


'''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     :  Union[str, Any]											=    logging.get_logger(__name__)


lowerCAmelCase     :  List[str]											=    Dict[str, Any]
lowerCAmelCase     :  int											=    List[Prediction]
@add_end_docstrings(SCREAMING_SNAKE_CASE_						)
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(*snake_case__   ,						**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 a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

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







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

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

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







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							model_inputs.pop('target_size' )
            _lowerCAmelCase     :				int       =							self.model(**snake_case__ )
            _lowerCAmelCase     :				Dict       =							outputs.__class__({'target_size': target_size, **outputs} )
            if self.tokenizer is not None:
                   _lowerCAmelCase     :				int       =							model_inputs['bbox']
            return model_outputs







     def a						(			self   ,						snake_case__   ,						snake_case__=0.9 ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							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     :				Optional[Any]       =							target_size[0].tolist()

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

                   _lowerCAmelCase     :				List[Any]       =							scores.tolist()
                   _lowerCAmelCase     :				Optional[int]       =							[self.model.config.idalabel[label.item()] for label in labels]
                   _lowerCAmelCase     :				Optional[Any]       =							[self._get_bounding_box(snake_case__ ) for box in boxes]

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

            return annotation







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

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





'''simple docstring'''







from __future__ import annotations

from math import gcd






def       lowercase					(_A ,    _A = 2 ,    _A = 1 ,    _A = 3 ,    ):







       """simple docstring"""







       if num < 2:
              raise ValueError('The input value cannot be less than 2'       )

       # Because of the relationship between ``f(f(x))`` and ``f(x)``, this
       # algorithm struggles to find factors that are divisible by two.
       # As a workaround, we specifically check for two and even inputs.
       #   See: https://math.stackexchange.com/a/2856214/165820
       if num > 2 and num % 2 == 0:
              return 2

       # Pollard's Rho algorithm requires a function that returns pseudorandom
       # values between 0 <= X < ``num``.  It doesn't need to be random in the
       # sense that the output value is cryptographically secure or difficult
       # to calculate, it only needs to be random in the sense that all output
       # values should be equally likely to appear.
       # For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
       # However, the success of Pollard's algorithm isn't guaranteed and is
       # determined in part by the initial seed and the chosen random function.
       # To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
       # where ``C`` is a value that we can modify between each attempt.
       def rand_fn(_A ,    _A ,    _A       ) -> int:
              return (pow(_A ,    2       ) + step) % modulus

       for _ in range(_A       ):
              # These track the position within the cycle detection logic.
              _lowerCAmelCase     :				Dict       =							seed
              _lowerCAmelCase     :				int       =							seed

              while True:
                     # At each iteration, the tortoise moves one step and the hare moves two.
                     _lowerCAmelCase     :				str       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Optional[int]       =							rand_fn(_A ,    _A ,    _A       )
                     _lowerCAmelCase     :				Union[str, Any]       =							rand_fn(_A ,    _A ,    _A       )

                     # At some point both the tortoise and the hare will enter a cycle whose
                     # length ``p`` is a divisor of ``num``.  Once in that cycle, at some point
                     # the tortoise and hare will end up on the same value modulo ``p``.
                     # We can detect when this happens because the position difference between
                     # the tortoise and the hare will share a common divisor with ``num``.
                     _lowerCAmelCase     :				Optional[int]       =							gcd(hare - tortoise ,    _A       )

                     if divisor == 1:
                            # No common divisor yet, just keep searching.
                            continue
                     else:
                            # We found a common divisor!
                            if divisor == num:
                                   # Unfortunately, the divisor is ``num`` itself and is useless.
                                   break
                            else:
                                   # The divisor is a nontrivial factor of ``num``!
                                   return divisor

        # If we made it here, then this attempt failed.
        # We need to pick a new starting seed for the tortoise and hare
        # in addition to a new step value for the random function.
        # To keep this example implementation deterministic, the
        # new values will be generated based on currently available
        # values instead of using something like ``random.randint``.

        # We can use the hare's position as the new seed.
        # This is actually what Richard Brent's the "optimized" variant does.
              _lowerCAmelCase     :				Tuple       =							hare

              # The new step value for the random function can just be incremented.
              # At first the results will be similar to what the old function would
              # have produced, but the value will quickly diverge after a bit.
              step += 1

       # We haven't found a divisor within the requested number of attempts.
       # We were unlucky or ``num`` itself is actually prime.
       return None


if __name__ == "__main__":
       import argparse

       lowerCAmelCase     :  Tuple											=    argparse.ArgumentParser()
       parser.add_argument(
           """num""",
           type=int,
           help="""The value to find a divisor of""",
       )
       parser.add_argument(
           """--attempts""",
           type=int,
           default=3,
           help="""The number of attempts before giving up""",
       )
       lowerCAmelCase     :  List[str]											=    parser.parse_args()

       lowerCAmelCase     :  List[str]											=    pollard_rho(args.num, attempts=args.attempts)
       if divisor is None:
              print(F'''{args.num} is probably prime''')
       else:
              lowerCAmelCase     :  Union[str, Any]											=    args.num // divisor
              print(F'''{args.num} = {divisor} * {quotient}''')

