



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

_snake_case		=  logging.get_logger(__name__)



@add_end_docstrings(a)
class   UpperCAmelCase_    (       a):







							def __init__(   self,			*__a,			**__a):

														'''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    snake_case__      (   self,			__a=None):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    {}
														if top_k is not None:
																					_lowerCAmelCase     :							Dict    =    top_k
														return {}, {}, postprocess_params







							def __call__(   self,			__a,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    load_image(__a)
														_lowerCAmelCase     :							Tuple    =    self.image_processor(images=__a,			return_tensors=self.framework)
														return model_inputs







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.model(**__a)
														return model_outputs







							def    snake_case__      (   self,			__a,			__a=5):

														'''simple docstring'''

														if top_k > self.model.config.num_labels:
																					_lowerCAmelCase     :							Any    =    self.model.config.num_labels

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

														_lowerCAmelCase     :							Dict    =    scores.tolist()
														_lowerCAmelCase     :							List[Any]    =    ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__a,			__a)]






"""simple docstring"""


import os
from typing import Dict, List, Tuple, TypeVar, Union


A_						=				TypeVar('''T''')

A_						=				Union[List[T], Tuple[T, ...]]
A_						=				Union[T, List[T], Dict[str, T]]
A_						=				Union[str, bytes, os.PathLike]


