

'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__      				=			logging.get_logger(__name__)
lowerCAmelCase__      				=			{"vocab_file": "spiece.model"}

lowerCAmelCase__      				=			{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

lowerCAmelCase__      				=			{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

lowerCAmelCase__      				=			"▁"



class        lowercase    (	a_			):
							_lowerCamelCase		:   Tuple= VOCAB_FILES_NAMES
							_lowerCamelCase		:   List[str]= PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:   List[Any]= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_lowerCamelCase		:   List[str]= ["""input_ids""", """token_type_ids"""]


							def __init__(      self     ,      _snake_case     ,      _snake_case=False     ,      _snake_case=True     ,      _snake_case=True     ,      _snake_case="<unk>"     ,      _snake_case="[SEP]"     ,      _snake_case="<pad>"     ,      _snake_case="[CLS]"     ,      _snake_case="[MASK]"     ,      _snake_case = None     ,      **_snake_case     ,      )				->					Optional[int]:
								# Mask token behave like a normal word, i.e. include the space before it and
								# is included in the raw text, there should be a match in a non-normalized sentence.
								UpperCAmelCase_		:   int													=       (
								    AddedToken(a_     ,      lstrip=a_     ,      rstrip=a_     ,      normalized=a_)
								    if isinstance(a_     ,      a_)
								    else mask_token
								)

								UpperCAmelCase_		:   List[str]													=       {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    do_lower_case=a_     ,      remove_space=a_     ,      keep_accents=a_     ,      unk_token=a_     ,      sep_token=a_     ,      pad_token=a_     ,      cls_token=a_     ,      mask_token=a_     ,      sp_model_kwargs=self.sp_model_kwargs     ,      **a_     ,      )

								UpperCAmelCase_		:   Dict													=       do_lower_case
								UpperCAmelCase_		:   List[str]													=       remove_space
								UpperCAmelCase_		:   Any													=       keep_accents
								UpperCAmelCase_		:   Optional[int]													=       vocab_file

								UpperCAmelCase_		:   int													=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(a_)


							@property
							def     _snake_case				(      self)				->					Any:
								return len(self.sp_model)


							def     _snake_case				(      self)				->					Union[str, Any]:
								UpperCAmelCase_		:   List[str]													=       {self.convert_ids_to_tokens(a_): i for i in range(self.vocab_size)}
								vocab.update(self.added_tokens_encoder)
								return vocab


							def __getstate__(      self)				->					List[str]:
								UpperCAmelCase_		:   List[Any]													=       self.__dict__.copy()
								UpperCAmelCase_		:   List[str]													=       None
								return state


							def __setstate__(      self     ,      _snake_case)				->					List[str]:
								UpperCAmelCase_		:   Tuple													=       d

								# for backward compatibility
								if not hasattr(self     ,      'sp_model_kwargs'):
									UpperCAmelCase_		:   Any													=       {}

								UpperCAmelCase_		:   List[str]													=       spm.SentencePieceProcessor(**self.sp_model_kwargs)
								self.sp_model.Load(self.vocab_file)


							def     _snake_case				(      self     ,      _snake_case)				->					int:
								if self.remove_space:
									UpperCAmelCase_		:   Dict													=       ' '.join(inputs.strip().split())
								else:
									UpperCAmelCase_		:   Optional[Any]													=       inputs
								UpperCAmelCase_		:   Tuple													=       outputs.replace('``'     ,      '\"').replace('\'\''     ,      '\"')

								if not self.keep_accents:
									UpperCAmelCase_		:   Optional[Any]													=       unicodedata.normalize('NFKD'     ,      a_)
									UpperCAmelCase_		:   Any													=       ''.join([c for c in outputs if not unicodedata.combining(a_)])
								if self.do_lower_case:
									UpperCAmelCase_		:   Dict													=       outputs.lower()

								return outputs


							def     _snake_case				(      self     ,      _snake_case)				->					Optional[Any]:
								UpperCAmelCase_		:   Optional[int]													=       self.preprocess_text(a_)
								UpperCAmelCase_		:   Dict													=       self.sp_model.encode(a_     ,      out_type=a_)
								UpperCAmelCase_		:   Any													=       []
								for piece in pieces:
									if len(a_) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
										UpperCAmelCase_		:   Optional[Any]													=       self.sp_model.EncodeAsPieces(piece[:-1].replace(a_     ,      ''))
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
											if len(cur_pieces[0]) == 1:
												UpperCAmelCase_		:   Any													=       cur_pieces[1:]
											else:
												UpperCAmelCase_		:   Optional[int]													=       cur_pieces[0][1:]
										cur_pieces.append(piece[-1])
										new_pieces.extend(a_)
									else:
										new_pieces.append(a_)

								return new_pieces


							def     _snake_case				(      self     ,      _snake_case)				->					Any:
								return self.sp_model.PieceToId(a_)


							def     _snake_case				(      self     ,      _snake_case)				->					Optional[Any]:
								return self.sp_model.IdToPiece(a_)


							def     _snake_case				(      self     ,      _snake_case)				->					int:
								UpperCAmelCase_		:   List[str]													=       []
								UpperCAmelCase_		:   Tuple													=       ''
								UpperCAmelCase_		:   Dict													=       False
								for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
										if not prev_is_special:
											out_string += " "
										out_string += self.sp_model.decode(a_) + token
										UpperCAmelCase_		:   Any													=       True
										UpperCAmelCase_		:   int													=       []
									else:
										current_sub_tokens.append(a_)
										UpperCAmelCase_		:   List[str]													=       False
								out_string += self.sp_model.decode(a_)
								return out_string.strip()


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = False     ,      _snake_case = None     ,      _snake_case = True     ,      **_snake_case     ,      )				->					str:
								UpperCAmelCase_		:   str													=       kwargs.pop('use_source_tokenizer'     ,      a_)

								UpperCAmelCase_		:   Union[str, Any]													=       self.convert_ids_to_tokens(a_     ,      skip_special_tokens=a_)

								# To avoid mixing byte-level and unicode for byte-level BPT
								# we need to build string separately for added tokens and byte-level tokens
								# cf. https://github.com/huggingface/transformers/issues/1133
								UpperCAmelCase_		:   Tuple													=       []
								UpperCAmelCase_		:   str													=       []
								for token in filtered_tokens:
									if skip_special_tokens and token in self.all_special_ids:
										continue
									if token in self.added_tokens_encoder:
										if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(a_))
											UpperCAmelCase_		:   Dict													=       []
										sub_texts.append(a_)
									else:
										current_sub_text.append(a_)
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(a_))

								# Mimic the behavior of the Rust tokenizer:
								# No space after <unk>
								if spaces_between_special_tokens:
									UpperCAmelCase_		:   List[Any]													=       re.sub(r'(<unk>) '     ,      r'\1'     ,      ' '.join(a_))
								else:
									UpperCAmelCase_		:   str													=       ''.join(a_)

								UpperCAmelCase_		:   Optional[Any]													=       (
								    clean_up_tokenization_spaces
								    if clean_up_tokenization_spaces is not None
								    else self.clean_up_tokenization_spaces
								)
								if clean_up_tokenization_spaces:
									UpperCAmelCase_		:   Tuple													=       self.clean_up_tokenization(a_)
									return clean_text
								else:
									return text


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Optional[Any]:
								UpperCAmelCase_		:   Dict													=       [self.sep_token_id]
								UpperCAmelCase_		:   Optional[Any]													=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None     ,      _snake_case = False)				->					str:

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

								if token_ids_a is not None:
									return [1] + ([0] * len(a_)) + [1] + ([0] * len(a_)) + [1]
								return [1] + ([0] * len(a_)) + [1]


							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Union[str, Any]:
								UpperCAmelCase_		:   int													=       [self.sep_token_id]
								UpperCAmelCase_		:   Optional[int]													=       [self.cls_token_id]

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





							def     _snake_case				(      self     ,      _snake_case     ,      _snake_case = None)				->					Union[str, Any]:
								if not os.path.isdir(a_):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
									return
								UpperCAmelCase_		:   Dict													=       os.path.join(
								    a_     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

								if os.path.abspath(self.vocab_file) != os.path.abspath(a_) and os.path.isfile(self.vocab_file):
									copyfile(self.vocab_file     ,      a_)
								elif not os.path.isfile(self.vocab_file):
									with open(a_     ,      'wb') as fi:
										UpperCAmelCase_		:   Union[str, Any]													=       self.sp_model.serialized_model_proto()
										fi.write(a_)

								return (out_vocab_file,)











from typing import Any

import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_		):
											# Check if RHS is negative
											if np.any(tableau[:, -1]	,	where=tableau[:, -1] < 0		):
															raise ValueError("""RHS must be > 0"""		)

											a__					   =		tableau
											a__   ,      a__					   =		tableau.shape

											# Number of decision variables x1, x2, x3...
											a__					   =		n_vars

											# Number of artificial variables to be minimised
											a__					   =		len(np.where(tableau[self.n_vars : -1] == -1		)[0]		)

											# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
											a__					   =		(self.n_art_vars > 0) + 1

											# Number of slack variables added to make inequalities into equalities
											a__					   =		self.n_rows - self.n_stages

											# Objectives for each stage
											a__					   =		["""max"""]

											# In two stage simplex, first minimise then maximise
											if self.n_art_vars:
															self.objectives.append("""min"""		)

											a__					   =		[""""""]

											# Index of current pivot row and column
											a__					   =		None
											a__					   =		None

											# Does objective row only contain (non)-negative values?
											a__					   =		False


							@staticmethod
							def _a   (				*a_		):
											if len(a_		) != 3:
															raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""		)

											if not all(x >= 0 and isinstance(a_	,	a_		) for x in args		):
															raise ValueError("""All arguments must be non-negative integers"""		)

											# decision | slack | artificial
											a__					   =		["""x""", """s""", """a"""]
											a__					   =		[]
											for i in range(3		):
															for j in range(args[i]		):
																			titles.append(string_starts[i] + str(j + 1		)		)
											titles.append("""RHS"""		)
											return titles


							def _a   (				self	,	a_		):
											a__					   =		self.objectives[-1]

											# Find entries of highest magnitude in objective rows
											a__					   =		(objective == """min""") - (objective == """max""")
											a__					   =		np.argmax(sign * tableau[0, : self.n_vars]		)

											# Choice is only valid if below 0 for maximise, and above for minimise
											if sign * self.tableau[0, col_idx] <= 0:
															a__					   =		True
															return 0, 0

											# Pivot row is chosen as having the lowest quotient when elements of
											# the pivot column divide the right-hand side

											# Slice excluding the objective rows
											a__					   =		slice(self.n_stages	,	self.n_rows		)

											# RHS
											a__					   =		tableau[s, -1]

											# Elements of pivot column within slice
											a__					   =		tableau[s, col_idx]

											# Array filled with nans
											a__					   =		np.full(self.n_rows - self.n_stages	,	np.nan		)

											# If element in pivot column is greater than zeron_stages, return
											# quotient or nan otherwise
											a__					   =		np.divide(a_	,	a_	,	out=a_	,	where=divisor > 0		)

											# Arg of minimum quotient excluding the nan values. n_stages is added
											# to compensate for earlier exclusion of objective columns
											a__					   =		np.nanargmin(a_		) + self.n_stages
											return row_idx, col_idx


							def _a   (				self	,	a_	,	a_	,	a_		):
											a__					   =		tableau[row_idx].copy()

											a__					   =		piv_row[col_idx]

											# Entry becomes 1
											piv_row *= 1 / piv_val

											# Variable in pivot column becomes basic, ie the only non-zero entry
											for idx, coeff in enumerate(tableau[:, col_idx]		):
															tableau[idx] += -coeff * piv_row
											a__					   =		piv_row
											return tableau


							def _a   (				self	,	a_		):
											self.objectives.pop()

											if not self.objectives:
															return tableau

											# Slice containing ids for artificial columns
											a__					   =		slice(-self.n_art_vars - 1	,	-1		)

											# Delete the artificial variable columns
											a__					   =		np.delete(a_	,	a_	,	axis=1		)

											# Delete the objective row of the first stage
											a__					   =		np.delete(a_	,	0	,	axis=0		)

											a__					   =		1
											self.n_rows -= 1
											a__					   =		0
											a__					   =		False
											return tableau


							def _a   (				self		):
											# Stop simplex algorithm from cycling.
											for _ in range(100		):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																			a__					   =		self.generate_col_titles(
																			    self.n_vars	,	self.n_slack	,	self.n_art_vars		)

																			# Find the values of each variable at optimal solution
																			return self.interpret_tableau(self.tableau	,	self.col_titles		)

															a__   ,      a__					   =		self.find_pivot(self.tableau		)

															# If there are no more negative values in objective row
															if self.stop_iter:
																			# Delete artificial variable columns and rows. Update attributes
																			a__					   =		self.change_stage(self.tableau		)
															else:
																			a__					   =		self.pivot(self.tableau	,	a_	,	a_		)
											return {}







							def _a   (				self	,	a_	,	a_		):
											a__					   =		{"""P""": abs(tableau[0, -1]		)}

											for i in range(self.n_vars		):
															# Gives ids of nonzero entries in the ith column
															a__					   =		np.nonzero(tableau[:, i]		)
															a__					   =		len(nonzero[0]		)

															# First entry in the nonzero ids
															a__					   =		nonzero[0][0]
															a__					   =		tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																			a__					   =		tableau[nonzero_rowidx, -1]
																			a__					   =		rhs_val

        # Check for basic variables
											for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																			output_dict.setdefault(a_	,	0		)
											return output_dict


if __name__ == "__main__":
	import doctest

	doctest.testmod()

