



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


UpperCAmelCase :				Optional[Any]							=logging.get_logger(__name__)

UpperCAmelCase :				Optional[Any]							={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase :				Union[str, Any]							={
    """vocab_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase :				Any							={
    """microsoft/mpnet-base""": 512,
}

UpperCAmelCase :				Optional[int]							={
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}


class    _lowercase (a_   ):
				'''simple docstring'''







				lowercase__     =    VOCAB_FILES_NAMES
				lowercase__     =    PRETRAINED_VOCAB_FILES_MAP
				lowercase__     =    PRETRAINED_INIT_CONFIGURATION
				lowercase__     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__     =    MPNetTokenizer
				lowercase__     =    ["""input_ids""", """attention_mask"""]

				def __init__(			self  ,			snake_case__=None  ,			snake_case__=None  ,			snake_case__=True  ,			snake_case__="<s>"  ,			snake_case__="</s>"  ,			snake_case__="</s>"  ,			snake_case__="<s>"  ,			snake_case__="[UNK]"  ,			snake_case__="<pad>"  ,			snake_case__="<mask>"  ,			snake_case__=True  ,			snake_case__=None  ,			**snake_case__  ,			):

									'''simple docstring'''


									super().__init__(
									    snake_case__  ,			tokenizer_file=snake_case__  ,			do_lower_case=snake_case__  ,			bos_token=snake_case__  ,			eos_token=snake_case__  ,			sep_token=snake_case__  ,			cls_token=snake_case__  ,			unk_token=snake_case__  ,			pad_token=snake_case__  ,			mask_token=snake_case__  ,			tokenize_chinese_chars=snake_case__  ,			strip_accents=snake_case__  ,			**snake_case__  ,			)

									UpperCamelCase_			= json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
									if (
									    pre_tok_state.get("lowercase"  ,			snake_case__     ) != do_lower_case
									    or pre_tok_state.get("strip_accents"  ,			snake_case__     ) != strip_accents
									):
														UpperCamelCase_			= getattr(snake_case__  ,			pre_tok_state.pop("type"     )     )
														UpperCamelCase_			= do_lower_case
														UpperCamelCase_			= strip_accents
														UpperCamelCase_			= pre_tok_class(**snake_case__     )

									UpperCamelCase_			= do_lower_case

				@property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									if self._mask_token is None:
														if self.verbose:
																			logger.error("Using mask_token, but it is not set yet."     )
														return None
									return str(self._mask_token     )

				@mask_token.setter
				def    _lowerCamelCase       (			self  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= AddedToken(snake_case__  ,			lstrip=snake_case__  ,			rstrip=snake_case__     ) if isinstance(snake_case__  ,			snake_case__     ) else value
									UpperCamelCase_			= value

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__=None     ):

									'''simple docstring'''


									UpperCamelCase_			= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__ = None     ):

									'''simple docstring'''


									UpperCamelCase_			= [self.sep_token_id]
									UpperCamelCase_			= [self.cls_token_id]

									if token_ids_a is None:
														return len(cls + token_ids_a + sep     ) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__ = None     ):

									'''simple docstring'''


									UpperCamelCase_			= self._tokenizer.model.save(snake_case__  ,			name=snake_case__     )
									return tuple(snake_case__     )

"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

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


