





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

_lowerCamelCase :    List[Any]  							= logging.get_logger(__name__)


@add_end_docstrings(__lowerCamelCase      )
class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							def __init__( self     :					int,			*_UpperCAmelCase     :					Tuple,			**_UpperCAmelCase     :					Tuple						)				->			Dict:





														"""simple docstring"""
														super().__init__(*_UpperCAmelCase,			**_UpperCAmelCase						)
														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     :					Optional[int],			_UpperCAmelCase     :					List[Any]=None						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{}
														if top_k is not None:
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						top_k
														return {}, {}, postprocess_params






							def __call__( self     :					List[str],			_UpperCAmelCase     :					Union[str, List[str], "Image.Image", List["Image.Image"]],			**_UpperCAmelCase     :					List[str]						)				->			int:





														"""simple docstring"""
														return super().__call__(_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					List[str]						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						load_image(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.image_processor(images=_UpperCAmelCase,			return_tensors=self.framework						)
														return model_inputs






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					List[str]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.model(**_UpperCAmelCase						)
														return model_outputs






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Union[str, Any]=5						)				->			Optional[int]:





														"""simple docstring"""
														if top_k > self.model.config.num_labels:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						self.model.config.num_labels

														if self.framework == "pt":
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						model_outputs.logits.softmax(-1						)[0]
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						probs.topk(_UpperCAmelCase						)
														elif self.framework == "tf":
																					SCREAMING_SNAKE_CASE__       :      str     =						stable_softmax(model_outputs.logits,			axis=-1						)[0]
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tf.math.top_k(_UpperCAmelCase,			k=_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'''Unsupported framework: {self.framework}'''						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						scores.tolist()
														SCREAMING_SNAKE_CASE__       :      int     =						ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCAmelCase,			_UpperCAmelCase						)]













def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str       )	->    int:

							'''simple docstring'''






							if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
														raise ValueError("String lengths must match!"       )

							SCREAMING_SNAKE_CASE__       :      Dict     =						0

							for chara, chara in zip(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
														if chara != chara:
																					count += 1

							return count


if __name__ == "__main__":
		import doctest

		doctest.testmod()






