





from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
							import datasets





class 				a			(      _SCREAMING_SNAKE_CASE			):






				@slow
				@require_torch
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''			, '''prajjwal1/bert-tiny'''					)
										__SCREAMING_SNAKE_CASE:				Dict           =   BertTokenizer.from_pretrained('''bert-base-uncased'''					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   bertabert.config.encoder.vocab_size
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   tokenizer.sep_token_id
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   tokenizer.cls_token_id
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   128

										__SCREAMING_SNAKE_CASE:				str           =   datasets.load_dataset('''cnn_dailymail'''			, '''3.0.0'''			, split='''train[:1%]'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   datasets.load_dataset('''cnn_dailymail'''			, '''3.0.0'''			, split='''validation[:1%]'''					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   train_dataset.select(range(32					)					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   val_dataset.select(range(16					)					)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   4

										def _map_to_encoder_decoder_inputs(_lowerCAmelCase					):
																# Tokenizer will automatically set [BOS] <text> [EOS]
																__SCREAMING_SNAKE_CASE:				List[Any]           =   tokenizer(batch['''article''']			, padding='''max_length'''			, truncation=_lowerCAmelCase			, max_length=512					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer(batch['''highlights''']			, padding='''max_length'''			, truncation=_lowerCAmelCase			, max_length=128					)
																__SCREAMING_SNAKE_CASE:				Dict           =   inputs.input_ids
																__SCREAMING_SNAKE_CASE:				Dict           =   inputs.attention_mask

																__SCREAMING_SNAKE_CASE:				List[str]           =   outputs.input_ids
																__SCREAMING_SNAKE_CASE:				str           =   outputs.input_ids.copy()
																__SCREAMING_SNAKE_CASE:				Any           =   [
																    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
																]
																__SCREAMING_SNAKE_CASE:				List[str]           =   outputs.attention_mask

																assert all(len(_lowerCAmelCase					) == 512 for x in inputs.input_ids					)
																assert all(len(_lowerCAmelCase					) == 128 for x in outputs.input_ids					)

																return batch

										def _compute_metrics(_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Dict           =   pred.label_ids
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   pred.predictions

																# all unnecessary tokens are removed
																__SCREAMING_SNAKE_CASE:				Any           =   tokenizer.batch_decode(_lowerCAmelCase			, skip_special_tokens=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   tokenizer.batch_decode(_lowerCAmelCase			, skip_special_tokens=_lowerCAmelCase					)

																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   sum([int(pred_str[i] == label_str[i]					) for i in range(len(_lowerCAmelCase					)					)]					) / len(_lowerCAmelCase					)

																return {"accuracy": accuracy}

										# map train dataset
										__SCREAMING_SNAKE_CASE:				Dict           =   train_dataset.map(
										    _map_to_encoder_decoder_inputs			, batched=_lowerCAmelCase			, batch_size=_lowerCAmelCase			, remove_columns=['''article''', '''highlights''']			, )
										train_dataset.set_format(
										    type='''torch'''			, columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']			, )

										# same for validation dataset
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   val_dataset.map(
										    _map_to_encoder_decoder_inputs			, batched=_lowerCAmelCase			, batch_size=_lowerCAmelCase			, remove_columns=['''article''', '''highlights''']			, )
										val_dataset.set_format(
										    type='''torch'''			, columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']			, )

										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_auto_remove_tmp_dir()

										__SCREAMING_SNAKE_CASE:				int           =   SeqaSeqTrainingArguments(
										    output_dir=_lowerCAmelCase			, per_device_train_batch_size=_lowerCAmelCase			, per_device_eval_batch_size=_lowerCAmelCase			, predict_with_generate=_lowerCAmelCase			, evaluation_strategy='''steps'''			, do_train=_lowerCAmelCase			, do_eval=_lowerCAmelCase			, warmup_steps=0			, eval_steps=2			, logging_steps=2			, )

										# instantiate trainer
										__SCREAMING_SNAKE_CASE:				int           =   SeqaSeqTrainer(
										    model=_lowerCAmelCase			, args=_lowerCAmelCase			, compute_metrics=_compute_metrics			, train_dataset=_lowerCAmelCase			, eval_dataset=_lowerCAmelCase			, tokenizer=_lowerCAmelCase			, )

										# start training
										trainer.train()









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   lowerCamelCase__					:  Optional[Any]      							=				None

lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)
lowerCamelCase__					:  List[str]      							=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					:  List[str]      							=				{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					:  List[Any]      							=				'▁'





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       ["input_ids", "token_type_ids"]
 lowercase_							       =       FNetTokenizer



 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						List[str]=False							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						List[Any]="<unk>"							,							_lowerCAmelCase    :						Optional[Any]="[SEP]"							,							_lowerCAmelCase    :						Optional[Any]="<pad>"							,							_lowerCAmelCase    :						Optional[int]="[CLS]"							,							_lowerCAmelCase    :						Optional[Any]="[MASK]"							,							**_lowerCAmelCase    :						Any							,							):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       SCREAMING_SNAKE_CASE_      		=     (
           AddedToken(_lowerCAmelCase							,							lstrip=_lowerCAmelCase							,							rstrip=_lowerCAmelCase							,							normalized=_lowerCAmelCase		)
           if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		)
           else mask_token
       )

       super().__init__(
           _lowerCAmelCase							,							tokenizer_file=_lowerCAmelCase							,							do_lower_case=_lowerCAmelCase							,							remove_space=_lowerCAmelCase							,							keep_accents=_lowerCAmelCase							,							unk_token=_lowerCAmelCase							,							sep_token=_lowerCAmelCase							,							pad_token=_lowerCAmelCase							,							cls_token=_lowerCAmelCase							,							mask_token=_lowerCAmelCase							,							**_lowerCAmelCase							,							)

       SCREAMING_SNAKE_CASE_      		=     do_lower_case
       SCREAMING_SNAKE_CASE_      		=     remove_space
       SCREAMING_SNAKE_CASE_      		=     keep_accents
       SCREAMING_SNAKE_CASE_      		=     vocab_file
       SCREAMING_SNAKE_CASE_      		=     False if not self.vocab_file else True



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_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		) * [0] + len(token_ids_a + sep		) * [1]




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       if not os.path.isdir(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
             return
       SCREAMING_SNAKE_CASE_      		=     os.path.join(
           _lowerCAmelCase							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		):
             copyfile(self.vocab_file							,							_lowerCAmelCase		)

       return (out_vocab_file,)