


'''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 		UpperCamelCase__    (       tf.keras.layers.Layer		):



     """simple docstring"""






     def __init__(       self					,		snake_case__					,		snake_case__					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = None					,		snake_case__ = "longest"					,		snake_case__ = True					,		snake_case__ = 512					,		snake_case__ = None					,		snake_case__ = True					,		snake_case__ = True					,		snake_case__ = True					,		**snake_case__					,		):

            '''simple docstring'''

            super().__init__()
            if use_fast_bert_tokenizer:
                   _lowerCAmelCase			:							List[str]      	=				FastBertTokenizer(
                       _SCREAMING_SNAKE_CASE					,		token_out_type=tf.intaa					,		lower_case_nfd_strip_accents=_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE      )
            else:
                   _lowerCAmelCase			:							Dict      	=				tf.lookup.StaticVocabularyTable(
                       tf.lookup.KeyValueTensorInitializer(
                           keys=_SCREAMING_SNAKE_CASE					,		key_dtype=tf.string					,		values=tf.range(tf.size(_SCREAMING_SNAKE_CASE					,		out_type=tf.intaa      )					,		dtype=tf.intaa      )					,		value_dtype=tf.intaa					,		)					,		num_oov_buckets=1					,		)
                   _lowerCAmelCase			:							List[Any]      	=				BertTokenizerLayer(
                       _SCREAMING_SNAKE_CASE					,		token_out_type=tf.intaa					,		lower_case=_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE      )

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







     @classmethod
     def a		(       cls					,		snake_case__					,		**snake_case__      ):  # noqa: F821

            '''simple docstring'''

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

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







     @classmethod
     def a		(       cls					,		snake_case__					,		*snake_case__					,		**snake_case__      ):

            '''simple docstring'''

            try:
                   _lowerCAmelCase			:							Optional[int]      	=				BertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE					,		*_SCREAMING_SNAKE_CASE					,		**_SCREAMING_SNAKE_CASE      )
            except:  # noqa: E722
                   from .tokenization_bert_fast import BertTokenizerFast

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







     def a		(       self					,		snake_case__      ):

            '''simple docstring'''

            if self.do_lower_case:
                   _lowerCAmelCase			:							List[str]      	=				case_fold_utfa(_SCREAMING_SNAKE_CASE      )
            _lowerCAmelCase			:							str      	=				self.tf_tokenizer.tokenize(_SCREAMING_SNAKE_CASE      )
            return tokens.merge_dims(1					,		-1      )







     def a		(       self					,		snake_case__					,		snake_case__=None					,		snake_case__=None					,		snake_case__=None					,		snake_case__=None					,		snake_case__=None					,		snake_case__=None					,		snake_case__=None					,		):

            '''simple docstring'''

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

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







     def a		(       self      ):

            '''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,
            }








def        lowerCAmelCase_       (							UpperCamelCase_    ,						UpperCamelCase_     )   ->						list:
       UpperCamelCase_      				=     sorted(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[2]    ,						reverse=UpperCamelCase_     )

       # Create a list of size equal to the maximum deadline
       # and initialize it with -1
       UpperCamelCase_      				=     max(UpperCamelCase_    ,						key=lambda UpperCamelCase_     : value[1]     )[1]
       UpperCamelCase_      				=     [-1] * max_deadline

       # Finding the maximum profit and the count of jobs
       UpperCamelCase_      				=     0
       UpperCamelCase_      				=     0
       for job in jobs:
              # Find a free time slot for this job
              # (Note that we start from the last possible slot)
              for i in range(job[1] - 1    ,						-1    ,						-1     ):
                     if time_slots[i] == -1:
                            UpperCamelCase_      				=     job[0]
                            count += 1
                            max_profit += job[2]
                            break
       return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

