



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

SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_  )
class 	lowerCamelCase      (						UpperCamelCase_  ):







					'''simple docstring'''







					def __init__(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							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    A__		(			self    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				{}
							if top_k is not None:
									UpperCAmelCase_						=				top_k
							return {}, {}, postprocess_params



					def __call__(			self    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							return super().__call__(__a    ,	**__a     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				load_image(__a     )
							UpperCAmelCase_						=				self.image_processor(images=__a    ,	return_tensors=self.framework     )
							return model_inputs



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				self.model(**__a     )
							return model_outputs



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=5     ):






							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_						=				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_						=				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 snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				int(__SCREAMING_SNAKE_CASE )
		if decimal in (0, 1):  # Exit cases for the recursion
				return str(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_					,						UpperCAmelCase_						=				divmod(__SCREAMING_SNAKE_CASE			,     2 )
		return binary_recursive(__SCREAMING_SNAKE_CASE ) + str(__SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							str:
		UpperCAmelCase_						=				str(__SCREAMING_SNAKE_CASE ).strip()
		if not number:
				raise ValueError("No input value was provided" )
		UpperCAmelCase_						=				"-" if number.startswith("-" ) else ""
		UpperCAmelCase_						=				number.lstrip("-" )
		if not number.isnumeric():
				raise ValueError("Input value is not an integer" )
		return f'''{negative}0b{binary_recursive(int(__SCREAMING_SNAKE_CASE ) )}'''


if __name__ == "__main__":
			from doctest import testmod

			testmod()

