





'''simple docstring'''



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


__magic_name__   :						str    =			logging.get_logger(__name__)

__magic_name__   :						Optional[int]    =			{"""vocab_file""": """vocab.txt"""}

__magic_name__   :						Optional[int]    =			{
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

__magic_name__   :						List[str]    =			{
    """openbmb/cpm-ant-10b""": 1_024,
}




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







		'''simple docstring'''



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


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



					'''simple docstring'''






					def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<unk>"						,   lowerCamelCase=200	):
							_snake_case     		=				vocab
							_snake_case     		=				unk_token
							_snake_case     		=				max_input_chars_per_word






					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							_snake_case     		=				list(lowerCamelCase	)
							if len(lowerCamelCase	) > self.max_input_chars_per_word:
									return [self.unk_token]

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

							return sub_tokens







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



					'''simple docstring'''






					UpperCAmelCase__       :						str      =   VOCAB_FILES_NAMES
					UpperCAmelCase__       :						Dict      =   PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__       :						Union[str, Any]      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__       :						List[str]      =   ['''input_ids''', '''attention_mask''']
					UpperCAmelCase__       :						Optional[Any]      =   False

					def __init__(					self						,   lowerCamelCase						,   lowerCamelCase="<d>"						,   lowerCamelCase="</d>"						,   lowerCamelCase="<s>"						,   lowerCamelCase="</s>"						,   lowerCamelCase="<pad>"						,   lowerCamelCase="<unk>"						,   lowerCamelCase="</n>"						,   lowerCamelCase="</_>"						,   lowerCamelCase="left"						,   **lowerCamelCase						,   ):
							requires_backends(self						,   ["jieba"]	)
							super().__init__(
							    bod_token=lowerCamelCase						,   eod_token=lowerCamelCase						,   bos_token=lowerCamelCase						,   eos_token=lowerCamelCase						,   pad_token=lowerCamelCase						,   unk_token=lowerCamelCase						,   line_token=lowerCamelCase						,   space_token=lowerCamelCase						,   padding_side=lowerCamelCase						,   **lowerCamelCase						,   )
							_snake_case     		=				bod_token
							_snake_case     		=				eod_token
							_snake_case     		=				load_vocab(lowerCamelCase	)
							_snake_case     		=				self.encoder[space_token]
							_snake_case     		=				self.encoder[line_token]

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

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

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

					@property
					def 	UpperCamelCase(					self	):
							return self.encoder[self.bod_token]

					@property
					def 	UpperCamelCase(					self	):
							return self.encoder[self.eod_token]

					@property
					def 	UpperCamelCase(					self	):
							return self.encoder["\n"]

					@property
					def 	UpperCamelCase(					self	):
							return len(self.encoder	)

					def 	UpperCamelCase(					self	):
							return dict(self.encoder						,   **self.added_tokens_encoder	)

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							_snake_case     		=				[]
							for x in jieba.cut(lowerCamelCase						,   cut_all=lowerCamelCase	):
									output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase	)	)
							return output_tokens

					def 	UpperCamelCase(					self						,   lowerCamelCase						,   **lowerCamelCase	):
							_snake_case     		=				[i for i in token_ids if i >= 0]
							_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(lowerCamelCase						,   **lowerCamelCase	)

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							return token in self.encoder

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							return "".join(lowerCamelCase	)

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							return self.encoder.get(lowerCamelCase						,   self.encoder.get(self.unk_token	)	)

					def 	UpperCamelCase(					self						,   lowerCamelCase	):
							return self.decoder.get(lowerCamelCase						,   self.unk_token	)

					def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
							if os.path.isdir(lowerCamelCase	):
									_snake_case     		=				os.path.join(
									    lowerCamelCase						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
							else:
									_snake_case     		=				(filename_prefix + "-" if filename_prefix else "") + save_directory
							_snake_case     		=				0
							if " " in self.encoder:
									_snake_case     		=				self.encoder[" "]
									del self.encoder[" "]
							if "\n" in self.encoder:
									_snake_case     		=				self.encoder["\n"]
									del self.encoder["\n"]
							_snake_case     		=				collections.OrderedDict(sorted(self.encoder.items()						,   key=lambda lowerCamelCase	: x[1]	)	)
							with open(lowerCamelCase						,   "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!"	)
													_snake_case     		=				token_index
											writer.write(token + "\n"	)
											index += 1
							return (vocab_file,)

					def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None	):
							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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = False	):

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

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









'''simple docstring'''



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__magic_name__   :						Any    =			{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __magic_name__   :						List[Any]    =			[
     """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTNeoXJapaneseForCausalLM""",
     """GPTNeoXJapaneseLayer""",
     """GPTNeoXJapaneseModel""",
     """GPTNeoXJapanesePreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
 from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox_japanese import (
      GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXJapaneseForCausalLM,
      GPTNeoXJapaneseLayer,
      GPTNeoXJapaneseModel,
      GPTNeoXJapanesePreTrainedModel,
  )


else:
 import sys

 __magic_name__   :						Union[str, Any]    =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


