






'''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 (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    List[Any]				       =					["image_processor", "tokenizer"]
      SCREAMING_SNAKE_CASE				:    Union[str, Any]				       =					"OneFormerImageProcessor"
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					("CLIPTokenizer", "CLIPTokenizerFast")




      def __init__(					self  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase = 77  ,		_UpperCAmelCase = 77  ,		**_UpperCAmelCase						):
             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__	:   str												=	max_seq_length
             UpperCAmelCase__	:   List[str]												=	task_seq_length

             super().__init__(_UpperCAmelCase  ,		_UpperCAmelCase						)




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

             UpperCAmelCase__	:   Tuple												=	self.tokenizer(_UpperCAmelCase  ,		padding='''max_length'''  ,		max_length=_UpperCAmelCase  ,		truncation=_UpperCAmelCase						)

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

             UpperCAmelCase__	:   List[str]												=	[]
             for attn_mask, input_id in zip(_UpperCAmelCase  ,		_UpperCAmelCase						):
                    UpperCAmelCase__	:   Tuple												=	torch.tensor(_UpperCAmelCase						) * torch.tensor(_UpperCAmelCase						)
                    token_inputs.append(token.unsqueeze(0						)						)

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




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

             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(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		**_UpperCAmelCase						)

             if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						):
                    UpperCAmelCase__	:   Any												=	[task_inputs]

             if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) and all(isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) for task_input in task_inputs						):
                    UpperCAmelCase__	:   str												=	[]
                    for task in task_inputs:
                           UpperCAmelCase__	:   str												=	F"""the task is {task}"""
                           task_token_inputs.append(_UpperCAmelCase						)
                    UpperCAmelCase__	:   Tuple												=	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'''						):
                    UpperCAmelCase__	:   Optional[Any]												=	encoded_inputs.text_inputs

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

                    UpperCAmelCase__	:   int												=	torch.cat(_UpperCAmelCase  ,		dim=0						)

             return encoded_inputs




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase=None  ,		**_UpperCAmelCase						):

             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__	:   Optional[Any]												=	self.image_processor.encode_inputs(_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		**_UpperCAmelCase						)

             if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						):
                    UpperCAmelCase__	:   Dict												=	[task_inputs]

             if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) and all(isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) for task_input in task_inputs						):
                    UpperCAmelCase__	:   Optional[Any]												=	[]
                    for task in task_inputs:
                           UpperCAmelCase__	:   int												=	F"""the task is {task}"""
                           task_token_inputs.append(_UpperCAmelCase						)
                    UpperCAmelCase__	:   str												=	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'''						):
                    UpperCAmelCase__	:   Tuple												=	encoded_inputs.text_inputs

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

                    UpperCAmelCase__	:   Tuple												=	torch.cat(_UpperCAmelCase  ,		dim=0						)

             return encoded_inputs




      def 							lowerCamelCase     (					self  ,		*_UpperCAmelCase  ,		**_UpperCAmelCase						):
             return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase  ,		**_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		*_UpperCAmelCase  ,		**_UpperCAmelCase						):
             return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase  ,		**_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		*_UpperCAmelCase  ,		**_UpperCAmelCase						):
             return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase  ,		**_UpperCAmelCase						)







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_					=				{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				["FlavaFeatureExtractor"]
  UpperCamelCase_					=				["FlavaImageProcessor"]
  UpperCamelCase_					=				["FlavaProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				[
      "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlavaForPreTraining",
      "FlavaImageCodebook",
      "FlavaImageModel",
      "FlavaModel",
      "FlavaMultimodalModel",
      "FlavaPreTrainedModel",
      "FlavaTextModel",
  ]

if TYPE_CHECKING:
  from .configuration_flava import (
      FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
      FlavaConfig,
      FlavaImageCodebookConfig,
      FlavaImageConfig,
      FlavaMultimodalConfig,
      FlavaTextConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_flava import FlavaFeatureExtractor
    from .image_processing_flava import FlavaImageProcessor
    from .processing_flava import FlavaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flava import (
        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlavaForPreTraining,
        FlavaImageCodebook,
        FlavaImageModel,
        FlavaModel,
        FlavaMultimodalModel,
        FlavaPreTrainedModel,
        FlavaTextModel,
    )

else:
  import sys

  UpperCamelCase_					=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)