


"""simple docstring"""


import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A  :     Dict    					=    logging.get_logger(__name__)

A  :     List[Any]    					=    "▁"

A  :     Optional[Any]    					=    {"vocab_file": "prophetnet.tokenizer"}

A  :     int    					=    {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

A  :     Union[str, Any]    					=    {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

A  :     Dict    					=    {
    "microsoft/xprophetnet-large-wiki100-cased": 5_1_2,
}


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    collections.OrderedDict()
			with open(UpperCAmelCase__					,				"r"					,				encoding="utf-8"   ) as reader:
						__lowerCAmelCase       					=    reader.readlines()
			for index, token in enumerate(UpperCAmelCase__   ):
						__lowerCAmelCase       					=    token.rstrip("\n"   )
						__lowerCAmelCase       					=    index
			return vocab


class 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Tuple          =VOCAB_FILES_NAMES
							__UpperCAmelCase		:					str          =PRETRAINED_VOCAB_FILES_MAP
							__UpperCAmelCase		:					Dict          =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__UpperCAmelCase		:					List[str]          =["""input_ids""", """attention_mask"""]

							def __init__(  self					,     __a					,     __a="[SEP]"					,     __a="[SEP]"					,     __a="[SEP]"					,     __a="[UNK]"					,     __a="[PAD]"					,     __a="[CLS]"					,     __a="[MASK]"					,     __a = None					,     **__a					,     ):
										__lowerCAmelCase       					=    {} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=__UpperCamelCase					,     eos_token=__UpperCamelCase					,     sep_token=__UpperCamelCase					,     unk_token=__UpperCamelCase					,     pad_token=__UpperCamelCase					,     cls_token=__UpperCamelCase					,     mask_token=__UpperCamelCase					,     sp_model_kwargs=self.sp_model_kwargs					,     **__UpperCamelCase					,     )

										try:
													import sentencepiece as spm
										except ImportError:
													logger.warning(
													    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
													    " pip install sentencepiece"			)
													raise

										__lowerCAmelCase       					=    spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(str(__UpperCamelCase			)			)
										__lowerCAmelCase       					=    vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# put special tokens and [unused] tokens into the vocab
										__lowerCAmelCase       					=    {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

										for i in range(10			):
													__lowerCAmelCase       					=    f"[unused{i}]"
													__lowerCAmelCase       					=    5 + i

										# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
										__lowerCAmelCase       					=    12
										__lowerCAmelCase       					=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}
										for k in self.fairseq_tokens_to_ids.keys():
													self.unique_no_split_tokens.append(__UpperCamelCase			)

							def __getstate__(  self			):
										__lowerCAmelCase       					=    self.__dict__.copy()
										__lowerCAmelCase       					=    None
										return state

							def __setstate__(  self					,     __a			):
										__lowerCAmelCase       					=    d
										try:
													import sentencepiece as spm
										except ImportError:
													logger.warning(
													    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
													    " pip install sentencepiece"			)
													raise

										# for backward compatibility
										if not hasattr(self					,     "sp_model_kwargs"			):
													__lowerCAmelCase       					=    {}

										__lowerCAmelCase       					=    spm.SentencePieceProcessor(**self.sp_model_kwargs			)
										self.sp_model.Load(self.vocab_file			)

							def     snake_case       (  self					,     __a					,     __a = None					,     __a = False			):

										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__UpperCamelCase					,     token_ids_a=__UpperCamelCase					,     already_has_special_tokens=__UpperCamelCase			)

										if token_ids_a is None:
													return ([0] * len(__UpperCamelCase			)) + [1]
										return ([0] * len(__UpperCamelCase			)) + [1] + ([0] * len(__UpperCamelCase			)) + [1]

							def     snake_case       (  self					,     __a					,     __a = None			):
										__lowerCAmelCase       					=    [self.sep_token_id]

										if token_ids_a is None:
													return len(token_ids_a + sep			) * [0]
										return len(token_ids_a + sep + sep + token_ids_a + sep			) * [0]

							@property
							def     snake_case       (  self			):
										return len(self.sp_model			) + self.fairseq_offset

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    {self.convert_ids_to_tokens(__UpperCamelCase			): i for i in range(self.vocab_size			)}
										vocab.update(self.added_tokens_encoder			)
										return vocab

							def     snake_case       (  self					,     __a			):
										return self.sp_model.encode(__UpperCamelCase					,     out_type=__UpperCamelCase			)

							def     snake_case       (  self					,     __a			):
										if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
										__lowerCAmelCase       					=    self.sp_model.PieceToId(__UpperCamelCase			)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

							def     snake_case       (  self					,     __a			):
										if index in self.fairseq_ids_to_tokens:
													return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset			)

							def     snake_case       (  self					,     __a			):
										__lowerCAmelCase       					=    "".join(__UpperCamelCase			).replace(__UpperCamelCase					,     " "			).strip()
										return out_string

							def     snake_case       (  self					,     __a					,     __a = None			):
										if not os.path.isdir(__UpperCamelCase			):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
													return
										__lowerCAmelCase       					=    os.path.join(
										    __UpperCamelCase					,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

										if os.path.abspath(self.vocab_file			) != os.path.abspath(__UpperCamelCase			) and os.path.isfile(self.vocab_file			):
													copyfile(self.vocab_file					,     __UpperCamelCase			)
										elif not os.path.isfile(self.vocab_file			):
													with open(__UpperCamelCase					,     "wb"			) as fi:
																__lowerCAmelCase       					=    self.sp_model.serialized_model_proto()
																fi.write(__UpperCamelCase			)

										return (out_vocab_file,)



							def     snake_case       (  self					,     __a					,     __a = None			):

										if token_ids_a is None:
													return token_ids_a + [self.sep_token_id]
										__lowerCAmelCase       					=    [self.sep_token_id]
										return token_ids_a + sep + token_ids_a + sep








"""simple docstring"""


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    len(_UpperCamelCase   )
			print("The following activities are selected:"   )

			# The first activity is always selected
			__lowerCAmelCase       					=    0
			print(_UpperCamelCase					,				end=","   )

			# Consider rest of the activities
			for j in range(_UpperCamelCase   ):
						# If this activity has start time greater than
						# or equal to the finish time of previously
						# selected activity, then select it
						if start[j] >= finish[i]:
									print(_UpperCamelCase					,				end=","   )
									__lowerCAmelCase       					=    j


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A  :     Dict    					=    [1, 3, 0, 5, 8, 5]
	A  :     int    					=    [2, 4, 6, 7, 9, 9]
	print_max_activities(start, finish)




