





"""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


A	     =					get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')




@require_sentencepiece
@require_tokenizers
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					PegasusTokenizer
					__lowerCAmelCase					=					PegasusTokenizerFast
					__lowerCAmelCase					=					True
					__lowerCAmelCase					=					True







					def 	_lowerCamelCase						(  self       ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									__a	:  Tuple         =					PegasusTokenizer(_UpperCAmelCase       )
									tokenizer.save_pretrained(self.tmpdirname       )







					@cached_property
					def 	_lowerCamelCase						(  self       ):
									return PegasusTokenizer.from_pretrained('''google/pegasus-large'''       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									return PegasusTokenizer.from_pretrained(self.tmpdirname		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return ("This is a test", "This is a test")







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					'''</s>'''
									__a	:  List[str]         =					1

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







					def 	_lowerCamelCase						(  self       ):
									__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(_UpperCAmelCase       )		,     1103       )







					def 	_lowerCamelCase						(  self       ):
									self.assertEqual(self.get_tokenizer().vocab_size		,     1103       )







					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[Any]         =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
									__a	:  int         =					self.tokenizer_class.from_pretrained(self.tmpdirname       )
									__a	:  Optional[int]         =					(
									    '''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	:  List[str]         =					rust_tokenizer([raw_input_str]		,     return_tensors=_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase       ).input_ids[0]
									__a	:  Tuple         =					py_tokenizer([raw_input_str]		,     return_tensors=_UpperCAmelCase		,     add_special_tokens=_UpperCAmelCase       ).input_ids[0]
									self.assertListEqual(_UpperCAmelCase		,     _UpperCAmelCase       )







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







					def 	_lowerCamelCase						(  self       ):
									__a	:  Any         =					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	:  Union[str, Any]         =					'''To ensure a smooth flow of bank resolutions.'''
									__a	:  List[str]         =					[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
									__a	:  List[str]         =					tokenizer([raw_input_str]		,     return_tensors=_UpperCAmelCase       ).input_ids[0]
									self.assertListEqual(_UpperCAmelCase		,     _UpperCAmelCase       )
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







					@require_torch
					def 	_lowerCamelCase						(  self       ):
									__a	:  Optional[int]         =					['''This is going to be way too long.''' * 150, '''short example''']
									__a	:  List[str]         =					['''not super long but more than 5 tokens''', '''tiny''']
									__a	:  int         =					self._large_tokenizer(_UpperCAmelCase		,     padding=_UpperCAmelCase		,     truncation=_UpperCAmelCase		,     return_tensors='''pt'''       )
									__a	:  Any         =					self._large_tokenizer(
									    text_target=_UpperCAmelCase		,     max_length=5		,     padding=_UpperCAmelCase		,     truncation=_UpperCAmelCase		,     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(_UpperCAmelCase       ) == 2  # input_ids, attention_mask.







					@slow
					def 	_lowerCamelCase						(  self       ):
									# fmt: off
									__a	:  Optional[int]         =					{'''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=_UpperCAmelCase		,     model_name='''google/bigbird-pegasus-large-arxiv'''		,     revision='''ba85d0851d708441f91440d509690f1ab6353415'''		,     )






@require_sentencepiece
@require_tokenizers
class 			__lowercase     (     _UpperCamelCase				, unittest.TestCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					PegasusTokenizer
					__lowerCAmelCase					=					PegasusTokenizerFast
					__lowerCAmelCase					=					True
					__lowerCAmelCase					=					True







					def 	_lowerCamelCase						(  self       ):
									super().setUp()

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







					@cached_property
					def 	_lowerCamelCase						(  self       ):
									return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv'''       )







					def 	_lowerCamelCase						(  self		,     **_UpperCAmelCase       ):
									return PegasusTokenizer.from_pretrained(self.tmpdirname		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									return ("This is a test", "This is a test")







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







					@require_torch
					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					['''This is going to be way too long.''' * 1000, '''short example''']
									__a	:  int         =					['''not super long but more than 5 tokens''', '''tiny''']
									__a	:  List[Any]         =					self._large_tokenizer(_UpperCAmelCase		,     padding=_UpperCAmelCase		,     truncation=_UpperCAmelCase		,     return_tensors='''pt'''       )
									__a	:  List[str]         =					self._large_tokenizer(
									    text_target=_UpperCAmelCase		,     max_length=5		,     padding=_UpperCAmelCase		,     truncation=_UpperCAmelCase		,     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(_UpperCAmelCase       ) == 2  # input_ids, attention_mask.







					def 	_lowerCamelCase						(  self       ):
									__a	:  Dict         =					(
									    '''This is an example string that is used to test the original TF implementation against the HF'''
									    ''' implementation'''
									)

									__a	:  int         =					self._large_tokenizer(_UpperCAmelCase       ).input_ids

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






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A	     =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					['''MvpTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MvpForCausalLM''',
		    '''MvpForConditionalGeneration''',
		    '''MvpForQuestionAnswering''',
		    '''MvpForSequenceClassification''',
		    '''MvpModel''',
		    '''MvpPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
		from .tokenization_mvp import MvpTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mvp_fast import MvpTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mvp import (
				    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MvpForCausalLM,
				    MvpForConditionalGeneration,
				    MvpForQuestionAnswering,
				    MvpForSequenceClassification,
				    MvpModel,
				    MvpPreTrainedModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)