'''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


lowerCAmelCase_ : str						  =      logging.get_logger(__name__)





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
   lowerCamelCase_    					=field(
       metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
   lowerCamelCase_    					=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_    					=field(
       default=a__ ,				metadata={'help': 'Overwrite the cached training and evaluation sets'} )


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Dict:
         lowercase_     =      self.task_name.lower()





class 							lowercase						(						a__ ):
   lowerCamelCase_    					="train"
   lowerCamelCase_    					="dev"
   lowerCamelCase_    					="test"





class 							lowercase						(						a__ ):
   lowerCamelCase_    					=42
   lowerCamelCase_    					=42
   lowerCamelCase_    					=42


   def __init__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	int = Split.train   ,		__lowerCAmelCase				:	Optional[Any] = None   ,		)      ->					Any:
         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"   ,		lowerCamelCase_   ,		)
         lowercase_     =      args
         lowercase_     =      glue_processors[args.task_name]()
         lowercase_     =      glue_output_modes[args.task_name]
         if isinstance(lowerCamelCase_   ,		lowerCamelCase_):
               try:
                     lowercase_     =      Split[mode]
               except KeyError:
                     raise KeyError("mode is not a valid split name")
        # Load data features from cache or dataset file
         lowercase_     =      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}'   ,		)
         lowercase_     =      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)
               lowercase_ ,							lowercase_     =      label_list[2], label_list[1]
         lowercase_     =      label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         lowercase_     =      cached_features_file + ".lock"
         with FileLock(lowerCamelCase_):
               if os.path.exists(lowerCamelCase_) and not args.overwrite_cache:
                     lowercase_     =      time.time()
                     lowercase_     =      torch.load(lowerCamelCase_)
                     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:
                           lowercase_     =      self.processor.get_dev_examples(args.data_dir)
                     elif mode == Split.test:
                           lowercase_     =      self.processor.get_test_examples(args.data_dir)
                     else:
                           lowercase_     =      self.processor.get_train_examples(args.data_dir)
                     if limit_length is not None:
                           lowercase_     =      examples[:limit_length]
                     lowercase_     =      glue_convert_examples_to_features(
                         lowerCamelCase_   ,		lowerCamelCase_   ,		max_length=args.max_seq_length   ,		label_list=lowerCamelCase_   ,		output_mode=self.output_mode   ,		)
                     lowercase_     =      time.time()
                     torch.save(self.features   ,		lowerCamelCase_)
                     # ^ 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				:	str)      ->					Tuple:
         return len(self.features)


   def __getitem__(   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any])      ->					InputFeatures:
         return self.features[i]


   def __UpperCAmelCase       (   self				:	int)      ->					Union[str, Any]:
         return self.label_list







'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


lowerCAmelCase_ : Optional[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Any						  =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_ : Tuple						  =      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_ : Dict						  =      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=['input_ids', 'attention_mask']
   lowerCamelCase_    					=GPTaTokenizer


   def __init__(   self				:	Dict   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any="<|endoftext|>"   ,		__lowerCAmelCase				:	Union[str, Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	List[Any]="<|endoftext|>"   ,		__lowerCAmelCase				:	Optional[Any]=False   ,		**__lowerCAmelCase				:	Dict   ,		)      ->					int:
         super().__init__(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		tokenizer_file=__lowerCAmelCase   ,		unk_token=__lowerCAmelCase   ,		bos_token=__lowerCAmelCase   ,		eos_token=__lowerCAmelCase   ,		add_prefix_space=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

         lowercase_     =      kwargs.pop("add_bos_token"   ,		__lowerCAmelCase)

         lowercase_     =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
         if pre_tok_state.get("add_prefix_space"   ,		__lowerCAmelCase) != add_prefix_space:
               lowercase_     =      getattr(__lowerCAmelCase   ,		pre_tok_state.pop("type"))
               lowercase_     =      add_prefix_space
               lowercase_     =      pre_tok_class(**__lowerCAmelCase)

         lowercase_     =      add_prefix_space


   def __UpperCAmelCase       (   self				:	List[str]   ,		*__lowerCAmelCase				:	List[str]   ,		**__lowerCAmelCase				:	str)      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)
         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(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		*__lowerCAmelCase				:	Union[str, Any]   ,		**__lowerCAmelCase				:	Union[str, Any])      ->					BatchEncoding:
         lowercase_     =      kwargs.get("is_split_into_words"   ,		__lowerCAmelCase)

         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(*__lowerCAmelCase   ,		**__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[str] = None)      ->					Tuple[str]:
         lowercase_     =      self._tokenizer.model.save(__lowerCAmelCase   ,		name=__lowerCAmelCase)
         return tuple(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	"Conversation")      ->					List[int]:
         lowercase_     =      []
         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:
               lowercase_     =      input_ids[-self.model_max_length :]
         return input_ids






