






from typing import List

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


if is_torch_available():
   import torch




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	List[Any]								=['''image_processor''', '''tokenizer''']
       a       :	Dict								='''OneFormerImageProcessor'''
       a       :	str								=('''CLIPTokenizer''', '''CLIPTokenizerFast''')





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase = 7_7						,    _lowerCamelCase = 7_7						,    **_lowerCamelCase							):
         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`.'							)

         UpperCamelCase_:      Optional[int]					   =       max_seq_length
         UpperCamelCase_:      List[Any]					   =       task_seq_length

         super().__init__(_lowerCamelCase						,    _lowerCamelCase							)





       def     _a					(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=7_7							):
         if text_list is None:
           raise ValueError('tokens cannot be None.'							)

         UpperCamelCase_:      List[str]					   =       self.tokenizer(_lowerCamelCase						,    padding='max_length'						,    max_length=_lowerCamelCase						,    truncation=_lowerCamelCase							)

         UpperCamelCase_:      Union[str, Any]					   =       tokens['attention_mask'], tokens['input_ids']

         UpperCamelCase_:      Any					   =       []
         for attn_mask, input_id in zip(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      List[Any]					   =       torch.tensor(_lowerCamelCase							) * torch.tensor(_lowerCamelCase							)
           token_inputs.append(token.unsqueeze(0							)							)

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





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

         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.'							)

         UpperCamelCase_:      Dict					   =       self.image_processor(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							)

         if isinstance(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      Optional[int]					   =       [task_inputs]

         if isinstance(_lowerCamelCase						,    _lowerCamelCase							) and all(isinstance(_lowerCamelCase						,    _lowerCamelCase							) for task_input in task_inputs							):
           UpperCamelCase_:      Optional[Any]					   =       []
           for task in task_inputs:
             UpperCamelCase_:      Optional[Any]					   =       f'''the task is {task}'''
             task_token_inputs.append(_lowerCamelCase							)
           UpperCamelCase_:      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'							):
           UpperCamelCase_:      Tuple					   =       encoded_inputs.text_inputs

           UpperCamelCase_:      int					   =       []
           for texts in texts_list:
             UpperCamelCase_:      List[str]					   =       self._preprocess_text(_lowerCamelCase						,    max_length=self.max_seq_length							)
             text_inputs.append(text_input_list.unsqueeze(0							)							)

           UpperCamelCase_:      List[str]					   =       torch.cat(_lowerCamelCase						,    dim=0							)

         return encoded_inputs





       def     _a					(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    **_lowerCamelCase							):

         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.'							)

         UpperCamelCase_:      List[Any]					   =       self.image_processor.encode_inputs(_lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							)

         if isinstance(_lowerCamelCase						,    _lowerCamelCase							):
           UpperCamelCase_:      Tuple					   =       [task_inputs]

         if isinstance(_lowerCamelCase						,    _lowerCamelCase							) and all(isinstance(_lowerCamelCase						,    _lowerCamelCase							) for task_input in task_inputs							):
           UpperCamelCase_:      Optional[int]					   =       []
           for task in task_inputs:
             UpperCamelCase_:      Tuple					   =       f'''the task is {task}'''
             task_token_inputs.append(_lowerCamelCase							)
           UpperCamelCase_:      str					   =       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'							):
           UpperCamelCase_:      List[Any]					   =       encoded_inputs.text_inputs

           UpperCamelCase_:      Union[str, Any]					   =       []
           for texts in texts_list:
             UpperCamelCase_:      int					   =       self._preprocess_text(_lowerCamelCase						,    max_length=self.max_seq_length							)
             text_inputs.append(text_input_list.unsqueeze(0							)							)

           UpperCamelCase_:      Dict					   =       torch.cat(_lowerCamelCase						,    dim=0							)

         return encoded_inputs





       def     _a					(      self						,    *_lowerCamelCase						,    **_lowerCamelCase							):
         return self.image_processor.post_process_semantic_segmentation(*_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self						,    *_lowerCamelCase						,    **_lowerCamelCase							):
         return self.image_processor.post_process_instance_segmentation(*_lowerCamelCase						,    **_lowerCamelCase							)





       def     _a					(      self						,    *_lowerCamelCase						,    **_lowerCamelCase							):
         return self.image_processor.post_process_panoptic_segmentation(*_lowerCamelCase						,    **_lowerCamelCase							)







# Lint as: python3

import hashlib
from typing import Union

def 						snake_case      (UpperCAmelCase__						) ->       bytes:
  if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # Data already in bytes, returns as it as
    return hash_data
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    # We keep the data as it as for it ot be later encoded to UTF-8
    # However replace `\\` with `/` for Windows compatibility
    UpperCamelCase_:      List[str]					   =       hash_data.replace('\\'			,					'/'						)
  elif isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
    UpperCamelCase_:      Union[str, Any]					   =       str(UpperCAmelCase__						)
  else:
    # If data is not of the required type, raise error
    raise InvalidKeyError(UpperCAmelCase__						)

  return hash_data.encode('utf-8'						)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       '\nFAILURE TO GENERATE DATASET: Invalid key type detected'
         UpperCamelCase_:      Dict					   =       f'''\nFound Key {hash_data} of type {type(_lowerCamelCase							)}'''
         UpperCamelCase_:      Any					   =       '\nKeys should be either str, int or bytes type'
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase						,    _lowerCamelCase						,    _lowerCamelCase=""							):
         UpperCamelCase_:      int					   =       key
         UpperCamelCase_:      Dict					   =       duplicate_key_indices
         UpperCamelCase_:      int					   =       fix_msg
         UpperCamelCase_:      List[Any]					   =       'Found multiple examples generated with the same key'
         if len(_lowerCamelCase							) <= 2_0:
           UpperCamelCase_:      Tuple					   =       f'''\nThe examples at index {', '.join(_lowerCamelCase							)} have the key {key}'''
         else:
           UpperCamelCase_:      Optional[int]					   =       f'''\nThe examples at index {', '.join(duplicate_key_indices[:2_0]							)}... ({len(_lowerCamelCase							) - 2_0} more) have the key {key}'''
         UpperCamelCase_:      List[Any]					   =       '\n' + fix_msg if fix_msg else ''
         super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)







class        _lowerCAmelCase:





       """simple docstring"""







       def __init__(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      int					   =       hashlib.mda(_as_bytes(_lowerCamelCase							)							)





       def     _a					(      self						,    _lowerCamelCase							):
         UpperCamelCase_:      Optional[int]					   =       self._split_mda.copy()
         UpperCamelCase_:      str					   =       _as_bytes(_lowerCamelCase							)
         mda.update(_lowerCamelCase							)
         # Convert to integer with hexadecimal conversion
         return int(mda.hexdigest()						,    1_6							)