


from typing import List

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


if is_torch_available():
  import torch







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     ['''image_processor''', '''tokenizer''']
     SCREAMING_SNAKE_CASE__      =     '''OneFormerImageProcessor'''
     SCREAMING_SNAKE_CASE__      =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





     def __init__(self						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	UpperCAmelCase = 7_7						,	UpperCAmelCase = 7_7						,	**UpperCAmelCase			)	->  Dict:
      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`.'''			)

      _lowercase				 =max_seq_length
      _lowercase				 =task_seq_length

      super().__init__(UpperCAmelCase						,	UpperCAmelCase			)





     def 	__A   (self						,	UpperCAmelCase=None						,	UpperCAmelCase=7_7			)	->  Dict:
      if text_list is None:
       raise ValueError('''tokens cannot be None.'''			)

      _lowercase				 =self.tokenizer(UpperCAmelCase						,	padding='''max_length'''						,	max_length=UpperCAmelCase						,	truncation=UpperCAmelCase			)

      _lowercase ,       _lowercase				 =tokens['''attention_mask'''], tokens['''input_ids''']

      _lowercase				 =[]
      for attn_mask, input_id in zip(UpperCAmelCase						,	UpperCAmelCase			):
       _lowercase				 =torch.tensor(UpperCAmelCase			) * torch.tensor(UpperCAmelCase			)
       token_inputs.append(token.unsqueeze(0			)			)

      _lowercase				 =torch.cat(UpperCAmelCase						,	dim=0			)
      return token_inputs





     def __call__(self						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	**UpperCAmelCase			)	->  Tuple:

      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.'''			)

      _lowercase				 =self.image_processor(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	**UpperCAmelCase			)

      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       _lowercase				 =[task_inputs]

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

      if hasattr(UpperCAmelCase						,	'''text_inputs'''			):
       _lowercase				 =encoded_inputs.text_inputs

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

       _lowercase				 =torch.cat(UpperCAmelCase						,	dim=0			)

      return encoded_inputs





     def 	__A   (self						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	**UpperCAmelCase			)	->  Optional[Any]:

      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.'''			)

      _lowercase				 =self.image_processor.encode_inputs(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	**UpperCAmelCase			)

      if isinstance(UpperCAmelCase						,	UpperCAmelCase			):
       _lowercase				 =[task_inputs]

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

      if hasattr(UpperCAmelCase						,	'''text_inputs'''			):
       _lowercase				 =encoded_inputs.text_inputs

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

       _lowercase				 =torch.cat(UpperCAmelCase						,	dim=0			)

      return encoded_inputs





     def 	__A   (self						,	*UpperCAmelCase						,	**UpperCAmelCase			)	->  Tuple:
      return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	*UpperCAmelCase						,	**UpperCAmelCase			)	->  Tuple:
      return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase						,	**UpperCAmelCase			)





     def 	__A   (self						,	*UpperCAmelCase						,	**UpperCAmelCase			)	->  Optional[Any]:
      return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase						,	**UpperCAmelCase			)





from __future__ import annotations
def 							UpperCAmelCase_   (				__snake_case		)	->     list[int]:







 """simple docstring"""


 if not list_of_ints:
  return []

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     list[int]:







 """simple docstring"""


 if bit_position == 0 or len(__snake_case		) in [0, 1]:
  return list_of_ints

 _lowercase				 =[]
 _lowercase				 =[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(__snake_case		)
  else:
   # number has a zero at bit bit_position
   zeros.append(__snake_case		)

    # recursively split both lists further
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)

 # recombine lists
 _lowercase				 =zeros
 res.extend(__snake_case		)

 return res
def 							UpperCAmelCase_   (				__snake_case		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =len(__snake_case		)
 if not list_of_ints or length == 1:
  return

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		0	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 _lowercase				 =begin_index
 _lowercase				 =end_index - 1
 while i <= j:
  _lowercase				 =False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   _lowercase				 =True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   _lowercase				 =True

  if changed:
   continue

  _lowercase ,       _lowercase				 =list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

