"""simple docstring"""


import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


__UpperCamelCase       :    str         =		logging.get_logger(__name__)

__UpperCamelCase       :    int         =		{
    '''source_spm''': '''source.spm''',
    '''target_spm''': '''target.spm''',
    '''vocab''': '''vocab.json''',
    '''target_vocab_file''': '''target_vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

__UpperCamelCase       :    Tuple         =		{
    '''source_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'''
    },
    '''target_spm''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'''
    },
    '''vocab''': {
        '''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'''
    },
    '''tokenizer_config_file''': {
        '''Helsinki-NLP/opus-mt-en-de''': (
            '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'''
        )
    },
}

__UpperCamelCase       :    List[Any]         =		{'''Helsinki-NLP/opus-mt-en-de''': 5_1_2}
__UpperCamelCase       :    List[str]         =		{}
class   UpperCAmelCase_	(				snake_case_   ):
    snake_case_											=      VOCAB_FILES_NAMES
    snake_case_											=      PRETRAINED_VOCAB_FILES_MAP
    snake_case_											=      PRETRAINED_INIT_CONFIGURATION
    snake_case_											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    snake_case_											=      ["""input_ids""", """attention_mask"""]
    snake_case_											=      re.compile(""">>.+<<"""   )  # type: re.Pattern
    def __init__(	self			:       int   ,						_lowercase			:       str   ,						_lowercase			:       List[str]   ,						_lowercase			:       List[Any]   ,						_lowercase			:       List[str]=None   ,						_lowercase			:       Optional[int]=None   ,						_lowercase			:       Tuple=None   ,						_lowercase			:       Dict="<unk>"   ,						_lowercase			:       int="</s>"   ,						_lowercase			:       Dict="<pad>"   ,						_lowercase			:       List[str]=5_1_2   ,						_lowercase			:       Any = None   ,						_lowercase			:       Any=False   ,						**_lowercase			:       int   ,						)						->    None:
       _lowercase							     =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
           source_lang=UpperCAmelCase__   ,						target_lang=UpperCAmelCase__   ,						unk_token=UpperCAmelCase__   ,						eos_token=UpperCAmelCase__   ,						pad_token=UpperCAmelCase__   ,						model_max_length=UpperCAmelCase__   ,						sp_model_kwargs=self.sp_model_kwargs   ,						target_vocab_file=UpperCAmelCase__   ,						separate_vocabs=UpperCAmelCase__   ,						**UpperCAmelCase__   ,						)
       assert Path(UpperCAmelCase__		).exists(), f"""cannot find spm source {source_spm}"""

       _lowercase							     =       separate_vocabs
       _lowercase							     =       load_json(UpperCAmelCase__		)
       if self.unk_token not in self.encoder:
          raise KeyError("<unk> token must be in vocab"		)
       assert self.pad_token in self.encoder

       if separate_vocabs:
          _lowercase							     =       load_json(UpperCAmelCase__		)
          _lowercase							     =       {v: k for k, v in self.target_encoder.items()}
          _lowercase							     =       []
       else:
          _lowercase							     =       {v: k for k, v in self.encoder.items()}
          _lowercase							     =       [k for k in self.encoder if k.startswith(">>"		) and k.endswith("<<"		)]

       _lowercase							     =       source_lang
       _lowercase							     =       target_lang
       _lowercase							     =       [source_spm, target_spm]

       # load SentencePiece model for pre-processing
       _lowercase							     =       load_spm(UpperCAmelCase__   ,						self.sp_model_kwargs		)
       _lowercase							     =       load_spm(UpperCAmelCase__   ,						self.sp_model_kwargs		)
       _lowercase							     =       self.spm_source
       _lowercase							     =       self.encoder

       # Multilingual target side: default to using first supported language code.

       self._setup_normalizer()
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    str:
       try:
          from sacremoses import MosesPunctNormalizer

          _lowercase							     =       MosesPunctNormalizer(self.source_lang		).normalize
       except (ImportError, FileNotFoundError):
          warnings.warn("Recommended: pip install sacremoses."		)
          _lowercase							     =       lambda _lowercase		: x
    def 		_lowerCamelCase      (	self			:       Dict   ,						_lowercase			:       str		)						->    str:
       return self.punc_normalizer(UpperCAmelCase__		) if x else ""
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       int		)						->    List[str]:
       return self.current_encoder.get(UpperCAmelCase__   ,						self.current_encoder[self.unk_token]		)
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       List[Any]		)						->    str:
       _lowercase							     =       self.language_code_re.match(UpperCAmelCase__		)
       _lowercase							     =       [match.group(0		)] if match else []
       return code, self.language_code_re.sub(""   ,						UpperCAmelCase__		)
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Union[str, Any]		)						->    List[str]:
       _lowercase							     =       self.remove_language_code(UpperCAmelCase__		)
       _lowercase							     =       self.current_spm.encode(UpperCAmelCase__   ,						out_type=UpperCAmelCase__		)
       return code + pieces
    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Tuple		)						->    str:
       return self.decoder.get(UpperCAmelCase__   ,						self.unk_token		)
    def 		_lowerCamelCase      (	self			:       int   ,						_lowercase			:       Tuple   ,						**_lowercase			:       Optional[Any]		)						->    List[Any]:
       return super().batch_decode(UpperCAmelCase__   ,						**UpperCAmelCase__		)
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       List[str]   ,						**_lowercase			:       Tuple		)						->    Any:
       return super().decode(UpperCAmelCase__   ,						**UpperCAmelCase__		)
    def 		_lowerCamelCase      (	self			:       Dict   ,						_lowercase			:       Dict		)						->    str:
       _lowercase							     =       self.spm_source if self._decode_use_source_tokenizer else self.spm_target
       _lowercase							     =       []
       _lowercase							     =       ''''''
       for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
             out_string += sp_model.decode_pieces(UpperCAmelCase__		) + token + " "
             _lowercase							     =       []
          else:
             current_sub_tokens.append(UpperCAmelCase__		)
       out_string += sp_model.decode_pieces(UpperCAmelCase__		)
       return out_string.strip()
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Any   ,						_lowercase			:       Dict=None		)						->    List[int]:
       if token_ids_a is None:
          return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Tuple:
       _lowercase							     =       self.spm_source
       _lowercase							     =       self.encoder
    def 		_lowerCamelCase      (	self			:       List[str]		)						->    Union[str, Any]:
       _lowercase							     =       self.spm_target
       if self.separate_vocabs:
          _lowercase							     =       self.target_encoder
    @property
    def 		_lowerCamelCase      (	self			:       int		)						->    int:
       return len(self.encoder		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       str = None		)						->    Tuple[str]:
       if not os.path.isdir(UpperCAmelCase__		):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
          return
       _lowercase							     =       []

       if self.separate_vocabs:
          _lowercase							     =       os.path.join(
              UpperCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]   ,						)
          _lowercase							     =       os.path.join(
              UpperCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]   ,						)
          save_json(self.encoder   ,						UpperCAmelCase__		)
          save_json(self.target_encoder   ,						UpperCAmelCase__		)
          saved_files.append(UpperCAmelCase__		)
          saved_files.append(UpperCAmelCase__		)
       else:
          _lowercase							     =       os.path.join(
              UpperCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]		)
          save_json(self.encoder   ,						UpperCAmelCase__		)
          saved_files.append(UpperCAmelCase__		)

       for spm_save_filename, spm_orig_path, spm_model in zip(
           [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]   ,						self.spm_files   ,						[self.spm_source, self.spm_target]   ,						):
          _lowercase							     =       os.path.join(
              UpperCAmelCase__   ,						(filename_prefix + "-" if filename_prefix else "") + spm_save_filename		)
          if os.path.abspath(UpperCAmelCase__		) != os.path.abspath(UpperCAmelCase__		) and os.path.isfile(UpperCAmelCase__		):
             copyfile(UpperCAmelCase__   ,						UpperCAmelCase__		)
             saved_files.append(UpperCAmelCase__		)
          elif not os.path.isfile(UpperCAmelCase__		):
             with open(UpperCAmelCase__   ,						"wb"		) as fi:
                _lowercase							     =       spm_model.serialized_model_proto()
                fi.write(UpperCAmelCase__		)
             saved_files.append(UpperCAmelCase__		)

       return tuple(UpperCAmelCase__		)
    def 		_lowerCamelCase      (	self			:       Any		)						->    Dict:
       return self.get_src_vocab()
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    int:
       return dict(self.encoder   ,						**self.added_tokens_encoder		)
    def 		_lowerCamelCase      (	self			:       List[str]		)						->    Tuple:
       return dict(self.target_encoder   ,						**self.added_tokens_decoder		)
    def __getstate__(	self			:       List[str]		)						->    Dict:
       _lowercase							     =       self.__dict__.copy()
       state.update(
           {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}		)
       return state
    def __setstate__(	self			:       Dict   ,						_lowercase			:       List[Any]		)						->    None:
       _lowercase							     =       d

       # for backward compatibility
       if not hasattr(self   ,						"sp_model_kwargs"		):
          _lowercase							     =       {}

       _lowercase							     =       (load_spm(UpperCAmelCase__   ,						self.sp_model_kwargs		) for f in self.spm_files)
       _lowercase							     =       self.spm_source
       self._setup_normalizer()
    def 		_lowerCamelCase      (	self			:       int   ,						*_lowercase			:       List[str]   ,						**_lowercase			:       int		)						->    List[str]:
       return 1
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Any		)						->    List[Any]:
       _lowercase							     =       set(self.all_special_ids		)  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special
       return [1 if x in all_special_ids else 0 for x in seq]




    def 		_lowerCamelCase      (	self			:       List[str]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       Any = None   ,						_lowercase			:       Union[str, Any] = False		)						->    List[int]:
       if already_has_special_tokens:
          return self._special_token_mask(UpperCAmelCase__		)
       elif token_ids_a is None:
          return self._special_token_mask(UpperCAmelCase__		) + [1]
       else:
          return self._special_token_mask(token_ids_a + token_ids_a		) + [1]


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       Dict[str, Any]       ):
   _lowercase							     =       sentencepiece.SentencePieceProcessor(**_snake_case       )
   spm.Load(_snake_case       )
   return spm


def   __UpperCAmelCase     (	_snake_case		:       Any,				_snake_case		:       str       ):


   with open(_snake_case,				"w"       ) as f:
      json.dump(_snake_case,				_snake_case,				indent=2       )


def   __UpperCAmelCase     (	_snake_case		:       str       ):


   with open(_snake_case,				"r"       ) as f:
      return json.load(_snake_case       )






'''simple docstring'''






import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
	print('''#################### AVAILABLE DEVICES ####################''')
	print(jax.devices())
	print('''###########################################################''')

	# setup for wandb sweep
	__UpperCamelCase      :   str								=     Args()
	__UpperCamelCase      :   Optional[int]								=     wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
	__UpperCamelCase      :   Dict								=     dict(logger.config)
	del wandb_args["batch_size"]
	__UpperCamelCase      :   int								=     replace(args, **wandb_args)
	__UpperCamelCase      :   str								=     args.base_dir + '''-''' + wandb.run.id
	__UpperCamelCase      :   List[Any]								=     replace(args, base_dir=base_dir)
	print(args)

	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.tr_data_path)['''train''']
	__UpperCamelCase      :   Any								=     load_dataset('''json''', data_files=args.val_data_path)['''train''']

	# drop extra batch for now
	__UpperCamelCase      :   int								=     range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
	__UpperCamelCase      :   Optional[int]								=     tr_dataset.shuffle().select(indices)
	__UpperCamelCase      :   str								=     range(len(val_dataset) - len(val_dataset) % args.batch_size)
	__UpperCamelCase      :   int								=     val_dataset.shuffle().select(indices)

	if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
		__UpperCamelCase      :   str								=     tr_dataset.shuffle().select(range(80000))
		__UpperCamelCase      :   List[Any]								=     val_dataset.shuffle().select(range(8000))

	print(tr_dataset)
	print(val_dataset)

	__UpperCamelCase      :   Tuple								=     FlaxBigBirdForNaturalQuestions.from_pretrained(
	    args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
	)
	__UpperCamelCase      :   Optional[Any]								=     BigBirdTokenizerFast.from_pretrained(args.model_id)
	__UpperCamelCase      :   int								=     DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)

	__UpperCamelCase      :   Dict								=     {
	    '''lr''': args.lr,
	    '''init_lr''': args.init_lr,
	    '''warmup_steps''': args.warmup_steps,
	    '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
	    '''weight_decay''': args.weight_decay,
	}
	__UpperCamelCase							,  __UpperCamelCase      :   str								=     build_tx(**tx_args)

	__UpperCamelCase      :   List[str]								=     Trainer(
	    args=args,
	    data_collator=data_collator,
	    model_save_fn=model.save_pretrained,
	    train_step_fn=train_step,
	    val_step_fn=val_step,
	    logger=logger,
	    scheduler_fn=lr,
	)

	__UpperCamelCase      :   List[Any]								=     None
	__UpperCamelCase      :   str								=     trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
	try:
		trainer.train(state, tr_dataset, val_dataset)
	except KeyboardInterrupt:
		print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

	print('''SAVING WEIGHTS IN `final-weights`''')
	__UpperCamelCase      :   List[Any]								=     jax_utils.unreplicate(state.params)
	model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)
