





import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class 						UpperCamelCase__					( unittest.TestCase				, lowerCAmelCase_  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		load_tool("""document-question-answering"""			)
           self.tool.setup()
           SCREAMING_SNAKE_CASE						=		load_tool("""document-question-answering"""		,remote=lowerCamelCase__			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		load_dataset("""hf-internal-testing/example-documents"""		,split="""test"""			)
           SCREAMING_SNAKE_CASE						=		dataset[0]["""image"""]

           SCREAMING_SNAKE_CASE						=		self.tool(lowerCamelCase__		,"""When is the coffee break?"""			)
           self.assertEqual(lowerCamelCase__		,"""11-14 to 11:39 a.m."""			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		load_dataset("""hf-internal-testing/example-documents"""		,split="""test"""			)
           SCREAMING_SNAKE_CASE						=		dataset[0]["""image"""]

           SCREAMING_SNAKE_CASE						=		self.remote_tool(lowerCamelCase__		,"""When is the coffee break?"""			)
           self.assertEqual(lowerCamelCase__		,"""11-14 to 11:39 a.m."""			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		load_dataset("""hf-internal-testing/example-documents"""		,split="""test"""			)
           SCREAMING_SNAKE_CASE						=		dataset[0]["""image"""]

           self.tool(document=lowerCamelCase__		,question="""When is the coffee break?"""			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		load_dataset("""hf-internal-testing/example-documents"""		,split="""test"""			)
           SCREAMING_SNAKE_CASE						=		dataset[0]["""image"""]

           SCREAMING_SNAKE_CASE						=		self.remote_tool(document=lowerCamelCase__		,question="""When is the coffee break?"""			)
           self.assertEqual(lowerCamelCase__		,"""11-14 to 11:39 a.m."""			)









import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


