




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


UpperCamelCase__ :   List[Any]						=							logging.get_logger(__name__)

UpperCamelCase__ :   str						=							{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase__ :   Optional[Any]						=							{
    '''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'''
    },
}

UpperCamelCase__ :   Optional[int]						=							{'''mobilebert-uncased''': 5_12}


UpperCamelCase__ :   Optional[int]						=							{}







class 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Optional[int]						=     VOCAB_FILES_NAMES
				__a     :       Optional[Any]						=     PRETRAINED_VOCAB_FILES_MAP
				__a     :       List[str]						=     PRETRAINED_INIT_CONFIGURATION
				__a     :       Tuple						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a     :       List[str]						=     MobileBertTokenizer






				def __init__(			self      ,snake_case__=None      ,snake_case__=None      ,snake_case__=True      ,snake_case__="[UNK]"      ,snake_case__="[SEP]"      ,snake_case__="[PAD]"      ,snake_case__="[CLS]"      ,snake_case__="[MASK]"      ,snake_case__=True      ,snake_case__=None      ,**snake_case__      ,):
							super().__init__(
							    snake_case__      ,tokenizer_file=snake_case__      ,do_lower_case=snake_case__      ,unk_token=snake_case__      ,sep_token=snake_case__      ,pad_token=snake_case__      ,cls_token=snake_case__      ,mask_token=snake_case__      ,tokenize_chinese_chars=snake_case__      ,strip_accents=snake_case__      ,**snake_case__      ,)

							SCREAMING_SNAKE_CASE_   : Any					  =      json.loads(self.backend_tokenizer.normalizer.__getstate__() )
							if (
							    normalizer_state.get('lowercase'      ,snake_case__ ) != do_lower_case
							    or normalizer_state.get('strip_accents'      ,snake_case__ ) != strip_accents
							    or normalizer_state.get('handle_chinese_chars'      ,snake_case__ ) != tokenize_chinese_chars
							):
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      getattr(snake_case__      ,normalizer_state.pop('type' ) )
										SCREAMING_SNAKE_CASE_   : int					  =      do_lower_case
										SCREAMING_SNAKE_CASE_   : Any					  =      strip_accents
										SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenize_chinese_chars
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      normalizer_class(**snake_case__ )

							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      do_lower_case






				def 		snake_case	(			self      ,snake_case__      ,snake_case__=None ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      [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 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Dict					  =      [self.sep_token_id]
							SCREAMING_SNAKE_CASE_   : int					  =      [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 		snake_case	(			self      ,snake_case__      ,snake_case__ = None ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self._tokenizer.model.save(snake_case__      ,name=snake_case__ )
							return tuple(snake_case__ )





from typing import TYPE_CHECKING

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


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


