






'''simple docstring'''







import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__A      :       Dict      =	logging.get_logger(__name__)

if is_vision_available():
       import PIL.Image
       from PIL import Image
       from PIL.Image import Image as ImageType
else:
       __A      :       Tuple      =	object

if is_torch_available():
       import torch

if is_soundfile_availble():
       import soundfile as sf






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	str				,				lowercase__    :	Optional[Any]					)  ->       str:
          __A		   =      value






     def __str__(self    :	Tuple					)  ->       Any:
          return self.to_string()






     def     _UpperCamelCase   (self    :	str					)  ->       Optional[int]:
          logger.error(
              "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"					)
          return self._value






     def     _UpperCamelCase   (self    :	Union[str, Any]					)  ->       str:
          logger.error(
              "This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable"					)
          return str(self._value					)







class 					a__    (			lowerCamelCase ,						lowerCamelCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	Optional[Any]					)  ->       List[str]:
          return self._value






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Optional[Any]:
          return self._value







class 					a__    (			lowerCamelCase ,						lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[int]				,				lowercase__    :	Optional[Any]					)  ->       str:
          super().__init__(lowercase__					)

          if not is_vision_available():
               raise ImportError("PIL must be installed in order to handle images."					)

          __A		   =      None
          __A		   =      None
          __A		   =      None

          if isinstance(lowercase__				,				lowercase__					):
               __A		   =      value
          elif isinstance(lowercase__				,				(str, pathlib.Path)					):
               __A		   =      value
          elif isinstance(lowercase__				,				torch.Tensor					):
               __A		   =      value
          else:
               raise ValueError(f"Unsupported type for {self.__class__.__name__}: {type(lowercase__					)}"					)






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Any=None				,				lowercase__    :	Union[str, Any]=None					)  ->       Optional[int]:
          from IPython.display import Image, display

          display(Image(self.to_string()					)					)






     def     _UpperCamelCase   (self    :	int					)  ->       List[Any]:
          if self._raw is not None:
               return self._raw

          if self._path is not None:
               __A		   =      Image.open(self._path					)
               return self._raw






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
          if self._path is not None:
               return self._path

          if self._raw is not None:
               __A		   =      tempfile.mkdtemp()
               __A		   =      os.path.join(lowercase__				,				str(uuid.uuida()					) + ".png"					)
               self._raw.save(self._path					)

               return self._path

          if self._tensor is not None:
               __A		   =      self._tensor.cpu().detach().numpy()

               # There is likely simpler than load into image into save
               __A		   =      Image.fromarray((array * 255).astype(np.uinta					)					)

               __A		   =      tempfile.mkdtemp()
               __A		   =      os.path.join(lowercase__				,				str(uuid.uuida()					) + ".png"					)

               img.save(self._path					)

               return self._path







class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[int]				,				lowercase__    :	Tuple				,				lowercase__    :	Optional[int]=16000					)  ->       Union[str, Any]:
          super().__init__(lowercase__					)

          if not is_soundfile_availble():
               raise ImportError("soundfile must be installed in order to handle audio."					)

          __A		   =      None
          __A		   =      None

          __A		   =      samplerate

          if isinstance(lowercase__				,				(str, pathlib.Path)					):
               __A		   =      value
          elif isinstance(lowercase__				,				torch.Tensor					):
               __A		   =      value
          else:
               raise ValueError(f"Unsupported audio type: {type(lowercase__					)}"					)






     def     _UpperCamelCase   (self    :	List[Any]				,				lowercase__    :	Optional[Any]=None				,				lowercase__    :	Union[str, Any]=None					)  ->       List[Any]:
          from IPython.display import Audio, display

          display(Audio(self.to_string()				,				rate=self.samplerate					)					)






     def     _UpperCamelCase   (self    :	List[str]					)  ->       Dict:
          if self._tensor is not None:
               return self._tensor

          if self._path is not None:
               __A   ,				__A		   =      sf.read(self._path					)
               __A		   =      torch.tensor(lowercase__					)
               return self._tensor






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       List[Any]:
          if self._path is not None:
               return self._path

          if self._tensor is not None:
               __A		   =      tempfile.mkdtemp()
               __A		   =      os.path.join(lowercase__				,				str(uuid.uuida()					) + ".wav"					)
               sf.write(self._path				,				self._tensor				,				samplerate=self.samplerate					)
               return self._path







__A      :       Tuple      =	{'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
__A      :       Optional[int]      =	{str: AgentText}

if is_vision_available():
       __A      :       Dict      =	AgentImage


def 						_UpperCamelCase							(     *snake_case__							,			**snake_case__							):
     __A		   =      [(arg.to_raw() if isinstance(snake_case__							,			snake_case__							) else arg) for arg in args]
     __A		   =      {k: (v.to_raw() if isinstance(snake_case__							,			snake_case__							) else v) for k, v in kwargs.items()}
     return args, kwargs





def 						_UpperCamelCase							(     snake_case__							,			snake_case__=None							):
     if isinstance(snake_case__							,			snake_case__							):
          __A		   =      {}
          for i, (k, v) in enumerate(outputs.items()							):
               if output_types is not None:
                    # If the class has defined outputs, we can map directly according to the class definition
                    if output_types[i] in AGENT_TYPE_MAPPING:
                         __A		   =      AGENT_TYPE_MAPPING[output_types[i]](snake_case__							)
                    else:
                         __A		   =      AgentType(snake_case__							)

               else:
                    # If the class does not have defined output, then we map according to the type
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(snake_case__							,			_k							):
                              __A		   =      _v(snake_case__							)
                    if k not in decoded_outputs:
                         __A		   =      AgentType[v]

     elif isinstance(snake_case__							,			(list, tuple)							):
          __A		   =      type(snake_case__							)()
          for i, v in enumerate(snake_case__							):
               if output_types is not None:
                    # If the class has defined outputs, we can map directly according to the class definition
                    if output_types[i] in AGENT_TYPE_MAPPING:
                         decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](snake_case__							)							)
                    else:
                         decoded_outputs.append(AgentType(snake_case__							)							)
               else:
                    # If the class does not have defined output, then we map according to the type
                    __A		   =      False
                    for _k, _v in INSTANCE_TYPE_MAPPING.items():
                         if isinstance(snake_case__							,			_k							):
                              decoded_outputs.append(_v(snake_case__							)							)
                              __A		   =      True

                    if not found:
                         decoded_outputs.append(AgentType(snake_case__							)							)

     else:
          if output_types[0] in AGENT_TYPE_MAPPING:
               # If the class has defined outputs, we can map directly according to the class definition
               __A		   =      AGENT_TYPE_MAPPING[output_types[0]](snake_case__							)

          else:
               # If the class does not have defined output, then we map according to the type
               for _k, _v in INSTANCE_TYPE_MAPPING.items():
                    if isinstance(snake_case__							,			_k							):
                         return _v(snake_case__							)
               return AgentType(snake_case__							)

     return decoded_outputs










'''simple docstring'''







from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       from ..models.auto.modeling_auto import (
           MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
           MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
           MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
           MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
       )


__A      :       int      =	logging.get_logger(__name__)


__A      :       Optional[int]      =	Dict[str, Any]
__A      :       Any      =	List[Prediction]






@add_end_docstrings(lowerCamelCase						)
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           super().__init__(*lowercase__				,				**lowercase__					)

           if self.framework == "tf":
                 raise ValueError(f"The {self.__class__} is only available in PyTorch."					)

           requires_backends(self				,				"vision"					)
           self.check_model_type(
               dict(
                   MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                   + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)






     def     _UpperCamelCase   (self    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






     def __call__(self    :	int				,				lowercase__    :	Any				,				**lowercase__    :	Dict					)  ->       Union[Predictions, List[Prediction]]:
           return super().__call__(lowercase__				,				**lowercase__					)






     def     _UpperCamelCase   (self    :	Optional[Any]				,				lowercase__    :	int				,				lowercase__    :	Union[str, Any]=None					)  ->       Any:
           __A		   =      load_image(lowercase__					)
           __A		   =      [(image.height, image.width)]
           if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                       __A		   =      {}
                 else:
                       __A		   =      {"task_inputs": [subtask]}
                 __A		   =      self.image_processor(images=[image]				,				return_tensors="pt"				,				**lowercase__					)
                 __A		   =      self.tokenizer(
                     inputs["task_inputs"]				,				padding="max_length"				,				max_length=self.model.config.task_seq_len				,				return_tensors=self.framework				,				)["input_ids"]
           else:
                 __A		   =      self.image_processor(images=[image]				,				return_tensors="pt"					)
           __A		   =      target_size
           return inputs






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

           if fn is not None:
                 __A		   =      fn(
                     lowercase__				,				threshold=lowercase__				,				mask_threshold=lowercase__				,				overlap_mask_area_threshold=lowercase__				,				target_sizes=model_outputs["target_size"]				,				)[0]

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                       __A		   =      (segmentation == segment["id"]) * 255
                       __A		   =      Image.fromarray(mask.numpy().astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[segment["label_id"]]
                       __A		   =      segment["score"]
                       annotation.append({"score": score, "label": label, "mask": mask}					)

           elif subtask in {"semantic", None} and hasattr(self.image_processor				,				"post_process_semantic_segmentation"					):
                 __A		   =      self.image_processor.post_process_semantic_segmentation(
                     lowercase__				,				target_sizes=model_outputs["target_size"]					)[0]

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

                 for label in labels:
                       __A		   =      (segmentation == label) * 255
                       __A		   =      Image.fromarray(mask.astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[label]
                       annotation.append({"score": None, "label": label, "mask": mask}					)
           else:
                 raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model					)}"					)
           return annotation


