




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,
				)


lowercase_		=							logging.get_logger(__name__)


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





@add_end_docstrings(__SCREAMING_SNAKE_CASE			)
class 		A__    (       __SCREAMING_SNAKE_CASE			):






	def __init__(     self    ,					*lowerCamelCase    ,					**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 			lowercase				(     self    ,					**lowerCamelCase						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							{}
						__magic_name__ :							Tuple		       =							{}
						if "subtask" in kwargs:
											__magic_name__ :							Tuple		       =							kwargs['''subtask''']
											__magic_name__ :							str		       =							kwargs['''subtask''']
						if "threshold" in kwargs:
											__magic_name__ :							Optional[Any]		       =							kwargs['''threshold''']
						if "mask_threshold" in kwargs:
											__magic_name__ :							Tuple		       =							kwargs['''mask_threshold''']
						if "overlap_mask_area_threshold" in kwargs:
											__magic_name__ :							Union[str, Any]		       =							kwargs['''overlap_mask_area_threshold''']

						return preprocess_kwargs, {}, postprocess_kwargs






	def __call__(     self    ,					lowerCamelCase    ,					**lowerCamelCase						)				->       Union[Predictions, List[Prediction]]:




						"""simple docstring"""






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






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None						)				->       Optional[Any]:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							load_image(lowerCamelCase						)
						__magic_name__ :							Dict		       =							[(image.height, image.width)]
						if self.model.config.__class__.__name__ == "OneFormerConfig":
											if subtask is None:
																__magic_name__ :							Optional[Any]		       =							{}
											else:
																__magic_name__ :							int		       =							{'''task_inputs''': [subtask]}
											__magic_name__ :							List[Any]		       =							self.image_processor(images=[image]    ,					return_tensors='''pt'''    ,					**lowerCamelCase						)
											__magic_name__ :							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:
											__magic_name__ :							str		       =							self.image_processor(images=[image]    ,					return_tensors='''pt'''						)
						__magic_name__ :							List[str]		       =							target_size
						return inputs






	def 			lowercase				(     self    ,					lowerCamelCase						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							model_inputs.pop('''target_size'''						)
						__magic_name__ :							int		       =							self.model(**lowerCamelCase						)
						__magic_name__ :							Dict		       =							target_size
						return model_outputs






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase=None    ,					lowerCamelCase=0.9    ,					lowerCamelCase=0.5    ,					lowerCamelCase=0.5						)				->       List[str]:




						"""simple docstring"""






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

						if fn is not None:
											__magic_name__ :							Union[str, Any]		       =							fn(
											    lowerCamelCase    ,					threshold=lowerCamelCase    ,					mask_threshold=lowerCamelCase    ,					overlap_mask_area_threshold=lowerCamelCase    ,					target_sizes=model_outputs['''target_size''']    ,					)[0]

											__magic_name__ :							int		       =							[]
											__magic_name__ :							Union[str, Any]		       =							outputs['''segmentation''']

											for segment in outputs["segments_info"]:
																__magic_name__ :							Optional[int]		       =							(segmentation == segment['''id''']) * 255
																__magic_name__ :							Dict		       =							Image.fromarray(mask.numpy().astype(np.uinta						)    ,					mode='''L'''						)
																__magic_name__ :							str		       =							self.model.config.idalabel[segment['''label_id''']]
																__magic_name__ :							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'''						):
											__magic_name__ :							List[Any]		       =							self.image_processor.post_process_semantic_segmentation(
											    lowerCamelCase    ,					target_sizes=model_outputs['''target_size''']						)[0]

											__magic_name__ :							str		       =							[]
											__magic_name__ :							Optional[Any]		       =							outputs.numpy()
											__magic_name__ :							Tuple		       =							np.unique(lowerCamelCase						)

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









import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
				from PIL import Image





class 		A__    (       unittest.TestCase		,					__SCREAMING_SNAKE_CASE			):






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							load_tool('''image-question-answering'''						)
						self.tool.setup()
						__magic_name__ :							Any		       =							load_tool('''image-question-answering'''    ,					remote=lowerCamelCase						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							int		       =							self.remote_tool(lowerCamelCase    ,					'''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							int		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)






	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''						)						) / '''000000039769.png'''						)
						__magic_name__ :							Tuple		       =							self.remote_tool(image=lowerCamelCase    ,					question='''How many cats are sleeping on the couch?'''						)
						self.assertEqual(lowerCamelCase    ,					'''2'''						)



