"""simple docstring"""





import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    BigBirdConfig
      lowerCamelCase__		:    jnp.dtype   =  jnp.floataa
      lowerCamelCase__		:    bool   =  True


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             super().setup()
             lowercase__			:		Optional[int]       =	nn.Dense(5		,			dtype=self.dtype							)


      def __call__(   self		,			*a		,			**a							)       ->					Union[str, Any]:
             lowercase__			:		List[str]       =	super().__call__(*a		,			**a							)
             lowercase__			:		str       =	self.cls(outputs[2]							)
             return outputs[:2] + (cls_out,)





class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Optional[int]   =  FlaxBigBirdForNaturalQuestionsModule





def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : str     ):





       '''simple docstring'''

       def cross_entropy(_lowerCAmelCase  : int   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : str=None     ):
              lowercase__			:		List[str]       =	logits.shape[-1]
              lowercase__			:		Any       =	(labels[..., None] == jnp.arange(_lowerCAmelCase     )[None]).astype('f4'     )
              lowercase__			:		Any       =	jax.nn.log_softmax(_lowerCAmelCase   , axis=-1     )
              lowercase__			:		str       =	-jnp.sum(labels * logits   , axis=-1     )
              if reduction is not None:
                     lowercase__			:		List[Any]       =	reduction(_lowerCAmelCase     )
              return loss

       lowercase__			:		int       =	partial(_lowerCAmelCase   , reduction=jnp.mean     )
       lowercase__			:		Tuple       =	cross_entropy(_lowerCAmelCase   , _lowerCAmelCase     )
       lowercase__			:		int       =	cross_entropy(_lowerCAmelCase   , _lowerCAmelCase     )
       lowercase__			:		List[Any]       =	cross_entropy(_lowerCAmelCase   , _lowerCAmelCase     )
       return (start_loss + end_loss + pooled_loss) / 3


@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    str   =  "google/bigbird-roberta-base"
      lowerCamelCase__		:    int   =  3_0_0_0
      lowerCamelCase__		:    int   =  1_0_5_0_0

      lowerCamelCase__		:    int   =  1_2_8
      lowerCamelCase__		:    int   =  3

      lowerCamelCase__		:    int   =  1
      lowerCamelCase__		:    int   =  5

      # tx_args
      lowerCamelCase__		:    float   =  3E-5
      lowerCamelCase__		:    float   =  0.0
      lowerCamelCase__		:    int   =  2_0_0_0_0
      lowerCamelCase__		:    float   =  0.0095

      lowerCamelCase__		:    str   =  "bigbird-roberta-natural-questions"
      lowerCamelCase__		:    str   =  "training-expt"
      lowerCamelCase__		:    str   =  "data/nq-training.jsonl"
      lowerCamelCase__		:    str   =  "data/nq-validation.jsonl"


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             os.makedirs(self.base_dir		,			exist_ok=a							)
             lowercase__			:		Tuple       =	os.path.join(self.base_dir		,			self.save_dir							)
             lowercase__			:		int       =	self.batch_size_per_device * jax.device_count()





@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    int
      lowerCamelCase__		:    int   =  4_0_9_6  # no dynamic padding on TPUs


      def __call__(   self		,			a							)       ->					List[str]:
             lowercase__			:		Any       =	self.collate_fn(a							)
             lowercase__			:		Dict       =	jax.tree_util.tree_map(a		,			a							)
             return batch


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Union[str, Any]:
             lowercase__    ,      lowercase__			:		Any       =	self.fetch_inputs(features['input_ids']							)
             lowercase__			:		Optional[Any]       =	{
                 'input_ids': jnp.array(a		,			dtype=jnp.intaa							),
                 'attention_mask': jnp.array(a		,			dtype=jnp.intaa							),
                 'start_labels': jnp.array(features['start_token']		,			dtype=jnp.intaa							),
                 'end_labels': jnp.array(features['end_token']		,			dtype=jnp.intaa							),
                 'pooled_labels': jnp.array(features['category']		,			dtype=jnp.intaa							),
             }
             return batch


      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[str]:
             lowercase__			:		List[Any]       =	[self._fetch_inputs(a							) for ids in input_ids]
             return zip(*a							)


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Tuple:
             lowercase__			:		Any       =	[1 for _ in range(len(a							)							)]
             while len(a							) < self.max_length:
                    input_ids.append(self.pad_id							)
                    attention_mask.append(0							)
             return input_ids, attention_mask





def 				a_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : Union[str, Any]=None     ):





       '''simple docstring'''

       if seed is not None:
              lowercase__			:		Tuple       =	dataset.shuffle(seed=_lowerCAmelCase     )
       for i in range(len(_lowerCAmelCase     ) // batch_size     ):
              lowercase__			:		Optional[int]       =	dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(_lowerCAmelCase     )





@partial(jax.pmap   , axis_name='batch'     )
def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Union[str, Any]   , **_lowerCAmelCase  : Optional[int]     ):





       '''simple docstring'''

       def loss_fn(_lowerCAmelCase  : Union[str, Any]     ):
              lowercase__			:		Any       =	model_inputs.pop('start_labels'     )
              lowercase__			:		List[str]       =	model_inputs.pop('end_labels'     )
              lowercase__			:		Any       =	model_inputs.pop('pooled_labels'     )

              lowercase__			:		Dict       =	state.apply_fn(**_lowerCAmelCase   , params=_lowerCAmelCase   , dropout_rng=_lowerCAmelCase   , train=_lowerCAmelCase     )
              lowercase__    ,      lowercase__    ,      lowercase__			:		List[Any]       =	outputs

              return state.loss_fn(
                  _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , )

       lowercase__    ,      lowercase__			:		Dict       =	jax.random.split(_lowerCAmelCase     )
       lowercase__			:		Dict       =	jax.value_and_grad(_lowerCAmelCase     )
       lowercase__    ,      lowercase__			:		Dict       =	grad_fn(state.params     )
       lowercase__			:		Optional[Any]       =	jax.lax.pmean({'loss': loss}   , axis_name='batch'     )
       lowercase__			:		Tuple       =	jax.lax.pmean(_lowerCAmelCase   , 'batch'     )

       lowercase__			:		Tuple       =	state.apply_gradients(grads=_lowerCAmelCase     )
       return state, metrics, new_drp_rng





@partial(jax.pmap   , axis_name='batch'     )
def 				a_     (      _lowerCAmelCase  : Tuple   , **_lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

       lowercase__			:		List[str]       =	model_inputs.pop('start_labels'     )
       lowercase__			:		int       =	model_inputs.pop('end_labels'     )
       lowercase__			:		str       =	model_inputs.pop('pooled_labels'     )

       lowercase__			:		Optional[int]       =	state.apply_fn(**_lowerCAmelCase   , params=state.params   , train=_lowerCAmelCase     )
       lowercase__    ,      lowercase__    ,      lowercase__			:		List[str]       =	outputs

       lowercase__			:		Optional[Any]       =	state.loss_fn(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
       lowercase__			:		int       =	jax.lax.pmean({'loss': loss}   , axis_name='batch'     )
       return metrics


class      UpperCAmelCase_	(    train_state.TrainState):
      lowerCamelCase__		:    Callable   =  struct.field(pytree_node=_a)





@dataclass
class      UpperCAmelCase_	:
      lowerCamelCase__		:    Args
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    Callable
      lowerCamelCase__		:    wandb
      lowerCamelCase__		:    Callable   =  None


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a		,			a=None							)       ->					Dict:
             lowercase__			:		Optional[int]       =	model.params
             lowercase__			:		Tuple       =	TrainState.create(
                 apply_fn=model.__call__		,			params=a		,			tx=a		,			loss_fn=a		,			)
             if ckpt_dir is not None:
                    lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Optional[Any]       =	restore_checkpoint(a		,			a							)
                    lowercase__			:		List[str]       =	{
                        'lr': args.lr,
                        'init_lr': args.init_lr,
                        'warmup_steps': args.warmup_steps,
                        'num_train_steps': num_train_steps,
                        'weight_decay': args.weight_decay,
                    }
                    lowercase__    ,      lowercase__			:		str       =	build_tx(**a							)
                    lowercase__			:		Tuple       =	train_state.TrainState(
                        step=a		,			apply_fn=model.__call__		,			params=a		,			tx=a		,			opt_state=a		,			)
                    lowercase__			:		Optional[int]       =	args
                    lowercase__			:		Any       =	data_collator
                    lowercase__			:		Union[str, Any]       =	lr
                    lowercase__			:		List[Any]       =	params
             lowercase__			:		Tuple       =	jax_utils.replicate(a							)
             return state


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Optional[int]:
             lowercase__			:		str       =	self.args
             lowercase__			:		str       =	len(a							) // args.batch_size

             lowercase__			:		List[Any]       =	jax.random.PRNGKey(0							)
             lowercase__			:		Tuple       =	jax.random.split(a		,			jax.device_count()							)
             for epoch in range(args.max_epochs							):
                    lowercase__			:		Any       =	jnp.array(0		,			dtype=jnp.floataa							)
                    lowercase__			:		List[Any]       =	get_batched_dataset(a		,			args.batch_size		,			seed=a							)
                    lowercase__			:		int       =	0
                    for batch in tqdm(a		,			total=a		,			desc=f"""Running EPOCH-{epoch}"""							):
                           lowercase__			:		Dict       =	self.data_collator(a							)
                           lowercase__    ,      lowercase__    ,      lowercase__			:		List[Any]       =	self.train_step_fn(a		,			a		,			**a							)
                           running_loss += jax_utils.unreplicate(metrics['loss']							)
                           i += 1
                           if i % args.logging_steps == 0:
                                  lowercase__			:		int       =	jax_utils.unreplicate(state.step							)
                                  lowercase__			:		Dict       =	running_loss.item() / i
                                  lowercase__			:		List[Any]       =	self.scheduler_fn(state_step - 1							)

                                  lowercase__			:		Dict       =	self.evaluate(a		,			a							)
                                  lowercase__			:		Tuple       =	{
                                      'step': state_step.item(),
                                      'eval_loss': eval_loss.item(),
                                      'tr_loss': tr_loss,
                                      'lr': lr.item(),
                                  }
                                  tqdm.write(str(a							)							)
                                  self.logger.log(a		,			commit=a							)

                           if i % args.save_steps == 0:
                                  self.save_checkpoint(args.save_dir + f"""-e{epoch}-s{i}"""		,			state=a							)


      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					List[Any]:
             lowercase__			:		Any       =	get_batched_dataset(a		,			self.args.batch_size							)
             lowercase__			:		Optional[int]       =	len(a							) // self.args.batch_size
             lowercase__			:		Union[str, Any]       =	jnp.array(0		,			dtype=jnp.floataa							)
             lowercase__			:		Optional[int]       =	0
             for batch in tqdm(a		,			total=a		,			desc='Evaluating ... '							):
                    lowercase__			:		int       =	self.data_collator(a							)
                    lowercase__			:		Optional[int]       =	self.val_step_fn(a		,			**a							)
                    running_loss += jax_utils.unreplicate(metrics['loss']							)
                    i += 1
             return running_loss / i


      def 		_UpperCAmelCase       (   self		,			a		,			a							)       ->					List[Any]:
             lowercase__			:		int       =	jax_utils.unreplicate(a							)
             print(f"""SAVING CHECKPOINT IN {save_dir}"""		,			end=' ... '							)
             self.model_save_fn(a		,			params=state.params							)
             with open(os.path.join(a		,			'opt_state.msgpack'							)		,			'wb'							) as f:
                    f.write(to_bytes(state.opt_state							)							)
             joblib.dump(self.args		,			os.path.join(a		,			'args.joblib'							)							)
             joblib.dump(self.data_collator		,			os.path.join(a		,			'data_collator.joblib'							)							)
             with open(os.path.join(a		,			'training_state.json'							)		,			'w'							) as f:
                    json.dump({'step': state.step.item()}		,			a							)
             print('DONE'							)





def 				a_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       print(f"""RESTORING CHECKPOINT FROM {save_dir}"""   , end=' ... '     )
       with open(os.path.join(_lowerCAmelCase   , 'flax_model.msgpack'     )   , 'rb'     ) as f:
              lowercase__			:		Tuple       =	from_bytes(state.params   , f.read()     )

       with open(os.path.join(_lowerCAmelCase   , 'opt_state.msgpack'     )   , 'rb'     ) as f:
              lowercase__			:		List[str]       =	from_bytes(state.opt_state   , f.read()     )

       lowercase__			:		int       =	joblib.load(os.path.join(_lowerCAmelCase   , 'args.joblib'     )     )
       lowercase__			:		Optional[Any]       =	joblib.load(os.path.join(_lowerCAmelCase   , 'data_collator.joblib'     )     )

       with open(os.path.join(_lowerCAmelCase   , 'training_state.json'     )   , 'r'     ) as f:
              lowercase__			:		Union[str, Any]       =	json.load(_lowerCAmelCase     )
       lowercase__			:		Tuple       =	training_state['step']

       print('DONE'     )
       return params, opt_state, step, args, data_collator





def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	num_train_steps - warmup_steps
       lowercase__			:		int       =	optax.linear_schedule(init_value=_lowerCAmelCase   , end_value=_lowerCAmelCase   , transition_steps=_lowerCAmelCase     )
       lowercase__			:		Optional[Any]       =	optax.linear_schedule(init_value=_lowerCAmelCase   , end_value=1E-7   , transition_steps=_lowerCAmelCase     )
       lowercase__			:		int       =	optax.join_schedules(schedules=[warmup_fn, decay_fn]   , boundaries=[warmup_steps]     )
       return lr






def 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : int     ):





       '''simple docstring'''

       def weight_decay_mask(_lowerCAmelCase  : Optional[int]     ):
              lowercase__			:		Tuple       =	traverse_util.flatten_dict(_lowerCAmelCase     )
              lowercase__			:		Any       =	{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
              return traverse_util.unflatten_dict(_lowerCAmelCase     )

       lowercase__			:		List[str]       =	scheduler_fn(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )

       lowercase__			:		List[Any]       =	optax.adamw(learning_rate=_lowerCAmelCase   , weight_decay=_lowerCAmelCase   , mask=_lowerCAmelCase     )
       return tx, lr



'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer





def 					UpperCAmelCase_		(__a      :    List[Any] ):






   """simple docstring"""

   if (
       (cp >= 0x4E_00 and cp <= 0x9F_FF)
       or (cp >= 0x34_00 and cp <= 0x4D_BF)  #
       or (cp >= 0x2_00_00 and cp <= 0x2_A6_DF)  #
       or (cp >= 0x2_A7_00 and cp <= 0x2_B7_3F)  #
       or (cp >= 0x2_B7_40 and cp <= 0x2_B8_1F)  #
       or (cp >= 0x2_B8_20 and cp <= 0x2_CE_AF)  #
       or (cp >= 0xF9_00 and cp <= 0xFA_FF)
       or (cp >= 0x2_F8_00 and cp <= 0x2_FA_1F)  #
   ):  #
      return True

   return False





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   for char in word:
      _a				:	Union[str, Any]			=   ord(__a )
      if not _is_chinese_char(__a ):
         return 0
   return 1





def 					UpperCAmelCase_		(__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   set()

   for token in tokens:
      _a				:	str			=   len(__a ) > 1 and is_chinese(__a )
      if chinese_word:
         word_set.add(__a )
   _a				:	Optional[Any]			=   list(__a )
   return word_list





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    set() ):






   """simple docstring"""

   if not chinese_word_set:
      return bert_tokens
   _a				:	Optional[Any]			=   max([len(__a ) for w in chinese_word_set] )

   _a				:	Optional[int]			=   bert_tokens
   _a,  _a				:	Any			=   0, len(__a )
   while start < end:
      _a				:	Tuple			=   True
      if is_chinese(bert_word[start] ):
         _a				:	Union[str, Any]			=   min(end - start ,	__a )
         for i in range(__a ,	1 ,	-1 ):
            _a				:	Optional[Any]			=   ''.join(bert_word[start : start + i] )
            if whole_word in chinese_word_set:
               for j in range(start + 1 ,	start + i ):
                  _a				:	Any			=   '##' + bert_word[j]
               _a				:	Union[str, Any]			=   start + i
               _a				:	int			=   False
               break
      if single_word:
         start += 1
   return bert_word





def 					UpperCAmelCase_		(__a      :    List[str] ,	__a      :    LTP ,	__a      :    BertTokenizer ):






   """simple docstring"""

   _a				:	int			=   []

   for i in range(0 ,	len(__a ) ,	1_0_0 ):
      _a				:	Union[str, Any]			=   ltp_tokenizer.seg(lines[i : i + 1_0_0] )[0]
      _a				:	Optional[Any]			=   [get_chinese_word(__a ) for r in res]
      ltp_res.extend(__a )
   assert len(__a ) == len(__a )

   _a				:	str			=   []
   for i in range(0 ,	len(__a ) ,	1_0_0 ):
      _a				:	List[str]			=   bert_tokenizer(lines[i : i + 1_0_0] ,	add_special_tokens=__a ,	truncation=__a ,	max_length=5_1_2 )
      bert_res.extend(res['input_ids'] )
   assert len(__a ) == len(__a )

   _a				:	List[str]			=   []
   for input_ids, chinese_word in zip(__a ,	__a ):
      _a				:	int			=   []
      for id in input_ids:
         _a				:	Optional[int]			=   bert_tokenizer._convert_id_to_token(__a )
         input_tokens.append(__a )
      _a				:	List[str]			=   add_sub_symbol(__a ,	__a )
      _a				:	Tuple			=   []
      # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
      for i, token in enumerate(__a ):
         if token[:2] == "##":
            _a				:	str			=   token[2:]
            # save chinese tokens' pos
            if len(__a ) == 1 and _is_chinese_char(ord(__a ) ):
               ref_id.append(__a )
      ref_ids.append(__a )

   assert len(__a ) == len(__a )

   return ref_ids





def 					UpperCAmelCase_		(__a      :    Optional[Any] ):






   """simple docstring"""

   with open(args.file_name ,	'r' ,	encoding='utf-8' ) as f:
      _a				:	Dict			=   f.readlines()
   _a				:	int			=   [line.strip() for line in data if len(__a ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   _a				:	int			=   LTP(args.ltp )  # faster in GPU device
   _a				:	Tuple			=   BertTokenizer.from_pretrained(args.bert )

   _a				:	int			=   prepare_ref(__a ,	__a ,	__a )

   with open(args.save_path ,	'w' ,	encoding='utf-8' ) as f:
      _a				:	Optional[Any]			=   [json.dumps(__a ) + '\n' for ref in ref_ids]
      f.writelines(__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser(description="""prepare_chinese_ref""")
  parser.add_argument(
      """--file_name""",
      type=str,
      default="""./resources/chinese-demo.txt""",
      help="""file need process, same as training data in lm""",
  )
  parser.add_argument(
      """--ltp""", type=str, default="""./resources/ltp""", help="""resources for LTP tokenizer, usually a path"""
  )
  parser.add_argument("""--bert""", type=str, default="""./resources/robert""", help="""resources for Bert tokenizer""")
  parser.add_argument("""--save_path""", type=str, default="""./resources/ref.txt""", help="""path to save res""")

  __lowerCAmelCase         =    parser.parse_args()
  main(args)



