
import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english





@require_tokenizers
class 		__lowerCamelCase     (   lowercase_							, unittest.TestCase		):
							lowerCamelCase__:				str      =							RealmTokenizer
							lowerCamelCase__:				Union[str, Any]      =							RealmTokenizerFast
							lowerCamelCase__:				List[str]      =							True
							lowerCamelCase__:				Optional[int]      =							True
							lowerCamelCase__:				Union[str, Any]      =							filter_non_english



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								super().setUp()

								UpperCAmelCase:					Any 						=     [
								    '[UNK]',
								    '[CLS]',
								    '[SEP]',
								    '[PAD]',
								    '[MASK]',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								UpperCAmelCase:					Optional[Any] 						=     os.path.join(self.tmpdirname	,				VOCAB_FILES_NAMES["vocab_file"]							)
								with open(self.vocab_file	,				"w"	,				encoding="utf-8"							) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]							)							)



							def     A__ (     self	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     'UNwant\u00E9d,running'
								UpperCAmelCase:					Optional[int] 						=     'unwanted, running'
								return input_text, output_text



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






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     self.tokenizer_class(self.vocab_file							)

								UpperCAmelCase:					List[Any] 						=     tokenizer.tokenize("UNwant\u00E9d,running"							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				["un", "##want", "##ed", ",", "runn", "##ing"]							)
								self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_							)	,				[9, 6, 7, 1_2, 1_0, 1_1]							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								if not self.test_rust_tokenizer:
									return

								UpperCAmelCase:					Any 						=     self.get_tokenizer()
								UpperCAmelCase:					List[str] 						=     self.get_rust_tokenizer()

								UpperCAmelCase:					List[Any] 						=     'UNwant\u00E9d,running'

								UpperCAmelCase:					List[str] 						=     tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Optional[int] 						=     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)

								UpperCAmelCase:					List[Any] 						=     tokenizer.encode(SCREAMING_SNAKE_CASE_	,				add_special_tokens=SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					int 						=     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_	,				add_special_tokens=SCREAMING_SNAKE_CASE_							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)

								UpperCAmelCase:					int 						=     self.get_rust_tokenizer()
								UpperCAmelCase:					str 						=     tokenizer.encode(SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Optional[int] 						=     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)

								# With lower casing
								UpperCAmelCase:					List[str] 						=     self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Dict 						=     self.get_rust_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_							)

								UpperCAmelCase:					List[str] 						=     'UNwant\u00E9d,running'

								UpperCAmelCase:					str 						=     tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Optional[int] 						=     rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)

								UpperCAmelCase:					Tuple 						=     tokenizer.encode(SCREAMING_SNAKE_CASE_	,				add_special_tokens=SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					List[str] 						=     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_	,				add_special_tokens=SCREAMING_SNAKE_CASE_							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)

								UpperCAmelCase:					Dict 						=     self.get_rust_tokenizer()
								UpperCAmelCase:					List[str] 						=     tokenizer.encode(SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Union[str, Any] 						=     rust_tokenizer.encode(SCREAMING_SNAKE_CASE_							)
								self.assertListEqual(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)



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






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     BasicTokenizer()

								self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"							)	,				["ah", "\u535A", "\u63A8", "zz"]							)



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)	,				["hello", "!", "how", "are", "you", "?"]							)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)	,				["hello"]							)



							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_	,				strip_accents=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)	,				["hällo", "!", "how", "are", "you", "?"]							)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)	,				["h\u00E9llo"]							)



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






								"""simple docstring"""



								UpperCAmelCase:					int 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_	,				strip_accents=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)	,				["hallo", "!", "how", "are", "you", "?"]							)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)	,				["hello"]							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)	,				["hallo", "!", "how", "are", "you", "?"]							)
								self.assertListEqual(tokenizer.tokenize("H\u00E9llo"							)	,				["hello"]							)



							def     A__ (     self							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					int 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "							)	,				["HeLLo", "!", "how", "Are", "yoU", "?"]							)



							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_	,				strip_accents=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)	,				["HäLLo", "!", "how", "Are", "yoU", "?"]							)



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_	,				strip_accents=SCREAMING_SNAKE_CASE_							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "							)	,				["HaLLo", "!", "how", "Are", "yoU", "?"]							)



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






								"""simple docstring"""



								UpperCAmelCase:					Any 						=     BasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE_	,				never_split=["[UNK]"]							)

								self.assertListEqual(
								    tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"							)	,				["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]							)



							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

								UpperCAmelCase:					Any 						=     {}
								for i, token in enumerate(SCREAMING_SNAKE_CASE_							):
									UpperCAmelCase:					Any 						=     i
								UpperCAmelCase:					Dict 						=     WordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE_	,				unk_token="[UNK]"							)

								self.assertListEqual(tokenizer.tokenize(""							)	,				[]							)

								self.assertListEqual(tokenizer.tokenize("unwanted running"							)	,				["un", "##want", "##ed", "runn", "##ing"]							)

								self.assertListEqual(tokenizer.tokenize("unwantedX running"							)	,				["[UNK]", "runn", "##ing"]							)



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






								"""simple docstring"""



								self.assertTrue(_is_whitespace(" "							)							)
								self.assertTrue(_is_whitespace("\t"							)							)
								self.assertTrue(_is_whitespace("\r"							)							)
								self.assertTrue(_is_whitespace("\n"							)							)
								self.assertTrue(_is_whitespace("\u00A0"							)							)

								self.assertFalse(_is_whitespace("A"							)							)
								self.assertFalse(_is_whitespace("-"							)							)



							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								self.assertTrue(_is_control("\u0005"							)							)

								self.assertFalse(_is_control("A"							)							)
								self.assertFalse(_is_control(" "							)							)
								self.assertFalse(_is_control("\t"							)							)
								self.assertFalse(_is_control("\r"							)							)



							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								self.assertTrue(_is_punctuation("-"							)							)
								self.assertTrue(_is_punctuation("$"							)							)
								self.assertTrue(_is_punctuation("`"							)							)
								self.assertTrue(_is_punctuation("."							)							)

								self.assertFalse(_is_punctuation("A"							)							)
								self.assertFalse(_is_punctuation(" "							)							)



							def     A__ (     self							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     self.get_tokenizer()

								# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
								self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE_							) for t in ["Test", "\xad", "test"]]	,				[["[UNK]"], [], ["[UNK]"]]							)

								if self.test_rust_tokenizer:
									UpperCAmelCase:					int 						=     self.get_rust_tokenizer()
									self.assertListEqual(
									    [rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_							) for t in ["Test", "\xad", "test"]]	,				[["[UNK]"], [], ["[UNK]"]]							)



							@slow
							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     self.tokenizer_class.from_pretrained("bert-base-uncased"							)

								UpperCAmelCase:					Union[str, Any] 						=     tokenizer.encode("sequence builders"	,				add_special_tokens=SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Union[str, Any] 						=     tokenizer.encode("multi-sequence build"	,				add_special_tokens=SCREAMING_SNAKE_CASE_							)

								UpperCAmelCase:					str 						=     tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_							)
								UpperCAmelCase:					Dict 						=     tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_	,				SCREAMING_SNAKE_CASE_							)

								assert encoded_sentence == [1_0_1] + text + [1_0_2]
								assert encoded_pair == [1_0_1] + text + [1_0_2] + text_a + [1_0_2]



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
										UpperCAmelCase:					Union[str, Any] 						=     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_	,				**SCREAMING_SNAKE_CASE_							)

										UpperCAmelCase:					List[Any] 						=     F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
										UpperCAmelCase:					Optional[Any] 						=     tokenizer_r.encode_plus(
										    SCREAMING_SNAKE_CASE_	,				return_attention_mask=SCREAMING_SNAKE_CASE_	,				return_token_type_ids=SCREAMING_SNAKE_CASE_	,				return_offsets_mapping=SCREAMING_SNAKE_CASE_	,				add_special_tokens=SCREAMING_SNAKE_CASE_	,				)

										UpperCAmelCase:					Tuple 						=     tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE_	,				"do_lower_case"							) else False
										UpperCAmelCase:					List[str] 						=     (
										    [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), 'A'),
										        ((1, 2), ','),
										        ((3, 5), 'na'),
										        ((5, 6), '##ï'),
										        ((6, 8), '##ve'),
										        ((9, 1_5), tokenizer_r.mask_token),
										        ((1_6, 2_1), 'Allen'),
										        ((2_1, 2_3), '##NL'),
										        ((2_3, 2_4), '##P'),
										        ((2_5, 3_3), 'sentence'),
										        ((3_3, 3_4), '.'),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										    if not do_lower_case
										    else [
										        ((0, 0), tokenizer_r.cls_token),
										        ((0, 1), 'a'),
										        ((1, 2), ','),
										        ((3, 8), 'naive'),
										        ((9, 1_5), tokenizer_r.mask_token),
										        ((1_6, 2_1), 'allen'),
										        ((2_1, 2_3), '##nl'),
										        ((2_3, 2_4), '##p'),
										        ((2_5, 3_3), 'sentence'),
										        ((3_3, 3_4), '.'),
										        ((0, 0), tokenizer_r.sep_token),
										    ]
										)

										self.assertEqual(
										    [e[1] for e in expected_results]	,				tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]							)							)
										self.assertEqual([e[0] for e in expected_results]	,				tokens["offset_mapping"]							)




							@slow
							def     A__ (     self							)						->     Tuple:






								"""simple docstring"""



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
										UpperCAmelCase:					Optional[Any] 						=     self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_	,				**SCREAMING_SNAKE_CASE_							)
										UpperCAmelCase:					Any 						=     self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_	,				**SCREAMING_SNAKE_CASE_							)
										UpperCAmelCase:					Optional[int] 						=     [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

										UpperCAmelCase:					str 						=     tokenizer_r.batch_encode_candidates(SCREAMING_SNAKE_CASE_	,				max_length=1_0	,				return_tensors="np"							)
										UpperCAmelCase:					str 						=     tokenizer_p.batch_encode_candidates(SCREAMING_SNAKE_CASE_	,				max_length=1_0	,				return_tensors="np"							)

										UpperCAmelCase:					Optional[int] 						=     (2, 2, 1_0)

										self.assertEqual(encoded_sentence_r["input_ids"].shape	,				SCREAMING_SNAKE_CASE_							)
										self.assertEqual(encoded_sentence_r["attention_mask"].shape	,				SCREAMING_SNAKE_CASE_							)
										self.assertEqual(encoded_sentence_r["token_type_ids"].shape	,				SCREAMING_SNAKE_CASE_							)

										self.assertEqual(encoded_sentence_p["input_ids"].shape	,				SCREAMING_SNAKE_CASE_							)
										self.assertEqual(encoded_sentence_p["attention_mask"].shape	,				SCREAMING_SNAKE_CASE_							)
										self.assertEqual(encoded_sentence_p["token_type_ids"].shape	,				SCREAMING_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
     lowercase__						:							Any        =      Args()
     lowercase__						:							List[Any]        =      wandb.init(project="""bigbird-natural-questions""", config=args.__dict__)
     lowercase__						:							List[Any]        =      dict(logger.config)
     del wandb_args["batch_size"]
     lowercase__						:							List[str]        =      replace(args, **wandb_args)
     lowercase__						:							Any        =      args.base_dir + """-""" + wandb.run.id
     lowercase__						:							Tuple        =      replace(args, base_dir=base_dir)
     print(args)

     lowercase__						:							Optional[Any]        =      load_dataset("""json""", data_files=args.tr_data_path)["""train"""]
     lowercase__						:							Union[str, Any]        =      load_dataset("""json""", data_files=args.val_data_path)["""train"""]

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

     if os.environ.get("""TRAIN_ON_SMALL""", """false""") == "true":
          lowercase__						:							Optional[int]        =      tr_dataset.shuffle().select(range(8_0_0_0_0))
          lowercase__						:							Any        =      val_dataset.shuffle().select(range(8_0_0_0))

     print(tr_dataset)
     print(val_dataset)

     lowercase__						:							Any        =      FlaxBigBirdForNaturalQuestions.from_pretrained(
         args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
     )
     lowercase__						:							int        =      BigBirdTokenizerFast.from_pretrained(args.model_id)
     lowercase__						:							Union[str, Any]        =      DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_0_9_6)

     lowercase__						:							List[Any]        =      {
         """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,
     }
     lowercase__       ,	lowercase__						:							str        =      build_tx(**tx_args)

     lowercase__						:							List[Any]        =      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,
     )

     lowercase__						:							Dict        =      None
     lowercase__						:							Optional[int]        =      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`""")
     lowercase__						:							Any        =      jax_utils.unreplicate(state.params)
     model.save_pretrained(os.path.join(args.base_dir, """final-weights"""), params=params)





