
import random







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   tuple:
       lowercase     ,      lowercase     ,      lowercase							:					List[str]	     =				[], [], []
       for element in data:
              if element < pivot:
                     less.append(SCREAMING_SNAKE_CASE__    )
              elif element > pivot:
                     greater.append(SCREAMING_SNAKE_CASE__    )
              else:
                     equal.append(SCREAMING_SNAKE_CASE__    )
       return less, equal, greater







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Dict:
       # index = len(items) // 2 when trying to find the median
       #   (value of index when items is sorted)

       # invalid input
       if index >= len(SCREAMING_SNAKE_CASE__    ) or index < 0:
              return None

       lowercase							:					int	     =				items[random.randint(0 ,     len(SCREAMING_SNAKE_CASE__    ) - 1    )]
       lowercase							:					Optional[int]	     =				0
       lowercase     ,      lowercase     ,      lowercase							:					Union[str, Any]	     =				_partition(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[int]	     =				len(SCREAMING_SNAKE_CASE__    )
       lowercase							:					List[str]	     =				len(SCREAMING_SNAKE_CASE__    )

       # index is the pivot
       if m <= index < m + count:
              return pivot
       # must be in smaller
       elif m > index:
              return quick_select(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       # must be in larger
       else:
              return quick_select(SCREAMING_SNAKE_CASE__ ,     index - (m + count)    )


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Optional[int]=    None
    _a		:						Optional[Any]=    BloomTokenizerFast
    _a		:						Tuple=    BloomTokenizerFast
    _a		:						str=    True
    _a		:						Optional[int]=    False
    _a		:						List[Any]=    "tokenizer_file"
    _a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map				)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_rust_tokenizer()

           lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

           lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					Optional[Any]	     =				[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

                         lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
           lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

           lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
           lowercase							:					str	     =				list(sample_data.values()				)

           lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
           lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)
