'''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__	(							_snake_case      ):






   """simple docstring"""
   A				:							List[str]			     =     ['''image_processor''', '''tokenizer''']
   A				:							List[str]			     =     '''OneFormerImageProcessor'''
   A				:							Union[str, Any]			     =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
   def __init__(self							,					_a=None							,					_a=None							,					_a = 77							,					_a = 77							,					**_a					)     ->  List[Any]:
        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_       :							Tuple  					=       max_seq_length
        lowercase_       :							Any  					=       task_seq_length

        super().__init__(_a							,					_a					)
   def 		_lowerCamelCase			(self							,					_a=None							,					_a=77					)     ->  Dict:
        if text_list is None:
             raise ValueError('tokens cannot be None.'					)

        lowercase_       :							Any  					=       self.tokenizer(_a							,					padding='max_length'							,					max_length=_a							,					truncation=_a					)

        lowercase_					,lowercase_       :							List[Any]  					=       tokens['attention_mask'], tokens['input_ids']

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

        lowercase_       :							Any  					=       torch.cat(_a							,					dim=0					)
        return token_inputs
   def __call__(self							,					_a=None							,					_a=None							,					_a=None							,					**_a					)     ->  List[str]:

        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_       :							str  					=       self.image_processor(_a							,					_a							,					_a							,					**_a					)

        if isinstance(_a							,					_a					):
             lowercase_       :							List[Any]  					=       [task_inputs]

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

        if hasattr(_a							,					'text_inputs'					):
             lowercase_       :							List[Any]  					=       encoded_inputs.text_inputs

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

             lowercase_       :							Dict  					=       torch.cat(_a							,					dim=0					)

        return encoded_inputs
   def 		_lowerCamelCase			(self							,					_a=None							,					_a=None							,					_a=None							,					**_a					)     ->  int:

        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_       :							Any  					=       self.image_processor.encode_inputs(_a							,					_a							,					_a							,					**_a					)

        if isinstance(_a							,					_a					):
             lowercase_       :							Optional[int]  					=       [task_inputs]

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

        if hasattr(_a							,					'text_inputs'					):
             lowercase_       :							Union[str, Any]  					=       encoded_inputs.text_inputs

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

             lowercase_       :							Dict  					=       torch.cat(_a							,					dim=0					)

        return encoded_inputs
   def 		_lowerCamelCase			(self							,					*_a							,					**_a					)     ->  Dict:
        return self.image_processor.post_process_semantic_segmentation(*_a							,					**_a					)
   def 		_lowerCamelCase			(self							,					*_a							,					**_a					)     ->  Any:
        return self.image_processor.post_process_instance_segmentation(*_a							,					**_a					)





   def 		_lowerCamelCase			(self							,					*_a							,					**_a					)     ->  Optional[int]:
        return self.image_processor.post_process_panoptic_segmentation(*_a							,					**_a					)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

 _A										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




