



import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class 			__magic_name__		(					tf.keras.layers.Layer     ):







							def __init__(							self      : Optional[Any]			,_UpperCAmelCase      : List			,_UpperCAmelCase      : bool			,_UpperCAmelCase      : int = None			,_UpperCAmelCase      : int = None			,_UpperCAmelCase      : int = None			,_UpperCAmelCase      : str = "longest"			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : int = 512			,_UpperCAmelCase      : int = None			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : bool = True			,_UpperCAmelCase      : bool = True			,**_UpperCAmelCase      : Optional[int]			,):
									super().__init__()
									if use_fast_bert_tokenizer:
											_a  :   str				 =							FastBertTokenizer(
											    _lowerCAmelCase			,token_out_type=tf.intaa			,lower_case_nfd_strip_accents=_lowerCAmelCase			,**_lowerCAmelCase	)
									else:
											_a  :   Dict				 =							tf.lookup.StaticVocabularyTable(
											    tf.lookup.KeyValueTensorInitializer(
											        keys=_lowerCAmelCase			,key_dtype=tf.string			,values=tf.range(tf.size(_lowerCAmelCase			,out_type=tf.intaa	)			,dtype=tf.intaa	)			,value_dtype=tf.intaa			,)			,num_oov_buckets=1			,)
											_a  :   str				 =							BertTokenizerLayer(
											    _lowerCAmelCase			,token_out_type=tf.intaa			,lower_case=_lowerCAmelCase			,**_lowerCAmelCase	)

									_a  :   int				 =							vocab_list
									_a  :   int				 =							do_lower_case
									_a  :   Optional[int]				 =							cls_token_id or vocab_list.index('[CLS]'	)
									_a  :   Any				 =							sep_token_id or vocab_list.index('[SEP]'	)
									_a  :   Optional[Any]				 =							pad_token_id or vocab_list.index('[PAD]'	)
									_a  :   List[str]				 =							ShrinkLongestTrimmer(max_length - 3			,axis=1	)  # Allow room for special tokens
									_a  :   Optional[int]				 =							max_length
									_a  :   Optional[int]				 =							padding
									_a  :   Dict				 =							truncation
									_a  :   str				 =							pad_to_multiple_of
									_a  :   Any				 =							return_token_type_ids
									_a  :   str				 =							return_attention_mask







							@classmethod
							def 							__lowercase    (							cls      : int			,_UpperCAmelCase      : "PreTrainedTokenizerBase"			,**_UpperCAmelCase      : List[str]	):  # noqa: F821
									_a  :   Optional[int]				 =							kwargs.pop('do_lower_case'			,_lowerCAmelCase	)
									_a  :   Any				 =							tokenizer.do_lower_case if do_lower_case is None else do_lower_case
									_a  :   List[str]				 =							kwargs.pop('cls_token_id'			,_lowerCAmelCase	)
									_a  :   Any				 =							tokenizer.cls_token_id if cls_token_id is None else cls_token_id
									_a  :   List[Any]				 =							kwargs.pop('sep_token_id'			,_lowerCAmelCase	)
									_a  :   Union[str, Any]				 =							tokenizer.sep_token_id if sep_token_id is None else sep_token_id
									_a  :   Any				 =							kwargs.pop('pad_token_id'			,_lowerCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.pad_token_id if pad_token_id is None else pad_token_id

									_a  :   int				 =							tokenizer.get_vocab()
									_a  :   Any				 =							sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]			,key=lambda _UpperCAmelCase	: x[1]	)
									_a  :   Dict				 =							[entry[0] for entry in vocab]
									return cls(
									    vocab_list=_lowerCAmelCase			,do_lower_case=_lowerCAmelCase			,cls_token_id=_lowerCAmelCase			,sep_token_id=_lowerCAmelCase			,pad_token_id=_lowerCAmelCase			,**_lowerCAmelCase			,)







							@classmethod
							def 							__lowercase    (							cls      : Dict			,_UpperCAmelCase      : Union[str, os.PathLike]			,*_UpperCAmelCase      : Optional[Any]			,**_UpperCAmelCase      : Optional[Any]	):
									try:
											_a  :   List[str]				 =							BertTokenizer.from_pretrained(_lowerCAmelCase			,*_lowerCAmelCase			,**_lowerCAmelCase	)
									except:  # noqa: E722
											from .tokenization_bert_fast import BertTokenizerFast

											_a  :   List[Any]				 =							BertTokenizerFast.from_pretrained(_lowerCAmelCase			,*_lowerCAmelCase			,**_lowerCAmelCase	)
									return cls.from_tokenizer(_lowerCAmelCase			,**_lowerCAmelCase	)







							def 							__lowercase    (							self      : Optional[Any]			,_UpperCAmelCase      : Dict	):
									if self.do_lower_case:
											_a  :   List[Any]				 =							case_fold_utfa(_lowerCAmelCase	)
									_a  :   Optional[Any]				 =							self.tf_tokenizer.tokenize(_lowerCAmelCase	)
									return tokens.merge_dims(1			,-1	)







							def 							__lowercase    (							self      : Optional[int]			,_UpperCAmelCase      : Union[str, Any]			,_UpperCAmelCase      : Optional[int]=None			,_UpperCAmelCase      : Dict=None			,_UpperCAmelCase      : Dict=None			,_UpperCAmelCase      : str=None			,_UpperCAmelCase      : List[str]=None			,_UpperCAmelCase      : Union[str, Any]=None			,_UpperCAmelCase      : List[str]=None			,):
									if padding is None:
											_a  :   int				 =							self.padding
									if padding not in ("longest", "max_length"):
											raise ValueError('Padding must be either \'longest\' or \'max_length\'!'	)
									if max_length is not None and text_pair is not None:
											# Because we have to instantiate a Trimmer to do it properly
											raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'	)
									if max_length is None:
											_a  :   List[Any]				 =							self.max_length
									if truncation is None:
											_a  :   Optional[Any]				 =							self.truncation
									if pad_to_multiple_of is None:
											_a  :   Tuple				 =							self.pad_to_multiple_of
									if return_token_type_ids is None:
											_a  :   Any				 =							self.return_token_type_ids
									if return_attention_mask is None:
											_a  :   Tuple				 =							self.return_attention_mask
									if not isinstance(_lowerCAmelCase			,tf.Tensor	):
											_a  :   List[str]				 =							tf.convert_to_tensor(_lowerCAmelCase	)
									if text_pair is not None and not isinstance(_lowerCAmelCase			,tf.Tensor	):
											_a  :   List[str]				 =							tf.convert_to_tensor(_lowerCAmelCase	)
									if text_pair is not None:
											if text.shape.rank > 1:
													raise ValueError('text argument should not be multidimensional when a text pair is supplied!'	)
											if text_pair.shape.rank > 1:
													raise ValueError('text_pair should not be multidimensional!'	)
									if text.shape.rank == 2:
											_a ,      _a  :   int				 =							text[:, 0], text[:, 1]
									_a  :   Union[str, Any]				 =							self.unpaired_tokenize(_lowerCAmelCase	)
									if text_pair is None:  # Unpaired text
											if truncation:
													_a  :   Any				 =							text[:, : max_length - 2]  # Allow room for special tokens
											_a ,      _a  :   List[Any]				 =							combine_segments(
											    (text,)			,start_of_sequence_id=self.cls_token_id			,end_of_segment_id=self.sep_token_id	)
									else:  # Paired text
											_a  :   int				 =							self.unpaired_tokenize(_lowerCAmelCase	)
											if truncation:
													_a ,      _a  :   Optional[int]				 =							self.paired_trimmer.trim([text, text_pair]	)
											_a ,      _a  :   Any				 =							combine_segments(
											    (text, text_pair)			,start_of_sequence_id=self.cls_token_id			,end_of_segment_id=self.sep_token_id	)
									if padding == "longest":
											_a  :   int				 =							input_ids.bounding_shape(axis=1	)
											if pad_to_multiple_of is not None:
													# No ceiling division in tensorflow, so we negate floordiv instead
													_a  :   Dict				 =							pad_to_multiple_of * (-tf.math.floordiv(-pad_length			,_lowerCAmelCase	))
									else:
											_a  :   int				 =							max_length

									_a ,      _a  :   Optional[int]				 =							pad_model_inputs(_lowerCAmelCase			,max_seq_length=_lowerCAmelCase			,pad_value=self.pad_token_id	)
									_a  :   str				 =							{'input_ids': input_ids}
									if return_attention_mask:
											_a  :   Optional[int]				 =							attention_mask
									if return_token_type_ids:
											_a ,      _a  :   List[str]				 =							pad_model_inputs(
											    _lowerCAmelCase			,max_seq_length=_lowerCAmelCase			,pad_value=self.pad_token_id	)
											_a  :   Optional[Any]				 =							token_type_ids
									return output







							def 							__lowercase    (							self      : List[Any]	):
									return {
									    "vocab_list": self.vocab_list,
									    "do_lower_case": self.do_lower_case,
									    "cls_token_id": self.cls_token_id,
									    "sep_token_id": self.sep_token_id,
									    "pad_token_id": self.pad_token_id,
									}









'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase		    =     {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5Tokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5TokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''T5EncoderModel''',
							    '''T5ForConditionalGeneration''',
							    '''T5Model''',
							    '''T5PreTrainedModel''',
							    '''load_tf_weights_in_t5''',
							    '''T5ForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFT5EncoderModel''',
							    '''TFT5ForConditionalGeneration''',
							    '''TFT5Model''',
							    '''TFT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''FlaxT5EncoderModel''',
							    '''FlaxT5ForConditionalGeneration''',
							    '''FlaxT5Model''',
							    '''FlaxT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

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




