



'''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

UpperCAmelCase     		=   logging.get_logger(__name__)


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

@add_end_docstrings(_lowerCAmelCase )
class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def __init__( self     ,	*A_     ,	**A_				)     ->     int:
							super().__init__(*A_     ,	**A_				)

							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     ,	**A_				)     ->     List[Any]:
							lowerCAmelCase   =     {}
							if "threshold" in kwargs:
								lowerCAmelCase   =     kwargs["""threshold"""]
							return {}, {}, postprocess_kwargs


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


						def     __snake_case   ( self     ,	A_				)     ->     Optional[Any]:
							lowerCAmelCase   =     load_image(A_				)
							lowerCAmelCase   =     torch.IntTensor([[image.height, image.width]]				)
							lowerCAmelCase   =     self.image_processor(images=[image]     ,	return_tensors="""pt"""				)
							if self.tokenizer is not None:
								lowerCAmelCase   =     self.tokenizer(text=inputs["""words"""]     ,	boxes=inputs["""boxes"""]     ,	return_tensors="""pt"""				)
							lowerCAmelCase   =     target_size
							return inputs


						def     __snake_case   ( self     ,	A_				)     ->     Tuple:
							lowerCAmelCase   =     model_inputs.pop("""target_size"""				)
							lowerCAmelCase   =     self.model(**A_				)
							lowerCAmelCase   =     outputs.__class__({"""target_size""": target_size, **outputs}				)
							if self.tokenizer is not None:
								lowerCAmelCase   =     model_inputs["""bbox"""]
							return model_outputs


						def     __snake_case   ( self     ,	A_     ,	A_=0.9				)     ->     Dict:
							lowerCAmelCase   =     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.
								lowerCAmelCase,       lowerCAmelCase   =     target_size[0].tolist()

								def unnormalize(A_				):
									return self._get_bounding_box(
									    torch.Tensor(
									        [
									            (width * bbox[0] / 1000),
									            (height * bbox[1] / 1000),
									            (width * bbox[2] / 1000),
									            (height * bbox[3] / 1000),
									        ]				)				)

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

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

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

							return annotation




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




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase     		=   logging.get_logger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		"timm_backbone"


						def __init__( self     ,	A_=None     ,	A_=3     ,	A_=True     ,	A_=True     ,	A_=None     ,	**A_     ,	)     ->     int:
							super().__init__(**A_				)
							lowerCAmelCase   =     backbone
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     features_only
							lowerCAmelCase   =     use_pretrained_backbone
							lowerCAmelCase   =     True
							lowerCAmelCase   =     out_indices if out_indices is not None else (-1,)