






"""simple docstring"""
from typing import Any, Dict, List, Union

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 ..image_utils import load_image


if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCamelCase_       			=   logging.get_logger(__name__)


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







@add_end_docstrings(__A  )
class     UpperCamelCase_ (__A  ):





	def __init__(   self      :				List[Any]			,			*lowerCAmelCase_      :				List[str]			,			**lowerCAmelCase_      :				int		)		->    List[Any]:
				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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    List[str]:
				UpperCAmelCase_  :				Any      =      {}
				if "threshold" in kwargs:
							UpperCAmelCase_  :				str      =      kwargs["threshold"]
				return {}, {}, postprocess_kwargs





	def __call__(   self      :				Dict			,			*lowerCAmelCase_      :				Union[str, Any]			,			**lowerCAmelCase_      :				Optional[int]		)		->    Union[Predictions, List[Prediction]]:
				return super().__call__(*lowerCAmelCase_			,			**lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict			,			lowerCAmelCase_      :				Tuple		)		->    List[Any]:
				UpperCAmelCase_  :				List[str]      =      load_image(lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      torch.IntTensor([[image.height, image.width]]		)
				UpperCAmelCase_  :				Optional[int]      =      self.image_processor(images=[image]			,			return_tensors="pt"		)
				if self.tokenizer is not None:
							UpperCAmelCase_  :				Optional[int]      =      self.tokenizer(text=inputs["words"]			,			boxes=inputs["boxes"]			,			return_tensors="pt"		)
				UpperCAmelCase_  :				List[str]      =      target_size
				return inputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				Tuple		)		->    str:
				UpperCAmelCase_  :				int      =      model_inputs.pop("target_size"		)
				UpperCAmelCase_  :				int      =      self.model(**lowerCAmelCase_		)
				UpperCAmelCase_  :				int      =      outputs.__class__({"target_size": target_size, **outputs}		)
				if self.tokenizer is not None:
							UpperCAmelCase_  :				Tuple      =      model_inputs["bbox"]
				return model_outputs





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Optional[int]=0.9		)		->    int:
				UpperCAmelCase_  :				Tuple      =      model_outputs["target_size"]
				if self.tokenizer is not None:
							# This is a LayoutLMForTokenClassification variant.
							# The OCR got the boxes and the model classified the words.
							UpperCAmelCase_  ,			UpperCAmelCase_  :				Any      =      target_size[0].tolist()

							def unnormalize(lowerCAmelCase_      :				int		):
										return self._get_bounding_box(
										    torch.Tensor(
										        [
										            (width * bbox[0] / 1_000),
										            (height * bbox[1] / 1_000),
										            (width * bbox[2] / 1_000),
										            (height * bbox[3] / 1_000),
										        ]		)		)

							UpperCAmelCase_  ,			UpperCAmelCase_  :				Optional[int]      =      model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
							UpperCAmelCase_  :				Union[str, Any]      =      [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
							UpperCAmelCase_  :				str      =      [unnormalize(lowerCAmelCase_		) for bbox in model_outputs["bbox"].squeeze(0		)]
							UpperCAmelCase_  :				List[Any]      =      ["score", "label", "box"]
							UpperCAmelCase_  :				Optional[int]      =      [dict(zip(lowerCAmelCase_			,			lowerCAmelCase_		)		) for vals in zip(scores.tolist()			,			lowerCAmelCase_			,			lowerCAmelCase_		) if vals[0] > threshold]
				else:
							# This is a regular ForObjectDetectionModel
							UpperCAmelCase_  :				Tuple      =      self.image_processor.post_process_object_detection(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)
							UpperCAmelCase_  :				Union[str, Any]      =      raw_annotations[0]
							UpperCAmelCase_  :				Tuple      =      raw_annotation["scores"]
							UpperCAmelCase_  :				Optional[Any]      =      raw_annotation["labels"]
							UpperCAmelCase_  :				Optional[int]      =      raw_annotation["boxes"]

							UpperCAmelCase_  :				int      =      scores.tolist()
							UpperCAmelCase_  :				Optional[int]      =      [self.model.config.idalabel[label.item()] for label in labels]
							UpperCAmelCase_  :				List[str]      =      [self._get_bounding_box(lowerCAmelCase_		) for box in boxes]

							# {"scores": [...], ...} --> [{"score":x, ...}, ...]
							UpperCAmelCase_  :				Any      =      ["score", "label", "box"]
							UpperCAmelCase_  :				List[str]      =      [
							    dict(zip(lowerCAmelCase_			,			lowerCAmelCase_		)		)
							    for vals in zip(raw_annotation["scores"]			,			raw_annotation["labels"]			,			raw_annotation["boxes"]		)
							]

				return annotation





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]			,			lowerCAmelCase_      :				"torch.Tensor"		)		->    Dict[str, int]:
				if self.framework != "pt":
							raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
				UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				List[Any]      =      box.int().tolist()
				UpperCAmelCase_  :				List[Any]      =      {
				    "xmin": xmin,
				    "ymin": ymin,
				    "xmax": xmax,
				    "ymax": ymax,
				}
				return bbox











"""simple docstring"""
from ..utils import DummyObject, requires_backends







class     UpperCamelCase_ (metaclass=__A  ):
	__magic_name__			   =       ['''onnx''']





	def __init__(   self      :				List[Any]			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				Dict		)		->    Dict:
				requires_backends(self			,			["onnx"]		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				Optional[Any]			,			*lowerCAmelCase_      :				Dict			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    int:
				requires_backends(cls			,			["onnx"]		)





	@classmethod
	def   _SCREAMING_SNAKE_CASE   (   cls      :				str			,			*lowerCAmelCase_      :				Optional[Any]			,			**lowerCAmelCase_      :				str		)		->    Optional[Any]:
				requires_backends(cls			,			["onnx"]		)



