


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LayoutLMTokenizer
						UpperCAmelCase             =						LayoutLMTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Optional[Any]:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Optional[Any]				,    **_a					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										return LayoutLMTokenizer.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      int				,    _a					:      Optional[int]  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Tuple:


										"""simple docstring"""





										pass



from datetime import datetime

import matplotlib.pyplot as plt
import torch







def 						lowerCamelCase(   a__):
				for param in module.parameters():
								_SCREAMING_SNAKE_CASE			  =False







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''cuda''' if torch.cuda.is_available() else '''cpu'''
				if torch.backends.mps.is_available() and torch.backends.mps.is_built():
								_SCREAMING_SNAKE_CASE			  ='''mps'''
				if device == "mps":
								print(
								    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
								    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
								    ''' with generations.''')
				return device







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =plt.imshow(a__)
				fig.axes.get_xaxis().set_visible(a__)
				fig.axes.get_yaxis().set_visible(a__)
				plt.show()







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  =datetime.now()
				_SCREAMING_SNAKE_CASE			  =current_time.strftime('''%H:%M:%S''')
				return timestamp