






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 :      Union[str, Any]										=				logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_						)
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					def __init__( self					:  str							,		*__A					:  Any							,		**__A					:  Tuple	):
						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 	_lowercase					( self					:  str							,		__A					:  Dict=None	):
						snake_case__			:				Optional[int]       =  {}
						if top_k is not None:
							snake_case__			:				Dict       =  top_k
						return {}, {}, postprocess_params



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



					def 	_lowercase					( self					:  Union[str, Any]							,		__A					:  Tuple	):
						snake_case__			:				List[str]       =  load_image(__A	)
						snake_case__			:				Optional[int]       =  self.image_processor(images=__A							,		return_tensors=self.framework	)
						return model_inputs



					def 	_lowercase					( self					:  Dict							,		__A					:  Dict	):
						snake_case__			:				List[Any]       =  self.model(**__A	)
						return model_outputs






					def 	_lowercase					( self					:  Dict							,		__A					:  Union[str, Any]							,		__A					:  Union[str, Any]=5	):
						if top_k > self.model.config.num_labels:
							snake_case__			:				str       =  self.model.config.num_labels

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

						snake_case__			:				Optional[Any]       =  scores.tolist()
						snake_case__			:				List[str]       =  ids.tolist()
						return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__A							,		__A	)]











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



