__A       (     self:      Tuple						,   __A:      Dict						,   __A:      Optional[Any]=None      )			-> Optional[int]:
												_A									=       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																	return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



							def 					__A       (     self:      Optional[int]						,   __A:      List[int]						,   __A:      Optional[List[int]] = None      )			-> List[int]:
												_A									=       [self.sep_token_id]
												_A									=       [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep      ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]



