


'''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, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

UpperCAmelCase_ :     int								=      logging.get_logger(__name__)

@add_end_docstrings(snake_case_				)
class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 def __init__(      self      ,      **__lowercase						):

  """simple docstring"""







  super().__init__(**__lowercase						)

  if self.framework == "tf":
   raise ValueError(F"""The {self.__class__} is only available in PyTorch."""						)

  requires_backends(self      ,      'vision'						)
  self.check_model_type(__lowercase						)


 def __call__(      self      ,      __lowercase      ,      __lowercase = None      ,      **__lowercase      ,      ):

  """simple docstring"""







  if "text_queries" in kwargs:
   __A  :		int       =			kwargs.pop('text_queries'						)

  if isinstance(__lowercase      ,      (str, Image.Image)						):
   __A  :		Optional[Any]       =			{'image': image, 'candidate_labels': candidate_labels}
  else:
   __A  :		List[str]       =			image
  __A  :		Any       =			super().__call__(__lowercase      ,      **__lowercase						)
  return results


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  __A  :		int       =			{}
  if "threshold" in kwargs:
   __A  :		List[str]       =			kwargs['threshold']
  if "top_k" in kwargs:
   __A  :		Optional[int]       =			kwargs['top_k']
  return {}, {}, postprocess_params


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Tuple       =			load_image(inputs['image']						)
  __A  :		Optional[Any]       =			inputs['candidate_labels']
  if isinstance(__lowercase      ,      __lowercase						):
   __A  :		List[Any]       =			candidate_labels.split(','						)

  __A  :		int       =			torch.tensor([[image.height, image.width]]      ,      dtype=torch.intaa						)
  for i, candidate_label in enumerate(__lowercase						):
   __A  :		Optional[Any]       =			self.tokenizer(__lowercase      ,      return_tensors=self.framework						)
   __A  :		Dict       =			self.image_processor(__lowercase      ,      return_tensors=self.framework						)
   yield {
       "is_last": i == len(__lowercase						) - 1,
       "target_size": target_size,
       "candidate_label": candidate_label,
       **text_inputs,
       **image_features,
   }


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[int]       =			model_inputs.pop('target_size'						)
  __A  :		Any       =			model_inputs.pop('candidate_label'						)
  __A  :		str       =			model_inputs.pop('is_last'						)

  __A  :		List[str]       =			self.model(**__lowercase						)

  __A  :		List[str]       =			{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
  return model_outputs


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase=0.1      ,      __lowercase=None						):

  """simple docstring"""







  __A  :		str       =			[]
  for model_output in model_outputs:
   __A  :		str       =			model_output['candidate_label']
   __A  :		str       =			BaseModelOutput(__lowercase						)
   __A  :		int       =			self.image_processor.post_process_object_detection(
       outputs=__lowercase      ,      threshold=__lowercase      ,      target_sizes=model_output['target_size']						)[0]

   for index in outputs["scores"].nonzero():
    __A  :		Optional[int]       =			outputs['scores'][index].item()
    __A  :		List[str]       =			self._get_bounding_box(outputs['boxes'][index][0]						)

    __A  :		str       =			{'score': score, 'label': label, 'box': box}
    results.append(__lowercase						)

  __A  :		List[Any]       =			sorted(__lowercase      ,      key=lambda __lowercase						: x["score"]      ,      reverse=__lowercase						)
  if top_k:
   __A  :		Tuple       =			results[:top_k]

  return results







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







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










'''simple docstring'''
from __future__ import annotations


def 			_lowercase       (				UpperCamelCase__       :					list,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int,				UpperCamelCase__       :					int			):
 __A  :		Dict       =			[]
 __A     ,__A  :		Dict       =			input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0			)			)
 __A  :		Optional[Any]       =			result + left + right
 return input_list


def 			_lowercase       (				UpperCamelCase__       :					list			):
 if len(UpperCamelCase__			) <= 1:
  return input_list
 __A  :		Any       =			list(UpperCamelCase__			)

 # iteration for two-way merging
 __A  :		Dict       =			2
 while p <= len(UpperCamelCase__			):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0,				len(UpperCamelCase__			),				UpperCamelCase__			):
   __A  :		Union[str, Any]       =			i
   __A  :		Tuple       =			i + p - 1
   __A  :		Union[str, Any]       =			(low + high + 1) // 2
   __A  :		Tuple       =			merge(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase__			):
   __A  :		Optional[int]       =			i
   __A  :		str       =			merge(UpperCamelCase__,				0,				UpperCamelCase__,				len(UpperCamelCase__			) - 1			)
   break
  p *= 2

 return input_list


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      input('Enter numbers separated by a comma:\n').strip()
 if user_input == "":
  UpperCAmelCase_ :     Union[str, Any]								=      []
 else:
  UpperCAmelCase_ :     str								=      [int(item.strip()) for item in user_input.split(',')]
 print(iter_merge_sort(unsorted))






