






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								=						logging.get_logger(__name__)



@add_end_docstrings(_snake_case			)
class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		**_UpperCamelCase   )	->							Optional[int]:
							super().__init__(**_UpperCamelCase   )

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

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



		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Any:
							if "text_queries" in kwargs:
												UpperCAmelCase_    :      Tuple            =						kwargs.pop('text_queries'   )

							if isinstance(_UpperCamelCase				,		(str, Image.Image)   ):
												UpperCAmelCase_    :      Optional[Any]            =						{'image': image, 'candidate_labels': candidate_labels}
							else:
												UpperCAmelCase_    :      Union[str, Any]            =						image
							UpperCAmelCase_    :      Tuple            =						super().__call__(_UpperCamelCase				,		**_UpperCamelCase   )
							return results



		def 					__UpperCAmelCase			(							self				,		**_UpperCamelCase   )	->							str:
							UpperCAmelCase_    :      Tuple            =						{}
							if "threshold" in kwargs:
												UpperCAmelCase_    :      str            =						kwargs['threshold']
							if "top_k" in kwargs:
												UpperCAmelCase_    :      str            =						kwargs['top_k']
							return {}, {}, postprocess_params



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Union[str, Any]:
							UpperCAmelCase_    :      Optional[Any]            =						load_image(inputs['image']   )
							UpperCAmelCase_    :      List[Any]            =						inputs['candidate_labels']
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Optional[Any]            =						candidate_labels.split(','   )

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							UpperCAmelCase_    :      List[str]            =						model_inputs.pop('target_size'   )
							UpperCAmelCase_    :      Dict            =						model_inputs.pop('candidate_label'   )
							UpperCAmelCase_    :      Any            =						model_inputs.pop('is_last'   )

							UpperCAmelCase_    :      Tuple            =						self.model(**_UpperCamelCase   )

							UpperCAmelCase_    :      Any            =						{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
							return model_outputs



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase=0.1				,		_UpperCamelCase=None   )	->							Tuple:
							UpperCAmelCase_    :      Optional[Any]            =						[]
							for model_output in model_outputs:
												UpperCAmelCase_    :      Optional[Any]            =						model_output['candidate_label']
												UpperCAmelCase_    :      List[str]            =						BaseModelOutput(_UpperCamelCase   )
												UpperCAmelCase_    :      Optional[int]            =						self.image_processor.post_process_object_detection(
												    outputs=_UpperCamelCase				,		threshold=_UpperCamelCase				,		target_sizes=model_output['target_size']   )[0]

												for index in outputs["scores"].nonzero():
																	UpperCAmelCase_    :      List[Any]            =						outputs['scores'][index].item()
																	UpperCAmelCase_    :      Dict            =						self._get_bounding_box(outputs['boxes'][index][0]   )

																	UpperCAmelCase_    :      List[Any]            =						{'score': score, 'label': label, 'box': box}
																	results.append(_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						sorted(_UpperCamelCase				,		key=lambda _UpperCamelCase   : x["score"]				,		reverse=_UpperCamelCase   )
							if top_k:
												UpperCAmelCase_    :      List[str]            =						results[:top_k]

							return results



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Dict[str, int]:
							if self.framework != "pt":
												raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
							UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						box.int().tolist()
							UpperCAmelCase_    :      Optional[Any]            =						{
							    'xmin': xmin,
							    'ymin': ymin,
							    'xmax': xmax,
							    'ymax': ymax,
							}
							return bbox














def      lowercase__      (     __snake_case    :							int  ):


					'''simple docstring'''


					UpperCAmelCase_    :      list[list[int]]            =						[[0 for _ in range(__snake_case  )] for _ in range(m + 1  )]
					for i in range(m + 1  ):
										UpperCAmelCase_    :      Optional[Any]            =						1

					for n in range(m + 1  ):
										for k in range(1     ,       __snake_case  ):
															memo[n][k] += memo[n][k - 1]
															if n - k > 0:
																				memo[n][k] += memo[n - k - 1][k]

					return memo[m][m - 1]


if __name__ == "__main__":
		import sys

		if len(sys.argv) == 1:
				try:
						__UpperCAmelCase								=						int(input('Enter a number: ').strip())
						print(partition(n))
				except ValueError:
						print('Please enter a number.')
		else:
				try:
						__UpperCAmelCase								=						int(sys.argv[1])
						print(partition(n))
				except ValueError:
						print('Please pass a number.')






