




"""simple docstring"""

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  _snake_case				(							tf.keras.layers.Layer       ):

				'''simple docstring'''




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

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





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

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





				@classmethod
				def 				snake_case_ (							cls      :  Any					,							snake_case      :  Union[str, os.PathLike]					,							*snake_case      :  int					,							**snake_case      :  str     ):
								try:
												UpperCAmelCase_					:Optional[int]      			=      BertTokenizer.from_pretrained(snake_case					,							*snake_case					,							**snake_case     )
								except:  # noqa: E722
												from .tokenization_bert_fast import BertTokenizerFast

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





				def 				snake_case_ (							self      :  Dict					,							snake_case      :  Tuple     ):
								if self.do_lower_case:
												UpperCAmelCase_					:int      			=      case_fold_utfa(snake_case     )
								UpperCAmelCase_					:int      			=      self.tf_tokenizer.tokenize(snake_case     )
								return tokens.merge_dims(1					,							-1     )





				def 				snake_case_ (							self      :  Tuple					,							snake_case      :  Tuple					,							snake_case      :  List[Any]=None					,							snake_case      :  Optional[int]=None					,							snake_case      :  Tuple=None					,							snake_case      :  Tuple=None					,							snake_case      :  Dict=None					,							snake_case      :  Any=None					,							snake_case      :  Any=None					,							):
								if padding is None:
												UpperCAmelCase_					: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:
												UpperCAmelCase_					:int      			=      self.max_length
								if truncation is None:
												UpperCAmelCase_					:Dict      			=      self.truncation
								if pad_to_multiple_of is None:
												UpperCAmelCase_					:List[str]      			=      self.pad_to_multiple_of
								if return_token_type_ids is None:
												UpperCAmelCase_					:List[Any]      			=      self.return_token_type_ids
								if return_attention_mask is None:
												UpperCAmelCase_					:Dict      			=      self.return_attention_mask
								if not isinstance(snake_case					,							tf.Tensor     ):
												UpperCAmelCase_					:str      			=      tf.convert_to_tensor(snake_case     )
								if text_pair is not None and not isinstance(snake_case					,							tf.Tensor     ):
												UpperCAmelCase_					:Optional[Any]      			=      tf.convert_to_tensor(snake_case     )
								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:
												UpperCAmelCase_					:List[Any]      			=      text[:, 0], text[:, 1]
								UpperCAmelCase_					:List[Any]      			=      self.unpaired_tokenize(snake_case     )
								if text_pair is None:  # Unpaired text
												if truncation:
																UpperCAmelCase_					:List[str]      			=      text[:, : max_length - 2]  # Allow room for special tokens
												UpperCAmelCase_					:List[str]      			=      combine_segments(
												    (text,)					,							start_of_sequence_id=self.cls_token_id					,							end_of_segment_id=self.sep_token_id     )
								else:  # Paired text
												UpperCAmelCase_					:int      			=      self.unpaired_tokenize(snake_case     )
												if truncation:
																UpperCAmelCase_					:Any      			=      self.paired_trimmer.trim([text, text_pair]     )
												UpperCAmelCase_					:Optional[int]      			=      combine_segments(
												    (text, text_pair)					,							start_of_sequence_id=self.cls_token_id					,							end_of_segment_id=self.sep_token_id     )
								if padding == "longest":
												UpperCAmelCase_					:List[Any]      			=      input_ids.bounding_shape(axis=1     )
												if pad_to_multiple_of is not None:
																# No ceiling division in tensorflow, so we negate floordiv instead
																UpperCAmelCase_					:Any      			=      pad_to_multiple_of * (-tf.math.floordiv(-pad_length					,							snake_case     ))
								else:
												UpperCAmelCase_					:Union[str, Any]      			=      max_length

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





				def 				snake_case_ (							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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase								=						{
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BartForCausalLM",
						    "BartForConditionalGeneration",
						    "BartForQuestionAnswering",
						    "BartForSequenceClassification",
						    "BartModel",
						    "BartPretrainedModel",
						    "PretrainedBartModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "TFBartForConditionalGeneration",
						    "TFBartForSequenceClassification",
						    "TFBartModel",
						    "TFBartPretrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "FlaxBartDecoderPreTrainedModel",
						    "FlaxBartForCausalLM",
						    "FlaxBartForConditionalGeneration",
						    "FlaxBartForQuestionAnswering",
						    "FlaxBartForSequenceClassification",
						    "FlaxBartModel",
						    "FlaxBartPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

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


