






from random import randint
from tempfile import TemporaryFile

import numpy as np


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   str						    =     0
  if start < end:
    SCREAMING_SNAKE_CASE							:   int						    =     randint(lowercase    ,  lowercase					)
    SCREAMING_SNAKE_CASE							:   int						    =     a[end]
    SCREAMING_SNAKE_CASE							:   str						    =     a[pivot]
    SCREAMING_SNAKE_CASE							:   Dict						    =     temp

    SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   str						    =     _in_place_partition(lowercase    ,  lowercase    ,  lowercase					)
    count += _in_place_quick_sort(lowercase    ,  lowercase    ,  p - 1					)
    count += _in_place_quick_sort(lowercase    ,  p + 1    ,  lowercase					)
  return count


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   int						    =     0
  SCREAMING_SNAKE_CASE							:   Any						    =     randint(lowercase    ,  lowercase					)
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     a[end]
  SCREAMING_SNAKE_CASE							:   Any						    =     a[pivot]
  SCREAMING_SNAKE_CASE							:   List[str]						    =     temp
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     start - 1
  for index in range(lowercase    ,  lowercase					):
    count += 1
    if a[index] < a[end]:  # check if current val is less than pivot value
      SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     new_pivot_index + 1
      SCREAMING_SNAKE_CASE							:   Dict						    =     a[new_pivot_index]
      SCREAMING_SNAKE_CASE							:   List[Any]						    =     a[index]
      SCREAMING_SNAKE_CASE							:   Optional[int]						    =     temp

  SCREAMING_SNAKE_CASE							:   Any						    =     a[new_pivot_index + 1]
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     a[end]
  SCREAMING_SNAKE_CASE							:   Tuple						    =     temp
  return new_pivot_index + 1, count


snake_case     					=		TemporaryFile()
snake_case     					=		100  # 1000 elements are to be sorted


snake_case					, snake_case     					=		0, 1  # mean and standard deviation
snake_case     					=		np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
snake_case     					=		np.load(outfile)
snake_case     					=		len(M) - 1
snake_case     					=		_in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)

print(z)














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"""					)




