





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					:				Tuple				=			logging.get_logger(__name__)


_A					:				Any				=			Dict[str, Any]
_A					:				str				=			List[Prediction]



@add_end_docstrings(__SCREAMING_SNAKE_CASE    )
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				*A_   ,				**A_     ):





        '''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  lowercase_					(       self   ,				**A_     ):





        '''simple docstring'''

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



    def __call__(       self   ,				*A_   ,				**A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

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



    def  lowercase_					(       self   ,				A_   ,				A_=0.9     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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__											=			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),
                        ]     )     )

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

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

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

        return annotation




    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        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__											=			box.int().tolist()
        SCREAMING_SNAKE_CASE__											=			{
            '''xmin''': xmin,
            '''ymin''': ymin,
            '''xmax''': xmax,
            '''ymax''': ymax,
        }
        return bbox



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''			)
											if not isinstance(features[self.label_column] ,  snake_case			):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''			)
											snake_case_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




