



"""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__		:				Optional[Any]          =	logging.get_logger(__name__)


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







@add_end_docstrings(lowercase_  )
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      def __init__(      self				:      Tuple    ,			*SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        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				:      Dict    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				Any								= {}
        if "threshold" in kwargs:
          lowerCAmelCase_   :				Union[str, Any]								= kwargs['threshold']
        return {}, {}, postprocess_kwargs



      def __call__(      self				:      str    ,			*SCREAMING_SNAKE_CASE_				:      str    ,			**SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        return super().__call__(*__UpperCamelCase    ,			**__UpperCamelCase       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Optional[Any]								= load_image(__UpperCamelCase       )
        lowerCAmelCase_   :				str								= torch.IntTensor([[image.height, image.width]]       )
        lowerCAmelCase_   :				Tuple								= self.image_processor(images=[image]    ,			return_tensors='pt'       )
        if self.tokenizer is not None:
          lowerCAmelCase_   :				str								= self.tokenizer(text=inputs['words']    ,			boxes=inputs['boxes']    ,			return_tensors='pt'       )
        lowerCAmelCase_   :				int								= target_size
        return inputs



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



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

          def unnormalize(SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
            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),
                    ]       )       )

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

          lowerCAmelCase_   :				Tuple								= scores.tolist()
          lowerCAmelCase_   :				str								= [self.model.config.idalabel[label.item()] for label in labels]
          lowerCAmelCase_   :				Optional[int]								= [self._get_bounding_box(__UpperCamelCase       ) for box in boxes]

          # {"scores": [...], ...} --> [{"score":x, ...}, ...]
          lowerCAmelCase_   :				int								= ['score', 'label', 'box']
          lowerCAmelCase_   :				Optional[int]								= [
              dict(zip(__UpperCamelCase    ,			__UpperCamelCase       )       )
              for vals in zip(raw_annotation['scores']    ,			raw_annotation['labels']    ,			raw_annotation['boxes']       )
          ]

        return annotation





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Any       ):


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






"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int | float], int | float]     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int = 1_00     ,			):
     lowercase_	:				Dict													=	x_start
     lowercase_	:				Optional[Any]													=	fnc(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	0.0

     for _ in range(__SCREAMING_SNAKE_CASE      ):
          # Approximates curve as a sequence of linear lines and sums their length
          lowercase_	:				Union[str, Any]													=	(x_end - x_start) / steps + xa
          lowercase_	:				str													=	fnc(__SCREAMING_SNAKE_CASE      )
          length += math.hypot(xa - xa     ,			fxa - fxa      )

          # Increment step
          lowercase_	:				List[Any]													=	xa
          lowercase_	:				Optional[Any]													=	fxa

     return length


if __name__ == "__main__":



       def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
            return math.sin(10 * x      )

       print("f(x) = sin(10 * x)")
       print("The length of the curve from x = -10 to x = 10 is:")
       __SCREAMING_SNAKE_CASE    	=10
       while i <= 10_0000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10

