




"""simple docstring"""







from typing import Any

import numpy as np






class  SCREAMING_SNAKE_CASE__   :


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     np.ndarray  ,  lowerCAmelCase			:     int   ):




										if np.any(tableau[:, -1]  ,  where=tableau[:, -1] < 0   ):
														raise ValueError("""RHS must be > 0"""   )

										lowerCAmelCase		=			tableau
										lowerCAmelCase		=			tableau.shape

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

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

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

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

										# Objectives for each stage
										lowerCAmelCase		=			['max']

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

										lowerCAmelCase		=			['']

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

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


						@staticmethod
						def 					__lowercase	(							*lowerCAmelCase			:     int   ):




										if len(lowercase_   ) != 3:
														raise ValueError("""Must provide n_vars, n_slack, and n_art_vars"""   )

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

										# decision | slack | artificial
										lowerCAmelCase		=			['x', 's', 'a']
										lowerCAmelCase		=			[]
										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 					__lowercase	(							self			:     Any  ,  lowerCAmelCase			:     np.ndarray   ):
										lowerCAmelCase		=			self.objectives[-1]

										# Find entries of highest magnitude in objective rows
										lowerCAmelCase		=			(objective == 'min') - (objective == 'max')
										lowerCAmelCase		=			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:
														lowerCAmelCase		=			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
										lowerCAmelCase		=			slice(self.n_stages  ,  self.n_rows   )

										# RHS
										lowerCAmelCase		=			tableau[s, -1]

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

										# Array filled with nans
										lowerCAmelCase		=			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
										lowerCAmelCase		=			np.divide(lowercase_  ,  lowercase_  ,  out=lowercase_  ,  where=divisor > 0   )

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


						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     np.ndarray  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     int   ):
										lowerCAmelCase		=			tableau[row_idx].copy()

										lowerCAmelCase		=			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
										lowerCAmelCase		=			piv_row
										return tableau


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     np.ndarray   ):
										self.objectives.pop()

										if not self.objectives:
														return tableau

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

										# Delete the artificial variable columns
										lowerCAmelCase		=			np.delete(lowercase_  ,  lowercase_  ,  axis=1   )

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

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


						def 					__lowercase	(							self			:     Dict   ):




										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:
																		lowerCAmelCase		=			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   )

														lowerCAmelCase		=			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
																		lowerCAmelCase		=			self.change_stage(self.tableau   )
														else:
																		lowerCAmelCase		=			self.pivot(self.tableau  ,  lowercase_  ,  lowercase_   )
										return {}




						def 					__lowercase	(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     np.ndarray  ,  lowerCAmelCase			:     list[str]   ):
										lowerCAmelCase		=			{'P': abs(tableau[0, -1]   )}

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

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

														# If there is only one nonzero value in column, which is one
														if n_nonzero == nonzero_val == 1:
																		lowerCAmelCase		=			tableau[nonzero_rowidx, -1]
																		lowerCAmelCase		=			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(lowercase_  ,  0   )
										return output_dict


if __name__ == "__main__":
		import doctest

		doctest.testmod()











"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


a								=							logging.get_logger(__name__)

a								=							{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a								=							{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

a								=							{
    'allenai/longformer-base-4096': 4_0_9_6,
    'allenai/longformer-large-4096': 4_0_9_6,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
}






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  VOCAB_FILES_NAMES
						_a        =  PRETRAINED_VOCAB_FILES_MAP
						_a        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a        =  ['input_ids', 'attention_mask']
						_a        =  LongformerTokenizer


						def __init__(							self			:     Optional[Any]  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     Any=None  ,  lowerCAmelCase			:     List[Any]=None  ,  lowerCAmelCase			:     Optional[Any]="replace"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     int="</s>"  ,  lowerCAmelCase			:     Any="</s>"  ,  lowerCAmelCase			:     Union[str, Any]="<s>"  ,  lowerCAmelCase			:     List[str]="<unk>"  ,  lowerCAmelCase			:     Optional[Any]="<pad>"  ,  lowerCAmelCase			:     List[str]="<mask>"  ,  lowerCAmelCase			:     Tuple=False  ,  lowerCAmelCase			:     List[str]=True  ,  **lowerCAmelCase			:     Optional[int]  ,  ):
										super().__init__(
										    lowerCAmelCase  ,  lowerCAmelCase  ,  tokenizer_file=lowerCAmelCase  ,  errors=lowerCAmelCase  ,  bos_token=lowerCAmelCase  ,  eos_token=lowerCAmelCase  ,  sep_token=lowerCAmelCase  ,  cls_token=lowerCAmelCase  ,  unk_token=lowerCAmelCase  ,  pad_token=lowerCAmelCase  ,  mask_token=lowerCAmelCase  ,  add_prefix_space=lowerCAmelCase  ,  trim_offsets=lowerCAmelCase  ,  **lowerCAmelCase  ,  )

										lowerCAmelCase		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
										if pre_tok_state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
														lowerCAmelCase		=			getattr(lowerCAmelCase  ,  pre_tok_state.pop("""type"""   )   )
														lowerCAmelCase		=			add_prefix_space
														lowerCAmelCase		=			pre_tok_class(**lowerCAmelCase   )

										lowerCAmelCase		=			add_prefix_space

										lowerCAmelCase		=			"""post_processor"""
										lowerCAmelCase		=			getattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )
										if tokenizer_component_instance:
														lowerCAmelCase		=			json.loads(tokenizer_component_instance.__getstate__()   )

														# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
														if "sep" in state:
																		lowerCAmelCase		=			tuple(state["""sep"""]   )
														if "cls" in state:
																		lowerCAmelCase		=			tuple(state["""cls"""]   )

														lowerCAmelCase		=			False

														if state.get("""add_prefix_space"""  ,  lowerCAmelCase   ) != add_prefix_space:
																		lowerCAmelCase		=			add_prefix_space
																		lowerCAmelCase		=			True

														if state.get("""trim_offsets"""  ,  lowerCAmelCase   ) != trim_offsets:
																		lowerCAmelCase		=			trim_offsets
																		lowerCAmelCase		=			True

														if changes_to_apply:
																		lowerCAmelCase		=			getattr(lowerCAmelCase  ,  state.pop("""type"""   )   )
																		lowerCAmelCase		=			component_class(**lowerCAmelCase   )
																		setattr(self.backend_tokenizer  ,  lowerCAmelCase  ,  lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										if self._mask_token is None:
														if self.verbose:
																		logger.error("""Using mask_token, but it is not set yet."""   )
														return None
										return str(self._mask_token   )


						@mask_token.setter
						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Dict   ):
										lowerCAmelCase		=			AddedToken(lowerCAmelCase  ,  lstrip=lowerCAmelCase  ,  rstrip=lowerCAmelCase   ) if isinstance(lowerCAmelCase  ,  lowerCAmelCase   ) else value
										lowerCAmelCase		=			value


						def 					__lowercase	(							self			:     Tuple  ,  *lowerCAmelCase			:     Optional[int]  ,  **lowerCAmelCase			:     Optional[Any]   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )
										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     str  ,  **lowerCAmelCase			:     str   ):
										lowerCAmelCase		=			kwargs.get("""is_split_into_words"""  ,  lowerCAmelCase   )

										assert self.add_prefix_space or not is_split_into_words, (
										    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     str  ,  lowerCAmelCase			:     Optional[str] = None   ):
										lowerCAmelCase		=			self._tokenizer.model.save(lowerCAmelCase  ,  name=lowerCAmelCase   )
										return tuple(lowerCAmelCase   )


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     Optional[int]=None   ):
										lowerCAmelCase		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
														return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




						def 					__lowercase	(							self			:     Optional[int]  ,  lowerCAmelCase			:     List[int]  ,  lowerCAmelCase			:     Optional[List[int]] = None   ):
										lowerCAmelCase		=			[self.sep_token_id]
										lowerCAmelCase		=			[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 + sep + token_ids_a + sep   ) * [0]





