





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

lowerCAmelCase_						   =    logging.get_logger(__name__)


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



@add_end_docstrings(_UpperCAmelCase							)
class        _lowerCAmelCase    (			_UpperCAmelCase							):


  '''simple docstring'''







  def __init__(      self      :					str      ,				*UpperCamelCase      :					int      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    super().__init__(*lowercase__      ,				**lowercase__							)

    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      :					Optional[int]      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				Any														=      {}
    if "threshold" in kwargs:
      _snake_case   :				Optional[int]														=      kwargs["threshold"]
    return {}, {}, postprocess_kwargs







  def __call__(      self      :					int      ,				*UpperCamelCase      :					int      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





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







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				List[str]														=      load_image(lowercase__							)
    _snake_case   :				Dict														=      torch.IntTensor([[image.height, image.width]]							)
    _snake_case   :				int														=      self.image_processor(images=[image]      ,				return_tensors='pt'							)
    if self.tokenizer is not None:
      _snake_case   :				List[str]														=      self.tokenizer(text=inputs['words']      ,				boxes=inputs['boxes']      ,				return_tensors='pt'							)
    _snake_case   :				List[Any]														=      target_size
    return inputs







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Dict														=      model_inputs.pop('target_size'							)
    _snake_case   :				int														=      self.model(**lowercase__							)
    _snake_case   :				Dict														=      outputs.__class__({'target_size': target_size, **outputs}							)
    if self.tokenizer is not None:
      _snake_case   :				str														=      model_inputs["bbox"]
    return model_outputs







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					List[Any]=0.9							):


    '''simple docstring'''





    _snake_case   :				Tuple														=      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.
      _snake_case   :				Union[str, Any]														=      target_size[0].tolist()

      def unnormalize(UpperCamelCase      :					Tuple							):
        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),
                ]							)							)

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

      _snake_case   :				Dict														=      scores.tolist()
      _snake_case   :				Optional[Any]														=      [self.model.config.idalabel[label.item()] for label in labels]
      _snake_case   :				List[Any]														=      [self._get_bounding_box(lowercase__							) for box in boxes]

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

    return annotation







  def UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Optional[int]							):


    '''simple docstring'''





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











from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =    logging.get_logger(__name__)

lowerCAmelCase_						   =    {
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       int      					="""roberta"""







  def __init__(      self      :					int      ,				UpperCamelCase      :					Tuple=5_02_65      ,				UpperCamelCase      :					Any=7_68      ,				UpperCamelCase      :					List[Any]=12      ,				UpperCamelCase      :					str=12      ,				UpperCamelCase      :					Dict=30_72      ,				UpperCamelCase      :					Any="gelu"      ,				UpperCamelCase      :					List[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=0.1      ,				UpperCamelCase      :					Optional[Any]=5_12      ,				UpperCamelCase      :					List[str]=2      ,				UpperCamelCase      :					Optional[Any]=0.02      ,				UpperCamelCase      :					Tuple=1e-1_2      ,				UpperCamelCase      :					str=1      ,				UpperCamelCase      :					int=0      ,				UpperCamelCase      :					Any=2      ,				UpperCamelCase      :					int="absolute"      ,				UpperCamelCase      :					int=True      ,				UpperCamelCase      :					List[Any]=None      ,				**UpperCamelCase      :					Any      ,				):


    '''simple docstring'''





    super().__init__(pad_token_id=UpperCamelCase      ,				bos_token_id=UpperCamelCase      ,				eos_token_id=UpperCamelCase      ,				**UpperCamelCase							)

    _snake_case   :				Any														=	vocab_size
    _snake_case   :				List[str]														=	hidden_size
    _snake_case   :				List[str]														=	num_hidden_layers
    _snake_case   :				Dict														=	num_attention_heads
    _snake_case   :				List[str]														=	hidden_act
    _snake_case   :				Union[str, Any]														=	intermediate_size
    _snake_case   :				Union[str, Any]														=	hidden_dropout_prob
    _snake_case   :				Optional[int]														=	attention_probs_dropout_prob
    _snake_case   :				Dict														=	max_position_embeddings
    _snake_case   :				Optional[int]														=	type_vocab_size
    _snake_case   :				Tuple														=	initializer_range
    _snake_case   :				int														=	layer_norm_eps
    _snake_case   :				Dict														=	position_embedding_type
    _snake_case   :				Union[str, Any]														=	use_cache
    _snake_case   :				str														=	classifier_dropout




class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    if self.task == "multiple-choice":
      _snake_case   :				Optional[Any]														=	{0: 'batch', 1: 'choice', 2: 'sequence'}
    else:
      _snake_case   :				Dict														=	{0: 'batch', 1: 'sequence'}
    return OrderedDict(
        [
            ('input_ids', dynamic_axis),
            ('attention_mask', dynamic_axis),
        ]							)




