





"""simple docstring"""
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
       import torch

__UpperCamelCase    :	str						=     logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
       from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

       def       _SCREAMING_SNAKE_CASE (cls				:						Any ):
        return cls

def       _SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Union[Tuple, List]     ,    _UpperCAmelCase				:						Optional[Union[List["torch.Generator"], "torch.Generator"]] = None     ,    _UpperCAmelCase				:						Optional["torch.device"] = None     ,    _UpperCAmelCase				:						Optional["torch.dtype"] = None     ,    _UpperCAmelCase				:						Optional["torch.layout"] = None     ,    ):
 lowerCAmelCase													=   device
 lowerCAmelCase													=   shape[0]

 lowerCAmelCase													=   layout or torch.strided
 lowerCAmelCase													=   device or torch.device('cpu' )

 if generator is not None:
  lowerCAmelCase													=   generator.device.type if not isinstance(_UpperCAmelCase     ,    _UpperCAmelCase ) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   lowerCAmelCase													=   "cpu"
   if device != "mps":
    logger.info(
        F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
        F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
        F' slighly speed up this function by passing a generator that was created on the {device} device.' )
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.' )

 if isinstance(_UpperCAmelCase     ,    _UpperCAmelCase ):
  lowerCAmelCase													=   (1,) + shape[1:]
  lowerCAmelCase													=   [
      torch.randn(_UpperCAmelCase     ,    generator=generator[i]     ,    device=_UpperCAmelCase     ,    dtype=_UpperCAmelCase     ,    layout=_UpperCAmelCase )
      for i in range(_UpperCAmelCase )
  ]
  lowerCAmelCase													=   torch.cat(_UpperCAmelCase     ,    dim=0 ).to(_UpperCAmelCase )
 else:
  lowerCAmelCase													=   torch.randn(_UpperCAmelCase     ,    generator=_UpperCAmelCase     ,    device=_UpperCAmelCase     ,    dtype=_UpperCAmelCase     ,    layout=_UpperCAmelCase ).to(_UpperCAmelCase )

 return latents




def       _SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						str ):



 if is_torch_version('<'     ,    '2.0.0' ) or not hasattr(_UpperCAmelCase     ,    '_dynamo' ):
  return False
 return isinstance(_UpperCAmelCase     ,    torch._dynamo.eval_frame.OptimizedModule )












"""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,
       )


__UpperCamelCase    :	str						=     logging.get_logger(__name__)


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







@add_end_docstrings(a__     )
class 			a       (      a__     ):



 def __init__(    self							,					*_snake_case							,					**_snake_case    ):







  """simple docstring"""






  super().__init__(*_snake_case							,					**_snake_case    )

  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							,					**_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   {}
  lowerCAmelCase													=   {}
  if "subtask" in kwargs:
   lowerCAmelCase													=   kwargs['subtask']
   lowerCAmelCase													=   kwargs['subtask']
  if "threshold" in kwargs:
   lowerCAmelCase													=   kwargs['threshold']
  if "mask_threshold" in kwargs:
   lowerCAmelCase													=   kwargs['mask_threshold']
  if "overlap_mask_area_threshold" in kwargs:
   lowerCAmelCase													=   kwargs['overlap_mask_area_threshold']

  return preprocess_kwargs, {}, postprocess_kwargs



 def __call__(    self							,					_snake_case							,					**_snake_case    ):







  """simple docstring"""






  return super().__call__(_snake_case							,					**_snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






  lowerCAmelCase													=   load_image(_snake_case    )
  lowerCAmelCase													=   [(image.height, image.width)]
  if self.model.config.__class__.__name__ == "OneFormerConfig":
   if subtask is None:
    lowerCAmelCase													=   {}
   else:
    lowerCAmelCase													=   {'task_inputs': [subtask]}
   lowerCAmelCase													=   self.image_processor(images=[image]							,					return_tensors='pt'							,					**_snake_case    )
   lowerCAmelCase													=   self.tokenizer(
       inputs['task_inputs']							,					padding='max_length'							,					max_length=self.model.config.task_seq_len							,					return_tensors=self.framework							,					)['input_ids']
  else:
   lowerCAmelCase													=   self.image_processor(images=[image]							,					return_tensors='pt'    )
  lowerCAmelCase													=   target_size
  return inputs



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   model_inputs.pop('target_size'    )
  lowerCAmelCase													=   self.model(**_snake_case    )
  lowerCAmelCase													=   target_size
  return model_outputs



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None							,					_snake_case=0.9							,					_snake_case=0.5							,					_snake_case=0.5    ):







  """simple docstring"""






  lowerCAmelCase													=   None
  if subtask in {"panoptic", None} and hasattr(self.image_processor							,					'post_process_panoptic_segmentation'    ):
   lowerCAmelCase													=   self.image_processor.post_process_panoptic_segmentation
  elif subtask in {"instance", None} and hasattr(self.image_processor							,					'post_process_instance_segmentation'    ):
   lowerCAmelCase													=   self.image_processor.post_process_instance_segmentation

  if fn is not None:
   lowerCAmelCase													=   fn(
       _snake_case							,					threshold=_snake_case							,					mask_threshold=_snake_case							,					overlap_mask_area_threshold=_snake_case							,					target_sizes=model_outputs['target_size']							,					)[0]

   lowerCAmelCase													=   []
   lowerCAmelCase													=   outputs['segmentation']

   for segment in outputs["segments_info"]:
    lowerCAmelCase													=   (segmentation == segment['id']) * 2_55
    lowerCAmelCase													=   Image.fromarray(mask.numpy().astype(np.uinta    )							,					mode='L'    )
    lowerCAmelCase													=   self.model.config.idalabel[segment['label_id']]
    lowerCAmelCase													=   segment['score']
    annotation.append({'score': score, 'label': label, 'mask': mask}    )

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

   lowerCAmelCase													=   []
   lowerCAmelCase													=   outputs.numpy()
   lowerCAmelCase													=   np.unique(_snake_case    )

   for label in labels:
    lowerCAmelCase													=   (segmentation == label) * 2_55
    lowerCAmelCase													=   Image.fromarray(mask.astype(np.uinta    )							,					mode='L'    )
    lowerCAmelCase													=   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





