

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 		__magic_name__     (				_UpperCamelCase						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[Any]        =  BartTokenizer
   lowerCamelCase__					:      Dict        =  BartTokenizerFast
   lowerCamelCase__					:      Dict        =  True
   lowerCamelCase__					:      Union[str, Any]        =  filter_roberta_detectors



   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






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

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



   def 					_UpperCAmelCase					(							self,       **lowercase_			)				->       Optional[int]:






     """simple docstring"""






     kwargs.update(self.special_tokens_map			)
     return self.tokenizer_class.from_pretrained(self.tmpdirname,       **_SCREAMING_SNAKE_CASE			)



   def 					_UpperCAmelCase					(							self,       **lowercase_			)				->       Union[str, Any]:






     """simple docstring"""






     kwargs.update(self.special_tokens_map			)
     return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,       **_SCREAMING_SNAKE_CASE			)



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       Optional[int]:






     """simple docstring"""






     return "lower newer", "lower newer"



   @cached_property
   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     return BartTokenizer.from_pretrained('''facebook/bart-large'''			)



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






     """simple docstring"""






     return BartTokenizerFast.from_pretrained('''facebook/bart-large'''			)



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     a__    =[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

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



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       a__    =tokenizer(_SCREAMING_SNAKE_CASE,       padding=_SCREAMING_SNAKE_CASE,       return_tensors='''pt'''			)
       # check if input_ids are returned and no labels
       self.assertIn('''input_ids''',       _SCREAMING_SNAKE_CASE			)
       self.assertIn('''attention_mask''',       _SCREAMING_SNAKE_CASE			)
       self.assertNotIn('''labels''',       _SCREAMING_SNAKE_CASE			)
       self.assertNotIn('''decoder_attention_mask''',       _SCREAMING_SNAKE_CASE			)



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    =[
         '''Summary of the text.''',
         '''Another summary.''',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       a__    =tokenizer(text_target=_SCREAMING_SNAKE_CASE,       max_length=32,       padding='''max_length''',       return_tensors='''pt'''			)
       self.assertEqual(32,       targets['''input_ids'''].shape[1]			)



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






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



   @require_torch
   def 					_UpperCAmelCase					(							self			)				->       int:






     """simple docstring"""






     a__    =['''A long paragraph for summarization.''']
     a__    =[
         '''Summary of the text.''',
     ]
     for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       a__    =tokenizer(_SCREAMING_SNAKE_CASE,       return_tensors='''pt'''			)
       a__    =tokenizer(text_target=_SCREAMING_SNAKE_CASE,       return_tensors='''pt'''			)
       a__    =inputs['''input_ids''']
       a__    =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 					_UpperCAmelCase					(							self			)				->       Union[str, Any]:






     """simple docstring"""






     pass



   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
         a__    =self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE,       **_SCREAMING_SNAKE_CASE			)
         a__    =self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE,       **_SCREAMING_SNAKE_CASE			)
         a__    ='''A, <mask> AllenNLP sentence.'''
         a__    =tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE,       add_special_tokens=_SCREAMING_SNAKE_CASE,       return_token_type_ids=_SCREAMING_SNAKE_CASE			)
         a__    =tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE,       add_special_tokens=_SCREAMING_SNAKE_CASE,       return_token_type_ids=_SCREAMING_SNAKE_CASE			)

         # 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__    =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
         a__    =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, 50264, 3823, 487, 21992, 3645, 4, 2]			)
         self.assertSequenceEqual(tokens_r['''input_ids'''],       [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

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

'''simple docstring'''




from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__snake_case  : Union[str, Any]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : List[str]		       =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__snake_case  : Optional[int]		       =    {
    'openai-gpt': 512,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   str  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[Any]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							OpenAIGPTTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Optional[Any]:
           return True

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)






