



"""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

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




@add_end_docstrings(lowerCAmelCase__)
class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: Any						,  **lowercase_	: Optional[Any]       ):
     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	: Optional[int]						,  lowercase_	: Union[str, "Image.Image", List[Dict[str, Any]]]						,  lowercase_	: Union[str, List[str]] = None						,  **lowercase_	: int						,  ):
     if "text_queries" in kwargs:
         snake_case_				:  str       =			kwargs.pop('''text_queries'''       )

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

 def 		_snake_case      (  self	: List[str]						,  **lowercase_	: str       ):
     snake_case_				:  Optional[int]       =			{}
     if "threshold" in kwargs:
         snake_case_				:  Tuple       =			kwargs['''threshold''']
     if "top_k" in kwargs:
         snake_case_				:  Dict       =			kwargs['''top_k''']
     return {}, {}, postprocess_params

 def 		_snake_case      (  self	: str						,  lowercase_	: List[Any]       ):
     snake_case_				:  Optional[Any]       =			load_image(inputs['''image''']       )
     snake_case_				:  Tuple       =			inputs['''candidate_labels''']
     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  Optional[int]       =			candidate_labels.split(''','''       )

     snake_case_				:  Union[str, Any]       =			torch.tensor([[image.height, image.width]]						,  dtype=torch.intaa       )
     for i, candidate_label in enumerate(lowercase_       ):
         snake_case_				:  Any       =			self.tokenizer(lowercase_						,  return_tensors=self.framework       )
         snake_case_				:  Tuple       =			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	: List[Any]						,  lowercase_	: Optional[int]       ):
     snake_case_				:  Optional[Any]       =			model_inputs.pop('''target_size'''       )
     snake_case_				:  Tuple       =			model_inputs.pop('''candidate_label'''       )
     snake_case_				:  int       =			model_inputs.pop('''is_last'''       )

     snake_case_				:  List[Any]       =			self.model(**lowercase_       )

     snake_case_				:  Optional[int]       =			{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
     return model_outputs

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[int]						,  lowercase_	: List[str]=0.1						,  lowercase_	: Optional[int]=None       ):
     snake_case_				:  Any       =			[]
     for model_output in model_outputs:
         snake_case_				:  Any       =			model_output['''candidate_label''']
         snake_case_				:  Tuple       =			BaseModelOutput(lowercase_       )
         snake_case_				:  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():
             snake_case_				:  str       =			outputs['''scores'''][index].item()
             snake_case_				:  Dict       =			self._get_bounding_box(outputs['''boxes'''][index][0]       )

             snake_case_				:  List[str]       =			{'''score''': score, '''label''': label, '''box''': box}
             results.append(lowercase_       )

     snake_case_				:  str       =			sorted(lowercase_						,  key=lambda lowercase_       : x["score"]						,  reverse=lowercase_       )
     if top_k:
         snake_case_				:  Optional[Any]       =			results[:top_k]

     return results






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










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





