



'''simple docstring'''



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

a__    :      List[Any]      				=      logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase_)
class 							UpperCAmelCase__	(     UpperCAmelCase_):



 def __init__(	self		,						**lowercase      )	->				List[Any]:
  super().__init__(**_snake_case      )

  requires_backends(self		,						"""vision"""      )
  self.check_model_type(
      TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
      if self.framework == """tf"""
      else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING      )



 def __call__(	self		,						lowercase		,						**lowercase      )	->				Optional[int]:
  return super().__call__(_snake_case		,						**_snake_case      )



 def  __lowerCamelCase					(	self		,						**lowercase      )	->				Dict:
  __UpperCamelCase					   =	{}
  if "candidate_labels" in kwargs:
   __UpperCamelCase					   =	kwargs["""candidate_labels"""]
  if "hypothesis_template" in kwargs:
   __UpperCamelCase					   =	kwargs["""hypothesis_template"""]

  return preprocess_params, {}, {}



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase=None		,						lowercase="This is a photo of {}."      )	->				str:
  __UpperCamelCase					   =	load_image(_snake_case      )
  __UpperCamelCase					   =	self.image_processor(images=[image]		,						return_tensors=self.framework      )
  __UpperCamelCase					   =	candidate_labels
  __UpperCamelCase					   =	[hypothesis_template.format(_snake_case      ) for x in candidate_labels]
  __UpperCamelCase					   =	self.tokenizer(_snake_case		,						return_tensors=self.framework		,						padding=_snake_case      )
  __UpperCamelCase					   =	[text_inputs]
  return inputs



 def  __lowerCamelCase					(	self		,						lowercase      )	->				Optional[Any]:
  __UpperCamelCase					   =	model_inputs.pop("""candidate_labels"""      )
  __UpperCamelCase					   =	model_inputs.pop("""text_inputs"""      )
  if isinstance(text_inputs[0]		,						_snake_case      ):
   __UpperCamelCase					   =	text_inputs[0]
  else:
   # Batching case.
   __UpperCamelCase					   =	text_inputs[0][0]

  __UpperCamelCase					   =	self.model(**_snake_case		,						**_snake_case      )

  __UpperCamelCase					   =	{
      """candidate_labels""": candidate_labels,
      """logits""": outputs.logits_per_image,
  }
  return model_outputs




 def  __lowerCamelCase					(	self		,						lowercase      )	->				Optional[Any]:
  __UpperCamelCase					   =	model_outputs.pop("""candidate_labels"""      )
  __UpperCamelCase					   =	model_outputs["""logits"""][0]
  if self.framework == "pt":
   __UpperCamelCase					   =	logits.softmax(dim=-1      ).squeeze(-1      )
   __UpperCamelCase					   =	probs.tolist()
   if not isinstance(_snake_case		,						_snake_case      ):
    __UpperCamelCase					   =	[scores]
  elif self.framework == "tf":
   __UpperCamelCase					   =	stable_softmax(_snake_case		,						axis=-1      )
   __UpperCamelCase					   =	probs.numpy().tolist()
  else:
   raise ValueError(f"Unsupported framework: {self.framework}"      )

  __UpperCamelCase					   =	[
      {"""score""": score, """label""": candidate_label}
      for score, candidate_label in sorted(zip(_snake_case		,						_snake_case      )		,						key=lambda lowercase      : -x[0]      )
  ]
  return result










from __future__ import annotations

from typing import Any


def A    (							__UpperCamelCase       )    ->   int:
			if not postfix_notation:
						return 0

			A__	  =     {'+', '-', '*', '/'}
			A__	  =     []

			for token in postfix_notation:
						if token in operations:
									A__  ,			A__	  =     stack.pop(), stack.pop()
									if token == "+":
												stack.append(a + b       )
									elif token == "-":
												stack.append(a - b       )
									elif token == "*":
												stack.append(a * b       )
									else:
												if a * b < 0 and a % b != 0:
															stack.append(a // b + 1       )
												else:
															stack.append(a // b       )
						else:
									stack.append(int(__UpperCamelCase       )       )

			return stack.pop()


if __name__ == "__main__":
							import doctest

							doctest.testmod()




