


'''simple docstring'''


from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
      import torch







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							['image_processor', 'tokenizer']
     lowerCamelCase__						=							'OneFormerImageProcessor'
     lowerCamelCase__						=							('CLIPTokenizer', 'CLIPTokenizerFast')


     def __init__(      self ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase = 77 ,    lowerCamelCase = 77 ,    **lowerCamelCase		):


          '''simple docstring'''





          if image_processor is None:
               raise ValueError("You need to specify an `image_processor`."		)
          if tokenizer is None:
               raise ValueError("You need to specify a `tokenizer`."		)

          __A  :					str						     =       max_seq_length
          __A  :					Dict						     =       task_seq_length

          super().__init__(lowerCamelCase ,    lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase=None ,    lowerCamelCase=77		):


          '''simple docstring'''





          if text_list is None:
               raise ValueError("tokens cannot be None."		)

          __A  :					List[str]						     =       self.tokenizer(lowerCamelCase ,    padding="max_length" ,    max_length=lowerCamelCase ,    truncation=lowerCamelCase		)

          __A     ,__A  :					Optional[int]						     =       tokens["attention_mask"], tokens["input_ids"]

          __A  :					Optional[int]						     =       []
          for attn_mask, input_id in zip(lowerCamelCase ,    lowerCamelCase		):
               __A  :					Union[str, Any]						     =       torch.tensor(lowerCamelCase		) * torch.tensor(lowerCamelCase		)
               token_inputs.append(token.unsqueeze(0		)		)

          __A  :					Union[str, Any]						     =       torch.cat(lowerCamelCase ,    dim=0		)
          return token_inputs


     def __call__(      self ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase=None ,    **lowerCamelCase		):


          '''simple docstring'''





          if task_inputs is None:
               raise ValueError("You have to specify the task_input. Found None."		)
          elif images is None:
               raise ValueError("You have to specify the image. Found None."		)

          if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs		):
               raise ValueError("task_inputs must be semantic, instance, or panoptic."		)

          __A  :					Tuple						     =       self.image_processor(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    **lowerCamelCase		)

          if isinstance(lowerCamelCase ,    lowerCamelCase		):
               __A  :					str						     =       [task_inputs]

          if isinstance(lowerCamelCase ,    lowerCamelCase		) and all(isinstance(lowerCamelCase ,    lowerCamelCase		) for task_input in task_inputs		):
               __A  :					Tuple						     =       []
               for task in task_inputs:
                    __A  :					List[Any]						     =       f"the task is {task}"
                    task_token_inputs.append(lowerCamelCase		)
               __A  :					int						     =       self._preprocess_text(lowerCamelCase ,    max_length=self.task_seq_length		)
          else:
               raise TypeError("Task Inputs should be a string or a list of strings."		)

          if hasattr(lowerCamelCase ,    "text_inputs"		):
               __A  :					Any						     =       encoded_inputs.text_inputs

               __A  :					List[str]						     =       []
               for texts in texts_list:
                    __A  :					Any						     =       self._preprocess_text(lowerCamelCase ,    max_length=self.max_seq_length		)
                    text_inputs.append(text_input_list.unsqueeze(0		)		)

               __A  :					str						     =       torch.cat(lowerCamelCase ,    dim=0		)

          return encoded_inputs


     def lowerCAmelCase__     (      self ,    lowerCamelCase=None ,    lowerCamelCase=None ,    lowerCamelCase=None ,    **lowerCamelCase		):


          '''simple docstring'''





          if task_inputs is None:
               raise ValueError("You have to specify the task_input. Found None."		)
          elif images is None:
               raise ValueError("You have to specify the image. Found None."		)

          if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs		):
               raise ValueError("task_inputs must be semantic, instance, or panoptic."		)

          __A  :					Any						     =       self.image_processor.encode_inputs(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    **lowerCamelCase		)

          if isinstance(lowerCamelCase ,    lowerCamelCase		):
               __A  :					Dict						     =       [task_inputs]

          if isinstance(lowerCamelCase ,    lowerCamelCase		) and all(isinstance(lowerCamelCase ,    lowerCamelCase		) for task_input in task_inputs		):
               __A  :					Union[str, Any]						     =       []
               for task in task_inputs:
                    __A  :					Dict						     =       f"the task is {task}"
                    task_token_inputs.append(lowerCamelCase		)
               __A  :					Union[str, Any]						     =       self._preprocess_text(lowerCamelCase ,    max_length=self.task_seq_length		)
          else:
               raise TypeError("Task Inputs should be a string or a list of strings."		)

          if hasattr(lowerCamelCase ,    "text_inputs"		):
               __A  :					Dict						     =       encoded_inputs.text_inputs

               __A  :					Dict						     =       []
               for texts in texts_list:
                    __A  :					Dict						     =       self._preprocess_text(lowerCamelCase ,    max_length=self.max_seq_length		)
                    text_inputs.append(text_input_list.unsqueeze(0		)		)

               __A  :					Tuple						     =       torch.cat(lowerCamelCase ,    dim=0		)

          return encoded_inputs


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return self.image_processor.post_process_semantic_segmentation(*lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return self.image_processor.post_process_instance_segmentation(*lowerCamelCase ,    **lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    *lowerCamelCase ,    **lowerCamelCase		):


          '''simple docstring'''





          return self.image_processor.post_process_panoptic_segmentation(*lowerCamelCase ,    **lowerCamelCase		)





'''simple docstring'''


import os
from string import ascii_letters

_UpperCamelCase          =     ascii_letters + """ \t\n"""


def 		_lowercase   ():




     '''simple docstring'''





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


_UpperCamelCase          =     load_dictionary()


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					int						     =       message.upper()
     __A  :					int						     =       remove_non_letters(SCREAMING_SNAKE_CASE       )
     __A  :					str						     =       message.split()
     __A  :					Tuple						     =       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       ):




     '''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       ):




     '''simple docstring'''





     __A  :					Optional[Any]						     =       get_english_count(SCREAMING_SNAKE_CASE       ) * 100 >= word_percentage
     __A  :					Optional[int]						     =       len(remove_non_letters(SCREAMING_SNAKE_CASE       )       )
     __A  :					Any						     =       (float(SCREAMING_SNAKE_CASE       ) / len(SCREAMING_SNAKE_CASE       )) * 100
     __A  :					int						     =       message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()

