





'''simple docstring'''


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

a__			:					Optional[int]       						=						logging.get_logger(__name__)






@add_end_docstrings(UpperCAmelCase_    )
class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''

       def __init__( self					:				Dict       ,     *a__					:				Optional[Any]       ,     **a__					:				List[Any]						):
              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					:				int       ,     a__					:				Union[str, Any]=None						):
              UpperCAmelCase  =						{}
              if top_k is not None:
                     UpperCAmelCase  =						top_k
              return {}, {}, postprocess_params

       def __call__( self					:				Any       ,     a__					:				Union[str, List[str], "Image.Image", List["Image.Image"]]       ,     **a__					:				List[str]						):
              return super().__call__(a__       ,     **a__						)

       def 			__snake_case      ( self					:				Union[str, Any]       ,     a__					:				Dict						):
              UpperCAmelCase  =						load_image(a__						)
              UpperCAmelCase  =						self.image_processor(images=a__       ,     return_tensors=self.framework						)
              return model_inputs

       def 			__snake_case      ( self					:				str       ,     a__					:				Optional[Any]						):
              UpperCAmelCase  =						self.model(**a__						)
              return model_outputs



       def 			__snake_case      ( self					:				Union[str, Any]       ,     a__					:				Tuple       ,     a__					:				Union[str, Any]=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,					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__						)]








import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script




@require_cpu
class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										debug_launcher(test_script.main			)


						def      A__    (		self :Dict			):
										'''simple docstring'''

										debug_launcher(test_ops.main			)




