


'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowercase:		Dict							=     {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		str							=     ["DeformableDetrFeatureExtractor"]
       __lowercase:		Tuple							=     ["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase:		Union[str, Any]							=     [
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       __lowercase:		List[str]							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''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 (       SCREAMING_SNAKE_CASE__):
 _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
 _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
 _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')

 def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):





    """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`."					)

    UpperCamelCase__ 						=						max_seq_length
    UpperCamelCase__ 						=						task_seq_length

    super().__init__(a_,					a_					)

 def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):





    """simple docstring"""



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

    UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)

    UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]

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

    UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
    return token_inputs

 def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):





    """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."					)

    UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						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"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

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

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):





    """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."					)

    UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)

    if isinstance(a_,					a_					):
       UpperCamelCase__ 						=						[task_inputs]

    if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
       UpperCamelCase__ 						=						[]
       for task in task_inputs:
          UpperCamelCase__ 						=						f'the task is {task}'
          task_token_inputs.append(a_					)
       UpperCamelCase__ 						=						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"					):
       UpperCamelCase__ 						=						encoded_inputs.text_inputs

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

       UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)

    return encoded_inputs

 def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):





    """simple docstring"""



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

 def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):





    """simple docstring"""



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