

"""simple docstring"""
from typing import List

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


if is_torch_available():
 import torch

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





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



  def __init__( self					: Optional[int]       ,       snake_case					: Optional[Any]=None       ,       snake_case					: Any=None       ,       snake_case					: int = 77       ,       snake_case					: int = 77       ,       **snake_case					: Dict      )	->			Union[str, Any]:


         '''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`.'''      )

         __magic_name__			:		Optional[int]        =     max_seq_length
         __magic_name__			:		List[str]        =     task_seq_length

         super().__init__(snake_case       ,       snake_case      )



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Dict=None       ,       snake_case					: Optional[int]=77      )	->			str:


         '''simple docstring'''





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

         __magic_name__			:		int        =     self.tokenizer(snake_case       ,       padding='''max_length'''       ,       max_length=snake_case       ,       truncation=snake_case      )

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

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

         __magic_name__			:		List[str]        =     torch.cat(snake_case       ,       dim=0      )
         return token_inputs



  def __call__( self					: Tuple       ,       snake_case					: Dict=None       ,       snake_case					: str=None       ,       snake_case					: Optional[int]=None       ,       **snake_case					: Dict      )	->			str:


         '''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.'''      )

         __magic_name__			:		Any        =     self.image_processor(snake_case       ,       snake_case       ,       snake_case       ,       **snake_case      )

         if isinstance(snake_case       ,       snake_case      ):
                __magic_name__			:		str        =     [task_inputs]

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

         if hasattr(snake_case       ,       '''text_inputs'''      ):
                __magic_name__			:		List[str]        =     encoded_inputs.text_inputs

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

                __magic_name__			:		int        =     torch.cat(snake_case       ,       dim=0      )

         return encoded_inputs



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: Optional[int]=None       ,       snake_case					: int=None       ,       snake_case					: List[Any]=None       ,       **snake_case					: int      )	->			Optional[int]:


         '''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.'''      )

         __magic_name__			:		List[str]        =     self.image_processor.encode_inputs(snake_case       ,       snake_case       ,       snake_case       ,       **snake_case      )

         if isinstance(snake_case       ,       snake_case      ):
                __magic_name__			:		Union[str, Any]        =     [task_inputs]

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

         if hasattr(snake_case       ,       '''text_inputs'''      ):
                __magic_name__			:		Tuple        =     encoded_inputs.text_inputs

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

                __magic_name__			:		Tuple        =     torch.cat(snake_case       ,       dim=0      )

         return encoded_inputs



  def 							_UpperCAmelCase      ( self					: str       ,       *snake_case					: Optional[int]       ,       **snake_case					: List[str]      )	->			Optional[int]:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: str       ,       *snake_case					: int       ,       **snake_case					: Tuple      )	->			Any:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: Any       ,       *snake_case					: Optional[int]       ,       **snake_case					: List[str]      )	->			List[Any]:


         '''simple docstring'''





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



"""simple docstring"""
from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A									=						logging.get_logger(__name__)

A									=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    'prophetnet'
  snake_case_        =    ['past_key_values']
  snake_case_        =    {
      'num_attention_heads': 'num_encoder_attention_heads',
  }



  def __init__( self					: Union[str, Any]       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[Union[str, Callable]] = "gelu"       ,       snake_case					: Optional[int] = 3_0522       ,       snake_case					: Optional[int] = 1024       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[int] = 4096       ,       snake_case					: Optional[int] = 12       ,       snake_case					: Optional[int] = 16       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[float] = 0.1       ,       snake_case					: Optional[int] = 512       ,       snake_case					: Optional[float] = 0.02       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 2       ,       snake_case					: Optional[int] = 32       ,       snake_case					: Optional[int] = 128       ,       snake_case					: Optional[bool] = False       ,       snake_case					: Optional[float] = 0.0       ,       snake_case					: Optional[bool] = True       ,       snake_case					: Optional[int] = 0       ,       snake_case					: Optional[int] = 1       ,       snake_case					: Optional[int] = 2       ,       **snake_case					: Optional[int]       ,       )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Union[str, Any]        =     vocab_size
         __magic_name__			:		Tuple        =     hidden_size
         __magic_name__			:		str        =     encoder_ffn_dim
         __magic_name__			:		Tuple        =     num_encoder_layers
         __magic_name__			:		int        =     num_encoder_attention_heads
         __magic_name__			:		int        =     decoder_ffn_dim
         __magic_name__			:		Optional[int]        =     num_decoder_layers
         __magic_name__			:		str        =     num_decoder_attention_heads
         __magic_name__			:		Optional[Any]        =     max_position_embeddings
         __magic_name__			:		Union[str, Any]        =     init_std  # Normal(0, this parameter)
         __magic_name__			:		Tuple        =     activation_function

         # parameters for prophetnet
         __magic_name__			:		int        =     ngram
         __magic_name__			:		str        =     num_buckets
         __magic_name__			:		Any        =     relative_max_distance
         __magic_name__			:		Optional[int]        =     disable_ngram_loss
         __magic_name__			:		str        =     eps

         # 3 Types of Dropout
         __magic_name__			:		Dict        =     attention_dropout
         __magic_name__			:		Optional[int]        =     activation_dropout
         __magic_name__			:		List[Any]        =     dropout

         __magic_name__			:		str        =     use_cache

         super().__init__(
             pad_token_id=snake_case       ,       bos_token_id=snake_case       ,       eos_token_id=snake_case       ,       is_encoder_decoder=snake_case       ,       add_cross_attention=snake_case       ,       decoder_start_token_id=snake_case       ,       **snake_case       ,       )



  @property
  def 							_UpperCAmelCase      ( self					: str      )	->			int:


         '''simple docstring'''





         return self.num_encoder_layers + self.num_decoder_layers




  @num_hidden_layers.setter
  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: int      )	->			Optional[int]:


         '''simple docstring'''





         raise NotImplementedError(
             '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
             ''' `num_decoder_layers`.'''      )
