


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

SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)


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



@add_end_docstrings(lowercase							)
class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     List[Any]   ,  *UpperCAmelCase				:     Any   ,  **UpperCAmelCase				:     Tuple):
				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 _snake_case		(						self				:     str   ,  **UpperCAmelCase				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{}
				if "threshold" in kwargs:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						kwargs["threshold"]
				return {}, {}, postprocess_kwargs






		def __call__(						self				:     Any   ,  *UpperCAmelCase				:     Any   ,  **UpperCAmelCase				:     List[str]):
				return super().__call__(*UpperCAmelCase   ,  **UpperCAmelCase)






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						load_image(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Dict				     =						torch.IntTensor([[image.height, image.width]])
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.image_processor(images=[image]   ,  return_tensors="pt")
				if self.tokenizer is not None:
						SCREAMING_SNAKE_CASE_				:Any				     =						self.tokenizer(text=inputs["words"]   ,  boxes=inputs["boxes"]   ,  return_tensors="pt")
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						target_size
				return inputs






		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model_inputs.pop("target_size")
				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.model(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						outputs.__class__({"target_size": target_size, **outputs})
				if self.tokenizer is not None:
						SCREAMING_SNAKE_CASE_				:str				     =						model_inputs["bbox"]
				return model_outputs






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     int=0.9):
				SCREAMING_SNAKE_CASE_				:Dict				     =						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.
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						target_size[0].tolist()

						def unnormalize(UpperCAmelCase				:     Dict):
								return self._get_bounding_box(
								    torch.Tensor(
								        [
								            (width * bbox[0] / 10_00),
								            (height * bbox[1] / 10_00),
								            (width * bbox[2] / 10_00),
								            (height * bbox[3] / 10_00),
								        ]))

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

						SCREAMING_SNAKE_CASE_				:List[Any]				     =						scores.tolist()
						SCREAMING_SNAKE_CASE_				:str				     =						[self.model.config.idalabel[label.item()] for label in labels]
						SCREAMING_SNAKE_CASE_				:Any				     =						[self._get_bounding_box(UpperCAmelCase) for box in boxes]

						# {"scores": [...], ...} --> [{"score":x, ...}, ...]
						SCREAMING_SNAKE_CASE_				:Tuple				     =						["score", "label", "box"]
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[
						    dict(zip(UpperCAmelCase   ,  UpperCAmelCase))
						    for vals in zip(raw_annotation["scores"]   ,  raw_annotation["labels"]   ,  raw_annotation["boxes"])
						]

				return annotation







		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     "torch.Tensor"):
				if self.framework != "pt":
						raise ValueError("The ObjectDetectionPipeline is only available in PyTorch.")
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						box.int().tolist()
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{
				    "xmin": xmin,
				    "ymin": ymin,
				    "xmax": xmax,
				    "ymax": ymax,
				}
				return bbox










import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
		import torch

		from transformers import AutoModelForImageClassification

if is_vision_available():
		from transformers import AutoImageProcessor



@require_torch
@require_vision
class 						_UpperCAmelCase      (     unittest.TestCase							):






		@slow
		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				SCREAMING_SNAKE_CASE_				:int				     =						AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				model.to(UpperCAmelCase)

				from datasets import load_dataset

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						load_dataset("nielsr/rvlcdip-demo")

				SCREAMING_SNAKE_CASE_				:Dict				     =						dataset["train"][0]["image"].convert("RGB")

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_processor(UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(**UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						outputs.logits

				SCREAMING_SNAKE_CASE_				:int				     =						torch.Size((1, 16))
				self.assertEqual(logits.shape   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.tensor(
				    [-0.4158, -0.4092, -0.4347]   ,  device=UpperCAmelCase   ,  dtype=torch.float   ,  )
				self.assertTrue(torch.allclose(logits[0, :3]   ,  UpperCAmelCase   ,  atol=1E-4))






