
"""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

_a														=		logging.get_logger(__name__)


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






@add_end_docstrings(lowerCamelCase     )
class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self    ,		*__a    ,		**__a)	->	Any:






							'''simple docstring'''
							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 						UpperCAmelCase							( self    ,		**__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  {}
							if "threshold" in kwargs:
												_UpperCamelCase										=  kwargs['''threshold''']
							return {}, {}, postprocess_kwargs



		def __call__( self    ,		*__a    ,		**__a)	->	Union[Predictions, List[Prediction]]:






							'''simple docstring'''
							return super().__call__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  load_image(__a)
							_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 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  model_inputs.pop('''target_size''')
							_UpperCamelCase										=  self.model(**__a)
							_UpperCamelCase										=  outputs.__class__({'''target_size''': target_size, **outputs})
							if self.tokenizer is not None:
												_UpperCamelCase										=  model_inputs['''bbox''']
							return model_outputs



		def 						UpperCAmelCase							( self    ,		__a    ,		__a=0.9)	->	Dict:






							'''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(__a):
																	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),
																	        ]))

												_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(__a) for bbox in model_outputs['''bbox'''].squeeze(0)]
												_UpperCamelCase										=  ['''score''', '''label''', '''box''']
												_UpperCamelCase										=  [dict(zip(__a    ,		__a)) for vals in zip(scores.tolist()    ,		__a    ,		__a) if vals[0] > threshold]
							else:
												# This is a regular ForObjectDetectionModel
												_UpperCamelCase										=  self.image_processor.post_process_object_detection(__a    ,		__a    ,		__a)
												_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(__a) for box in boxes]

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

							return annotation




		def 						UpperCAmelCase							( self    ,		__a)	->	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








"""simple docstring"""





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline






