"""simple docstring"""


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase									=			logging.get_logger(__name__)


__UpperCamelCase									=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all BART models at https://huggingface.co/models?filter=bart
__UpperCamelCase									=			{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
}

__UpperCamelCase									=			{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}







@lru_cache()
def 					UpperCAmelCase    (		)				->      Dict:
     snake_case_					       =					(
         list(range(ord('!'					)						,					ord('~'					) + 1					)					) + list(range(ord('¡'					)						,					ord('¬'					) + 1					)					) + list(range(ord('®'					)						,					ord('ÿ'					) + 1					)					)
     )
     snake_case_					       =					bs[:]
     snake_case_					       =					0
     for b in range(2**8					):
          if b not in bs:
               bs.append(UpperCAmelCase					)
               cs.append(2**8 + n					)
               n += 1
     snake_case_					       =					[chr(UpperCAmelCase					) for n in cs]
     return dict(zip(UpperCAmelCase						,					UpperCAmelCase					)					)







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      int:
     snake_case_					       =					set()
     snake_case_					       =					word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)					)
          snake_case_					       =					char
     return pairs




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_			=      PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_			=      ["input_ids", "attention_mask"]







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__="replace",							lowerCAmelCase__="<s>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="</s>",							lowerCAmelCase__="<s>",							lowerCAmelCase__="<unk>",							lowerCAmelCase__="<pad>",							lowerCAmelCase__="<mask>",							lowerCAmelCase__=False,							**lowerCAmelCase__,							)      ->      Union[str, Any]:
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else bos_token
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else eos_token
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else sep_token
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else cls_token
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else unk_token
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else pad_token

            # Mask token behave like a normal word, i.e. include the space before it
            snake_case_					       =					AddedToken(lowerCAmelCase__,							lstrip=lowerCAmelCase__,							rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,							lowerCAmelCase__) else mask_token

            super().__init__(
                errors=lowerCAmelCase__,							bos_token=lowerCAmelCase__,							eos_token=lowerCAmelCase__,							unk_token=lowerCAmelCase__,							sep_token=lowerCAmelCase__,							cls_token=lowerCAmelCase__,							pad_token=lowerCAmelCase__,							mask_token=lowerCAmelCase__,							add_prefix_space=lowerCAmelCase__,							**lowerCAmelCase__,							)

            with open(lowerCAmelCase__,							encoding='utf-8') as vocab_handle:
                 snake_case_					       =					json.load(lowerCAmelCase__)
            snake_case_					       =					{v: k for k, v in self.encoder.items()}
            snake_case_					       =					errors  # how to handle errors in decoding
            snake_case_					       =					bytes_to_unicode()
            snake_case_					       =					{v: k for k, v in self.byte_encoder.items()}
            with open(lowerCAmelCase__,							encoding='utf-8') as merges_handle:
                 snake_case_					       =					merges_handle.read().split('\n')[1:-1]
            snake_case_					       =					[tuple(merge.split()) for merge in bpe_merges]
            snake_case_					       =					dict(zip(lowerCAmelCase__,							range(len(lowerCAmelCase__))))
            snake_case_					       =					{}
            snake_case_					       =					add_prefix_space

            # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
            snake_case_					       =					re.compile(R'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')







       @property
       def 		a_						( self)      ->      Optional[int]:
            return len(self.encoder)







       def 		a_						( self)      ->      Optional[Any]:
            return dict(self.encoder,							**self.added_tokens_encoder)







       def 		a_						( self,							lowerCAmelCase__)      ->      Union[str, Any]:
            if token in self.cache:
                 return self.cache[token]
            snake_case_					       =					tuple(lowerCAmelCase__)
            snake_case_					       =					get_pairs(lowerCAmelCase__)

            if not pairs:
                 return token

            while True:
                 snake_case_					       =					min(lowerCAmelCase__,							key=lambda lowerCAmelCase__: self.bpe_ranks.get(lowerCAmelCase__,							float('inf')))
                 if bigram not in self.bpe_ranks:
                      break
                 snake_case_       ,	snake_case_					       =					bigram
                 snake_case_					       =					[]
                 snake_case_					       =					0
                 while i < len(lowerCAmelCase__):
                      try:
                           snake_case_					       =					word.index(lowerCAmelCase__,							lowerCAmelCase__)
                      except ValueError:
                           new_word.extend(word[i:])
                           break
                      else:
                           new_word.extend(word[i:j])
                           snake_case_					       =					j

                      if word[i] == first and i < len(lowerCAmelCase__) - 1 and word[i + 1] == second:
                           new_word.append(first + second)
                           i += 2
                      else:
                           new_word.append(word[i])
                           i += 1
                 snake_case_					       =					tuple(lowerCAmelCase__)
                 snake_case_					       =					new_word
                 if len(lowerCAmelCase__) == 1:
                      break
                 else:
                      snake_case_					       =					get_pairs(lowerCAmelCase__)
            snake_case_					       =					' '.join(lowerCAmelCase__)
            snake_case_					       =					word
            return word







       def 		a_						( self,							lowerCAmelCase__)      ->      Dict:
            snake_case_					       =					[]
            for token in re.findall(self.pat,							lowerCAmelCase__):
                 snake_case_					       =					''.join(
                     self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                 bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCAmelCase__).split(' '))
            return bpe_tokens







       def 		a_						( self,							lowerCAmelCase__)      ->      List[str]:
            return self.encoder.get(lowerCAmelCase__,							self.encoder.get(self.unk_token))







       def 		a_						( self,							lowerCAmelCase__)      ->      int:
            return self.decoder.get(lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					''.join(lowerCAmelCase__)
            snake_case_					       =					bytearray([self.byte_decoder[c] for c in text]).decode('utf-8',							errors=self.errors)
            return text







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      Tuple[str]:
            if not os.path.isdir(lowerCAmelCase__):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory')
                 return
            snake_case_					       =					os.path.join(
                lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
            snake_case_					       =					os.path.join(
                lowerCAmelCase__,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

            with open(lowerCAmelCase__,							'w',							encoding='utf-8') as f:
                 f.write(json.dumps(self.encoder,							indent=2,							sort_keys=lowerCAmelCase__,							ensure_ascii=lowerCAmelCase__) + '\n')

            snake_case_					       =					0
            with open(lowerCAmelCase__,							'w',							encoding='utf-8') as writer:
                 writer.write('#version: 0.2\n')
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),							key=lambda lowerCAmelCase__: kv[1]):
                      if index != token_index:
                           logger.warning(
                               f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
                               ' Please check that the tokenizer is not corrupted!')
                           snake_case_					       =					token_index
                      writer.write(' '.join(lowerCAmelCase__) + '\n')
                      index += 1

            return vocab_file, merge_file







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = 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]
            return cls + token_ids_a + sep + sep + token_ids_a + sep







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False)      ->      List[int]:
            if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowerCAmelCase__,							token_ids_a=lowerCAmelCase__,							already_has_special_tokens=lowerCAmelCase__)

            if token_ids_a is None:
                 return [1] + ([0] * len(lowerCAmelCase__)) + [1]
            return [1] + ([0] * len(lowerCAmelCase__)) + [1, 1] + ([0] * len(lowerCAmelCase__)) + [1]







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      List[int]:
            snake_case_					       =					[self.sep_token_id]
            snake_case_					       =					[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]







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__=False,							**lowerCAmelCase__)      ->      int:
            snake_case_					       =					kwargs.pop('add_prefix_space',							self.add_prefix_space)
            if (is_split_into_words or add_prefix_space) and (len(lowerCAmelCase__) > 0 and not text[0].isspace()):
                 snake_case_					       =					' ' + text
            return (text, kwargs)







import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset

from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes, glue_tasks_num_labels
from transformers import glue_processors as processors


SCREAMING_SNAKE_CASE__       :      Dict  =   logging.getLogger(__name__)




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[int]	     =      """sequence-classification"""







    def __init__(    self     ,  UpperCamelCase__     )     ->      List[Any]:
           if type(UpperCamelCase__     ) == dict:
                  lowerCamelCase   :      int			 =					Namespace(**UpperCamelCase__     )
           lowerCamelCase   :      str			 =					glue_output_modes[hparams.task]
           lowerCamelCase   :      int			 =					glue_tasks_num_labels[hparams.task]

           super().__init__(UpperCamelCase__     ,  UpperCamelCase__     ,  self.mode     )







    def     _lowercase     (    self     ,  **UpperCamelCase__     )     ->      Tuple:
           return self.model(**UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Tuple:
           lowerCamelCase   :      Union[str, Any]			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

           if self.config.model_type not in ["distilbert", "bart"]:
                  lowerCamelCase   :      List[str]			 =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

           lowerCamelCase   :      Optional[int]			 =					self(**UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					outputs[0]

           lowerCamelCase   :      str			 =					self.trainer.lr_schedulers[0]["scheduler"]
           lowerCamelCase   :      Optional[int]			 =					{"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
           return {"loss": loss, "log": tensorboard_logs}







    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Any			 =					self.hparams
           lowerCamelCase   :      Union[str, Any]			 =					processors[args.task]()
           lowerCamelCase   :      Optional[int]			 =					processor.get_labels()

           for mode in ["train", "dev"]:
                  lowerCamelCase   :      Optional[Any]			 =					self._feature_file(UpperCamelCase__     )
                  if os.path.exists(UpperCamelCase__     ) and not args.overwrite_cache:
                         logger.info("Loading features from cached file %s"     ,  UpperCamelCase__     )
                  else:
                         logger.info("Creating features from dataset file at %s"     ,  args.data_dir     )
                         lowerCamelCase   :      List[str]			 =					(
                             processor.get_dev_examples(args.data_dir     )
                             if mode == "dev"
                             else processor.get_train_examples(args.data_dir     )
                         )
                         lowerCamelCase   :      Dict			 =					convert_examples_to_features(
                             UpperCamelCase__     ,  self.tokenizer     ,  max_length=args.max_seq_length     ,  label_list=self.labels     ,  output_mode=args.glue_output_mode     ,  )
                         logger.info("Saving features into cached file %s"     ,  UpperCamelCase__     )
                         torch.save(UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__ = False     )     ->      DataLoader:
           lowerCamelCase   :      str			 =					"dev" if mode == "test" else mode

           lowerCamelCase   :      int			 =					self._feature_file(UpperCamelCase__     )
           logger.info("Loading features from cached file %s"     ,  UpperCamelCase__     )
           lowerCamelCase   :      str			 =					torch.load(UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					torch.tensor([f.input_ids for f in features]     ,  dtype=torch.long     )
           lowerCamelCase   :      str			 =					torch.tensor([f.attention_mask for f in features]     ,  dtype=torch.long     )
           lowerCamelCase   :      List[str]			 =					torch.tensor([f.token_type_ids for f in features]     ,  dtype=torch.long     )
           if self.hparams.glue_output_mode == "classification":
                  lowerCamelCase   :      Any			 =					torch.tensor([f.label for f in features]     ,  dtype=torch.long     )
           elif self.hparams.glue_output_mode == "regression":
                  lowerCamelCase   :      Union[str, Any]			 =					torch.tensor([f.label for f in features]     ,  dtype=torch.float     )

           return DataLoader(
               TensorDataset(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ,  batch_size=UpperCamelCase__     ,  shuffle=UpperCamelCase__     ,  )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[Any]:
           lowerCamelCase   :      Dict			 =					{"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

           if self.config.model_type not in ["distilbert", "bart"]:
                  lowerCamelCase   :      Tuple			 =					batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

           lowerCamelCase   :      Dict			 =					self(**UpperCamelCase__     )
           lowerCamelCase ,      lowerCamelCase   :      Any			 =					outputs[:2]
           lowerCamelCase   :      Union[str, Any]			 =					logits.detach().cpu().numpy()
           lowerCamelCase   :      Optional[Any]			 =					inputs["labels"].detach().cpu().numpy()

           return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      tuple:
           lowerCamelCase   :      Union[str, Any]			 =					torch.stack([x["val_loss"] for x in outputs]     ).mean().detach().cpu().item()
           lowerCamelCase   :      Optional[int]			 =					np.concatenate([x["pred"] for x in outputs]     ,  axis=0     )

           if self.hparams.glue_output_mode == "classification":
                  lowerCamelCase   :      Union[str, Any]			 =					np.argmax(UpperCamelCase__     ,  axis=1     )
           elif self.hparams.glue_output_mode == "regression":
                  lowerCamelCase   :      str			 =					np.squeeze(UpperCamelCase__     )

           lowerCamelCase   :      List[Any]			 =					np.concatenate([x["target"] for x in outputs]     ,  axis=0     )
           lowerCamelCase   :      List[str]			 =					[[] for _ in range(out_label_ids.shape[0]     )]
           lowerCamelCase   :      Optional[int]			 =					[[] for _ in range(out_label_ids.shape[0]     )]

           lowerCamelCase   :      Dict			 =					{**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task     ,  UpperCamelCase__     ,  UpperCamelCase__     )}

           lowerCamelCase   :      List[str]			 =					dict(results.items()     )
           lowerCamelCase   :      Optional[int]			 =					results
           return ret, preds_list, out_label_list







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					ret["log"]
           return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      dict:
           lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      str			 =					self._eval_end(UpperCamelCase__     )
           lowerCamelCase   :      str			 =					ret["log"]
           # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
           return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}







    @staticmethod
    def     _lowercase     (    UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           BaseTransformer.add_model_specific_args(UpperCamelCase__     ,  UpperCamelCase__     )
           parser.add_argument(
               "--max_seq_length"     ,  default=128     ,  type=UpperCamelCase__     ,  help=(
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )     ,  )

           parser.add_argument(
               "--task"     ,  default=""     ,  type=UpperCamelCase__     ,  required=UpperCamelCase__     ,  help="The GLUE task to run"     ,  )
           parser.add_argument(
               "--gpus"     ,  default=0     ,  type=UpperCamelCase__     ,  help="The number of GPUs allocated for this, it is by default 0 meaning none"     ,  )

           parser.add_argument(
               "--overwrite_cache"     ,  action="store_true"     ,  help="Overwrite the cached training and evaluation sets"     )

           return parser




def      A      (      )	-> int:
       lowerCamelCase   :      int			 =					argparse.ArgumentParser()
       add_generic_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					GLUETransformer.add_model_specific_args(_SCREAMING_SNAKE_CASE ,os.getcwd()    )
       lowerCamelCase   :      str			 =					parser.parse_args()

       # If output_dir not provided, a folder will be generated in pwd
       if args.output_dir is None:
              lowerCamelCase   :      int			 =					os.path.join(
                  "./results" ,f'''{args.task}_{time.strftime("%Y%m%d_%H%M%S"    )}''' ,)
              os.makedirs(args.output_dir    )

       lowerCamelCase   :      int			 =					GLUETransformer(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Dict			 =					generic_train(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Optionally, predict on dev set and write to output_dir
       if args.do_predict:
              lowerCamelCase   :      Optional[int]			 =					sorted(glob.glob(os.path.join(args.output_dir ,"checkpoint-epoch=*.ckpt"    ) ,recursive=_SCREAMING_SNAKE_CASE    )    )
              lowerCamelCase   :      Tuple			 =					model.load_from_checkpoint(checkpoints[-1]    )
              return trainer.test(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()






