
"""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      :							Dict          =					logging.get_logger(__name__)


UpperCamelCase      :							List[Any]          =					Dict[str, Any]
UpperCamelCase      :							List[Any]          =					List[Prediction]

@add_end_docstrings(_a     )
class __lowerCAmelCase						( _a     ):





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

      '''simple docstring'''




      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 		UpperCAmelCase   (      self      ,	**__UpperCAmelCase ):

      '''simple docstring'''




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

      return preprocess_kwargs, {}, postprocess_kwargs





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

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=None ):

      '''simple docstring'''




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





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase ):

      '''simple docstring'''




      __UpperCamelCase							    =			model_inputs.pop('target_size' )
      __UpperCamelCase							    =			self.model(**__lowerCamelCase )
      __UpperCamelCase							    =			target_size
      return model_outputs







     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=None      ,	__UpperCAmelCase=0.9      ,	__UpperCAmelCase=0.5      ,	__UpperCAmelCase=0.5 ):

      '''simple docstring'''




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

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

       __UpperCamelCase							    =			[]
       __UpperCamelCase							    =			outputs["""segmentation"""]

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

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

       __UpperCamelCase							    =			[]
       __UpperCamelCase							    =			outputs.numpy()
       __UpperCamelCase							    =			np.unique(__lowerCamelCase )

       for label in labels:
        __UpperCamelCase							    =			(segmentation == label) * 255
        __UpperCamelCase							    =			Image.fromarray(mask.astype(np.uinta )      ,	mode='L' )
        __UpperCamelCase							    =			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











# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_					:		int           = '''Update an existing config file with the latest defaults while maintaining the old configuration.'''

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str   )						->     Dict:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      args.config_file
	if config_file is None and Path(__magic_name__   ).exists():
		UpperCamelCase	:int		=      default_config_file
	elif not Path(__magic_name__   ).exists():
		raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""   )
	UpperCamelCase	:Dict		=      load_config_from_file(__magic_name__   )

	if config_file.endswith(""".json"""   ):
		config.to_json_file(__magic_name__   )
	else:
		config.to_yaml_file(__magic_name__   )
	return config_file

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Optional[Any]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      parser.add_parser("""update"""      ,     parents=__magic_name__      ,     help=__magic_name__      ,     formatter_class=__magic_name__   )
	parser.add_argument(
	    """--config_file"""      ,     default=__magic_name__      ,     help=(
	        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )      ,     )

	parser.set_defaults(func=__magic_name__   )
	return parser

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, Any]   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      update_config(__magic_name__   )
	print(f"""Sucessfully updated the configuration file at {config_file}."""   )
