
'''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

snake_case_ :  Tuple     =					logging.get_logger(__name__)


@add_end_docstrings(snake_case_   )
class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(**lowerCamelCase__   )

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

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







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

      '''simple docstring'''







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

      if isinstance(lowerCamelCase__ ,   (str, Image.Image)   ):
         UpperCamelCase       =  {'''image''': image, '''candidate_labels''': candidate_labels}
      else:
         UpperCamelCase       =  image
      UpperCamelCase       =  super().__call__(lowerCamelCase__ ,   **lowerCamelCase__   )
      return results







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  {}
      if "threshold" in kwargs:
         UpperCamelCase       =  kwargs['''threshold''']
      if "top_k" in kwargs:
         UpperCamelCase       =  kwargs['''top_k''']
      return {}, {}, postprocess_params







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  load_image(inputs['''image''']   )
      UpperCamelCase       =  inputs['''candidate_labels''']
      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  candidate_labels.split(''','''   )

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  model_inputs.pop('''target_size'''   )
      UpperCamelCase       =  model_inputs.pop('''candidate_label'''   )
      UpperCamelCase       =  model_inputs.pop('''is_last'''   )

      UpperCamelCase       =  self.model(**lowerCamelCase__   )

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      for model_output in model_outputs:
         UpperCamelCase       =  model_output['''candidate_label''']
         UpperCamelCase       =  BaseModelOutput(lowerCamelCase__   )
         UpperCamelCase       =  self.image_processor.post_process_object_detection(
             outputs=lowerCamelCase__ ,   threshold=lowerCamelCase__ ,   target_sizes=model_output['''target_size''']   )[0]

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

            UpperCamelCase       =  {'''score''': score, '''label''': label, '''box''': box}
            results.append(lowerCamelCase__   )

      UpperCamelCase       =  sorted(lowerCamelCase__ ,   key=lambda lowerCamelCase__   : x["score"] ,   reverse=lowerCamelCase__   )
      if top_k:
         UpperCamelCase       =  results[:top_k]

      return results







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







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







'''simple docstring'''


from random import randint
from tempfile import TemporaryFile

import numpy as np



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   if start < end:
      UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
      UpperCamelCase       =  a[end]
      UpperCamelCase       =  a[pivot]
      UpperCamelCase       =  temp

      UpperCamelCase     , UpperCamelCase       =  _in_place_partition(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase)
      count += _in_place_quick_sort(_UpperCAmelCase,      _UpperCAmelCase,      p - 1)
      count += _in_place_quick_sort(_UpperCAmelCase,      p + 1,      _UpperCAmelCase)
   return count



def  __snake_case     (    _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Any):
   UpperCamelCase       =  0
   UpperCamelCase       =  randint(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  a[pivot]
   UpperCamelCase       =  temp
   UpperCamelCase       =  start - 1
   for index in range(_UpperCAmelCase,      _UpperCAmelCase):
      count += 1
      if a[index] < a[end]:  # check if current val is less than pivot value
         UpperCamelCase       =  new_pivot_index + 1
         UpperCamelCase       =  a[new_pivot_index]
         UpperCamelCase       =  a[index]
         UpperCamelCase       =  temp

   UpperCamelCase       =  a[new_pivot_index + 1]
   UpperCamelCase       =  a[end]
   UpperCamelCase       =  temp
   return new_pivot_index + 1, count


snake_case_ :  List[Any]     =					TemporaryFile()
snake_case_ :  Optional[Any]     =					100  # 1000 elements are to be sorted


snake_case_  , snake_case_ :  List[str]     =					0, 1  # mean and standard deviation
snake_case_ :  Any     =					np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
snake_case_ :  Optional[int]     =					np.load(outfile)
snake_case_ :  Optional[int]     =					len(M) - 1
snake_case_ :  List[str]     =					_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)

print(z)





