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

UpperCamelCase__    							= logging.get_logger(__name__)


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





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


			def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
									"""simple docstring"""



									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       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
									"""simple docstring"""



									UpperCAmelCase__								=  {}
									if "threshold" in kwargs:
															UpperCAmelCase__								=  kwargs["threshold"]
									return {}, {}, postprocess_kwargs


			def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""



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


			def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
									UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
									UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
									if self.tokenizer is not None:
															UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
									UpperCAmelCase__								=  target_size
									return inputs


			def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  model_inputs.pop("target_size"		)
									UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
									UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
									if self.tokenizer is not None:
															UpperCAmelCase__								=  model_inputs["bbox"]
									return model_outputs


			def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
									"""simple docstring"""



									UpperCAmelCase__								=  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__								=  target_size[0].tolist()

															def unnormalize(__UpperCAmelCase					:						List[str]		):
																					return self._get_bounding_box(
																					    torch.Tensor(
																					        [
																					            (width * bbox[0] / 1_0_0_0),
																					            (height * bbox[1] / 1_0_0_0),
																					            (width * bbox[2] / 1_0_0_0),
																					            (height * bbox[3] / 1_0_0_0),
																					        ]		)		)

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

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

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

									return annotation





			def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
									"""simple docstring"""



									if self.framework != "pt":
															raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
									UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
									UpperCAmelCase__								=  {
									    "xmin": xmin,
									    "ymin": ymin,
									    "xmax": xmax,
									    "ymax": ymax,
									}
									return bbox




from __future__ import annotations




def        lowerCAmelCase_			(					__A,  __A,  __A,  )					->    tuple:







      '''simple docstring'''


      if (electron_conc, hole_conc, intrinsic_conc).count(0							) != 1:
            raise ValueError("You cannot supply more or less than 2 values"							)
      elif electron_conc < 0:
            raise ValueError("Electron concentration cannot be negative in a semiconductor"							)
      elif hole_conc < 0:
            raise ValueError("Hole concentration cannot be negative in a semiconductor"							)
      elif intrinsic_conc < 0:
            raise ValueError(
                "Intrinsic concentration cannot be negative in a semiconductor"							)
      elif electron_conc == 0:
            return (
                "electron_conc",
                intrinsic_conc**2 / hole_conc,
            )
      elif hole_conc == 0:
            return (
                "hole_conc",
                intrinsic_conc**2 / electron_conc,
            )
      elif intrinsic_conc == 0:
            return (
                "intrinsic_conc",
                (electron_conc * hole_conc) ** 0.5,
            )
      else:
            return (-1, -1)


if __name__ == "__main__":
      import doctest

      doctest.testmod()



