


"""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,
					)


lowercase  		=			logging.get_logger(__name__)


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


@add_end_docstrings(_lowercase)
class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							def __init__(						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''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	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      {}
								snake_case__  :						Optional[Any]		=      {}
								if "subtask" in kwargs:
									snake_case__  :						Optional[int]		=      kwargs["subtask"]
									snake_case__  :						List[str]		=      kwargs["subtask"]
								if "threshold" in kwargs:
									snake_case__  :						Any		=      kwargs["threshold"]
								if "mask_threshold" in kwargs:
									snake_case__  :						Optional[int]		=      kwargs["mask_threshold"]
								if "overlap_mask_area_threshold" in kwargs:
									snake_case__  :						List[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        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None)   ->	Any:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      model_inputs.pop("target_size")
								snake_case__  :						Tuple		=      self.model(**lowerCamelCase__)
								snake_case__  :						Tuple		=      target_size
								return model_outputs







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__=0.9	,					lowerCamelCase__=0.5	,					lowerCamelCase__=0.5)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[Any]		=      None
								if subtask in {"panoptic", None} and hasattr(self.image_processor	,					"post_process_panoptic_segmentation"):
									snake_case__  :						Union[str, 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__  :						Union[str, Any]		=      fn(
									    lowerCamelCase__	,					threshold=lowerCamelCase__	,					mask_threshold=lowerCamelCase__	,					overlap_mask_area_threshold=lowerCamelCase__	,					target_sizes=model_outputs["target_size"]	,					)[0]

									snake_case__  :						Tuple		=      []
									snake_case__  :						Optional[Any]		=      outputs["segmentation"]

									for segment in outputs["segments_info"]:
										snake_case__  :						Union[str, Any]		=      (segmentation == segment["id"]) * 255
										snake_case__  :						int		=      Image.fromarray(mask.numpy().astype(np.uinta)	,					mode="L")
										snake_case__  :						str		=      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__  :						Dict		=      self.image_processor.post_process_semantic_segmentation(
									    lowerCamelCase__	,					target_sizes=model_outputs["target_size"])[0]

									snake_case__  :						Optional[Any]		=      []
									snake_case__  :						Optional[Any]		=      outputs.numpy()
									snake_case__  :						List[str]		=      np.unique(lowerCamelCase__)

									for label in labels:
										snake_case__  :						List[Any]		=      (segmentation == label) * 255
										snake_case__  :						Tuple		=      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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return self.tokenizer.batch_decode(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.decode(*lowerCamelCase__	,					**lowerCamelCase__)







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





