






from __future__ import annotations

from typing import Any


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      Dict			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      float = 0					):


         """simple docstring"""



         __A     ,						__A       =						row, column
         __A       =						[[default_value for c in range(UpperCamelCase_					)] for r in range(UpperCamelCase_					)]




     def __str__(					self							:      Any					):


         """simple docstring"""



         __A       =						F"Matrix consist of {self.row} rows and {self.column} columns\n"

         # Make string identifier
         __A       =						0
         for row_vector in self.array:
             for obj in row_vector:
                 __A       =						max(UpperCamelCase_			,						len(str(UpperCamelCase_					)					)					)
         __A       =						F"%{max_element_length}s"

         # Make string and return
         def single_line(UpperCamelCase_							:      list[float]					) -> str:
             nonlocal string_format_identifier
             __A       =						"""["""
             line += ", ".join(string_format_identifier % (obj,) for obj in row_vector					)
             line += "]"
             return line

         s += "\n".join(single_line(UpperCamelCase_					) for row_vector in self.array					)
         return s




     def __repr__(					self							:      Optional[Any]					):


         """simple docstring"""



         return str(self					)




     def 		lowerCAmelCase_  (					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         if not (isinstance(UpperCamelCase_			,						(list, tuple)					) and len(UpperCamelCase_					) == 2):
             return False
         elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
             return False
         else:
             return True




     def __getitem__(					self							:      Optional[int]			,						UpperCamelCase_							:      tuple[int, int]					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         return self.array[loc[0]][loc[1]]




     def __setitem__(					self							:      str			,						UpperCamelCase_							:      tuple[int, int]			,						UpperCamelCase_							:      float					):


         """simple docstring"""



         assert self.validate_indicies(UpperCamelCase_					)
         __A       =						value




     def __add__(					self							:      Optional[int]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == another.row and self.column == another.column

         # Add
         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c] + another[r, c]
         return result




     def __neg__(					self							:      Any					):


         """simple docstring"""



         __A       =						Matrix(self.row			,						self.column					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						-self[r, c]
         return result




     def __sub__(					self							:      List[Any]			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         return self + (-another)




     def __mul__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      int | float | Matrix					):


         """simple docstring"""



         if isinstance(UpperCamelCase_			,						(int, float)					):  # Scalar multiplication
             __A       =						Matrix(self.row			,						self.column					)
             for r in range(self.row					):
                 for c in range(self.column					):
                     __A       =						self[r, c] * another
             return result
         elif isinstance(UpperCamelCase_			,						UpperCamelCase_					):  # Matrix multiplication
             assert self.column == another.row
             __A       =						Matrix(self.row			,						another.column					)
             for r in range(self.row					):
                 for c in range(another.column					):
                     for i in range(self.column					):
                         result[r, c] += self[r, i] * another[i, c]
             return result
         else:
             __A       =						F"Unsupported type given for another ({type(UpperCamelCase_					)})"
             raise TypeError(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						Matrix(self.column			,						self.row					)
         for r in range(self.row					):
             for c in range(self.column					):
                 __A       =						self[r, c]
         return result





     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Matrix			,						UpperCamelCase_							:      Matrix					):


         """simple docstring"""



         assert isinstance(UpperCamelCase_			,						UpperCamelCase_					) and isinstance(UpperCamelCase_			,						UpperCamelCase_					)
         assert self.row == self.column == u.row == v.row  # u, v should be column vector
         assert u.column == v.column == 1  # u, v should be column vector

         # Calculate
         __A       =						v.transpose()
         __A       =						(v_t * self * u)[0, 0] + 1
         if numerator_factor == 0:
             return None  # It's not invertable
         return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      __A       =						Matrix(3      ,					3      ,					0     )
      for i in range(3     ):
          __A       =						1
      print(f"a^(-1) is {ainv}"     )
      # u, v
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						1, 2, -3
      __A       =						Matrix(3      ,					1      ,					0     )
      __A     ,						__A     ,						__A       =						4, -2, 5
      print(f"u is {u}"     )
      print(f"v is {v}"     )
      print(f"uv^T is {u * v.transpose()}"     )
      # Sherman Morrison
      print(f"(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowercase      ,					__lowercase     )}"     )



  def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





      """simple docstring"""

      import doctest

      doctest.testmod()






  testa()












import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer


class    __lowercase   (						lowercase_	):







     '''simple docstring'''




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						tempfile.mkdtemp()
         __A       =						5

         # Realm tok
         __A       =						[
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """[PAD]""",
             """[MASK]""",
             """test""",
             """question""",
             """this""",
             """is""",
             """the""",
             """first""",
             """second""",
             """third""",
             """fourth""",
             """fifth""",
             """record""",
             """want""",
             """##want""",
             """##ed""",
             """wa""",
             """un""",
             """runn""",
             """##ing""",
             """,""",
             """low""",
             """lowest""",
         ]
         __A       =						os.path.join(self.tmpdirname			,						"""realm_tokenizer"""					)
         os.makedirs(UpperCamelCase_			,						exist_ok=UpperCamelCase_					)
         __A       =						os.path.join(UpperCamelCase_			,						VOCAB_FILES_NAMES["""vocab_file"""]					)
         with open(self.vocab_file			,						"""w"""			,						encoding="""utf-8"""					) as vocab_writer:
             vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

         __A       =						os.path.join(self.tmpdirname			,						"""realm_block_records"""					)
         os.makedirs(UpperCamelCase_			,						exist_ok=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname			,						"""realm_tokenizer"""					)					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         shutil.rmtree(self.tmpdirname					)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						RealmConfig(num_block_records=self.num_block_records					)
         return config




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						Dataset.from_dict(
             {
                 """id""": ["""0""", """1"""],
                 """question""": ["""foo""", """bar"""],
                 """answers""": [["""Foo""", """Bar"""], ["""Bar"""]],
             }					)
         return dataset




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						np.array(
             [
                 B"""This is the first record""",
                 B"""This is the second record""",
                 B"""This is the third record""",
                 B"""This is the fourth record""",
                 B"""This is the fifth record""",
                 B"""This is a longer longer longer record""",
             ]			,						dtype=UpperCamelCase_			,						)
         return block_records




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						RealmRetriever(
             block_records=self.get_dummy_block_records()			,						tokenizer=self.get_tokenizer()			,						)
         return retriever




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         __A       =						self.get_config()
         __A       =						self.get_dummy_retriever()
         __A       =						retriever.tokenizer

         __A       =						np.array([0, 3]			,						dtype="""long"""					)
         __A       =						tokenizer(["""Test question"""]					).input_ids
         __A       =						tokenizer(
             ["""the fourth"""]			,						add_special_tokens=UpperCamelCase_			,						return_token_type_ids=UpperCamelCase_			,						return_attention_mask=UpperCamelCase_			,						).input_ids
         __A       =						config.reader_seq_len

         __A     ,						__A     ,						__A     ,						__A       =						retriever(
             UpperCamelCase_			,						UpperCamelCase_			,						answer_ids=UpperCamelCase_			,						max_length=UpperCamelCase_			,						return_tensors="""np"""					)

         self.assertEqual(len(UpperCamelCase_					)			,						2					)
         self.assertEqual(len(UpperCamelCase_					)			,						2					)
         self.assertEqual(len(UpperCamelCase_					)			,						2					)
         self.assertEqual(concat_inputs.input_ids.shape			,						(2, 10)					)
         self.assertEqual(concat_inputs.attention_mask.shape			,						(2, 10)					)
         self.assertEqual(concat_inputs.token_type_ids.shape			,						(2, 10)					)
         self.assertEqual(concat_inputs.special_tokens_mask.shape			,						(2, 10)					)
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0]					)			,						["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """first""", """record""", """[SEP]"""]			,						)
         self.assertEqual(
             tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1]					)			,						["""[CLS]""", """test""", """question""", """[SEP]""", """this""", """is""", """the""", """fourth""", """record""", """[SEP]"""]			,						)




     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.get_config()
         __A       =						self.get_dummy_retriever()
         __A       =						retriever.tokenizer

         __A       =						np.array([0, 3, 5]			,						dtype="""long"""					)
         __A       =						tokenizer(["""Test question"""]					).input_ids
         __A       =						tokenizer(
             ["""the fourth""", """longer longer"""]			,						add_special_tokens=UpperCamelCase_			,						return_token_type_ids=UpperCamelCase_			,						return_attention_mask=UpperCamelCase_			,						).input_ids
         __A       =						config.reader_seq_len

         __A     ,						__A     ,						__A     ,						__A       =						retriever(
             UpperCamelCase_			,						UpperCamelCase_			,						answer_ids=UpperCamelCase_			,						max_length=UpperCamelCase_			,						return_tensors="""np"""					)

         self.assertEqual([False, True, True]			,						UpperCamelCase_					)
         self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]]			,						UpperCamelCase_					)
         self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]]			,						UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						self.get_dummy_retriever()
         retriever.save_pretrained(os.path.join(self.tmpdirname			,						"""realm_block_records"""					)					)

         # Test local path
         __A       =						retriever.from_pretrained(os.path.join(self.tmpdirname			,						"""realm_block_records"""					)					)
         self.assertEqual(retriever.block_records[0]			,						B"""This is the first record"""					)

         # Test mocked remote path
         with patch("""transformers.models.realm.retrieval_realm.hf_hub_download"""					) as mock_hf_hub_download:
             __A       =						os.path.join(
                 os.path.join(self.tmpdirname			,						"""realm_block_records"""					)			,						_REALM_BLOCK_RECORDS_FILENAME					)
             __A       =						RealmRetriever.from_pretrained("""google/realm-cc-news-pretrained-openqa"""					)

         self.assertEqual(retriever.block_records[0]			,						B"""This is the first record"""					)




