



"""simple docstring"""






import os
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ... import AutoTokenizer
from ...utils import logging


__UpperCAmelCase      :      List[str]						=					'block_records.npy'


__UpperCAmelCase      :      Dict						=					logging.get_logger(__name__)
def      A      (			_A,     _A							):




					"""simple docstring"""
					import tensorflow.compat.va as tf

					snake_case_						:Union[str, Any]								=						tf.data.TFRecordDataset(_A,     buffer_size=512 * 1_024 * 1_024							)
					snake_case_						:List[str]								=						blocks_dataset.batch(_A,     drop_remainder=_A							)
					snake_case_						:str								=						next(blocks_dataset.take(1							).as_numpy_iterator()							)

					return np_record

class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a    ,       a=2    ,       a=10_00    ,       a=1_00    ,       a=10_00_00    ,       ):




										"""simple docstring"""





										from scann.scann_ops.py.scann_ops_pybind import builder as Builder

										snake_case_						:Any								=						Builder(db=a    ,       num_neighbors=a    ,       distance_measure="dot_product"   )
										snake_case_						:List[Any]								=						builder.tree(
										    num_leaves=a    ,       num_leaves_to_search=a    ,       training_sample_size=a   )
										snake_case_						:Union[str, Any]								=						builder.score_ah(dimensions_per_block=a   )

										snake_case_						:Optional[int]								=						builder.build()







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:int								=						self.searcher.search_batched(question_projection.detach().cpu()   )
										return retrieved_block_ids.astype("int64"   )



class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a   ):




										"""simple docstring"""





										super().__init__()
										snake_case_						:str								=						block_records
										snake_case_						:int								=						tokenizer







					def __call__(      self    ,       a    ,       a    ,       a    ,       a=None    ,       a="pt"   ):




										"""simple docstring"""





										snake_case_						:Optional[Any]								=						np.take(self.block_records    ,       indices=a    ,       axis=0   )

										snake_case_						:Union[str, Any]								=						self.tokenizer.decode(question_input_ids[0]    ,       skip_special_tokens=a   )

										snake_case_						:Tuple								=						[]
										snake_case_						:Optional[Any]								=						[]
										for retrieved_block in retrieved_blocks:
															text.append(a   )
															text_pair.append(retrieved_block.decode()   )

										snake_case_						:Union[str, Any]								=						self.tokenizer(
										    a    ,       a    ,       padding=a    ,       truncation=a    ,       return_special_tokens_mask=a    ,       max_length=a   )
										snake_case_						:Tuple								=						concat_inputs.convert_to_tensors(a   )

										if answer_ids is not None:
															return self.block_has_answer(a    ,       a   ) + (concat_inputs_tensors,)
										else:
															return (None, None, None, concat_inputs_tensors)







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




										"""simple docstring"""





										if os.path.isdir(a   ):
															snake_case_						:str								=						os.path.join(a    ,       _REALM_BLOCK_RECORDS_FILENAME   )
										else:
															snake_case_						:List[str]								=						hf_hub_download(
															    repo_id=a    ,       filename=_REALM_BLOCK_RECORDS_FILENAME    ,       **a   )
										snake_case_						:str								=						np.load(a    ,       allow_pickle=a   )

										snake_case_						:Any								=						AutoTokenizer.from_pretrained(a    ,       *a    ,       **a   )

										return cls(a    ,       a   )







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										np.save(os.path.join(a    ,       _REALM_BLOCK_RECORDS_FILENAME   )    ,       self.block_records   )
										# save tokenizer
										self.tokenizer.save_pretrained(a   )







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Any								=						[]
										snake_case_						:Optional[int]								=						[]
										snake_case_						:Optional[Any]								=						[]
										snake_case_						:Tuple								=						0

										for input_id in concat_inputs.input_ids:
															snake_case_						:Dict								=						input_id.tolist()
															# Check answers between two [SEP] tokens
															snake_case_						:Optional[int]								=						input_id_list.index(self.tokenizer.sep_token_id   )
															snake_case_						:Any								=						first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id   )

															start_pos.append([]   )
															end_pos.append([]   )
															for answer in answer_ids:
																				for idx in range(first_sep_idx + 1    ,       a   ):
																									if answer[0] == input_id_list[idx]:
																														if input_id_list[idx : idx + len(a   )] == answer:
																																			start_pos[-1].append(a   )
																																			end_pos[-1].append(idx + len(a   ) - 1   )

															if len(start_pos[-1]   ) == 0:
																				has_answers.append(a   )
															else:
																				has_answers.append(a   )
																				if len(start_pos[-1]   ) > max_answers:
																									snake_case_						:List[str]								=						len(start_pos[-1]   )

        # Pad -1 to max_answers
										for start_pos_, end_pos_ in zip(a    ,       a   ):
															if len(start_pos_   ) < max_answers:
																				snake_case_						:Dict								=						[-1] * (max_answers - len(start_pos_   ))
																				start_pos_ += padded
																				end_pos_ += padded
										return has_answers, start_pos, end_pos






"""simple docstring"""






from typing import Any

import numpy as np

class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a   ):




										"""simple docstring"""





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

										snake_case_						:List[Any]								=						tableau
										snake_case_	,  snake_case_						:Dict								=						tableau.shape

										# Number of decision variables x1, x2, x3...
										snake_case_						:Union[str, Any]								=						n_vars

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

										# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
										snake_case_						:List[str]								=						(self.n_art_vars > 0) + 1

										# Number of slack variables added to make inequalities into equalities
										snake_case_						:Optional[int]								=						self.n_rows - self.n_stages

										# Objectives for each stage
										snake_case_						:Union[str, Any]								=						["max"]

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

										snake_case_						:Dict								=						[""]

										# Index of current pivot row and column
										snake_case_						:List[str]								=						None
										snake_case_						:List[Any]								=						None

										# Does objective row only contain (non)-negative values?
										snake_case_						:Union[str, Any]								=						False







					@staticmethod
					def 		_a (      *a   ):




										"""simple docstring"""





										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
										snake_case_						:Tuple								=						["x", "s", "a"]
										snake_case_						:Dict								=						[]
										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   ):




										"""simple docstring"""





										snake_case_						:str								=						self.objectives[-1]

										# Find entries of highest magnitude in objective rows
										snake_case_						:Dict								=						(objective == "min") - (objective == "max")
										snake_case_						:Optional[Any]								=						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:
															snake_case_						:Dict								=						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
										snake_case_						:str								=						slice(self.n_stages    ,       self.n_rows   )

										# RHS
										snake_case_						:Optional[Any]								=						tableau[s, -1]

										# Elements of pivot column within slice
										snake_case_						:Union[str, Any]								=						tableau[s, col_idx]

										# Array filled with nans
										snake_case_						:Optional[Any]								=						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
										snake_case_						:Dict								=						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
										snake_case_						:List[Any]								=						np.nanargmin(a   ) + self.n_stages
										return row_idx, col_idx







					def 		_a (      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						tableau[row_idx].copy()

										snake_case_						:Any								=						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
										snake_case_						:str								=						piv_row
										return tableau







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										self.objectives.pop()

										if not self.objectives:
															return tableau

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

										# Delete the artificial variable columns
										snake_case_						:Optional[int]								=						np.delete(a    ,       a    ,       axis=1   )

										# Delete the objective row of the first stage
										snake_case_						:List[str]								=						np.delete(a    ,       0    ,       axis=0   )

										snake_case_						:Optional[Any]								=						1
										self.n_rows -= 1
										snake_case_						:List[Any]								=						0
										snake_case_						:Dict								=						False
										return tableau







					def 		_a (      self   ):




										"""simple docstring"""





										for _ in range(1_00   ):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																				snake_case_						:str								=						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   )

															snake_case_	,  snake_case_						:Union[str, Any]								=						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
																				snake_case_						:str								=						self.change_stage(self.tableau   )
															else:
																				snake_case_						:str								=						self.pivot(self.tableau    ,       a    ,       a   )
										return {}







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Any								=						{"P": abs(tableau[0, -1]   )}

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

															# First entry in the nonzero ids
															snake_case_						:Any								=						nonzero[0][0]
															snake_case_						:int								=						tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																				snake_case_						:Tuple								=						tableau[nonzero_rowidx, -1]
																				snake_case_						:Tuple								=						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()

