
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_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

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

_UpperCAmelCase					: str      =       logging.get_logger(__name__)


@add_end_docstrings(_SCREAMING_SNAKE_CASE   )
class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __init__( self				,      *A_				,      **A_       )  ->					Tuple:
				"""simple docstring"""
				super().__init__(*A_				,      **A_       )
				requires_backends(self				,      'vision'       )
				self.check_model_type(
				    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				    if self.framework == 'tf'
				    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING       )



		def __UpperCamelCase ( self				,      A_=None       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     {}
				if top_k is not None:
						UpperCamelCase		=     top_k
				return {}, {}, postprocess_params



		def __call__( self				,      A_				,      **A_       )  ->					List[Any]:
				"""simple docstring"""
				return super().__call__(A_				,      **A_       )



		def __UpperCamelCase ( self				,      A_       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     load_image(A_       )
				UpperCamelCase		=     self.image_processor(images=A_				,      return_tensors=self.framework       )
				return model_inputs



		def __UpperCamelCase ( self				,      A_       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.model(**A_       )
				return model_outputs



		def __UpperCamelCase ( self				,      A_				,      A_=5       )  ->					Optional[Any]:
				"""simple docstring"""
				if top_k > self.model.config.num_labels:
						UpperCamelCase		=     self.model.config.num_labels

				if self.framework == "pt":
						UpperCamelCase		=     model_outputs.logits.softmax(-1       )[0]
						UpperCamelCase	,  UpperCamelCase		=     probs.topk(A_       )
				elif self.framework == "tf":
						UpperCamelCase		=     stable_softmax(model_outputs.logits				,      axis=-1       )[0]
						UpperCamelCase		=     tf.math.top_k(A_				,      k=A_       )
						UpperCamelCase	,  UpperCamelCase		=     topk.values.numpy(), topk.indices.numpy()
				else:
						raise ValueError(F'''Unsupported framework: {self.framework}'''       )

				UpperCamelCase		=     scores.tolist()
				UpperCamelCase		=     ids.tolist()
				return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A_				,      A_       )]


def       A       (					lowercase )   ->  str:
		'''simple docstring'''

		return " ".join(
		    ''.join(word[::-1] ) if len(lowercase ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(reverse_long_words("Hey wollef sroirraw"))
