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,
  )


UpperCAmelCase				 =   logging.get_logger(__name__)


UpperCAmelCase				 =   Dict[str, Any]
UpperCAmelCase				 =   List[Prediction]






@add_end_docstrings(snake_case__						)
class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   def __init__(     self				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->int:
        super().__init__(*lowerCAmelCase_				,  **lowerCAmelCase_     )

        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 _lowercase(     self				,  **lowerCAmelCase_     )						->Tuple:
        a	:     Tuple						      =    {}
        a	:     str						      =    {}
        if "subtask" in kwargs:
             a	:     Union[str, Any]						      =    kwargs['''subtask''']
             a	:     int						      =    kwargs['''subtask''']
        if "threshold" in kwargs:
             a	:     Tuple						      =    kwargs['''threshold''']
        if "mask_threshold" in kwargs:
             a	:     List[str]						      =    kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
             a	:     Optional[Any]						      =    kwargs['''overlap_mask_area_threshold''']

        return preprocess_kwargs, {}, postprocess_kwargs
   def __call__(     self				,  lowerCAmelCase_				,  **lowerCAmelCase_     )						->Union[Predictions, List[Prediction]]:
        return super().__call__(lowerCAmelCase_				,  **lowerCAmelCase_     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None     )						->List[Any]:
        a	:     Dict						      =    load_image(lowerCAmelCase_     )
        a	:     List[str]						      =    [(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
             if subtask is None:
                  a	:     Union[str, Any]						      =    {}
             else:
                  a	:     str						      =    {'''task_inputs''': [subtask]}
             a	:     Optional[Any]						      =    self.image_processor(images=[image]				,  return_tensors='''pt'''				,  **lowerCAmelCase_     )
             a	:     Optional[Any]						      =    self.tokenizer(
                 inputs['''task_inputs''']				,  padding='''max_length'''				,  max_length=self.model.config.task_seq_len				,  return_tensors=self.framework				,  )['''input_ids''']
        else:
             a	:     List[str]						      =    self.image_processor(images=[image]				,  return_tensors='''pt'''     )
        a	:     List[str]						      =    target_size
        return inputs
   def _lowercase(     self				,  lowerCAmelCase_     )						->Any:
        a	:     Dict						      =    model_inputs.pop('''target_size'''     )
        a	:     Any						      =    self.model(**lowerCAmelCase_     )
        a	:     List[str]						      =    target_size
        return model_outputs


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None				,  lowerCAmelCase_=0.9				,  lowerCAmelCase_=0.5				,  lowerCAmelCase_=0.5     )						->int:
        a	:     str						      =    None
        if subtask in {"panoptic", None} and hasattr(self.image_processor				,  '''post_process_panoptic_segmentation'''     ):
             a	:     Tuple						      =    self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor				,  '''post_process_instance_segmentation'''     ):
             a	:     Any						      =    self.image_processor.post_process_instance_segmentation

        if fn is not None:
             a	:     Optional[Any]						      =    fn(
                 lowerCAmelCase_				,  threshold=lowerCAmelCase_				,  mask_threshold=lowerCAmelCase_				,  overlap_mask_area_threshold=lowerCAmelCase_				,  target_sizes=model_outputs['''target_size''']				,  )[0]

             a	:     Optional[int]						      =    []
             a	:     str						      =    outputs['''segmentation''']

             for segment in outputs["segments_info"]:
                  a	:     Union[str, Any]						      =    (segmentation == segment['''id''']) * 2_55
                  a	:     Optional[Any]						      =    Image.fromarray(mask.numpy().astype(np.uinta     )				,  mode='''L'''     )
                  a	:     Dict						      =    self.model.config.idalabel[segment['''label_id''']]
                  a	:     Optional[int]						      =    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	:     int						      =    self.image_processor.post_process_semantic_segmentation(
                 lowerCAmelCase_				,  target_sizes=model_outputs['''target_size''']     )[0]

             a	:     Dict						      =    []
             a	:     str						      =    outputs.numpy()
             a	:     Any						      =    np.unique(lowerCAmelCase_     )

             for label in labels:
                  a	:     Optional[int]						      =    (segmentation == label) * 2_55
                  a	:     Union[str, Any]						      =    Image.fromarray(mask.astype(np.uinta     )				,  mode='''L'''     )
                  a	:     Optional[int]						      =    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
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase				 =   datasets.utils.logging.get_logger(__name__)






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilderConfig						):
   UpperCamelCase_					:    bool			      = None
   UpperCamelCase_					:    bool			      = None






class 				_SCREAMING_SNAKE_CASE( folder_based_builder.FolderBasedBuilder						):
   UpperCamelCase_					:    List[str]			      = datasets.Image()
   UpperCamelCase_					:    Any			      = 'image'
   UpperCamelCase_					:    str			      = ImageFolderConfig
   UpperCamelCase_					:    List[str]  # definition at the bottom of the script
   UpperCamelCase_					:    Union[str, Any]			      = ImageClassification(image_column='image'							,   label_column='label'						)

UpperCAmelCase				 =   [
    """.blp""",
    """.bmp""",
    """.dib""",
    """.bufr""",
    """.cur""",
    """.pcx""",
    """.dcx""",
    """.dds""",
    """.ps""",
    """.eps""",
    """.fit""",
    """.fits""",
    """.fli""",
    """.flc""",
    """.ftc""",
    """.ftu""",
    """.gbr""",
    """.gif""",
    """.grib""",
    """.h5""",
    """.hdf""",
    """.png""",
    """.apng""",
    """.jp2""",
    """.j2k""",
    """.jpc""",
    """.jpf""",
    """.jpx""",
    """.j2c""",
    """.icns""",
    """.ico""",
    """.im""",
    """.iim""",
    """.tif""",
    """.tiff""",
    """.jfif""",
    """.jpe""",
    """.jpg""",
    """.jpeg""",
    """.mpg""",
    """.mpeg""",
    """.msp""",
    """.pcd""",
    """.pxr""",
    """.pbm""",
    """.pgm""",
    """.ppm""",
    """.pnm""",
    """.psd""",
    """.bw""",
    """.rgb""",
    """.rgba""",
    """.sgi""",
    """.ras""",
    """.tga""",
    """.icb""",
    """.vda""",
    """.vst""",
    """.webp""",
    """.wmf""",
    """.emf""",
    """.xbm""",
    """.xpm""",
]
UpperCAmelCase				 =   IMAGE_EXTENSIONS