





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

_lowercase		:				List[str]      		=logging.get_logger(__name__)


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

@add_end_docstrings(A__		)
class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				List[str]:
									"""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 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						int							=  {}
									if "threshold" in kwargs:
												a__       :						Tuple							=  kwargs["""threshold"""]
									return {}, {}, postprocess_kwargs

						def __call__(       self ,       *__lowercase ,       **__lowercase  ) ->				Union[Predictions, List[Prediction]]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						int							=  load_image(__lowercase  )
									a__       :						int							=  torch.IntTensor([[image.height, image.width]]  )
									a__       :						Tuple							=  self.image_processor(images=[image] ,       return_tensors="""pt"""  )
									if self.tokenizer is not None:
												a__       :						str							=  self.tokenizer(text=inputs["""words"""] ,       boxes=inputs["""boxes"""] ,       return_tensors="""pt"""  )
									a__       :						str							=  target_size
									return inputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						str							=  model_inputs.pop("""target_size"""  )
									a__       :						Dict							=  self.model(**__lowercase  )
									a__       :						Any							=  outputs.__class__({"""target_size""": target_size, **outputs}  )
									if self.tokenizer is not None:
												a__       :						Union[str, Any]							=  model_inputs["""bbox"""]
									return model_outputs

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase=0.9  ) ->				int:
									"""simple docstring"""




									a__       :						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.
												a__       :						str							=  target_size[0].tolist()

												def unnormalize(__lowercase  ):
															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),
															        ]  )  )

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

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

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

									return annotation

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Dict[str, int]:
									"""simple docstring"""




									if self.framework != "pt":
												raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""  )
									a__       :						List[Any]							=  box.int().tolist()
									a__       :						Dict							=  {
									    """xmin""": xmin,
									    """ymin""": ymin,
									    """xmax""": xmax,
									    """ymax""": ymax,
									}
									return bbox









import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
		import ctypes
		import msvcrt  # noqa

		class        snake_case__    (ctypes.Structure		):







								"""simple docstring"""




								__lowerCAmelCase					:Dict      =					[("size", ctypes.c_int), ("visible", ctypes.c_byte)]

def      lowerCAmelCase_					(  )							->	List[Any]:







			"""simple docstring"""
			if os.name == "nt":
						a__       :						int							=  CursorInfo()
						a__       :						Union[str, Any]							=  ctypes.windll.kernelaa.GetStdHandle(-11)
						ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
						a__       :						List[str]							=  False
						ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
			elif os.name == "posix":
						sys.stdout.write("""\033[?25l""")
						sys.stdout.flush()

def      lowerCAmelCase_					(  )							->	Optional[Any]:







			"""simple docstring"""
			if os.name == "nt":
						a__       :						List[Any]							=  CursorInfo()
						a__       :						Optional[int]							=  ctypes.windll.kernelaa.GetStdHandle(-11)
						ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
						a__       :						Dict							=  True
						ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
			elif os.name == "posix":
						sys.stdout.write("""\033[?25h""")
						sys.stdout.flush()





@contextmanager
def      lowerCAmelCase_					(  )							->	Any:







			"""simple docstring"""
			try:
						hide_cursor()
						yield
			finally:
						show_cursor()




