
'''simple docstring'''




import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase			:				Union[str, Any]    		=			get_tests_dir('fixtures/test_sentencepiece_no_bos.model')





@require_sentencepiece
@require_tokenizers
class  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		PegasusTokenizer
     __magic_name__						=		PegasusTokenizerFast
     __magic_name__						=		True
     __magic_name__						=		True





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         super().setUp()

         # We have a SentencePiece fixture for testing
         A       :   List[Any]					=						PegasusTokenizer(SCREAMING_SNAKE_CASE	)
         tokenizer.save_pretrained(self.tmpdirname	)





     @cached_property
     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         return PegasusTokenizer.from_pretrained('''google/pegasus-large'''	)





     def 				__lowerCAmelCase							(							self						,			**SCREAMING_SNAKE_CASE	) ->				PegasusTokenizer:






         """simple docstring"""






         return PegasusTokenizer.from_pretrained(self.tmpdirname						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         return ("This is a test", "This is a test")





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         A       :   Any					=						'''</s>'''
         A       :   Dict					=						1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Union[str, Any]					=						list(self.get_tokenizer().get_vocab().keys()	)

         self.assertEqual(vocab_keys[0]						,			'''<pad>'''	)
         self.assertEqual(vocab_keys[1]						,			'''</s>'''	)
         self.assertEqual(vocab_keys[-1]						,			'''v'''	)
         self.assertEqual(len(SCREAMING_SNAKE_CASE	)						,			1103	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         self.assertEqual(self.get_tokenizer().vocab_size						,			1103	)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   Optional[int]					=						self.rust_tokenizer_class.from_pretrained(self.tmpdirname	)
         A       :   List[Any]					=						self.tokenizer_class.from_pretrained(self.tmpdirname	)
         A       :   List[Any]					=						(
             '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
             ''' </s> <pad> <pad> <pad>'''
         )
         A       :   Optional[int]					=						rust_tokenizer([raw_input_str]						,			return_tensors=SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	).input_ids[0]
         A       :   List[str]					=						py_tokenizer([raw_input_str]						,			return_tensors=SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	).input_ids[0]
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         A       :   Tuple					=						self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         A       :   Any					=						'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
         A       :   str					=						[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
         A       :   Dict					=						tokenizer([raw_input_str]						,			return_tensors=SCREAMING_SNAKE_CASE	).input_ids[0]
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Dict:






         """simple docstring"""






         A       :   str					=						self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 96103
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 103
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1024
         A       :   Optional[Any]					=						'''To ensure a smooth flow of bank resolutions.'''
         A       :   List[Any]					=						[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
         A       :   Tuple					=						tokenizer([raw_input_str]						,			return_tensors=SCREAMING_SNAKE_CASE	).input_ids[0]
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]	) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





     @require_torch
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         A       :   List[str]					=						['''This is going to be way too long.''' * 150, '''short example''']
         A       :   Any					=						['''not super long but more than 5 tokens''', '''tiny''']
         A       :   Optional[int]					=						self._large_tokenizer(SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	)
         A       :   Tuple					=						self._large_tokenizer(
             text_target=SCREAMING_SNAKE_CASE						,			max_length=5						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	)

         assert batch.input_ids.shape == (2, 1024)
         assert batch.attention_mask.shape == (2, 1024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(SCREAMING_SNAKE_CASE	) == 2  # input_ids, attention_mask.





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   str					=						{'''input_ids''': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=SCREAMING_SNAKE_CASE						,			model_name='''google/bigbird-pegasus-large-arxiv'''						,			revision='''ba85d0851d708441f91440d509690f1ab6353415'''						,			)





@require_sentencepiece
@require_tokenizers
class  A				(							__snake_case    ,			unittest.TestCase						):
     __magic_name__						=		PegasusTokenizer
     __magic_name__						=		PegasusTokenizerFast
     __magic_name__						=		True
     __magic_name__						=		True





     def 				__lowerCAmelCase							(							self	) ->				Optional[int]:






         """simple docstring"""






         super().setUp()

         # We have a SentencePiece fixture for testing
         A       :   List[Any]					=						PegasusTokenizer(SCREAMING_SNAKE_CASE						,			offset=0						,			mask_token_sent=SCREAMING_SNAKE_CASE						,			mask_token='''[MASK]'''	)
         tokenizer.save_pretrained(self.tmpdirname	)





     @cached_property
     def 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''	)





     def 				__lowerCAmelCase							(							self						,			**SCREAMING_SNAKE_CASE	) ->				PegasusTokenizer:






         """simple docstring"""






         return PegasusTokenizer.from_pretrained(self.tmpdirname						,			**SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				Union[str, Any]:






         """simple docstring"""






         return ("This is a test", "This is a test")





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Union[str, Any]					=						self.rust_tokenizer_class.from_pretrained(self.tmpdirname	)
         A       :   Optional[Any]					=						self.tokenizer_class.from_pretrained(self.tmpdirname	)
         A       :   int					=						(
             '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
             ''' <pad> <pad> <pad>'''
         )
         A       :   Any					=						rust_tokenizer([raw_input_str]						,			return_tensors=SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	).input_ids[0]
         A       :   Optional[Any]					=						py_tokenizer([raw_input_str]						,			return_tensors=SCREAMING_SNAKE_CASE						,			add_special_tokens=SCREAMING_SNAKE_CASE	).input_ids[0]
         self.assertListEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @require_torch
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   int					=						['''This is going to be way too long.''' * 1000, '''short example''']
         A       :   Dict					=						['''not super long but more than 5 tokens''', '''tiny''']
         A       :   List[str]					=						self._large_tokenizer(SCREAMING_SNAKE_CASE						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	)
         A       :   List[Any]					=						self._large_tokenizer(
             text_target=SCREAMING_SNAKE_CASE						,			max_length=5						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE						,			return_tensors='''pt'''	)

         assert batch.input_ids.shape == (2, 4096)
         assert batch.attention_mask.shape == (2, 4096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(SCREAMING_SNAKE_CASE	) == 2  # input_ids, attention_mask.





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Tuple					=						(
             '''This is an example string that is used to test the original TF implementation against the HF'''
             ''' implementation'''
         )

         A       :   List[Any]					=						self._large_tokenizer(SCREAMING_SNAKE_CASE	).input_ids

         self.assertListEqual(
             SCREAMING_SNAKE_CASE						,			[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]						,			)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset







@pytest.mark.parametrize('''dataset_size'''		,				[None, 400 * 2**20, 600 * 2**20]						)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,				['''default''', 0, 100 * 2**20, 900 * 2**20]						)
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    if input_in_memory_max_size != "default":
        monkeypatch.setattr(datasets.config		,				'''IN_MEMORY_MAX_SIZE'''		,				snake_case__						)
    A       :   Dict					=						datasets.config.IN_MEMORY_MAX_SIZE
    if input_in_memory_max_size == "default":
        assert in_memory_max_size == 0
    else:
        assert in_memory_max_size == input_in_memory_max_size
    if dataset_size and in_memory_max_size:
        A       :   Dict					=						dataset_size < in_memory_max_size
    else:
        A       :   Tuple					=						False
    A       :   int					=						is_small_dataset(snake_case__						)
    assert result == expected




