
"""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

__magic_name__       =			logging.get_logger(__name__)


__magic_name__       =			Dict[str, Any]
__magic_name__       =			List[Prediction]
@add_end_docstrings(__a )
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				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 	snake_case_     (     self							,  **lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = {}
				if "threshold" in kwargs:
						__SCREAMING_SNAKE_CASE            = kwargs["""threshold"""]
				return {}, {}, postprocess_kwargs




		def __call__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return super().__call__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = load_image(lowerCAmelCase__)
				__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 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = model_inputs.pop("""target_size""")
				__SCREAMING_SNAKE_CASE            = self.model(**lowerCAmelCase__)
				__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 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__=0.9):
				__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(lowerCAmelCase__):
								return self._get_bounding_box(
								    torch.Tensor(
								        [
								            (width * bbox[0] / 1_0_0_0),
								            (height * bbox[1] / 1_0_0_0),
								            (width * bbox[2] / 1_0_0_0),
								            (height * bbox[3] / 1_0_0_0),
								        ]))

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

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

				return annotation




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				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











'''simple docstring'''
from __future__ import annotations

from typing import Any

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 pass






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[Any]							,	__A       :							Any	):
        __UpperCamelCase       =  data
        __UpperCamelCase       =  None






 def __iter__(       self       :							Optional[Any]	):
        __UpperCamelCase       =  self
        __UpperCamelCase       =  []
        while node:
               if node in visited:
                      raise ContainsLoopError
               visited.append(__A	)
               yield node.data
               __UpperCamelCase       =  node.next_node






 @property
 def        _lowerCamelCase					(       self       :							List[str]	):
        try:
               list(self	)
               return False
        except ContainsLoopError:
               return True


if __name__ == "__main__":
    a__							:							Dict =Node(1)
    a__							:							Optional[int] =Node(2)
    a__							:							List[str] =Node(3)
    a__							:							Optional[int] =Node(4)
    print(root_node.has_loop)  # False
    a__							:							str =root_node.next_node
    print(root_node.has_loop)  # True

    a__							:							Optional[int] =Node(5)
    a__							:							List[Any] =Node(6)
    a__							:							int =Node(5)
    a__							:							Tuple =Node(6)
    print(root_node.has_loop)  # False

    a__							:							str =Node(1)
    print(root_node.has_loop)  # False
