


'''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

lowercase__				     =		logging.get_logger(__name__)


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





@add_end_docstrings(lowerCamelCase__ )
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def __init__(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Dict:
          super().__init__(*__UpperCamelCase     ,							**__UpperCamelCase		)

          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  a_					(			self     ,							**__UpperCamelCase		)       ->    Tuple:
          _a						=		{}
          if "threshold" in kwargs:
               _a						=		kwargs["threshold"]
          return {}, {}, postprocess_kwargs






     def __call__(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Union[Predictions, List[Prediction]]:
          return super().__call__(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		load_image(__UpperCamelCase		)
          _a						=		torch.IntTensor([[image.height, image.width]]		)
          _a						=		self.image_processor(images=[image]     ,							return_tensors="pt"		)
          if self.tokenizer is not None:
               _a						=		self.tokenizer(text=inputs["words"]     ,							boxes=inputs["boxes"]     ,							return_tensors="pt"		)
          _a						=		target_size
          return inputs






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Dict:
          _a						=		model_inputs.pop("target_size"		)
          _a						=		self.model(**__UpperCamelCase		)
          _a						=		outputs.__class__({"target_size": target_size, **outputs}		)
          if self.tokenizer is not None:
               _a						=		model_inputs["bbox"]
          return model_outputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=0.9		)       ->    Any:
          _a						=		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.
               _a			,			_a						=		target_size[0].tolist()

               def unnormalize(__UpperCamelCase		):
                    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),
                            ]		)		)

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

               _a						=		scores.tolist()
               _a						=		[self.model.config.idalabel[label.item()] for label in labels]
               _a						=		[self._get_bounding_box(__UpperCamelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               _a						=		["score", "label", "box"]
               _a						=		[
                   dict(zip(__UpperCamelCase     ,							__UpperCamelCase		)		)
                   for vals in zip(raw_annotation["scores"]     ,							raw_annotation["labels"]     ,							raw_annotation["boxes"]		)
               ]

          return annotation






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Dict[str, int]:
          if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
          _a			,			_a			,			_a			,			_a						=		box.int().tolist()
          _a						=		{
              "xmin": xmin,
              "ymin": ymin,
              "xmax": xmax,
              "ymax": ymax,
          }
          return bbox





'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ,			lowerCamelCase__ ):






     def  a_					(			self		)       ->    Tuple:
          _a						=		load_tool("text-classification"		)
          self.tool.setup()
          _a						=		load_tool("text-classification"     ,							remote=__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.remote_tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    str:
          _a						=		self.remote_tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)

