
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


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

SCREAMING_SNAKE_CASE							:List[str]										=    {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE							:Tuple										=    {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

SCREAMING_SNAKE_CASE							:Optional[Any]										=    {'''mobilebert-uncased''': 512}


SCREAMING_SNAKE_CASE							:List[Any]										=    {}





class 							UpperCAmelCase				(  a__  ):




	'''simple docstring'''





	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					PRETRAINED_INIT_CONFIGURATION
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					MobileBertTokenizer






	def __init__(	self :			Optional[int]							,A :			Optional[int]=None							,A :			Optional[int]=None							,A :			List[Any]=True							,A :			Dict="[UNK]"							,A :			Optional[Any]="[SEP]"							,A :			Dict="[PAD]"							,A :			Tuple="[CLS]"							,A :			Union[str, Any]="[MASK]"							,A :			str=True							,A :			str=None							,**A :			Optional[Any]							,):
								super().__init__(
								    _lowerCamelCase							,tokenizer_file=_lowerCamelCase							,do_lower_case=_lowerCamelCase							,unk_token=_lowerCamelCase							,sep_token=_lowerCamelCase							,pad_token=_lowerCamelCase							,cls_token=_lowerCamelCase							,mask_token=_lowerCamelCase							,tokenize_chinese_chars=_lowerCamelCase							,strip_accents=_lowerCamelCase							,**_lowerCamelCase							,)

								__A             =			json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
								if (
								    normalizer_state.get("lowercase"							,_lowerCamelCase			) != do_lower_case
								    or normalizer_state.get("strip_accents"							,_lowerCamelCase			) != strip_accents
								    or normalizer_state.get("handle_chinese_chars"							,_lowerCamelCase			) != tokenize_chinese_chars
								):
															__A             =			getattr(_lowerCamelCase							,normalizer_state.pop("type"			)			)
															__A             =			do_lower_case
															__A             =			strip_accents
															__A             =			tokenize_chinese_chars
															__A             =			normalizer_class(**_lowerCamelCase			)

								__A             =			do_lower_case






	def     UpperCamelCase_  (	self :			List[Any]							,A :			Optional[int]							,A :			List[Any]=None			):
								__A             =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output






	def     UpperCamelCase_  (	self :			str							,A :			List[Any]							,A :			str = None			):
								__A             =			[self.sep_token_id]
								__A             =			[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			) * [0] + len(token_ids_a + sep			) * [1]







	def     UpperCamelCase_  (	self :			Any							,A :			int							,A :			List[str] = None			):
								__A             =			self._tokenizer.model.save(_lowerCamelCase							,name=_lowerCamelCase			)
								return tuple(_lowerCamelCase			)


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:int										=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
