

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


lowerCAmelCase_	     =     logging.get_logger(__name__)

lowerCAmelCase_	     =     {'''vocab_file''': '''vocab.txt'''}

lowerCAmelCase_	     =     {
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

lowerCAmelCase_	     =     {
    '''tau/splinter-base''': 5_12,
    '''tau/splinter-base-qass''': 5_12,
    '''tau/splinter-large''': 5_12,
    '''tau/splinter-large-qass''': 5_12,
}

lowerCAmelCase_	     =     {
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}





class      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE					:       List[str]						      =    PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    PRETRAINED_INIT_CONFIGURATION
			SCREAMING_SNAKE_CASE					:       List[str]						      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE					:       Any						      =    SplinterTokenizer







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    Dict=None							,       _UpperCamelCase			:    Union[str, Any]=None							,       _UpperCamelCase			:    Union[str, Any]=True							,       _UpperCamelCase			:    List[Any]="[UNK]"							,       _UpperCamelCase			:    List[str]="[SEP]"							,       _UpperCamelCase			:    Optional[int]="[PAD]"							,       _UpperCamelCase			:    Tuple="[CLS]"							,       _UpperCamelCase			:    Optional[int]="[MASK]"							,       _UpperCamelCase			:    str="[QUESTION]"							,       _UpperCamelCase			:    Optional[Any]=True							,       _UpperCamelCase			:    Dict=None							,       **_UpperCamelCase			:    Optional[int]							,       )				->Union[str, Any]:
									super().__init__(
									    _UpperCamelCase							,       tokenizer_file=_UpperCamelCase							,       do_lower_case=_UpperCamelCase							,       unk_token=_UpperCamelCase							,       sep_token=_UpperCamelCase							,       pad_token=_UpperCamelCase							,       cls_token=_UpperCamelCase							,       mask_token=_UpperCamelCase							,       tokenize_chinese_chars=_UpperCamelCase							,       strip_accents=_UpperCamelCase							,       additional_special_tokens=(question_token,)							,       **_UpperCamelCase							,       )

									snake_case_       =     json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
									if (
									    pre_tok_state.get('''lowercase'''							,       _UpperCamelCase							) != do_lower_case
									    or pre_tok_state.get('''strip_accents'''							,       _UpperCamelCase							) != strip_accents
									):
															snake_case_       =     getattr(_UpperCamelCase							,       pre_tok_state.pop('''type'''							)							)
															snake_case_       =     do_lower_case
															snake_case_       =     strip_accents
															snake_case_       =     pre_tok_class(**_UpperCamelCase							)

									snake_case_       =     do_lower_case







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									return self.convert_tokens_to_ids(self.question_token							)







			def     snake_case__(					self			:    Dict							,       _UpperCamelCase			:    List[int]							,       _UpperCamelCase			:    Optional[List[int]] = None							)				->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									snake_case_       =     [self.cls_token_id]
									snake_case_       =     [self.sep_token_id]
									snake_case_       =     [self.question_token_id] + [self.convert_tokens_to_ids('''.'''							)]
									if self.padding_side == "right":
															# Input is question-then-context
															return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
									else:
															# Input is context-then-question
															return cls + token_ids_a + sep + token_ids_a + question_suffix + sep







			def     snake_case__(					self			:    str							,       _UpperCamelCase			:    List[int]							,       _UpperCamelCase			:    Optional[List[int]] = None							)				->List[int]:
									snake_case_       =     [self.sep_token_id]
									snake_case_       =     [self.cls_token_id]
									snake_case_       =     [self.question_token_id] + [self.convert_tokens_to_ids('''.'''							)]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep							) * [0]

									if self.padding_side == "right":
															# Input is question-then-context
															return len(cls + token_ids_a + question_suffix + sep							) * [0] + len(token_ids_a + sep							) * [1]
									else:
															# Input is context-then-question
															return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + question_suffix + sep							) * [1]







			def     snake_case__(					self			:    Union[str, Any]							,       _UpperCamelCase			:    str							,       _UpperCamelCase			:    Optional[str] = None							)				->Tuple[str]:
									snake_case_       =     self._tokenizer.model.save(_UpperCamelCase							,       name=_UpperCamelCase							)
									return tuple(_UpperCamelCase							)


import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class      snake_case_       (		__A     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    PreTrainedTokenizerFast
			SCREAMING_SNAKE_CASE					:       List[str]						      =    False
			SCREAMING_SNAKE_CASE					:       Tuple						      =    True
			SCREAMING_SNAKE_CASE					:       str						      =    "tokenizer_file"







			def     snake_case__(					self			:    Dict							)				->int:
									snake_case_       =     False  # because we don't have pretrained_vocab_files_map
									super().setUp()
									snake_case_       =     True

									snake_case_       =     ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
									snake_case_       =     '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

									# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
									snake_case_       =     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

									snake_case_       =     PreTrainedTokenizerFast.from_pretrained(model_paths[0]							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def     snake_case__(					self			:    Union[str, Any]							)				->Dict:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->Any:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    List[Any]							)				->str:
									# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
									# model
									pass







			def     snake_case__(					self			:    str							)				->List[Any]:
									# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
									pass







			def     snake_case__(					self			:    Any							)				->Union[str, Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    List[str]							)				->List[Any]:
									snake_case_       =     self.tmpdirname
									# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					try:
																											snake_case_       =     tempfile.mkdtemp()
																											snake_case_       =     self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,       **_UpperCamelCase							)

																											tokenizer.save_pretrained(self.tmpdirname							)
																											super().test_training_new_tokenizer_with_special_tokens_change()
																					finally:
																											# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
																											# is restored
																											shutil.rmtree(self.tmpdirname							)
																											snake_case_       =     tmpdirname_orig







			def     snake_case__(					self			:    Optional[int]							)				->Optional[Any]:
									snake_case_       =     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name							)

									snake_case_       =     ('''a''' for _ in range(1_0_0_0							))
									snake_case_       =     tokenizer.train_new_from_iterator(text_iterator=_UpperCamelCase							,       length=1_0_0_0							,       vocab_size=5_0							)

									snake_case_       =     new_tokenizer.encode('''a🤗'''							)
									self.assertEqual(_UpperCamelCase							,       [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5]							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    Optional[int]							)				->int:
									snake_case_       =     AutoTokenizer.from_pretrained('''bert-base-cased'''							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									snake_case_       =     len(_UpperCamelCase							)

									with tempfile.TemporaryDirectory() as tmp_dir:
															# Hack to save this in the tokenizer_config.json
															snake_case_       =     ['''tokenizer.4.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															json.dump(_UpperCamelCase							,       open(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       '''w'''							)							)

															# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							) + 1							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

															# Will need to be adjusted if we reach v42 and this test is still here.
															# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
															shutil.move(os.path.join(_UpperCamelCase							,       '''tokenizer.4.0.0.json'''							)							,       os.path.join(_UpperCamelCase							,       '''tokenizer.42.0.0.json'''							)							)
															snake_case_       =     ['''tokenizer.42.0.0.json''']
															tokenizer.save_pretrained(_UpperCamelCase							)
															snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
															self.assertEqual(len(_UpperCamelCase							)							,       len(_UpperCamelCase							)							)
															snake_case_       =     json.loads(new_tokenizer._tokenizer.to_str()							)
															self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)







			def     snake_case__(					self			:    List[str]							)				->Optional[Any]:
									# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
									snake_case_       =     '''hf-internal-testing/test-two-tokenizers'''

									# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
									snake_case_       =     AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_7							)
									snake_case_       =     json.loads(tokenizer._tokenizer.to_str()							)
									self.assertIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)

									# Testing an older version by monkey-patching the version in the module it's used.
									import transformers as old_transformers

									snake_case_       =     '''3.0.0'''
									snake_case_       =     old_transformers.models.auto.AutoTokenizer.from_pretrained(_UpperCamelCase							)
									self.assertEqual(len(_UpperCamelCase							)							,       2_8_9_9_6							)
									snake_case_       =     json.loads(old_tokenizer._tokenizer.to_str()							)
									self.assertNotIn('''huggingface'''							,       json_tokenizer['''model''']['''vocab''']							)






@require_tokenizers
class      snake_case_       (		unittest.TestCase ):







			'''simple docstring'''

			def     snake_case__(					self			:    str							)				->Optional[int]:
									# See https://github.com/huggingface/transformers/pull/12550
									# and https://github.com/huggingface/tokenizers/issues/537
									snake_case_       =     PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel'''							)
									snake_case_       =     '''The Matrix is a 1999 science fiction action film.'''

									with concurrent.futures.ThreadPoolExecutor() as executor:
															snake_case_       =     [executor.submit(self.fetch							,       _UpperCamelCase							,       _UpperCamelCase							) for i in range(1_0							)]
															snake_case_       =     [future.result() for future in futures]
															self.assertEqual(_UpperCamelCase							,       [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0							)]							)







			def     snake_case__(					self			:    Optional[int]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    str							)				->Tuple:
									return tokenizer.encode(_UpperCamelCase							,       truncation='''longest_first'''							,       padding='''longest'''							)