





import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				_UpperCamelCase       (						lowerCAmelCase_	,    unittest.TestCase  ):
       _UpperCamelCase					:       List[str]       = HerbertTokenizer
       _UpperCamelCase					:       Optional[int]       = HerbertTokenizerFast
       _UpperCamelCase					:       Optional[Any]       = True

       def lowercase			(		self:			Dict		)							->			Dict:




              """simple docstring"""


              super().setUp()

              # Use a simpler test file without japanese/chinese characters
              with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''       , encoding="utf-8"		) as f_data:
                     UpperCamelCase_ 							=	f_data.read().replace("\n\n"       , "\n"		).strip()

              UpperCamelCase_ 							=	[
                  "<s>",
                  "</s>",
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "w</w>",
                  "r</w>",
                  "t</w>",
                  "lo",
                  "low",
                  "er</w>",
                  "low</w>",
                  "lowest</w>",
                  "newer</w>",
                  "wider</w>",
                  ",</w>",
                  "<unk>",
              ]
              UpperCamelCase_ 							=	dict(zip(_SCREAMING_SNAKE_CASE       , range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
              UpperCamelCase_ 							=	["l o 123", "lo w 1456", "e r</w> 1789", ""]

              UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["vocab_file"]		)
              UpperCamelCase_ 							=	os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["merges_file"]		)
              with open(self.vocab_file       , "w"		) as fp:
                     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
              with open(self.merges_file       , "w"		) as fp:
                     fp.write("\n".join(_SCREAMING_SNAKE_CASE		)		)

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int		)							->			List[str]:




              """simple docstring"""


              UpperCamelCase_ 							=	"lower newer"
              UpperCamelCase_ 							=	"lower newer"
              return input_text, output_text

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




              """simple docstring"""


              UpperCamelCase_ 							=	self.tokenizer_class(vocab_file=self.vocab_file       , merges_file=self.merges_file		)

              UpperCamelCase_ 							=	"lower"
              UpperCamelCase_ 							=	["low", "er</w>"]
              UpperCamelCase_ 							=	tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	tokens + ["<unk>"]
              UpperCamelCase_ 							=	[16, 17, 23]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)       , _SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Union[str, Any]		)							->			int:




              """simple docstring"""


              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase_ 							=	self.get_tokenizer()
              UpperCamelCase_ 							=	self.get_rust_tokenizer()

              UpperCamelCase_ 							=	"lower,newer"

              UpperCamelCase_ 							=	tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	tokenizer.encode(_SCREAMING_SNAKE_CASE       , add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	rust_tokenizer.encode(_SCREAMING_SNAKE_CASE       , add_special_tokens=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	self.get_rust_tokenizer()
              UpperCamelCase_ 							=	tokenizer.encode(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	rust_tokenizer.encode(_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)



       @slow
       def lowercase			(		self:			List[str]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"		)

              UpperCamelCase_ 							=	tokenizer.encode("konstruowanie sekwencji"       , add_special_tokens=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tokenizer.encode("konstruowanie wielu sekwencji"       , add_special_tokens=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

              assert encoded_sentence == [0] + text + [2]
              assert encoded_pair == [0] + text + [2] + text_a + [2]








'''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)
