





"""simple docstring"""






import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin




class 					__lowercase      (      _UpperCamelCase ,   unittest.TestCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	ProphetNetTokenizer
					__lowerCAmelCase	=	False







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

									__a			:    List[Any]      							=	[
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									__a			:    str      							=	os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['''vocab_file''']  )
									with open(self.vocab_file						,      '''w'''						,      encoding='''utf-8'''  ) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a			:    List[str]      							=	'''UNwant\u00E9d,running'''
									__a			:    List[Any]      							=	'''unwanted, running'''
									return input_text, output_text







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	self.tokenizer_class(self.vocab_file  )

									__a			:    Any      							=	tokenizer.tokenize('''UNwant\u00E9d,running'''  )
									self.assertListEqual(_UpperCAmelCase						,      ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase  )						,      [9, 6, 7, 12, 10, 11]  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	BasicTokenizer()

									self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz'''  )						,      ['''ah''', '''\u535A''', '''\u63A8''', '''zz''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[int]      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )						,      ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )						,      ['''hello''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase						,      strip_accents=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )						,      ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )						,      ['''h\u00E9llo''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase						,      strip_accents=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )						,      ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )						,      ['''hello''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    int      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )						,      ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?''']  )
									self.assertListEqual(tokenizer.tokenize('''H\u00E9llo'''  )						,      ['''hello''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU?  '''  )						,      ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase						,      strip_accents=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )						,      ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    List[Any]      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase						,      strip_accents=_UpperCAmelCase  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHäLLo!how  \n Are yoU?  '''  )						,      ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	BasicTokenizer(do_lower_case=_UpperCAmelCase						,      never_split=['''[UNK]''']  )

									self.assertListEqual(
									    tokenizer.tokenize(''' \tHeLLo!how  \n Are yoU? [UNK]'''  )						,      ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]''']  )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']

									__a			:    str      							=	{}
									for i, token in enumerate(_UpperCAmelCase  ):
													__a			:    Tuple      							=	i
									__a			:    Dict      							=	WordpieceTokenizer(vocab=_UpperCAmelCase						,      unk_token='''[UNK]'''  )

									self.assertListEqual(tokenizer.tokenize(''''''  )						,      []  )

									self.assertListEqual(tokenizer.tokenize('''unwanted running'''  )						,      ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing''']  )

									self.assertListEqual(tokenizer.tokenize('''unwantedX running'''  )						,      ['''[UNK]''', '''runn''', '''##ing''']  )







					@require_torch
					def 			_lowerCamelCase		(    self  ):
									__a			:    Union[str, Any]      							=	self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''  )

									__a			:    Tuple      							=	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									__a			:    List[Any]      							=	[1037, 2146, 20423, 2005, 7680, 7849, 3989, 1012, 102]
									__a			:    List[str]      							=	tokenizer(_UpperCAmelCase						,      padding=_UpperCAmelCase						,      return_tensors='''pt'''  )
									self.assertIsInstance(_UpperCAmelCase						,      _UpperCAmelCase  )
									__a			:    Dict      							=	list(batch.input_ids.numpy()[0]  )
									self.assertListEqual(_UpperCAmelCase						,      _UpperCAmelCase  )

									self.assertEqual((2, 9)						,      batch.input_ids.shape  )
									self.assertEqual((2, 9)						,      batch.attention_mask.shape  )







					def 			_lowerCamelCase		(    self  ):
									self.assertTrue(_is_whitespace(''' '''  )  )
									self.assertTrue(_is_whitespace('''\t'''  )  )
									self.assertTrue(_is_whitespace('''\r'''  )  )
									self.assertTrue(_is_whitespace('''\n'''  )  )
									self.assertTrue(_is_whitespace('''\u00A0'''  )  )

									self.assertFalse(_is_whitespace('''A'''  )  )
									self.assertFalse(_is_whitespace('''-'''  )  )







					def 			_lowerCamelCase		(    self  ):
									self.assertTrue(_is_control('''\u0005'''  )  )

									self.assertFalse(_is_control('''A'''  )  )
									self.assertFalse(_is_control(''' '''  )  )
									self.assertFalse(_is_control('''\t'''  )  )
									self.assertFalse(_is_control('''\r'''  )  )







					def 			_lowerCamelCase		(    self  ):
									self.assertTrue(_is_punctuation('''-'''  )  )
									self.assertTrue(_is_punctuation('''$'''  )  )
									self.assertTrue(_is_punctuation('''`'''  )  )
									self.assertTrue(_is_punctuation('''.'''  )  )

									self.assertFalse(_is_punctuation('''A'''  )  )
									self.assertFalse(_is_punctuation(''' '''  )  )







					@slow
					def 			_lowerCamelCase		(    self  ):
									__a			:    Tuple      							=	self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased'''  )

									__a			:    List[Any]      							=	tokenizer.encode('''sequence builders'''						,      add_special_tokens=_UpperCAmelCase  )
									__a			:    Any      							=	tokenizer.encode('''multi-sequence build'''						,      add_special_tokens=_UpperCAmelCase  )

									__a			:    Optional[Any]      							=	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase  )
									__a			:    List[str]      							=	tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						,      _UpperCAmelCase  )

									assert encoded_sentence == text + [102]
									assert encoded_pair == text + [102] + text_a + [102]



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
   from transformers import TvltImageProcessor

if is_speech_available():
   from transformers import TvltFeatureExtractor

from transformers import TvltProcessor
@require_torch
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= '''ZinengTang/tvlt-base'''
       snake_case__						= tempfile.mkdtemp()

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:List[Any]   ):
       return TvltImageProcessor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		**_a:Tuple   ):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )
       processor.save_pretrained(self.tmpdirname   )
       snake_case__						= TvltProcessor.from_pretrained(self.tmpdirname   )

       self.assertIsInstance(processor.feature_extractor     ,		_a   )
       self.assertIsInstance(processor.image_processor     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )

       snake_case__						= feature_extractor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(audio=_a     ,		return_tensors='''np'''   )

       for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= image_processor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(images=_a     ,		return_tensors='''np'''   )

       for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()     ,		input_processor[key].sum()     ,		delta=1e-2   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       snake_case__						= np.ones([1_20_00]   )
       snake_case__						= np.ones([3, 2_24, 2_24]   )

       snake_case__						= processor(audio=_a     ,		images=_a   )

       self.assertListEqual(list(inputs.keys()   )     ,		['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']   )

       # test if it raises when no input is passed
       with pytest.raises(_a   ):
         processor()







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_image_processor()
       snake_case__						= self.get_feature_extractor()

       snake_case__						= TvltProcessor(image_processor=_a     ,		feature_extractor=_a   )

       self.assertListEqual(
           processor.model_input_names     ,		image_processor.model_input_names + feature_extractor.model_input_names     ,		msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,		)

