





'''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


__lowerCAmelCase							:	str			=logging.get_logger(__name__)

__lowerCAmelCase							:	int			="▁"

__lowerCAmelCase							:	str			={"vocab_file": "prophetnet.tokenizer"}

__lowerCAmelCase							:	List[Any]			={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__lowerCAmelCase							:	Tuple			={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__lowerCAmelCase							:	Optional[Any]			={
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}





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






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				VOCAB_FILES_NAMES
 __lowercase   		=				PRETRAINED_VOCAB_FILES_MAP
 __lowercase   		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase   		=				["""input_ids""", """attention_mask"""]
 def __init__(				self      :Any						,       lowercase_      :List[Any]						,       lowercase_      :List[str]="[SEP]"						,       lowercase_      :Any="[SEP]"						,       lowercase_      :List[Any]="[SEP]"						,       lowercase_      :List[Any]="[UNK]"						,       lowercase_      :Union[str, Any]="[PAD]"						,       lowercase_      :str="[CLS]"						,       lowercase_      :List[Any]="[MASK]"						,       lowercase_      :Optional[Dict[str, Any]] = None						,       **lowercase_      :Tuple						,       )->							None:
     A__				=			{} if sp_model_kwargs is None else sp_model_kwargs

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

     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

     A__				=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(str(lowercase_							)							)
     A__				=			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
     A__				=			{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

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

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     A__				=			12
     A__				=			{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(lowercase_							)
 def __getstate__(				self      :List[Any]							)->							Any:
     A__				=			self.__dict__.copy()
     A__				=			None
     return state
 def __setstate__(				self      :Union[str, Any]						,       lowercase_      :Tuple							)->							Any:
     A__				=			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"							):
         A__				=			{}

     A__				=			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(self.vocab_file							)
 def      UpperCAmelCase_							(				self      :Optional[Any]						,       lowercase_      :List[int]						,       lowercase_      :Optional[List[int]] = None						,       lowercase_      :bool = False							)->							List[int]:

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

     if token_ids_a is None:
         return ([0] * len(lowercase_							)) + [1]
     return ([0] * len(lowercase_							)) + [1] + ([0] * len(lowercase_							)) + [1]
 def      UpperCAmelCase_							(				self      :List[Any]						,       lowercase_      :List[int]						,       lowercase_      :Optional[List[int]] = None							)->							List[int]:
     A__				=			[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      UpperCAmelCase_							(				self      :str							)->							Any:
     return len(self.sp_model							) + self.fairseq_offset
 def      UpperCAmelCase_							(				self      :str							)->							Union[str, Any]:
     A__				=			{self.convert_ids_to_tokens(lowercase_							): i for i in range(self.vocab_size							)}
     vocab.update(self.added_tokens_encoder							)
     return vocab
 def      UpperCAmelCase_							(				self      :Optional[Any]						,       lowercase_      :str							)->							str:
     return self.sp_model.encode(lowercase_						,       out_type=lowercase_							)
 def      UpperCAmelCase_							(				self      :int						,       lowercase_      :Optional[int]							)->							Any:
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     A__				=			self.sp_model.PieceToId(lowercase_							)

     # 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      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :str							)->							List[Any]:
     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      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :Any							)->							Optional[Any]:
     A__				=			"".join(lowercase_							).replace(lowercase_						,       " "							).strip()
     return out_string
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :str						,       lowercase_      :Optional[str] = None							)->							Tuple[str]:
     if not os.path.isdir(lowercase_							):
         logger.error(F"Vocabulary path ({save_directory}) should be a directory"							)
         return
     A__				=			os.path.join(
         lowercase_						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

     return (out_vocab_file,)



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

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










'''simple docstring'''

from __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

     # a_{0} ... a_{k}
     A__				=			[1.0] + [0.0] * order
     # b_{0} ... b_{k}
     A__				=			[1.0] + [0.0] * order

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected a_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     if len(lowercase_							) != self.order + 1:
         A__				=			(
             F"Expected b_coeffs to have {self.order + 1} elements "
             F"for {self.order}-order filter, got {len(lowercase_							)}"
         )
         raise ValueError(lowercase_							)

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			0.0

     # Start at index 1 and do index 0 at the end.
     for i in range(1						,       self.order + 1							):
         result += (
             self.b_coeffs[i] * self.input_history[i - 1]
             - self.a_coeffs[i] * self.output_history[i - 1]
         )

     A__				=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

     A__				=			self.input_history[:-1]
     A__				=			self.output_history[:-1]

     A__				=			sample
     A__				=			result

     return result



