




"""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

_lowerCamelCase  :			Dict        =       logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase)
class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					str      ,     **_lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					super().__init__(**_lowerCamelCase       )

					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   :					Any      ,     _lowerCamelCase   :					Union[str, List[str], "Image", List["Image"]]      ,     **_lowerCamelCase   :					Optional[Any]       ):



					"""simple docstring"""

					return super().__call__(_lowerCamelCase      ,     **_lowerCamelCase       )







				def 					a_  (							self   :					List[str]      ,     **_lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				List[str]	   =		{}
					if "candidate_labels" in kwargs:
						A_  :				int	   =		kwargs['''candidate_labels''']
					if "hypothesis_template" in kwargs:
						A_  :				List[Any]	   =		kwargs['''hypothesis_template''']

					return preprocess_params, {}, {}







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Optional[Any]=None      ,     _lowerCamelCase   :					Any="This is a photo of {}."       ):



					"""simple docstring"""

					A_  :				Dict	   =		load_image(_lowerCamelCase       )
					A_  :				Union[str, Any]	   =		self.image_processor(images=[image]      ,     return_tensors=self.framework       )
					A_  :				Union[str, Any]	   =		candidate_labels
					A_  :				Optional[Any]	   =		[hypothesis_template.format(_lowerCamelCase       ) for x in candidate_labels]
					A_  :				List[Any]	   =		self.tokenizer(_lowerCamelCase      ,     return_tensors=self.framework      ,     padding=_lowerCamelCase       )
					A_  :				int	   =		[text_inputs]
					return inputs







				def 					a_  (							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

					A_  :				Tuple	   =		model_inputs.pop('''candidate_labels'''       )
					A_  :				Optional[Any]	   =		model_inputs.pop('''text_inputs'''       )
					if isinstance(text_inputs[0]      ,     _lowerCamelCase       ):
						A_  :				List[Any]	   =		text_inputs[0]
					else:
						# Batching case.
						A_  :				int	   =		text_inputs[0][0]

					A_  :				int	   =		self.model(**_lowerCamelCase      ,     **_lowerCamelCase       )

					A_  :				List[str]	   =		{
					    '''candidate_labels''': candidate_labels,
					    '''logits''': outputs.logits_per_image,
					}
					return model_outputs







				def 					a_  (							self   :					Tuple      ,     _lowerCamelCase   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		model_outputs.pop('''candidate_labels'''       )
					A_  :				List[str]	   =		model_outputs['''logits'''][0]
					if self.framework == "pt":
						A_  :				List[Any]	   =		logits.softmax(dim=-1       ).squeeze(-1       )
						A_  :				Tuple	   =		probs.tolist()
						if not isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
							A_  :				Union[str, Any]	   =		[scores]
					elif self.framework == "tf":
						A_  :				List[str]	   =		stable_softmax(_lowerCamelCase      ,     axis=-1       )
						A_  :				List[Any]	   =		probs.numpy().tolist()
					else:
						raise ValueError(F"""Unsupported framework: {self.framework}"""       )

					A_  :				Optional[Any]	   =		[
					    {'''score''': score, '''label''': candidate_label}
					    for score, candidate_label in sorted(zip(_lowerCamelCase      ,     _lowerCamelCase       )      ,     key=lambda _lowerCamelCase       : -x[0]       )
					]
					return result











"""simple docstring"""

from __future__ import annotations

from collections import namedtuple

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		namedtuple('''result'''  ,      '''name value'''	)
	if (voltage, current, power).count(0	) != 1:
		raise ValueError('''Only one argument must be 0'''	)
	elif power < 0:
		raise ValueError(
		    '''Power cannot be negative in any electrical/electronics system'''	)
	elif voltage == 0:
		return result('''voltage'''  ,      power / current	)
	elif current == 0:
		return result('''current'''  ,      power / voltage	)
	elif power == 0:
		return result('''power'''  ,      float(round(abs(voltage * current	)  ,      2	)	)	)
	else:
		raise ValueError('''Exactly one argument must be 0'''	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





