





"""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(__SCREAMING_SNAKE_CASE )
class 		SCREAMING_SNAKE_CASE_				(     __SCREAMING_SNAKE_CASE ):
				"""simple docstring"""




				def __init__(  self	:List[Any]    ,     *__lowercase	:int    ,     **__lowercase	:Any      ):
											super().__init__(*_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE      )

											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	:int    ,     **__lowercase	:Any      ):
											__lowerCamelCase   :   Optional[int]       ={}
											if "threshold" in kwargs:
																		__lowerCamelCase   :   Any       =kwargs["threshold"]
											return {}, {}, postprocess_kwargs




				def __call__(  self	:Optional[int]    ,     *__lowercase	:Dict    ,     **__lowercase	:int      ):
											return super().__call__(*_SCREAMING_SNAKE_CASE    ,     **_SCREAMING_SNAKE_CASE      )




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




				def        __lowercase	(  self	:Optional[Any]    ,     __lowercase	:Optional[int]      ):
											__lowerCamelCase   :   int       =model_inputs.pop('''target_size'''      )
											__lowerCamelCase   :   List[str]       =self.model(**_SCREAMING_SNAKE_CASE      )
											__lowerCamelCase   :   int       =outputs.__class__({'''target_size''': target_size, **outputs}      )
											if self.tokenizer is not None:
																		__lowerCamelCase   :   Tuple       =model_inputs["bbox"]
											return model_outputs




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:List[str]    ,     __lowercase	:Union[str, Any]=0.9      ):
											__lowerCamelCase   :   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.
																		__lowerCamelCase   :   List[str]       =target_size[0].tolist()

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

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

																		__lowerCamelCase   :   Union[str, Any]       =scores.tolist()
																		__lowerCamelCase   :   Optional[Any]       =[self.model.config.idalabel[label.item()] for label in labels]
																		__lowerCamelCase   :   Optional[Any]       =[self._get_bounding_box(_SCREAMING_SNAKE_CASE      ) for box in boxes]

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

											return annotation




				def        __lowercase	(  self	:Dict    ,     __lowercase	:"torch.Tensor"      ):

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









"""simple docstring"""






# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)