'''simple docstring'''




import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


__UpperCAmelCase												=logging.get_logger(__name__)




@dataclass
class    a__				:
     lowerCamelCase  :					str							=field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()			)}			)
     lowerCamelCase  :					str							=field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
     lowerCamelCase  :					int							=field(
         default=1_2_8							,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }							,       )
     lowerCamelCase  :					bool							=field(
         default=UpperCAmelCase__							,       metadata={"help": "Overwrite the cached training and evaluation sets"}			)






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict   ):





         """simple docstring"""



         __lowerCamelCase          =  self.task_name.lower()




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Optional[int]							="train"
     lowerCamelCase  :					str							="dev"
     lowerCamelCase  :					int							="test"




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					GlueDataTrainingArguments
     lowerCamelCase  :					str
     lowerCamelCase  :					List[InputFeatures]






     def __init__(			self		:		Dict  ,    a		:		GlueDataTrainingArguments  ,    a		:		PreTrainedTokenizerBase  ,    a		:		Optional[int] = None  ,    a		:		Union[str, Split] = Split.train  ,    a		:		Optional[str] = None  ,    ):





         """simple docstring"""



         warnings.warn(
             '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
             '''library. You can have a look at this example script for pointers: '''
             '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''  ,    a  ,    )
         __lowerCamelCase          =  args
         __lowerCamelCase          =  glue_processors[args.task_name]()
         __lowerCamelCase          =  glue_output_modes[args.task_name]
         if isinstance(a  ,    a   ):
             try:
                 __lowerCamelCase          =  Split[mode]
             except KeyError:
                 raise KeyError('''mode is not a valid split name'''   )
        # Load data features from cache or dataset file
         __lowerCamelCase          =  os.path.join(
             cache_dir if cache_dir is not None else args.data_dir  ,    f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""  ,    )
         __lowerCamelCase          =  self.processor.get_labels()
         if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
             "RobertaTokenizer",
             "RobertaTokenizerFast",
             "XLMRobertaTokenizer",
             "BartTokenizer",
             "BartTokenizerFast",
         ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             __lowerCamelCase     ,  __lowerCamelCase          =  label_list[2], label_list[1]
         __lowerCamelCase          =  label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         __lowerCamelCase          =  cached_features_file + '''.lock'''
         with FileLock(a   ):
             if os.path.exists(a   ) and not args.overwrite_cache:
                 __lowerCamelCase          =  time.time()
                 __lowerCamelCase          =  torch.load(a   )
                 logger.info(
                     f"""Loading features from cached file {cached_features_file} [took %.3f s]"""  ,    time.time() - start   )
             else:
                 logger.info(f"""Creating features from dataset file at {args.data_dir}"""   )

                 if mode == Split.dev:
                     __lowerCamelCase          =  self.processor.get_dev_examples(args.data_dir   )
                 elif mode == Split.test:
                     __lowerCamelCase          =  self.processor.get_test_examples(args.data_dir   )
                 else:
                     __lowerCamelCase          =  self.processor.get_train_examples(args.data_dir   )
                 if limit_length is not None:
                     __lowerCamelCase          =  examples[:limit_length]
                 __lowerCamelCase          =  glue_convert_examples_to_features(
                     a  ,    a  ,    max_length=args.max_seq_length  ,    label_list=a  ,    output_mode=self.output_mode  ,    )
                 __lowerCamelCase          =  time.time()
                 torch.save(self.features  ,    a   )
                 # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                 logger.info(
                     f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""   )






     def __len__(			self		:		Tuple   ):





         """simple docstring"""



         return len(self.features   )






     def __getitem__(			self		:		int  ,    a		:		Dict   ):





         """simple docstring"""



         return self.features[i]






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         return self.label_list

'''simple docstring'''




import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class    a__				(       UpperCAmelCase__							,       unittest.TestCase			):
     lowerCamelCase  :					Optional[Any]							=LongformerTokenizer
     lowerCamelCase  :					Optional[Any]							=True
     lowerCamelCase  :					List[str]							=LongformerTokenizerFast
     lowerCamelCase  :					Union[str, Any]							=True






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __lowerCamelCase          =  [
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''<unk>''',
         ]
         __lowerCamelCase          =  dict(zip(a  ,    range(len(a   )   )   )   )
         __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(a   ) + '''\n'''   )
         with open(self.merges_file  ,    '''w'''  ,    encoding='''utf-8'''   ) as fp:
             fp.write('''\n'''.join(a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    **a		:		int   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    **a		:		Dict   ):





         """simple docstring"""



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






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  '''lower newer'''
         __lowerCamelCase          =  '''lower newer'''
         return input_text, output_text






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.tokenizer_class(self.vocab_file  ,    self.merges_file  ,    **self.special_tokens_map   )
         __lowerCamelCase          =  '''lower newer'''
         __lowerCamelCase          =  ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         __lowerCamelCase          =  tokenizer.tokenize(a   )  # , add_prefix_space=True)
         self.assertListEqual(a  ,    a   )

         __lowerCamelCase          =  tokens + [tokenizer.unk_token]
         __lowerCamelCase          =  [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(a   )  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizer()

         self.assertListEqual(tokenizer.encode('''Hello world!'''  ,    add_special_tokens=a   )  ,    [0, 3_14_14, 2_32, 3_28, 2]   )
         self.assertListEqual(
             tokenizer.encode('''Hello world! cécé herlolip 418'''  ,    add_special_tokens=a   )  ,    [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]  ,    )






     @slow
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         __lowerCamelCase          =  self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''   )

         __lowerCamelCase          =  tokenizer.encode('''sequence builders'''  ,    add_special_tokens=a   )
         __lowerCamelCase          =  tokenizer.encode('''multi-sequence build'''  ,    add_special_tokens=a   )

         __lowerCamelCase          =  tokenizer.encode(
             '''sequence builders'''  ,    add_special_tokens=a  ,    add_prefix_space=a   )
         __lowerCamelCase          =  tokenizer.encode(
             '''sequence builders'''  ,    '''multi-sequence build'''  ,    add_special_tokens=a  ,    add_prefix_space=a   )

         __lowerCamelCase          =  tokenizer.build_inputs_with_special_tokens(a   )
         __lowerCamelCase          =  tokenizer.build_inputs_with_special_tokens(a  ,    a   )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.get_tokenizer()

         __lowerCamelCase          =  '''Encode this sequence.'''
         __lowerCamelCase          =  tokenizer.byte_encoder[''' '''.encode('''utf-8'''   )[0]]

         # Testing encoder arguments
         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a  ,    add_prefix_space=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
         self.assertNotEqual(a  ,    a   )

         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a  ,    add_prefix_space=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
         self.assertEqual(a  ,    a   )

         tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}   )
         __lowerCamelCase          =  tokenizer.encode(a  ,    add_special_tokens=a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
         self.assertNotEqual(a  ,    a   )

         # Testing spaces after special tokens
         __lowerCamelCase          =  '''<mask>'''
         tokenizer.add_special_tokens(
             {'''mask_token''': AddedToken(a  ,    lstrip=a  ,    rstrip=a   )}   )  # mask token has a left space
         __lowerCamelCase          =  tokenizer.convert_tokens_to_ids(a   )

         __lowerCamelCase          =  '''Encode <mask> sequence'''
         __lowerCamelCase          =  '''Encode <mask>sequence'''

         __lowerCamelCase          =  tokenizer.encode(a   )
         __lowerCamelCase          =  encoded.index(a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
         self.assertEqual(a  ,    a   )

         __lowerCamelCase          =  tokenizer.encode(a   )
         __lowerCamelCase          =  encoded.index(a   )
         __lowerCamelCase          =  tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
         self.assertNotEqual(a  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         pass






     def        SCREAMING_SNAKE_CASE__       (			self		:		Union[str, Any]   ):





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

                 # 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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
                 self.assertSequenceEqual(tokens_r['''input_ids''']  ,    [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

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






     def        SCREAMING_SNAKE_CASE__       (			self		:		str   ):





         """simple docstring"""



         for trim_offsets, add_prefix_space in itertools.product([True, False]  ,    repeat=2   ):
             __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                 self.tmpdirname  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )

             __lowerCamelCase          =  json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
             __lowerCamelCase          =  json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()   )

             self.assertEqual(pre_tokenizer_state['''add_prefix_space''']  ,    a   )

             self.assertEqual(post_processor_state['''add_prefix_space''']  ,    a   )
             self.assertEqual(post_processor_state['''trim_offsets''']  ,    a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                 __lowerCamelCase          =  '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                 __lowerCamelCase          =  f"""{text_of_1_token} {text_of_1_token}"""

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ) + 1, len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ) + 1, len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ), len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (len(a   ), len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  f""" {text}"""

                 # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                 #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                 # )
                 # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                 # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                 # self.assertEqual(
                 #     encoding.offset_mapping[1],
                 #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                 # )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (1, 1 + len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (1 + len(a   ) + 1, 1 + len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, 1 + len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (1 + len(a   ), 1 + len(a   ) + 1 + len(a   ))  ,    )

                 __lowerCamelCase          =  self.rust_tokenizer_class.from_pretrained(
                     a  ,    use_fast=a  ,    add_prefix_space=a  ,    trim_offsets=a   )
                 __lowerCamelCase          =  tokenizer_r(a  ,    return_offsets_mapping=a  ,    add_special_tokens=a   )
                 self.assertEqual(encoding.offset_mapping[0]  ,    (0, 1 + len(a   ))   )
                 self.assertEqual(
                     encoding.offset_mapping[1]  ,    (1 + len(a   ), 1 + len(a   ) + 1 + len(a   ))  ,    )
