


from ..utils import DummyObject, requires_backends

class 				a		(metaclass=lowercase_      ):




							"""simple docstring"""


							__UpperCAmelCase    :							Optional[Any]       =  ['''transformers''', '''torch''', '''note_seq''']

							def __init__(							self       :  str    ,	*lowerCamelCase       :  Any    ,	**lowerCamelCase       :  Tuple				)	->    Tuple:
														requires_backends(self    ,	["transformers", "torch", "note_seq"]				)

							@classmethod
							def     __snake_case					(							cls       :  Any    ,	*lowerCamelCase       :  Dict    ,	**lowerCamelCase       :  Optional[Any]				)	->    List[str]:
														requires_backends(cls    ,	["transformers", "torch", "note_seq"]				)






							@classmethod
							def     __snake_case					(							cls       :  Any    ,	*lowerCamelCase       :  Tuple    ,	**lowerCamelCase       :  Optional[Any]				)	->    Dict:
														requires_backends(cls    ,	["transformers", "torch", "note_seq"]				)




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  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			None
				__SCREAMING_SNAKE_CASE					: Any       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: int       			=			BloomTokenizerFast
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			True
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			'''tokenizer_file'''
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			{'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}




				def     a							(					self       ):
											super().setUp()
											snake_case_							=    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
											tokenizer.save_pretrained(self.tmpdirname       )




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




				def     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()

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

											snake_case_							=    tokenizer.batch_encode_plus(snake_case       )['input_ids']
											self.assertListEqual(snake_case      ,   snake_case       )

											snake_case_							=    tokenizer.batch_decode(snake_case       )
											self.assertListEqual(snake_case      ,   snake_case       )




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

																									snake_case_							=    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     a							(					self       ):
											snake_case_							=    self.get_rust_tokenizer()
											snake_case_							=    load_dataset('xnli'      ,   'all_languages'      ,   split='test'      ,   streaming=snake_case       )

											snake_case_							=    next(iter(snake_case       )       )['premise']  # pick up one data
											snake_case_							=    list(sample_data.values()       )

											snake_case_							=    list(map(tokenizer.encode      ,   snake_case       )       )
											snake_case_							=    [tokenizer.decode(snake_case      ,   clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
											self.assertListEqual(snake_case      ,   snake_case       )




				def     a							(					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       )




