


from typing import List

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


if is_torch_available():
     import torch







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




 def __init__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = 77      ,						_lowerCamelCase = 77      ,						**_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`.'''					)

       a		:int											= max_seq_length
       a		:List[Any]											= task_seq_length

       super().__init__(_lowerCamelCase      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=77					):
       if text_list is None:
             raise ValueError('''tokens cannot be None.'''					)

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

       a							,       a		:str											= tokens['''attention_mask'''], tokens['''input_ids''']

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

       a		:List[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.'''					)

       a		:Any											= self.image_processor(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase					)

       if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
             a		:int											= [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		:Union[str, Any]											= 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		:int											= encoded_inputs.text_inputs

             a		:Optional[int]											= []
             for texts in texts_list:
                   a		:Tuple											= self._preprocess_text(_lowerCamelCase      ,						max_length=self.max_seq_length					)
                   text_inputs.append(text_input_list.unsqueeze(0					)					)

             a		:Optional[Any]											= torch.cat(_lowerCamelCase      ,						dim=0					)

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	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.'''					)

       a		:List[str]											= self.image_processor.encode_inputs(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase					)

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

       if isinstance(_lowerCamelCase      ,						_lowerCamelCase					) and all(isinstance(_lowerCamelCase      ,						_lowerCamelCase					) for task_input in task_inputs					):
             a		:str											= []
             for task in task_inputs:
                   a		:Optional[int]											= 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		:List[Any]											= encoded_inputs.text_inputs

             a		:Optional[int]											= []
             for texts in texts_list:
                   a		:List[str]											= 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  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.image_processor.post_process_semantic_segmentation(*_lowerCamelCase      ,						**_lowerCamelCase					)




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




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









from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'MarkupLMFeatureExtractor'
 SCREAMING_SNAKE_CASE__   = ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
 SCREAMING_SNAKE_CASE__   = True




 def __call__(	self      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase=None      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = 0      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = None      ,						**_lowerCamelCase      ,						):
       # first, create nodes and xpaths
       if self.parse_html:
             if html_strings is None:
                   raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`'''					)

             if nodes is not None or xpaths is not None or node_labels is not None:
                   raise ValueError(
                       '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'''					)

             a		:int											= self.feature_extractor(_lowerCamelCase					)
             a		:Tuple											= features['''nodes''']
             a		:Dict											= features['''xpaths''']
       else:
             if html_strings is not None:
                   raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.'''					)
             if nodes is None or xpaths is None:
                   raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'''					)

        # # second, apply the tokenizer
       if questions is not None and self.parse_html:
             if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:Tuple											= [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

       a		:Optional[int]											= self.tokenizer(
           text=questions if questions is not None else nodes      ,						text_pair=nodes if questions is not None else None      ,						xpaths=_lowerCamelCase      ,						node_labels=_lowerCamelCase      ,						add_special_tokens=_lowerCamelCase      ,						padding=_lowerCamelCase      ,						truncation=_lowerCamelCase      ,						max_length=_lowerCamelCase      ,						stride=_lowerCamelCase      ,						pad_to_multiple_of=_lowerCamelCase      ,						return_token_type_ids=_lowerCamelCase      ,						return_attention_mask=_lowerCamelCase      ,						return_overflowing_tokens=_lowerCamelCase      ,						return_special_tokens_mask=_lowerCamelCase      ,						return_offsets_mapping=_lowerCamelCase      ,						return_length=_lowerCamelCase      ,						verbose=_lowerCamelCase      ,						return_tensors=_lowerCamelCase      ,						**_lowerCamelCase      ,						)

       return encoded_inputs




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.batch_decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						*_lowerCamelCase      ,						**_lowerCamelCase					):
       return self.tokenizer.decode(*_lowerCamelCase      ,						**_lowerCamelCase					)




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.tokenizer.model_input_names
       return tokenizer_input_names





