





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 SCREAMING_SNAKE_CASE_    (       tf.keras.layers.Layer       ):






						def __init__(   self   :   List[Any]		, _A   :   List		, _A   :   bool		, _A   :   int = None		, _A   :   int = None		, _A   :   int = None		, _A   :   str = "longest"		, _A   :   bool = True		, _A   :   int = 512		, _A   :   int = None		, _A   :   bool = True		, _A   :   bool = True		, _A   :   bool = True		, **_A   :   Any		, )				->     Tuple:







							"""simple docstring"""


							super().__init__()
							if use_fast_bert_tokenizer:
								snake_case_		:      Any           =						FastBertTokenizer(
								    _A		, token_out_type=tf.intaa		, lower_case_nfd_strip_accents=_A		, **_A						)
							else:
								snake_case_		:      int           =						tf.lookup.StaticVocabularyTable(
								    tf.lookup.KeyValueTensorInitializer(
								        keys=_A		, key_dtype=tf.string		, values=tf.range(tf.size(_A		, out_type=tf.intaa						)		, dtype=tf.intaa						)		, value_dtype=tf.intaa		, )		, num_oov_buckets=1		, )
								snake_case_		:      Any           =						BertTokenizerLayer(
								    _A		, token_out_type=tf.intaa		, lower_case=_A		, **_A						)

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






						@classmethod
						def UpperCAmelCase_					(   cls   :   Any		, _A   :   "PreTrainedTokenizerBase"		, **_A   :   str						)				->     List[str]:  # noqa: F821







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						kwargs.pop('do_lower_case'		, _A						)
							snake_case_		:      Tuple           =						tokenizer.do_lower_case if do_lower_case is None else do_lower_case
							snake_case_		:      List[str]           =						kwargs.pop('cls_token_id'		, _A						)
							snake_case_		:      Any           =						tokenizer.cls_token_id if cls_token_id is None else cls_token_id
							snake_case_		:      Tuple           =						kwargs.pop('sep_token_id'		, _A						)
							snake_case_		:      Tuple           =						tokenizer.sep_token_id if sep_token_id is None else sep_token_id
							snake_case_		:      List[str]           =						kwargs.pop('pad_token_id'		, _A						)
							snake_case_		:      List[str]           =						tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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






						@classmethod
						def UpperCAmelCase_					(   cls   :   Union[str, Any]		, _A   :   Union[str, os.PathLike]		, *_A   :   int		, **_A   :   List[str]						)				->     Tuple:







							"""simple docstring"""


							try:
								snake_case_		:      Any           =						BertTokenizer.from_pretrained(_A		, *_A		, **_A						)
							except:  # noqa: E722
								from .tokenization_bert_fast import BertTokenizerFast

								snake_case_		:      int           =						BertTokenizerFast.from_pretrained(_A		, *_A		, **_A						)
							return cls.from_tokenizer(_A		, **_A						)






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   List[str]						)				->     str:







							"""simple docstring"""


							if self.do_lower_case:
								snake_case_		:      Optional[int]           =						case_fold_utfa(_A						)
							snake_case_		:      Tuple           =						self.tf_tokenizer.tokenize(_A						)
							return tokens.merge_dims(1		, -1						)






						def UpperCAmelCase_					(   self   :   Optional[int]		, _A   :   Optional[int]		, _A   :   Optional[Any]=None		, _A   :   Optional[Any]=None		, _A   :   List[str]=None		, _A   :   List[Any]=None		, _A   :   Any=None		, _A   :   Optional[int]=None		, _A   :   int=None		, )				->     Optional[int]:







							"""simple docstring"""


							if padding is None:
								snake_case_		:      Union[str, Any]           =						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:
								snake_case_		:      Optional[Any]           =						self.max_length
							if truncation is None:
								snake_case_		:      str           =						self.truncation
							if pad_to_multiple_of is None:
								snake_case_		:      Tuple           =						self.pad_to_multiple_of
							if return_token_type_ids is None:
								snake_case_		:      List[Any]           =						self.return_token_type_ids
							if return_attention_mask is None:
								snake_case_		:      Dict           =						self.return_attention_mask
							if not isinstance(_A		, tf.Tensor						):
								snake_case_		:      Optional[Any]           =						tf.convert_to_tensor(_A						)
							if text_pair is not None and not isinstance(_A		, tf.Tensor						):
								snake_case_		:      List[Any]           =						tf.convert_to_tensor(_A						)
							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:
								snake_case_    ,snake_case_		:      Dict           =						text[:, 0], text[:, 1]
							snake_case_		:      str           =						self.unpaired_tokenize(_A						)
							if text_pair is None:  # Unpaired text
								if truncation:
									snake_case_		:      List[str]           =						text[:, : max_length - 2]  # Allow room for special tokens
								snake_case_    ,snake_case_		:      Dict           =						combine_segments(
								    (text,)		, start_of_sequence_id=self.cls_token_id		, end_of_segment_id=self.sep_token_id						)
							else:  # Paired text
								snake_case_		:      Tuple           =						self.unpaired_tokenize(_A						)
								if truncation:
									snake_case_    ,snake_case_		:      List[Any]           =						self.paired_trimmer.trim([text, text_pair]						)
								snake_case_    ,snake_case_		:      Optional[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":
								snake_case_		:      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
									snake_case_		:      Any           =						pad_to_multiple_of * (-tf.math.floordiv(-pad_length		, _A						))
							else:
								snake_case_		:      Optional[Any]           =						max_length

							snake_case_    ,snake_case_		:      Tuple           =						pad_model_inputs(_A		, max_seq_length=_A		, pad_value=self.pad_token_id						)
							snake_case_		:      str           =						{'input_ids': input_ids}
							if return_attention_mask:
								snake_case_		:      Any           =						attention_mask
							if return_token_type_ids:
								snake_case_    ,snake_case_		:      Dict           =						pad_model_inputs(
								    _A		, max_seq_length=_A		, pad_value=self.pad_token_id						)
								snake_case_		:      Union[str, Any]           =						token_type_ids
							return output






						def UpperCAmelCase_					(   self   :   Any						)				->     Any:







							"""simple docstring"""


							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,
							}








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

