"""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(A_   )
class        a				(		A_   ):


				def __init__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  Union[str, Any]   ,				**__lowerCAmelCase	:  List[Any]					):
						super().__init__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)

						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      lowerCAmelCase_  (					self	:  int   ,				**__lowerCAmelCase	:  str					):
						_UpperCAmelCase						 =      {}
						if "threshold" in kwargs:
								_UpperCAmelCase						 =      kwargs["""threshold"""]
						return {}, {}, postprocess_kwargs


				def __call__(					self	:  List[Any]   ,				*__lowerCAmelCase	:  Dict   ,				**__lowerCAmelCase	:  Optional[Any]					):
						return super().__call__(*__lowerCAmelCase   ,				**__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Optional[Any]   ,				__lowerCAmelCase	:  List[Any]					):
						_UpperCAmelCase						 =      load_image(__lowerCAmelCase					)
						_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      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]					):
						_UpperCAmelCase						 =      model_inputs.pop("""target_size"""					)
						_UpperCAmelCase						 =      self.model(**__lowerCAmelCase					)
						_UpperCAmelCase						 =      outputs.__class__({"""target_size""": target_size, **outputs}					)
						if self.tokenizer is not None:
								_UpperCAmelCase						 =      model_inputs["""bbox"""]
						return model_outputs


				def      lowerCAmelCase_  (					self	:  Any   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]=0.9					):
						_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						 =      target_size[0].tolist()

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

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

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

						return annotation


				def      lowerCAmelCase_  (					self	:  Tuple   ,				__lowerCAmelCase	:  Dict					):
						if self.framework != "pt":
								raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""					)
						_UpperCAmelCase						 =      box.int().tolist()
						_UpperCAmelCase						 =      {
						    """xmin""": xmin,
						    """ymin""": ymin,
						    """xmax""": xmax,
						    """ymax""": ymax,
						}
						return bbox

"""simple docstring"""


import random






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase = False		):







  """simple docstring"""
  _UpperCAmelCase						 =      {i: [] for i in range(lowercase		)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowercase		)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowercase		):
    for j in range(i + 1   ,lowercase		):
      if random.random() < probability:
        graph[i].append(lowercase		)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowercase		)
  return graph






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return {
      i: [j for j in range(lowercase		) if i != j] for i in range(lowercase		)
  }


if __name__ == "__main__":
    import doctest

    doctest.testmod()
