




'''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 : Optional[Any]  				=						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			:    int   )     ->				Optional[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			:    Optional[Any]   )     ->				int:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







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


														"""simple docstring"""


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







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


														"""simple docstring"""


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







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


														"""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			:    Dict   )     ->				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			:    str   )     ->				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			:    Dict   )     ->				List[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			:    Optional[Any]   )     ->				List[str]:


														"""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   )     ->				str:


														"""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			:    Tuple   )     ->				str:


														"""simple docstring"""


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







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


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    Tuple   )     ->				Dict:


														"""simple docstring"""


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







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


														"""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			:    int   )     ->				int:


														"""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]				,     )







'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_snake_case : Tuple  				=						logging.get_logger(__name__)

_snake_case : Optional[int]  				=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case : List[Any]  				=						{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case : Union[str, Any]  				=						{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


_snake_case : Tuple  				=						{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}

class      A       (						_a       ):
							lowercase_							     =							VOCAB_FILES_NAMES
							lowercase_							     =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_							     =							PRETRAINED_INIT_CONFIGURATION
							lowercase_							     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							     =							SqueezeBertTokenizer







							def __init__(    self			:    str				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    List[str]="[UNK]"				,     lowerCAmelCase_			:    Union[str, Any]="[SEP]"				,     lowerCAmelCase_			:    Optional[Any]="[PAD]"				,     lowerCAmelCase_			:    Any="[CLS]"				,     lowerCAmelCase_			:    List[str]="[MASK]"				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    List[Any]=None				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				int:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_				,     tokenizer_file=lowerCAmelCase_				,     do_lower_case=lowerCAmelCase_				,     unk_token=lowerCAmelCase_				,     sep_token=lowerCAmelCase_				,     pad_token=lowerCAmelCase_				,     cls_token=lowerCAmelCase_				,     mask_token=lowerCAmelCase_				,     tokenize_chinese_chars=lowerCAmelCase_				,     strip_accents=lowerCAmelCase_				,     **lowerCAmelCase_				,     )

														_a      	=							json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
														if (
														    normalizer_state.get('''lowercase'''				,     lowerCAmelCase_   ) != do_lower_case
														    or normalizer_state.get('''strip_accents'''				,     lowerCAmelCase_   ) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''				,     lowerCAmelCase_   ) != tokenize_chinese_chars
														):
																					_a      	=							getattr(lowerCAmelCase_				,     normalizer_state.pop('''type'''   )   )
																					_a      	=							do_lower_case
																					_a      	=							strip_accents
																					_a      	=							tokenize_chinese_chars
																					_a      	=							normalizer_class(**lowerCAmelCase_   )

														_a      	=							do_lower_case







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Optional[Any]=None   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None   )     ->				List[int]:


														"""simple docstring"""


														_a      	=							[self.sep_token_id]
														_a      	=							[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep   ) * [0]
														return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[str] = None   )     ->				Tuple[str]:


														"""simple docstring"""


														_a      	=							self._tokenizer.model.save(lowerCAmelCase_				,     name=lowerCAmelCase_   )
														return tuple(lowerCAmelCase_   )

