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

snake_case__				= logging.get_logger(__name__)


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



@add_end_docstrings(__lowercase )
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				Optional[int]:






           """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_						) ->				List[str]:






           """simple docstring"""
           _lowerCamelCase			    =				{}
           if "threshold" in kwargs:
                 _lowerCamelCase			    =				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_						) ->				Tuple:






           """simple docstring"""
           _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 		UpperCamelCase_						(					self				,				A_						) ->				Dict:






           """simple docstring"""
           _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 		UpperCamelCase_						(					self				,				A_				,				A_=0.9						) ->				Union[str, Any]:






           """simple docstring"""
           _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] / 10_00),
                                   (height * bbox[1] / 10_00),
                                   (width * bbox[2] / 10_00),
                                   (height * bbox[3] / 10_00),
                               ]						)						)

                 _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 		UpperCamelCase_						(					self				,				A_						) ->				Dict[str, int]:






           """simple docstring"""
           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
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)