




'''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_ 			=		logging.get_logger(__name__)


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






@add_end_docstrings(lowerCAmelCase_       )
class 					lowercase_			(	lowerCAmelCase_       ):





     def __init__(    self    :				Optional[int]    ,       *__lowerCamelCase    :				Optional[int]    ,       **__lowerCamelCase    :				Dict ):
           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  _lowerCAmelCase			(    self    :				Dict    ,       **__lowerCamelCase    :				Optional[Any] ):
           snake_case__					:      Union[str, Any]  			=						{}
           snake_case__					:      List[str]  			=						{}
           if "subtask" in kwargs:
                 snake_case__					:      Optional[Any]  			=						kwargs['subtask']
                 snake_case__					:      Union[str, Any]  			=						kwargs['subtask']
           if "threshold" in kwargs:
                 snake_case__					:      int  			=						kwargs['threshold']
           if "mask_threshold" in kwargs:
                 snake_case__					:      List[str]  			=						kwargs['mask_threshold']
           if "overlap_mask_area_threshold" in kwargs:
                 snake_case__					:      List[str]  			=						kwargs['overlap_mask_area_threshold']

           return preprocess_kwargs, {}, postprocess_kwargs





     def __call__(    self    :				Dict    ,       __lowerCamelCase    :				Union[str, Any]    ,       **__lowerCamelCase    :				int ):
           return super().__call__(__lowerCamelCase    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				Dict    ,       __lowerCamelCase    :				Tuple=None ):
           snake_case__					:      Optional[Any]  			=						load_image(__lowerCamelCase )
           snake_case__					:      Any  			=						[(image.height, image.width)]
           if self.model.config.__class__.__name__ == "OneFormerConfig":
                 if subtask is None:
                       snake_case__					:      Optional[int]  			=						{}
                 else:
                       snake_case__					:      Any  			=						{'task_inputs': [subtask]}
                 snake_case__					:      Tuple  			=						self.image_processor(images=[image]    ,       return_tensors='pt'    ,       **__lowerCamelCase )
                 snake_case__					:      List[str]  			=						self.tokenizer(
                     inputs['task_inputs']    ,       padding='max_length'    ,       max_length=self.model.config.task_seq_len    ,       return_tensors=self.framework    ,       )['input_ids']
           else:
                 snake_case__					:      Any  			=						self.image_processor(images=[image]    ,       return_tensors='pt' )
           snake_case__					:      str  			=						target_size
           return inputs





     def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				Optional[Any] ):
           snake_case__					:      Union[str, Any]  			=						model_inputs.pop('target_size' )
           snake_case__					:      Tuple  			=						self.model(**__lowerCamelCase )
           snake_case__					:      Optional[Any]  			=						target_size
           return model_outputs





     def  _lowerCAmelCase			(    self    :				Tuple    ,       __lowerCamelCase    :				Optional[Any]    ,       __lowerCamelCase    :				Tuple=None    ,       __lowerCamelCase    :				List[str]=0.9    ,       __lowerCamelCase    :				Union[str, Any]=0.5    ,       __lowerCamelCase    :				Any=0.5 ):
           snake_case__					:      Dict  			=						None
           if subtask in {"panoptic", None} and hasattr(self.image_processor    ,       'post_process_panoptic_segmentation' ):
                 snake_case__					:      Optional[Any]  			=						self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor    ,       'post_process_instance_segmentation' ):
                 snake_case__					:      Optional[Any]  			=						self.image_processor.post_process_instance_segmentation

           if fn is not None:
                 snake_case__					:      List[Any]  			=						fn(
                     __lowerCamelCase    ,       threshold=__lowerCamelCase    ,       mask_threshold=__lowerCamelCase    ,       overlap_mask_area_threshold=__lowerCamelCase    ,       target_sizes=model_outputs['target_size']    ,       )[0]

                 snake_case__					:      Optional[Any]  			=						[]
                 snake_case__					:      Union[str, Any]  			=						outputs['segmentation']

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

           elif subtask in {"semantic", None} and hasattr(self.image_processor    ,       'post_process_semantic_segmentation' ):
                 snake_case__					:      List[str]  			=						self.image_processor.post_process_semantic_segmentation(
                     __lowerCamelCase    ,       target_sizes=model_outputs['target_size'] )[0]

                 snake_case__					:      str  			=						[]
                 snake_case__					:      str  			=						outputs.numpy()
                 snake_case__					:      str  			=						np.unique(__lowerCamelCase )

                 for label in labels:
                       snake_case__					:      Optional[int]  			=						(segmentation == label) * 255
                       snake_case__					:      Optional[int]  			=						Image.fromarray(mask.astype(np.uinta )    ,       mode='L' )
                       snake_case__					:      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






'''simple docstring'''






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


A_ 			=		{
    "configuration_ernie_m": ["ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieMConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		["ErnieMTokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_ 			=		[
          "ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ErnieMForMultipleChoice",
          "ErnieMForQuestionAnswering",
          "ErnieMForSequenceClassification",
          "ErnieMForTokenClassification",
          "ErnieMModel",
          "ErnieMPreTrainedModel",
          "ErnieMForInformationExtraction",
      ]


if TYPE_CHECKING:
      from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_ernie_m import ErnieMTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie_m import (
                ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieMForInformationExtraction,
                ErnieMForMultipleChoice,
                ErnieMForQuestionAnswering,
                ErnieMForSequenceClassification,
                ErnieMForTokenClassification,
                ErnieMModel,
                ErnieMPreTrainedModel,
            )


else:
      import sys

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