


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    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			None
 SCREAMING_SNAKE_CASE__ =			BloomTokenizerFast
 SCREAMING_SNAKE_CASE__ =			BloomTokenizerFast
 SCREAMING_SNAKE_CASE__ =			True
 SCREAMING_SNAKE_CASE__ =			False
 SCREAMING_SNAKE_CASE__ =			'tokenizer_file'
 SCREAMING_SNAKE_CASE__ =			{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       super().setUp()
       a		:Optional[int]									=			BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''       )
       tokenizer.save_pretrained(self.tmpdirname       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    **_lowerCamelCase       ):
       kwargs.update(self.special_tokens_map       )
       return BloomTokenizerFast.from_pretrained(self.tmpdirname   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			self.get_rust_tokenizer()

       a		:str									=			['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
       a		:str									=			[[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

       a		:Optional[Any]									=			tokenizer.batch_encode_plus(_lowerCamelCase       )['''input_ids''']
       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )

       a		:List[Any]									=			tokenizer.batch_decode(_lowerCamelCase       )
       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase=6       ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   a		:List[str]									=			self.rust_tokenizer_class.from_pretrained(_lowerCamelCase   ,    **_lowerCamelCase       )
                   # tokenizer_r.pad_token = None # Hotfixing padding = None
                   # Simple input
                   a		:List[Any]									=			'''This is a simple input'''
                   a		:Union[str, Any]									=			['''This is a simple input 1''', '''This is a simple input 2''']
                   a		:str									=			('''This is a simple input''', '''This is a pair''')
                   a		:str									=			[
                       ('''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(_lowerCamelCase   ,    max_length=_lowerCamelCase       )
                         tokenizer_r.encode_plus(_lowerCamelCase   ,    max_length=_lowerCamelCase       )

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

                   a		:Optional[int]									=			None  # Hotfixing padding = None
                   self.assertRaises(_lowerCamelCase   ,    tokenizer_r.encode   ,    _lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding='''max_length'''       )

                   # Simple input
                   self.assertRaises(_lowerCamelCase   ,    tokenizer_r.encode_plus   ,    _lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding='''max_length'''       )

                   # Simple input
                   self.assertRaises(
                       _lowerCamelCase   ,    tokenizer_r.batch_encode_plus   ,    _lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding='''max_length'''   ,    )

                   # Pair input
                   self.assertRaises(_lowerCamelCase   ,    tokenizer_r.encode   ,    _lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding='''max_length'''       )

                   # Pair input
                   self.assertRaises(_lowerCamelCase   ,    tokenizer_r.encode_plus   ,    _lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding='''max_length'''       )

                   # Pair input
                   self.assertRaises(
                       _lowerCamelCase   ,    tokenizer_r.batch_encode_plus   ,    _lowerCamelCase   ,    max_length=_lowerCamelCase   ,    padding='''max_length'''   ,    )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Any									=			self.get_rust_tokenizer()
       a		:List[str]									=			load_dataset('''xnli'''   ,    '''all_languages'''   ,    split='''test'''   ,    streaming=_lowerCamelCase       )

       a		:Dict									=			next(iter(_lowerCamelCase       )       )['''premise''']  # pick up one data
       a		:List[Any]									=			list(sample_data.values()       )

       a		:Union[str, Any]									=			list(map(tokenizer.encode   ,    _lowerCamelCase       )       )
       a		:Optional[int]									=			[tokenizer.decode(_lowerCamelCase   ,    clean_up_tokenization_spaces=_lowerCamelCase       ) for x in output_tokens]
       self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
       # any sequence length constraints. This test of the parent class will fail since it relies on the
       # maximum sequence length of the positoonal embeddings.
       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       )









def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b == 0:
            return 1
      if (b % 2) == 0:
            return actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
      else:
            return a * actual_power(UpperCAmelCase_					,    int(b / 2		)		) * actual_power(UpperCAmelCase_					,    int(b / 2		)		)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      if b < 0:
            return 1 / actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)
      return actual_power(UpperCAmelCase_					,    UpperCAmelCase_		)


if __name__ == "__main__":
     print(power(-2, -3))





