





'''simple docstring'''






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A__							:  Optional[Any]    =get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')




@require_sentencepiece
@require_tokenizers
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Optional[int]			=  PegasusTokenizer
      _lowercase:     List[Any]			=  PegasusTokenizerFast
      _lowercase:     List[str]			=  True
      _lowercase:     str			=  True




      def 							lowercase__       (     self     :					Tuple    )							-> Any:
            super().setUp()

            # We have a SentencePiece fixture for testing
            _lowerCAmelCase					=				PegasusTokenizer(__snake_case    )
            tokenizer.save_pretrained(self.tmpdirname    )




      @cached_property
      def 							lowercase__       (     self     :					Tuple    )							-> Dict:
            return PegasusTokenizer.from_pretrained("""google/pegasus-large"""    )




      def 							lowercase__       (     self     :					Tuple     ,     **__snake_case     :					Optional[Any]    )							-> PegasusTokenizer:
            return PegasusTokenizer.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					List[Any]     ,     __snake_case     :					str    )							-> str:
            return ("This is a test", "This is a test")




      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""</s>"""
            _lowerCAmelCase					=				1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case    )     ,     __snake_case    )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Tuple    )							-> Optional[int]:
            _lowerCAmelCase					=				list(self.get_tokenizer().get_vocab().keys()    )

            self.assertEqual(vocab_keys[0]     ,     """<pad>"""    )
            self.assertEqual(vocab_keys[1]     ,     """</s>"""    )
            self.assertEqual(vocab_keys[-1]     ,     """v"""    )
            self.assertEqual(len(__snake_case    )     ,     11_03    )




      def 							lowercase__       (     self     :					Any    )							-> Dict:
            self.assertEqual(self.get_tokenizer().vocab_size     ,     11_03    )




      def 							lowercase__       (     self     :					Tuple    )							-> Tuple:
            _lowerCAmelCase					=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				(
                """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
                """ </s> <pad> <pad> <pad>"""
            )
            _lowerCAmelCase					=				rust_tokenizer([raw_input_str]     ,     return_tensors=__snake_case     ,     add_special_tokens=__snake_case    ).input_ids[0]
            _lowerCAmelCase					=				py_tokenizer([raw_input_str]     ,     return_tensors=__snake_case     ,     add_special_tokens=__snake_case    ).input_ids[0]
            self.assertListEqual(__snake_case     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Optional[int]:
            _lowerCAmelCase					=				self._large_tokenizer
            # <mask_1> masks whole sentence while <mask_2> masks single word
            _lowerCAmelCase					=				"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
            _lowerCAmelCase					=				[2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            _lowerCAmelCase					=				tokenizer([raw_input_str]     ,     return_tensors=__snake_case    ).input_ids[0]
            self.assertListEqual(__snake_case     ,     __snake_case    )




      def 							lowercase__       (     self     :					List[str]    )							-> Any:
            _lowerCAmelCase					=				self._large_tokenizer
            # The tracebacks for the following asserts are **better** without messages or self.assertEqual
            assert tokenizer.vocab_size == 9_61_03
            assert tokenizer.pad_token_id == 0
            assert tokenizer.eos_token_id == 1
            assert tokenizer.offset == 1_03
            assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
            assert tokenizer.unk_token == "<unk>"
            assert tokenizer.model_max_length == 10_24
            _lowerCAmelCase					=				"""To ensure a smooth flow of bank resolutions."""
            _lowerCAmelCase					=				[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            _lowerCAmelCase					=				tokenizer([raw_input_str]     ,     return_tensors=__snake_case    ).input_ids[0]
            self.assertListEqual(__snake_case     ,     __snake_case    )
            assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




      @require_torch
      def 							lowercase__       (     self     :					str    )							-> Tuple:
            _lowerCAmelCase					=				["""This is going to be way too long.""" * 1_50, """short example"""]
            _lowerCAmelCase					=				["""not super long but more than 5 tokens""", """tiny"""]
            _lowerCAmelCase					=				self._large_tokenizer(__snake_case     ,     padding=__snake_case     ,     truncation=__snake_case     ,     return_tensors="""pt"""    )
            _lowerCAmelCase					=				self._large_tokenizer(
                text_target=__snake_case     ,     max_length=5     ,     padding=__snake_case     ,     truncation=__snake_case     ,     return_tensors="""pt"""    )

            assert batch.input_ids.shape == (2, 10_24)
            assert batch.attention_mask.shape == (2, 10_24)
            assert targets["input_ids"].shape == (2, 5)
            assert len(__snake_case    ) == 2  # input_ids, attention_mask.




      @slow
      def 							lowercase__       (     self     :					Optional[int]    )							-> Optional[Any]:
            # fmt: off
            _lowerCAmelCase					=				{"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=__snake_case     ,     model_name="""google/bigbird-pegasus-large-arxiv"""     ,     revision="""ba85d0851d708441f91440d509690f1ab6353415"""     ,     )




@require_sentencepiece
@require_tokenizers
class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     Any			=  PegasusTokenizer
      _lowercase:     Optional[int]			=  PegasusTokenizerFast
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     int			=  True




      def 							lowercase__       (     self     :					int    )							-> str:
            super().setUp()

            # We have a SentencePiece fixture for testing
            _lowerCAmelCase					=				PegasusTokenizer(__snake_case     ,     offset=0     ,     mask_token_sent=__snake_case     ,     mask_token="""[MASK]"""    )
            tokenizer.save_pretrained(self.tmpdirname    )




      @cached_property
      def 							lowercase__       (     self     :					int    )							-> str:
            return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""    )




      def 							lowercase__       (     self     :					Dict     ,     **__snake_case     :					Optional[int]    )							-> PegasusTokenizer:
            return PegasusTokenizer.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					Optional[int]    )							-> Dict:
            return ("This is a test", "This is a test")




      def 							lowercase__       (     self     :					Dict    )							-> Any:
            _lowerCAmelCase					=				self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained(self.tmpdirname    )
            _lowerCAmelCase					=				(
                """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
                """ <pad> <pad> <pad>"""
            )
            _lowerCAmelCase					=				rust_tokenizer([raw_input_str]     ,     return_tensors=__snake_case     ,     add_special_tokens=__snake_case    ).input_ids[0]
            _lowerCAmelCase					=				py_tokenizer([raw_input_str]     ,     return_tensors=__snake_case     ,     add_special_tokens=__snake_case    ).input_ids[0]
            self.assertListEqual(__snake_case     ,     __snake_case    )




      @require_torch
      def 							lowercase__       (     self     :					Dict    )							-> Tuple:
            _lowerCAmelCase					=				["""This is going to be way too long.""" * 10_00, """short example"""]
            _lowerCAmelCase					=				["""not super long but more than 5 tokens""", """tiny"""]
            _lowerCAmelCase					=				self._large_tokenizer(__snake_case     ,     padding=__snake_case     ,     truncation=__snake_case     ,     return_tensors="""pt"""    )
            _lowerCAmelCase					=				self._large_tokenizer(
                text_target=__snake_case     ,     max_length=5     ,     padding=__snake_case     ,     truncation=__snake_case     ,     return_tensors="""pt"""    )

            assert batch.input_ids.shape == (2, 40_96)
            assert batch.attention_mask.shape == (2, 40_96)
            assert targets["input_ids"].shape == (2, 5)
            assert len(__snake_case    ) == 2  # input_ids, attention_mask.




      def 							lowercase__       (     self     :					List[Any]    )							-> Tuple:
            _lowerCAmelCase					=				(
                """This is an example string that is used to test the original TF implementation against the HF"""
                """ implementation"""
            )

            _lowerCAmelCase					=				self._large_tokenizer(__snake_case    ).input_ids

            self.assertListEqual(
                __snake_case     ,     [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]     ,     )













'''simple docstring'''






import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A__							:  List[str]    =logging.getLogger(__name__)
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				argparse.ArgumentParser(
          description="""Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."""    )
      parser.add_argument("""--file_path"""				,      type=lowerCAmelCase				,      default="""data/dump.txt"""				,      help="""The path to the data."""    )
      parser.add_argument("""--tokenizer_type"""				,      type=lowerCAmelCase				,      default="""bert"""				,      choices=["""bert""", """roberta""", """gpt2"""]    )
      parser.add_argument("""--tokenizer_name"""				,      type=lowerCAmelCase				,      default="""bert-base-uncased"""				,      help="""The tokenizer to use."""    )
      parser.add_argument("""--dump_file"""				,      type=lowerCAmelCase				,      default="""data/dump"""				,      help="""The dump file prefix."""    )
      _lowerCAmelCase					=				parser.parse_args()

      logger.info(f"Loading Tokenizer ({args.tokenizer_name})"    )
      if args.tokenizer_type == "bert":
            _lowerCAmelCase					=				BertTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `[CLS]`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            _lowerCAmelCase					=				RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""cls_token"""]  # `<s>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""sep_token"""]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            _lowerCAmelCase					=				GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""bos_token"""]  # `<|endoftext|>`
            _lowerCAmelCase					=				tokenizer.special_tokens_map["""eos_token"""]  # `<|endoftext|>`

      logger.info(f"Loading text from {args.file_path}"    )
      with open(args.file_path				,      """r"""				,      encoding="""utf8"""    ) as fp:
            _lowerCAmelCase					=				fp.readlines()

      logger.info("""Start encoding"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples to process."    )

      _lowerCAmelCase					=				[]
      _lowerCAmelCase					=				0
      _lowerCAmelCase					=				1_00_00
      _lowerCAmelCase					=				time.time()
      for text in data:
            _lowerCAmelCase					=				f"{bos} {text.strip()} {sep}"
            _lowerCAmelCase					=				tokenizer.encode(lowerCAmelCase				,      add_special_tokens=lowerCAmelCase    )
            rslt.append(lowerCAmelCase    )

            iter += 1
            if iter % interval == 0:
                  _lowerCAmelCase					=				time.time()
                  logger.info(f"{iter} examples processed. - {(end-start):.2f}s/{interval}expl"    )
                  _lowerCAmelCase					=				time.time()
      logger.info("""Finished binarization"""    )
      logger.info(f"{len(lowerCAmelCase    )} examples processed."    )

      _lowerCAmelCase					=				f"{args.dump_file}.{args.tokenizer_name}.pickle"
      _lowerCAmelCase					=				tokenizer.vocab_size
      if vocab_size < (1 << 16):
            _lowerCAmelCase					=				[np.uintaa(lowerCAmelCase    ) for d in rslt]
      else:
            _lowerCAmelCase					=				[np.intaa(lowerCAmelCase    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(f"Dump to {dp_file}"    )
      with open(lowerCAmelCase				,      """wb"""    ) as handle:
            pickle.dump(rslt_				,      lowerCAmelCase				,      protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
      main()






