
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		: Optional[Any]								=     get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')



@require_sentencepiece
@require_tokenizers
class    a_ (			snake_case_					,			unittest.TestCase    ):
			a	:			Optional[int]					   =							PegasusTokenizer
			a	:			List[Any]					   =							PegasusTokenizerFast
			a	:			List[str]					   =							True
			a	:			str					   =							True






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

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






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






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									return PegasusTokenizer.from_pretrained(self.tmpdirname				,			**__snake_case  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return ("This is a test", "This is a test")






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					"""</s>"""
									_lowercase					       =					1

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					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(__snake_case  )				,			1_103  )






			def    UpperCamelCase_				(      self  ):
									self.assertEqual(self.get_tokenizer().vocab_size				,			1_103  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
									_lowercase					       =					self.tokenizer_class.from_pretrained(self.tmpdirname  )
									_lowercase					       =					(
									    """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>"""
									)
									_lowercase					       =					rust_tokenizer([raw_input_str]				,			return_tensors=__snake_case				,			add_special_tokens=__snake_case  ).input_ids[0]
									_lowercase					       =					py_tokenizer([raw_input_str]				,			return_tensors=__snake_case				,			add_special_tokens=__snake_case  ).input_ids[0]
									self.assertListEqual(__snake_case				,			__snake_case  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self._large_tokenizer
									# <mask_1> masks whole sentence while <mask_2> masks single word
									_lowercase					       =					"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
									_lowercase					       =					[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
									_lowercase					       =					tokenizer([raw_input_str]				,			return_tensors=__snake_case  ).input_ids[0]
									self.assertListEqual(__snake_case				,			__snake_case  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self._large_tokenizer
									# The tracebacks for the following asserts are **better** without messages or self.assertEqual
									assert tokenizer.vocab_size == 96_103
									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 == 1_024
									_lowercase					       =					"""To ensure a smooth flow of bank resolutions."""
									_lowercase					       =					[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
									_lowercase					       =					tokenizer([raw_input_str]				,			return_tensors=__snake_case  ).input_ids[0]
									self.assertListEqual(__snake_case				,			__snake_case  )
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""This is going to be way too long.""" * 150, """short example"""]
									_lowercase					       =					["""not super long but more than 5 tokens""", """tiny"""]
									_lowercase					       =					self._large_tokenizer(__snake_case				,			padding=__snake_case				,			truncation=__snake_case				,			return_tensors="""pt"""  )
									_lowercase					       =					self._large_tokenizer(
									    text_target=__snake_case				,			max_length=5				,			padding=__snake_case				,			truncation=__snake_case				,			return_tensors="""pt"""  )

									assert batch.input_ids.shape == (2, 1_024)
									assert batch.attention_mask.shape == (2, 1_024)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__snake_case  ) == 2  # input_ids, attention_mask.






			@slow
			def    UpperCamelCase_				(      self  ):
									# fmt: off
									_lowercase					       =					{"""input_ids""": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 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, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 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=__snake_case				,			model_name="""google/bigbird-pegasus-large-arxiv"""				,			revision="""ba85d0851d708441f91440d509690f1ab6353415"""				,			)







@require_sentencepiece
@require_tokenizers
class    a_ (			snake_case_					,			unittest.TestCase    ):
			a	:			Any					   =							PegasusTokenizer
			a	:			Optional[int]					   =							PegasusTokenizerFast
			a	:			Union[str, Any]					   =							True
			a	:			int					   =							True






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

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






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






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									return PegasusTokenizer.from_pretrained(self.tmpdirname				,			**__snake_case  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return ("This is a test", "This is a test")






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






			@require_torch
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""This is going to be way too long.""" * 1_000, """short example"""]
									_lowercase					       =					["""not super long but more than 5 tokens""", """tiny"""]
									_lowercase					       =					self._large_tokenizer(__snake_case				,			padding=__snake_case				,			truncation=__snake_case				,			return_tensors="""pt"""  )
									_lowercase					       =					self._large_tokenizer(
									    text_target=__snake_case				,			max_length=5				,			padding=__snake_case				,			truncation=__snake_case				,			return_tensors="""pt"""  )

									assert batch.input_ids.shape == (2, 4_096)
									assert batch.attention_mask.shape == (2, 4_096)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__snake_case  ) == 2  # input_ids, attention_mask.






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					(
									    """This is an example string that is used to test the original TF implementation against the HF"""
									    """ implementation"""
									)

									_lowercase					       =					self._large_tokenizer(__snake_case  ).input_ids

									self.assertListEqual(
									    __snake_case				,			[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				,			)






'''simple docstring'''






import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					UpperCAmelCase	(     snake_case_				,						unittest.TestCase  ):
      _lowercase:     List[str]			=  DebertaTokenizer
      _lowercase:     Union[str, Any]			=  True
      _lowercase:     Union[str, Any]			=  DebertaTokenizerFast




      def 							lowercase__       (     self     :					Any    )							-> List[Any]:
            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            _lowerCAmelCase					=				[
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """[UNK]""",
            ]
            _lowerCAmelCase					=				dict(zip(__snake_case     ,     range(len(__snake_case    )    )    )    )
            _lowerCAmelCase					=				["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            _lowerCAmelCase					=				{"""unk_token""": """[UNK]"""}

            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]    )
            _lowerCAmelCase					=				os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""merges_file"""]    )
            with open(self.vocab_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write(json.dumps(__snake_case    ) + """\n"""    )
            with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""    ) as fp:
                  fp.write("""\n""".join(__snake_case    )    )




      def 							lowercase__       (     self     :					List[str]     ,     **__snake_case     :					Any    )							-> Dict:
            kwargs.update(self.special_tokens_map    )
            return self.tokenizer_class.from_pretrained(self.tmpdirname     ,     **__snake_case    )




      def 							lowercase__       (     self     :					Dict     ,     __snake_case     :					Dict    )							-> Optional[Any]:
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				"""lower newer"""
            return input_text, output_text




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> List[str]:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				"""lower newer"""
            _lowerCAmelCase					=				["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            _lowerCAmelCase					=				tokenizer.tokenize(__snake_case    )
            self.assertListEqual(__snake_case     ,     __snake_case    )

            _lowerCAmelCase					=				tokens + [tokenizer.unk_token]
            _lowerCAmelCase					=				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case    )     ,     __snake_case    )




      def 							lowercase__       (     self     :					Union[str, Any]    )							-> Tuple:
            _lowerCAmelCase					=				self.get_tokenizer()
            _lowerCAmelCase					=				tokenizer("""Hello"""     ,     """World"""    )
            _lowerCAmelCase					=				[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
            self.assertListEqual(tokd["""token_type_ids"""]     ,     __snake_case    )




      @slow
      def 							lowercase__       (     self     :					int    )							-> Union[str, Any]:
            _lowerCAmelCase					=				self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

            _lowerCAmelCase					=				tokenizer.encode("""sequence builders"""     ,     add_special_tokens=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode("""multi-sequence build"""     ,     add_special_tokens=__snake_case    )

            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )
            _lowerCAmelCase					=				tokenizer.encode(
                """sequence builders"""     ,     """multi-sequence build"""     ,     add_special_tokens=__snake_case     ,     add_prefix_space=__snake_case    )

            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case    )
            _lowerCAmelCase					=				tokenizer.build_inputs_with_special_tokens(__snake_case     ,     __snake_case    )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




      @slow
      def 							lowercase__       (     self     :					Any    )							-> str:
            _lowerCAmelCase					=				[self.tokenizer_class]
            if self.test_rust_tokenizer:
                  tokenizer_classes.append(self.rust_tokenizer_class    )

            for tokenizer_class in tokenizer_classes:
                  _lowerCAmelCase					=				tokenizer_class.from_pretrained("""microsoft/deberta-base"""    )

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  _lowerCAmelCase					=				tokenizer(__snake_case     ,     padding=__snake_case    )
                  _lowerCAmelCase					=				[tokenizer.decode(__snake_case     ,     skip_special_tokens=__snake_case    ) for seq in encoding["""input_ids"""]]

                  # fmt: off
                  _lowerCAmelCase					=				{
                      """input_ids""": [
                          [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
                      ],
                      """token_type_ids""": [
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                          [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
                      ]
                  }
                  # fmt: on

                  _lowerCAmelCase					=				[
                      """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
                      """ALBERT incorporates two parameter reduction techniques""",
                      """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
                      """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
                      """ vocabulary embedding.""",
                  ]

                  self.assertDictEqual(encoding.data     ,     __snake_case    )

                  for expected, decoded in zip(__snake_case     ,     __snake_case    ):
                        self.assertEqual(__snake_case     ,     __snake_case    )






