




'''simple docstring'''


from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     A					(	_a					):







							def __init__(						self :						Any							,    lowerCAmelCase_ :						Callable							,    lowerCAmelCase_ :						Optional[Features] = None							,    lowerCAmelCase_ :						str = None							,    lowerCAmelCase_ :						bool = False							,    lowerCAmelCase_ :						bool = False							,    lowerCAmelCase_ :						Optional[dict] = None							,    lowerCAmelCase_ :						Optional[int] = None							,    **lowerCAmelCase_ :						Union[str, Any]							,    )    ->							List[Any]:


														"""simple docstring"""


														super().__init__(
														    features=lowerCAmelCase_							,    cache_dir=lowerCAmelCase_							,    keep_in_memory=lowerCAmelCase_							,    streaming=lowerCAmelCase_							,    num_proc=lowerCAmelCase_							,    **lowerCAmelCase_							,    )
														_a			  =       Generator(
														    cache_dir=lowerCAmelCase_							,    features=lowerCAmelCase_							,    generator=lowerCAmelCase_							,    gen_kwargs=lowerCAmelCase_							,    **lowerCAmelCase_							,    )







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


														"""simple docstring"""


														if self.streaming:
																					_a			  =       self.builder.as_streaming_dataset(split='''train'''      )
														# Build regular (map-style) dataset
														else:
																					_a			  =       None
																					_a			  =       None
																					_a			  =       None
																					_a			  =       None

																					self.builder.download_and_prepare(
																					    download_config=lowerCAmelCase_							,    download_mode=lowerCAmelCase_							,    verification_mode=lowerCAmelCase_							,    base_path=lowerCAmelCase_							,    num_proc=self.num_proc							,    )
																					_a			  =       self.builder.as_dataset(
																					    split='''train'''							,    verification_mode=lowerCAmelCase_							,    in_memory=self.keep_in_memory      )
														return dataset







'''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


_snake_case   :   Tuple     							=	get_tests_dir('fixtures/test_sentencepiece_no_bos.model')

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







							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       PegasusTokenizer(lowerCAmelCase_      )
														tokenizer.save_pretrained(self.tmpdirname      )







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


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Optional[int]							,    **lowerCAmelCase_ :						List[str]      )    ->							PegasusTokenizer:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Optional[Any]							,    lowerCAmelCase_ :						List[str]      )    ->							Any:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a			  =       '''</s>'''
														_a			  =       1

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







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


														"""simple docstring"""


														_a			  =       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(lowerCAmelCase_      )							,    11_03      )







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


														"""simple docstring"""


														self.assertEqual(self.get_tokenizer().vocab_size							,    11_03      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							str:


														"""simple docstring"""


														_a			  =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       self.tokenizer_class.from_pretrained(self.tmpdirname      )
														_a			  =       (
														    '''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			  =       rust_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														_a			  =       py_tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_							,    add_special_tokens=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







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


														"""simple docstring"""


														_a			  =       self._large_tokenizer
														# <mask_1> masks whole sentence while <mask_2> masks single word
														_a			  =       '''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
														_a			  =       [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
														_a			  =       tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Dict:


														"""simple docstring"""


														_a			  =       self._large_tokenizer
														# The tracebacks for the following asserts are **better** without messages or self.assertEqual
														assert tokenizer.vocab_size == 9_61_03
														assert tokenizer.pad_token_id == 0
														assert tokenizer.eos_token_id == 1
														assert tokenizer.offset == 1_03
														assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
														assert tokenizer.unk_token == "<unk>"
														assert tokenizer.model_max_length == 10_24
														_a			  =       '''To ensure a smooth flow of bank resolutions.'''
														_a			  =       [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
														_a			  =       tokenizer([raw_input_str]							,    return_tensors=lowerCAmelCase_      ).input_ids[0]
														self.assertListEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
														assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]      ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







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


														"""simple docstring"""


														_a			  =       ['''This is going to be way too long.''' * 1_50, '''short example''']
														_a			  =       ['''not super long but more than 5 tokens''', '''tiny''']
														_a			  =       self._large_tokenizer(lowerCAmelCase_							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       self._large_tokenizer(
														    text_target=lowerCAmelCase_							,    max_length=5							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )

														assert batch.input_ids.shape == (2, 10_24)
														assert batch.attention_mask.shape == (2, 10_24)
														assert targets["input_ids"].shape == (2, 5)
														assert len(lowerCAmelCase_      ) == 2  # input_ids, attention_mask.







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


														"""simple docstring"""


														_a			  =       {'''input_ids''': [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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=lowerCAmelCase_							,    model_name='''google/bigbird-pegasus-large-arxiv'''							,    revision='''ba85d0851d708441f91440d509690f1ab6353415'''							,    )



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







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


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       PegasusTokenizer(lowerCAmelCase_							,    offset=0							,    mask_token_sent=lowerCAmelCase_							,    mask_token='''[MASK]'''      )
														tokenizer.save_pretrained(self.tmpdirname      )







							@cached_property
							def      __lowerCAmelCase    (						self :						Dict      )    ->							int:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Optional[int]							,    **lowerCAmelCase_ :						Tuple      )    ->							PegasusTokenizer:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Optional[int]      )    ->							Optional[Any]:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a			  =       ['''This is going to be way too long.''' * 10_00, '''short example''']
														_a			  =       ['''not super long but more than 5 tokens''', '''tiny''']
														_a			  =       self._large_tokenizer(lowerCAmelCase_							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )
														_a			  =       self._large_tokenizer(
														    text_target=lowerCAmelCase_							,    max_length=5							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    return_tensors='''pt'''      )

														assert batch.input_ids.shape == (2, 40_96)
														assert batch.attention_mask.shape == (2, 40_96)
														assert targets["input_ids"].shape == (2, 5)
														assert len(lowerCAmelCase_      ) == 2  # input_ids, attention_mask.







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


														"""simple docstring"""


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

														_a			  =       self._large_tokenizer(lowerCAmelCase_      ).input_ids

														self.assertListEqual(
														    lowerCAmelCase_							,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]							,    )

