"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


__A  =				logging.get_logger(__name__)

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

__A  =				[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

__A  =				{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
__A  =				{F"""funnel-transformer/{name}""": 512 for name in _model_names}
__A  =				{F"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}
class  _snake_case     (					a__		):
						snake_case__			  =			VOCAB_FILES_NAMES
						snake_case__			  =			PRETRAINED_VOCAB_FILES_MAP
						snake_case__			  =			PRETRAINED_INIT_CONFIGURATION
						snake_case__			  =			FunnelTokenizer
						snake_case__			  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__			  =			2






						def __init__(       self      :			List[Any]							,					UpperCAmelCase      :			List[str]=None							,					UpperCAmelCase      :			Optional[int]=None							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			int="<unk>"							,					UpperCAmelCase      :			List[Any]="<sep>"							,					UpperCAmelCase      :			List[str]="<pad>"							,					UpperCAmelCase      :			Union[str, Any]="<cls>"							,					UpperCAmelCase      :			int="<mask>"							,					UpperCAmelCase      :			List[str]="<s>"							,					UpperCAmelCase      :			Union[str, Any]="</s>"							,					UpperCAmelCase      :			List[Any]=True							,					UpperCAmelCase      :			Tuple=True							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			int="##"							,					**UpperCAmelCase      :			Dict							,					):
													super().__init__(
													    UpperCAmelCase							,					tokenizer_file=UpperCAmelCase							,					do_lower_case=UpperCAmelCase							,					unk_token=UpperCAmelCase							,					sep_token=UpperCAmelCase							,					pad_token=UpperCAmelCase							,					cls_token=UpperCAmelCase							,					mask_token=UpperCAmelCase							,					bos_token=UpperCAmelCase							,					eos_token=UpperCAmelCase							,					clean_text=UpperCAmelCase							,					tokenize_chinese_chars=UpperCAmelCase							,					strip_accents=UpperCAmelCase							,					wordpieces_prefix=UpperCAmelCase							,					**UpperCAmelCase							,					)

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

													__lowerCamelCase					:		List[Any]		   =     do_lower_case






						def       lowerCamelCase__     (       self      :			Union[str, Any]							,					UpperCAmelCase      :			Union[str, Any]							,					UpperCAmelCase      :			Tuple=None    ):
													__lowerCamelCase					:		List[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       lowerCamelCase__     (       self      :			Dict							,					UpperCAmelCase      :			List[int]							,					UpperCAmelCase      :			Optional[List[int]] = None    ):
													__lowerCamelCase					:		Tuple		   =     [self.sep_token_id]
													__lowerCamelCase					:		int		   =     [self.cls_token_id]
													if token_ids_a is None:
																				return len(cls    ) * [self.cls_token_type_id] + len(token_ids_a + sep    ) * [0]
													return len(cls    ) * [self.cls_token_type_id] + len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]







						def       lowerCamelCase__     (       self      :			Tuple							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			Optional[str] = None    ):
													__lowerCamelCase					:		List[Any]		   =     self._tokenizer.model.save(UpperCAmelCase							,					name=UpperCAmelCase    )
													return tuple(UpperCAmelCase    )
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A  =				{
    '''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
    '''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''BertTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''BertForMaskedLM''',
				    '''BertForMultipleChoice''',
				    '''BertForNextSentencePrediction''',
				    '''BertForPreTraining''',
				    '''BertForQuestionAnswering''',
				    '''BertForSequenceClassification''',
				    '''BertForTokenClassification''',
				    '''BertLayer''',
				    '''BertLMHeadModel''',
				    '''BertModel''',
				    '''BertPreTrainedModel''',
				    '''load_tf_weights_in_bert''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFBertEmbeddings''',
				    '''TFBertForMaskedLM''',
				    '''TFBertForMultipleChoice''',
				    '''TFBertForNextSentencePrediction''',
				    '''TFBertForPreTraining''',
				    '''TFBertForQuestionAnswering''',
				    '''TFBertForSequenceClassification''',
				    '''TFBertForTokenClassification''',
				    '''TFBertLMHeadModel''',
				    '''TFBertMainLayer''',
				    '''TFBertModel''',
				    '''TFBertPreTrainedModel''',
				]
try:
				if not is_tensorflow_text_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				['''TFBertTokenizer''']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A  =				[
				    '''FlaxBertForCausalLM''',
				    '''FlaxBertForMaskedLM''',
				    '''FlaxBertForMultipleChoice''',
				    '''FlaxBertForNextSentencePrediction''',
				    '''FlaxBertForPreTraining''',
				    '''FlaxBertForQuestionAnswering''',
				    '''FlaxBertForSequenceClassification''',
				    '''FlaxBertForTokenClassification''',
				    '''FlaxBertModel''',
				    '''FlaxBertPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
				from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_fast import BertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bert import (
								    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BertForMaskedLM,
								    BertForMultipleChoice,
								    BertForNextSentencePrediction,
								    BertForPreTraining,
								    BertForQuestionAnswering,
								    BertForSequenceClassification,
								    BertForTokenClassification,
								    BertLayer,
								    BertLMHeadModel,
								    BertModel,
								    BertPreTrainedModel,
								    load_tf_weights_in_bert,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bert import (
								    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFBertEmbeddings,
								    TFBertForMaskedLM,
								    TFBertForMultipleChoice,
								    TFBertForNextSentencePrediction,
								    TFBertForPreTraining,
								    TFBertForQuestionAnswering,
								    TFBertForSequenceClassification,
								    TFBertForTokenClassification,
								    TFBertLMHeadModel,
								    TFBertMainLayer,
								    TFBertModel,
								    TFBertPreTrainedModel,
								)

				try:
								if not is_tensorflow_text_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bert_tf import TFBertTokenizer

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bert import (
								    FlaxBertForCausalLM,
								    FlaxBertForMaskedLM,
								    FlaxBertForMultipleChoice,
								    FlaxBertForNextSentencePrediction,
								    FlaxBertForPreTraining,
								    FlaxBertForQuestionAnswering,
								    FlaxBertForSequenceClassification,
								    FlaxBertForTokenClassification,
								    FlaxBertModel,
								    FlaxBertPreTrainedModel,
								)

else:
				import sys

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