




'''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,
 )


lowerCamelCase_	 =		logging.get_logger(__name__)


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







@add_end_docstrings(A_     )
class 	lowercase_					(				A_     ):


   """simple docstring"""

   def __init__(					self      :			Dict						,   *__lowerCamelCase      :			Any						,   **__lowerCamelCase      :			Optional[Any]					):

      """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 							lowerCAmelCase_    (					self      :			Tuple						,   **__lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   {}
      _SCREAMING_SNAKE_CASE =   {}
      if "subtask" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs['''subtask''']
         _SCREAMING_SNAKE_CASE =   kwargs['''subtask''']
      if "threshold" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs['''threshold''']
      if "mask_threshold" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs['''mask_threshold''']
      if "overlap_mask_area_threshold" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs['''overlap_mask_area_threshold''']

      return preprocess_kwargs, {}, postprocess_kwargs



   def __call__(					self      :			Optional[int]						,   __lowerCamelCase      :			Tuple						,   **__lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      return super().__call__(__lowerCamelCase						,   **__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Tuple=None					):

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



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   model_inputs.pop("target_size"					)
      _SCREAMING_SNAKE_CASE =   self.model(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   target_size
      return model_outputs



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			List[str]						,   __lowerCamelCase      :			str=None						,   __lowerCamelCase      :			str=0.9						,   __lowerCamelCase      :			Dict=0.5						,   __lowerCamelCase      :			int=0.5					):

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

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

         _SCREAMING_SNAKE_CASE =   []
         _SCREAMING_SNAKE_CASE =   outputs['''segmentation''']

         for segment in outputs["segments_info"]:
            _SCREAMING_SNAKE_CASE =   (segmentation == segment['''id''']) * 2_5_5
            _SCREAMING_SNAKE_CASE =   Image.fromarray(mask.numpy().astype(np.uinta					)						,   mode="L"					)
            _SCREAMING_SNAKE_CASE =   self.model.config.idalabel[segment['''label_id''']]
            _SCREAMING_SNAKE_CASE =   segment['''score''']
            annotation.append({"score": score, "label": label, "mask": mask}					)

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

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

         for label in labels:
            _SCREAMING_SNAKE_CASE =   (segmentation == label) * 2_5_5
            _SCREAMING_SNAKE_CASE =   Image.fromarray(mask.astype(np.uinta					)						,   mode="L"					)
            _SCREAMING_SNAKE_CASE =   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'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils







class 	lowercase_					(				unittest.TestCase     ):


   """simple docstring"""

   @patch("diffusers.utils.hub_utils.get_full_repo_name"					)
   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Mock					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "full_repo_name"
      with TemporaryDirectory() as tmpdir:
         # Dummy args values
         _SCREAMING_SNAKE_CASE =   Mock()
         _SCREAMING_SNAKE_CASE =   tmpdir
         _SCREAMING_SNAKE_CASE =   0
         _SCREAMING_SNAKE_CASE =   "hub_token"
         _SCREAMING_SNAKE_CASE =   "dataset_name"
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0_0
         _SCREAMING_SNAKE_CASE =   1_0_0_0_0
         _SCREAMING_SNAKE_CASE =   0.0_1
         _SCREAMING_SNAKE_CASE =   0.0_2
         _SCREAMING_SNAKE_CASE =   0.0_3
         _SCREAMING_SNAKE_CASE =   0.0_0_0_5
         _SCREAMING_SNAKE_CASE =   0.0_0_0_0_0_1
         _SCREAMING_SNAKE_CASE =   1
         _SCREAMING_SNAKE_CASE =   1_0
         _SCREAMING_SNAKE_CASE =   0.0_0_1
         _SCREAMING_SNAKE_CASE =   0.1
         _SCREAMING_SNAKE_CASE =   0.2
         _SCREAMING_SNAKE_CASE =   True

         # Model card mush be rendered and saved
         diffusers.utils.hub_utils.create_model_card(__lowerCamelCase						,   model_name="model_name"					)
         self.assertTrue((Path(__lowerCamelCase					) / "README.md").is_file()					)

