





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__     =	{"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	["ReformerTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	["ReformerTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	[
					    "REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ReformerAttention",
					    "ReformerForMaskedLM",
					    "ReformerForQuestionAnswering",
					    "ReformerForSequenceClassification",
					    "ReformerLayer",
					    "ReformerModel",
					    "ReformerModelWithLMHead",
					    "ReformerPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_reformer import ReformerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_reformer_fast import ReformerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_reformer import (
										    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ReformerAttention,
										    ReformerForMaskedLM,
										    ReformerForQuestionAnswering,
										    ReformerForSequenceClassification,
										    ReformerLayer,
										    ReformerModel,
										    ReformerModelWithLMHead,
										    ReformerPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase__     =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{"vocab_file": "vocab.txt"}

UpperCamelCase__     =	{
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

UpperCamelCase__     =	{
    "openbmb/cpm-ant-10b": 1_024,
}





def       _UpperCamelCase       (a__				:Tuple    ):






							"""simple docstring"""





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

class     __SCREAMING_SNAKE_CASE   (       _a    ):






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







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     list(__lowerCAmelCase					)
														if len(__lowerCAmelCase					) > self.max_input_chars_per_word:
																					return [self.unk_token]

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

														return sub_tokens






class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : Optional[int]         =    VOCAB_FILES_NAMES
							snake_case       : Optional[Any]         =    PRETRAINED_VOCAB_FILES_MAP
							snake_case       : List[Any]         =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case       : Optional[int]         =    ["""input_ids""", """attention_mask"""]
							snake_case       : int         =    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					,	)
														UpperCamelCase__			       =     bod_token
														UpperCamelCase__			       =     eod_token
														UpperCamelCase__			       =     load_vocab(__lowerCAmelCase					)
														UpperCamelCase__			       =     self.encoder[space_token]
														UpperCamelCase__			       =     self.encoder[line_token]

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

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

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






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






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






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






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






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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     []
														for x in jieba.cut(__lowerCAmelCase					,	cut_all=__lowerCAmelCase					):
																					output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCAmelCase					)					)
														return output_tokens






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	**__lowerCAmelCase					):
														UpperCamelCase__			       =     [i for i in token_ids if i >= 0]
														UpperCamelCase__			       =     [
														    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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return token in self.encoder






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return "".join(__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return self.encoder.get(__lowerCAmelCase					,	self.encoder.get(self.unk_token					)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return self.decoder.get(__lowerCAmelCase					,	self.unk_token					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					):
														if os.path.isdir(__lowerCAmelCase					):
																					UpperCamelCase__			       =     os.path.join(
																					    __lowerCAmelCase					,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
														else:
																					UpperCamelCase__			       =     (filename_prefix + """-""" if filename_prefix else """""") + save_directory
														UpperCamelCase__			       =     0
														if " " in self.encoder:
																					UpperCamelCase__			       =     self.encoder[""" """]
																					del self.encoder[" "]
														if "\n" in self.encoder:
																					UpperCamelCase__			       =     self.encoder["""\n"""]
																					del self.encoder["\n"]
														UpperCamelCase__			       =     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!"""					)
																																			UpperCamelCase__			       =     token_index
																												writer.write(token + """\n"""					)
																												index += 1
														return (vocab_file,)






							def 			_lowerCamelCase						(  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 			_lowerCamelCase						(  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					))




