


"""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

UpperCAmelCase				=						logging.get_logger(__name__)


UpperCAmelCase				=						Dict[str, Any]
UpperCAmelCase				=						List[Prediction]




@add_end_docstrings(A_				)
class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  int:
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

      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 		UpperCamelCase_  (							self      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					int   =		{}
      if "threshold" in kwargs:
         _lowerCamelCase	:					List[Any]   =		kwargs["""threshold"""]
      return {}, {}, postprocess_kwargs





   def __call__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Union[Predictions, List[Prediction]]:
      return super().__call__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					int   =		load_image(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		torch.IntTensor([[image.height, image.width]])
      _lowerCamelCase	:					Dict   =		self.image_processor(images=[image]      ,   return_tensors="""pt""")
      if self.tokenizer is not None:
         _lowerCamelCase	:					Union[str, Any]   =		self.tokenizer(text=inputs["""words"""]      ,   boxes=inputs["""boxes"""]      ,   return_tensors="""pt""")
      _lowerCamelCase	:					Optional[Any]   =		target_size
      return inputs





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      _lowerCamelCase	:					int   =		model_inputs.pop("""target_size""")
      _lowerCamelCase	:					Optional[Any]   =		self.model(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		outputs.__class__({"""target_size""": target_size, **outputs})
      if self.tokenizer is not None:
         _lowerCamelCase	:					Union[str, Any]   =		model_inputs["""bbox"""]
      return model_outputs





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0.9)  ->  List[str]:
      _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   ,    _lowerCamelCase	:					Union[str, Any]   =		target_size[0].tolist()

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

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

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

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

      return annotation





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







from decimal import Decimal, getcontext
from math import ceil, factorial







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     if not isinstance(_lowercase					,		_lowercase ):
          raise TypeError('''Undefined for non-integers''' )
     elif precision < 1:
          raise ValueError('''Undefined for non-natural numbers''' )

     UpperCAmelCase_     :				Tuple  =     precision
     UpperCAmelCase_     :				Optional[Any]  =     ceil(precision / 14 )
     UpperCAmelCase_     :				int  =     426880 * Decimal(10005 ).sqrt()
     UpperCAmelCase_     :				Tuple  =     1
     UpperCAmelCase_     :				List[Any]  =     13591409
     UpperCAmelCase_     :				Optional[Any]  =     Decimal(_lowercase )
     for k in range(1					,		_lowercase ):
          UpperCAmelCase_     :				List[str]  =     factorial(6 * k ) // (factorial(3 * k ) * factorial(_lowercase ) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term ) / exponential_term
     return str(constant_term / partial_sum )[:-1]


if __name__ == "__main__":
     __a							     =					50
     print(F"""The first {n} digits of pi is: {pi(n)}""")