


import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class 	_snake_case						(   SCREAMING_SNAKE_CASE__		,					unittest.TestCase							):
 SCREAMING_SNAKE_CASE__   = BartTokenizer
 SCREAMING_SNAKE_CASE__   = BartTokenizerFast
 SCREAMING_SNAKE_CASE__   = True
 SCREAMING_SNAKE_CASE__   = filter_roberta_detectors




 def  SCREAMING_SNAKE_CASE__       (	self					):
       super().setUp()
       a		:Union[str, Any]											= [
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
       ]
       a		:str											= dict(zip(a_      ,						range(len(a_					)					)					)					)
       a		:Any											= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       a		:Tuple											= {'''unk_token''': '''<unk>'''}

       a		:Any											= os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['''vocab_file''']					)
       a		:Optional[int]											= 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(a_					) + '''\n'''					)
       with open(self.merges_file      ,						'''w'''      ,						encoding='''utf-8'''					) as fp:
             fp.write('''\n'''.join(a_					)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       kwargs.update(self.special_tokens_map					)
       return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**a_					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       kwargs.update(self.special_tokens_map					)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,						**a_					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       return "lower newer", "lower newer"




 @cached_property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return BartTokenizer.from_pretrained('''facebook/bart-large'''					)




 @cached_property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       return BartTokenizerFast.from_pretrained('''facebook/bart-large'''					)




 @require_torch
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       a		:Dict											= [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a		:Tuple											= tokenizer(a_      ,						max_length=len(a_					)      ,						padding=a_      ,						return_tensors='''pt'''					)
             self.assertIsInstance(a_      ,						a_					)

             self.assertEqual((2, 9)      ,						batch.input_ids.shape					)
             self.assertEqual((2, 9)      ,						batch.attention_mask.shape					)
             a		:Tuple											= batch.input_ids.tolist()[0]
             self.assertListEqual(a_      ,						a_					)
             # Test that special tokens are reset




 @require_torch
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a		:str											= tokenizer(a_      ,						padding=a_      ,						return_tensors='''pt'''					)
             # check if input_ids are returned and no labels
             self.assertIn('''input_ids'''      ,						a_					)
             self.assertIn('''attention_mask'''      ,						a_					)
             self.assertNotIn('''labels'''      ,						a_					)
             self.assertNotIn('''decoder_attention_mask'''      ,						a_					)




 @require_torch
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= [
           '''Summary of the text.''',
           '''Another summary.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a		:List[Any]											= tokenizer(text_target=a_      ,						max_length=32      ,						padding='''max_length'''      ,						return_tensors='''pt'''					)
             self.assertEqual(32      ,						targets['''input_ids'''].shape[1]					)




 @require_torch
 def  SCREAMING_SNAKE_CASE__       (	self					):



       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a		:Union[str, Any]											= tokenizer(
                 ['''I am a small frog''' * 1024, '''I am a small frog''']      ,						padding=a_      ,						truncation=a_      ,						return_tensors='''pt'''					)
             self.assertIsInstance(a_      ,						a_					)
             self.assertEqual(batch.input_ids.shape      ,						(2, 1024)					)




 @require_torch
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= ['''A long paragraph for summarization.''']
       a		:Optional[Any]											= [
           '''Summary of the text.''',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             a		:str											= tokenizer(a_      ,						return_tensors='''pt'''					)
             a		:Optional[Any]											= tokenizer(text_target=a_      ,						return_tensors='''pt'''					)
             a		:Any											= inputs['''input_ids''']
             a		:Optional[Any]											= targets['''input_ids''']
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()					)
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()					)
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()					)
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       pass




 def  SCREAMING_SNAKE_CASE__       (	self					):



       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
                   a		:List[Any]											= self.rust_tokenizer_class.from_pretrained(a_      ,						**a_					)
                   a		:Tuple											= self.tokenizer_class.from_pretrained(a_      ,						**a_					)
                   a		:Dict											= '''A, <mask> AllenNLP sentence.'''
                   a		:List[Any]											= tokenizer_r.encode_plus(a_      ,						add_special_tokens=a_      ,						return_token_type_ids=a_					)
                   a		:str											= tokenizer_p.encode_plus(a_      ,						add_special_tokens=a_      ,						return_token_type_ids=a_					)

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['''token_type_ids''']					)      ,						sum(tokens_p['''token_type_ids''']					)					)

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['''attention_mask''']					) / len(tokens_r['''attention_mask''']					)      ,						sum(tokens_p['''attention_mask''']					) / len(tokens_p['''attention_mask''']					)      ,						)

                   a		:Tuple											= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
                   a		:List[str]											= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['''input_ids''']      ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)
                   self.assertSequenceEqual(tokens_r['''input_ids''']      ,						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]					)

                   self.assertSequenceEqual(
                       a_      ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)
                   self.assertSequenceEqual(
                       a_      ,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']					)









from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = True




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = 0      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       # first, create nodes and xpaths
       if self.parse_html:
             if html_strings is None:
                   raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''					)

             if nodes is not None or xpaths is not None or node_labels is not None:
                   raise ValueError(
                       '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= features['''xpaths''']
       else:
             if html_strings is not None:
                   raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''					)
             if nodes is None or xpaths is None:
                   raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''					)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
             if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       a		:Optional[int]											= self.tokenizer(
           text=questions if questions is not None else nodes      ,						text_pair=nodes if questions is not None else None      ,						xpaths=_lowerCamelCase      ,						node_labels=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase      ,						padding=_lowerCamelCase      ,						truncation=_lowerCamelCase      ,						max_length=_lowerCamelCase      ,						stride=_lowerCamelCase      ,						pad_to_multiple_of=_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						return_attention_mask=_lowerCamelCase      ,						return_overflowing_tokens=_lowerCamelCase      ,						return_special_tokens_mask=_lowerCamelCase      ,						return_offsets_mapping=_lowerCamelCase      ,						return_length=_lowerCamelCase      ,						verbose=_lowerCamelCase      ,						return_tensors=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





