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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      @slow
      @require_torch
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""    ,  """prajjwal1/bert-tiny""")
          _lowerCamelCase     :		List[str] 						=						BertTokenizer.from_pretrained("""bert-base-uncased""")

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

          _lowerCamelCase     :		Dict 						=						datasets.load_dataset("""cnn_dailymail"""    ,  """3.0.0"""    ,  split="""train[:1%]""")
          _lowerCamelCase     :		List[str] 						=						datasets.load_dataset("""cnn_dailymail"""    ,  """3.0.0"""    ,  split="""validation[:1%]""")

          _lowerCamelCase     :		List[Any] 						=						train_dataset.select(range(32))
          _lowerCamelCase     :		Tuple 						=						val_dataset.select(range(16))

          _lowerCamelCase     :		Tuple 						=						4

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

              _lowerCamelCase     :		Tuple 						=						outputs.input_ids
              _lowerCamelCase     :		Optional[int] 						=						outputs.input_ids.copy()
              _lowerCamelCase     :		List[str] 						=						[
                  [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
              ]
              _lowerCamelCase     :		List[str] 						=						outputs.attention_mask

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

              return batch

          def _compute_metrics(_UpperCamelCase     :				str):
              _lowerCamelCase     :		List[str] 						=						pred.label_ids
              _lowerCamelCase     :		str 						=						pred.predictions

              # all unnecessary tokens are removed
              _lowerCamelCase     :		List[str] 						=						tokenizer.batch_decode(_UpperCamelCase    ,  skip_special_tokens=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase    ,  skip_special_tokens=_UpperCamelCase)

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

              return {"accuracy": accuracy}

          # map train dataset
          _lowerCamelCase     :		Any 						=						train_dataset.map(
              _map_to_encoder_decoder_inputs    ,  batched=_UpperCamelCase    ,  batch_size=_UpperCamelCase    ,  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
          _lowerCamelCase     :		Tuple 						=						val_dataset.map(
              _map_to_encoder_decoder_inputs    ,  batched=_UpperCamelCase    ,  batch_size=_UpperCamelCase    ,  remove_columns=["""article""", """highlights"""]    ,  )
          val_dataset.set_format(
              type="""torch"""    ,  columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]    ,  )

          _lowerCamelCase     :		int 						=						self.get_auto_remove_tmp_dir()

          _lowerCamelCase     :		Dict 						=						SeqaSeqTrainingArguments(
              output_dir=_UpperCamelCase    ,  per_device_train_batch_size=_UpperCamelCase    ,  per_device_eval_batch_size=_UpperCamelCase    ,  predict_with_generate=_UpperCamelCase    ,  evaluation_strategy="""steps"""    ,  do_train=_UpperCamelCase    ,  do_eval=_UpperCamelCase    ,  warmup_steps=0    ,  eval_steps=2    ,  logging_steps=2    ,  )

          # instantiate trainer
          _lowerCamelCase     :		Dict 						=						SeqaSeqTrainer(
              model=_UpperCamelCase    ,  args=_UpperCamelCase    ,  compute_metrics=_compute_metrics    ,  train_dataset=_UpperCamelCase    ,  eval_dataset=_UpperCamelCase    ,  tokenizer=_UpperCamelCase    ,  )

          # start training
          trainer.train()





import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

          return (vocab_file,)




