import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCamelCase    :				List[str]							     =	'''▁'''

__lowerCamelCase    :				Optional[int]							     =	get_tests_dir('''fixtures/test_sentencepiece.model''')







@require_sentencepiece
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							BertGenerationTokenizer
   lowerCAmelCase_														=							False
   lowerCAmelCase_														=							True




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     super().setUp()

     SCREAMING_SNAKE_CASE__    							=   BertGenerationTokenizer(_lowercase ,			keep_accents=_lowercase   )
     tokenizer.save_pretrained(self.tmpdirname   )




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """<s>"""
     SCREAMING_SNAKE_CASE__    							=   1

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




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   list(self.get_tokenizer().get_vocab().keys()   )

     self.assertEqual(vocab_keys[0] ,			"""<unk>"""   )
     self.assertEqual(vocab_keys[1] ,			"""<s>"""   )
     self.assertEqual(vocab_keys[-1] ,			"""<pad>"""   )
     self.assertEqual(len(_lowercase   ) ,			10_02   )




   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size ,			10_00   )




   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   BertGenerationTokenizer(_lowercase ,			keep_accents=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   tokenizer.tokenize("""This is a test"""   )
     self.assertListEqual(_lowercase ,			["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_lowercase   ) ,			[2_85, 46, 10, 1_70, 3_82] ,			)

     SCREAMING_SNAKE_CASE__    							=   tokenizer.tokenize("""I was born in 92000, and this is falsé."""   )
     self.assertListEqual(
         _lowercase ,			[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """9""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """é""",
             """.""",
         ] ,			)
     SCREAMING_SNAKE_CASE__    							=   tokenizer.convert_tokens_to_ids(_lowercase   )
     self.assertListEqual(
         _lowercase ,			[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] ,			)

     SCREAMING_SNAKE_CASE__    							=   tokenizer.convert_ids_to_tokens(_lowercase   )
     self.assertListEqual(
         _lowercase ,			[
             SPIECE_UNDERLINE + """I""",
             SPIECE_UNDERLINE + """was""",
             SPIECE_UNDERLINE + """b""",
             """or""",
             """n""",
             SPIECE_UNDERLINE + """in""",
             SPIECE_UNDERLINE + """""",
             """<unk>""",
             """2""",
             """0""",
             """0""",
             """0""",
             """,""",
             SPIECE_UNDERLINE + """and""",
             SPIECE_UNDERLINE + """this""",
             SPIECE_UNDERLINE + """is""",
             SPIECE_UNDERLINE + """f""",
             """al""",
             """s""",
             """<unk>""",
             """.""",
         ] ,			)




   @cached_property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""   )




   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """Hello World!"""
     SCREAMING_SNAKE_CASE__    							=   [1_85_36, 22_60, 1_01]

     self.assertListEqual(_lowercase ,			self.big_tokenizer.encode(_lowercase   )   )




   @slow
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   (
         """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
         """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
     )
     SCREAMING_SNAKE_CASE__    							=   [
         8_71,
         4_19,
         3_58,
         9_46,
         9_91,
         25_21,
         4_52,
         3_58,
         13_57,
         3_87,
         77_51,
         35_36,
         1_12,
         9_85,
         4_56,
         1_26,
         8_65,
         9_38,
         54_00,
         57_34,
         4_58,
         13_68,
         4_67,
         7_86,
         24_62,
         52_46,
         11_59,
         6_33,
         8_65,
         45_19,
         4_57,
         5_82,
         8_52,
         25_57,
         4_27,
         9_16,
         5_08,
         4_05,
         3_43_24,
         4_97,
         3_91,
         4_08,
         1_13_42,
         12_44,
         3_85,
         1_00,
         9_38,
         9_85,
         4_56,
         5_74,
         3_62,
         1_25_97,
         32_00,
         31_29,
         11_72,
     ]

     self.assertListEqual(_lowercase ,			self.big_tokenizer.encode(_lowercase   )   )




   @require_torch
   @slow
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     import torch

     from transformers import BertGenerationConfig, BertGenerationEncoder

     # Build sequence
     SCREAMING_SNAKE_CASE__    							=   list(self.big_tokenizer.get_vocab().keys()   )[:10]
     SCREAMING_SNAKE_CASE__    							=   """ """.join(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.big_tokenizer.encode_plus(_lowercase ,			return_tensors="""pt""" ,			return_token_type_ids=_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.big_tokenizer.batch_encode_plus(
         [sequence + """ """ + sequence] ,			return_tensors="""pt""" ,			return_token_type_ids=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   BertGenerationConfig()
     SCREAMING_SNAKE_CASE__    							=   BertGenerationEncoder(_lowercase   )

     assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

     with torch.no_grad():
       model(**_lowercase   )
       model(**_lowercase   )




   @slow
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   {"""input_ids""": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 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], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_lowercase ,			model_name="""google/bert_for_seq_generation_L-24_bbc_encoder""" ,			revision="""c817d1fd1be2ffa69431227a1fe320544943d4db""" ,			)

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

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


__lowerCamelCase    :				Any							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Optional[Any]							     =	{'''vocab_file''': '''vocab.txt'''}

__lowerCamelCase    :				Union[str, Any]							     =	{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

__lowerCamelCase    :				Optional[Any]							     =	{
    '''openbmb/cpm-ant-10b''': 1024,
}



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict()
  with open(__UpperCamelCase				,  """r"""				,  encoding="""utf-8"""			) as reader:
    SCREAMING_SNAKE_CASE__    							=   reader.readlines()
  for index, token in enumerate(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   token.rstrip("""\n"""			)
    SCREAMING_SNAKE_CASE__    							=   index
  return vocab







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int="<unk>" ,			_lowercase    :       int=2_00   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab
     SCREAMING_SNAKE_CASE__    							=   unk_token
     SCREAMING_SNAKE_CASE__    							=   max_input_chars_per_word




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   list(_lowercase   )
     if len(_lowercase   ) > self.max_input_chars_per_word:
       return [self.unk_token]

     SCREAMING_SNAKE_CASE__    							=   0
     SCREAMING_SNAKE_CASE__    							=   []
     while start < len(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   len(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   None
       while start < end:
         SCREAMING_SNAKE_CASE__    							=   """""".join(chars[start:end]   )
         if substr in self.vocab:
           SCREAMING_SNAKE_CASE__    							=   substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token   )
         start += 1
       else:
         sub_tokens.append(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   end

     return sub_tokens







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							False




   def __init__(					self    :       int ,			_lowercase    :       str ,			_lowercase    :       List[Any]="<d>" ,			_lowercase    :       List[Any]="</d>" ,			_lowercase    :       Union[str, Any]="<s>" ,			_lowercase    :       List[str]="</s>" ,			_lowercase    :       str="<pad>" ,			_lowercase    :       int="<unk>" ,			_lowercase    :       List[str]="</n>" ,			_lowercase    :       Tuple="</_>" ,			_lowercase    :       Any="left" ,			**_lowercase    :       Any ,			):



     """simple docstring"""






     requires_backends(self ,			["""jieba"""]   )
     super().__init__(
         bod_token=_lowercase ,			eod_token=_lowercase ,			bos_token=_lowercase ,			eos_token=_lowercase ,			pad_token=_lowercase ,			unk_token=_lowercase ,			line_token=_lowercase ,			space_token=_lowercase ,			padding_side=_lowercase ,			**_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   bod_token
     SCREAMING_SNAKE_CASE__    							=   eod_token
     SCREAMING_SNAKE_CASE__    							=   load_vocab(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.encoder[space_token]
     SCREAMING_SNAKE_CASE__    							=   self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict(sorted(self.encoder.items() ,			key=lambda _lowercase   : x[1]   )   )
     SCREAMING_SNAKE_CASE__    							=   {v: k for k, v in self.encoder.items()}

     SCREAMING_SNAKE_CASE__    							=   WordpieceTokenizer(vocab=self.encoder ,			unk_token=self.unk_token   )




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return self.encoder[self.bod_token]




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return self.encoder[self.eod_token]




   @property
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return self.encoder["\n"]




   @property
   def 			__a		(					self    :       Union[str, Any]   ):



     """simple docstring"""






     return len(self.encoder   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     return dict(self.encoder ,			**self.added_tokens_encoder   )




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     for x in jieba.cut(_lowercase ,			cut_all=_lowercase   ):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase   )   )
     return output_tokens




   def 			__a		(					self    :       int ,			_lowercase    :       Any ,			**_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [i for i in token_ids if i >= 0]
     SCREAMING_SNAKE_CASE__    							=   [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return token in self.encoder




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     return "".join(_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Any   ):



     """simple docstring"""






     return self.encoder.get(_lowercase ,			self.encoder.get(self.unk_token   )   )




   def 			__a		(					self    :       Tuple ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return self.decoder.get(_lowercase ,			self.unk_token   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if os.path.isdir(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   os.path.join(
           _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
     else:
       SCREAMING_SNAKE_CASE__    							=   (filename_prefix + """-""" if filename_prefix else """""") + save_directory
     SCREAMING_SNAKE_CASE__    							=   0
     if " " in self.encoder:
       SCREAMING_SNAKE_CASE__    							=   self.encoder[""" """]
       del self.encoder[" "]
     if "\n" in self.encoder:
       SCREAMING_SNAKE_CASE__    							=   self.encoder["""\n"""]
       del self.encoder["\n"]
     SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict(sorted(self.encoder.items() ,			key=lambda _lowercase   : x[1]   )   )
     with open(_lowercase ,			"""w""" ,			encoding="""utf-8"""   ) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               """ Please check that the vocabulary is not corrupted!"""   )
           SCREAMING_SNAKE_CASE__    							=   token_index
         writer.write(token + """\n"""   )
         index += 1
     return (vocab_file,)




   def 			__a		(					self    :       int ,			_lowercase    :       List[int] ,			_lowercase    :       List[int] = None   ):



     """simple docstring"""






     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None ,			_lowercase    :       bool = False   ):



     """simple docstring"""






     if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=_lowercase ,			token_ids_a=_lowercase ,			already_has_special_tokens=_lowercase   )

     if token_ids_a is not None:
       return [1] + ([0] * len(_lowercase   )) + [1] + ([0] * len(_lowercase   ))
     return [1] + ([0] * len(_lowercase   ))
