





'''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

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


lowercase__   :				Optional[Any]      =   Dict[str, Any]
lowercase__   :				Optional[Any]      =   List[Prediction]


@add_end_docstrings(__SCREAMING_SNAKE_CASE							)
class 			SCREAMING_SNAKE_CASE   (__SCREAMING_SNAKE_CASE							):


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

          '''simple docstring'''






          super().__init__(*UpperCamelCase__						,     **UpperCamelCase__)

          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						,     **_UpperCAmelCase):

          '''simple docstring'''






          __A						:			Any								=	{}
          if "threshold" in kwargs:
               __A						:			str								=	kwargs['''threshold''']
          return {}, {}, postprocess_kwargs


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

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	model_inputs.pop('target_size')
          __A						:			Dict								=	self.model(**UpperCamelCase__)
          __A						:			Union[str, Any]								=	outputs.__class__({'target_size': target_size, **outputs})
          if self.tokenizer is not None:
               __A						:			List[Any]								=	model_inputs['''bbox''']
          return model_outputs


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase=0.9):

          '''simple docstring'''






          __A						:			Dict								=	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						:			Any								=	target_size[0].tolist()

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

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

               __A						:			List[str]								=	scores.tolist()
               __A						:			Optional[int]								=	[self.model.config.idalabel[label.item()] for label in labels]
               __A						:			Union[str, Any]								=	[self._get_bounding_box(UpperCamelCase__) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               __A						:			Dict								=	['''score''', '''label''', '''box''']
               __A						:			List[str]								=	[
                   dict(zip(UpperCamelCase__						,     UpperCamelCase__))
                   for vals in zip(raw_annotation['scores']						,     raw_annotation['labels']						,     raw_annotation['boxes'])
               ]

          return annotation


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






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






'''simple docstring'''




from __future__ import annotations






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]      )					->					None:
     create_state_space_tree(__snake_case			,							[]			,							0			,							[0 for i in range(len(__snake_case      )      )]      )






def 	_lowerCAmelCase      (  __snake_case  :			list[int | str]			,							__snake_case  :			list[int | str]			,							__snake_case  :			int			,							__snake_case  :			list[int]			,							)					->					None:

     if index == len(__snake_case      ):
          print(__snake_case      )
          return

     for i in range(len(__snake_case      )      ):
          if not index_used[i]:
               current_sequence.append(sequence[i]      )
               __A						:			Any								=	True
               create_state_space_tree(__snake_case			,							__snake_case			,							index + 1			,							__snake_case      )
               current_sequence.pop()
               __A						:			Any								=	False
lowercase__   :				list[int | str]      =   [3, 1, 2, 4]
generate_all_permutations(sequence)

lowercase__   :				list[int | str]      =   ["A", "B", "C"]







generate_all_permutations(sequence_a)