




"""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

lowerCAmelCase							:    Optional[int]  							=							logging.get_logger(__name__)


lowerCAmelCase							:    Tuple  							=							Dict[str, Any]
lowerCAmelCase							:    Dict  							=							List[Prediction]






@add_end_docstrings(UpperCAmelCase__       )
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def __init__( self	,				*_a	,				**_a				):





							"""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      _lowerCAmelCase       ( self	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {}
							if "threshold" in kwargs:
										lowerCamelCase       =      kwargs["""threshold"""]
							return {}, {}, postprocess_kwargs





				def __call__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return super().__call__(*_a	,				**_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""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      _lowerCAmelCase       ( self	,				_a				):





							"""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      _lowerCAmelCase       ( self	,				_a	,				_a=0.9				):





							"""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] / 1_000),
													            (height * bbox[1] / 1_000),
													            (width * bbox[2] / 1_000),
													            (height * bbox[3] / 1_000),
													        ]				)				)

										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      _lowerCAmelCase       ( self	,				_a				):





							"""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









"""simple docstring"""




from collections.abc import Sequence



def 					a__				(       snake_case__ ,				snake_case__ = False       )		->      float:
			if not arr:
						return 0

			lowerCamelCase       =      0 if allow_empty_subarrays else float("""-inf"""       )
			lowerCamelCase       =      0.0
			for num in arr:
						lowerCamelCase       =      max(0 if allow_empty_subarrays else num ,				curr_sum + num       )
						lowerCamelCase       =      max(snake_case__ ,				snake_case__       )

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase							:    Optional[int]  							=							[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



