




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

UpperCAmelCase_									=	logging.get_logger(__name__)





@add_end_docstrings(__lowerCamelCase)
class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[str]    ,       *UpperCamelCase_: Any    ,       **UpperCamelCase_: Union[str, Any]   ):
             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 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Tuple=None   ):
             __lowerCamelCase   =						{}
             if top_k is not None:
                    __lowerCamelCase   =						top_k
             return {}, {}, postprocess_params
      def __call__(				self: Tuple    ,       UpperCamelCase_: Union[str, List[str], "Image.Image", List["Image.Image"]]    ,       **UpperCamelCase_: Optional[int]   ):
             return super().__call__(UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Union[str, Any]   ):
             __lowerCamelCase   =						load_image(UpperCamelCase_   )
             __lowerCamelCase   =						self.image_processor(images=UpperCamelCase_    ,       return_tensors=self.framework   )
             return model_inputs
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Any   ):
             __lowerCamelCase   =						self.model(**UpperCamelCase_   )
             return model_outputs




      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Union[str, Any]=5   ):
             if top_k > self.model.config.num_labels:
                    __lowerCamelCase   =						self.model.config.num_labels

             if self.framework == "pt":
                    __lowerCamelCase   =						model_outputs.logits.softmax(-1   )[0]
                    __lowerCamelCase,   __lowerCamelCase   =						probs.topk(UpperCamelCase_   )
             elif self.framework == "tf":
                    __lowerCamelCase   =						stable_softmax(model_outputs.logits    ,       axis=-1   )[0]
                    __lowerCamelCase   =						tf.math.top_k(UpperCamelCase_    ,       k=UpperCamelCase_   )
                    __lowerCamelCase,   __lowerCamelCase   =						topk.values.numpy(), topk.indices.numpy()
             else:
                    raise ValueError(F'Unsupported framework: {self.framework}'   )

             __lowerCamelCase   =						scores.tolist()
             __lowerCamelCase   =						ids.tolist()
             return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase_    ,       UpperCamelCase_   )]







import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


UpperCAmelCase_									=	logging.get_logger(__name__)

UpperCAmelCase_									=	{
    'tensor(bool)': np.bool_,
    'tensor(int8)': np.inta,
    'tensor(uint8)': np.uinta,
    'tensor(int16)': np.intaa,
    'tensor(uint16)': np.uintaa,
    'tensor(int32)': np.intaa,
    'tensor(uint32)': np.uintaa,
    'tensor(int64)': np.intaa,
    'tensor(uint64)': np.uintaa,
    'tensor(float16)': np.floataa,
    'tensor(float)': np.floataa,
    'tensor(double)': np.floataa,
}





class     lowerCamelCase__:
      def __init__(				self: str    ,       UpperCamelCase_: List[Any]=None    ,       **UpperCamelCase_: str   ):
             logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""   )
             __lowerCamelCase   =						model
             __lowerCamelCase   =						kwargs.get("""model_save_dir"""    ,       UpperCamelCase_   )
             __lowerCamelCase   =						kwargs.get("""latest_model_name"""    ,       UpperCamelCase_   )
      def __call__(				self: Dict    ,       **UpperCamelCase_: Any   ):
             __lowerCamelCase   =						{k: np.array(UpperCamelCase_   ) for k, v in kwargs.items()}
             return self.model.run(UpperCamelCase_    ,       UpperCamelCase_   )
      @staticmethod
      def 			lowerCAmelCase__						(				UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Tuple=None    ,       UpperCamelCase_: Tuple=None   ):
             if provider is None:
                    logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""   )
                    __lowerCamelCase   =						"""CPUExecutionProvider"""

             return ort.InferenceSession(UpperCamelCase_    ,       providers=[provider]    ,       sess_options=UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: Optional[int]   ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME

             __lowerCamelCase   =						self.model_save_dir.joinpath(self.latest_model_name   )
             __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
             try:
                    shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
             except shutil.SameFileError:
                    pass

             # copy external weights (for models >2GB)
             __lowerCamelCase   =						self.model_save_dir.joinpath(UpperCamelCase_   )
             if src_path.exists():
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).joinpath(UpperCamelCase_   )
                    try:
                           shutil.copyfile(UpperCamelCase_    ,       UpperCamelCase_   )
                    except shutil.SameFileError:
                           pass
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Union[str, os.PathLike]    ,       **UpperCamelCase_: Optional[Any]    ,       ):
             if os.path.isfile(UpperCamelCase_   ):
                    logger.error(F'Provided path ({save_directory}) should be a directory, not a file'   )
                    return

             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )

             # saving model weights/files
             self._save_pretrained(UpperCamelCase_    ,       **UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: str    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: Optional[Union[bool, str, None]] = None    ,       UpperCamelCase_: Optional[Union[str, None]] = None    ,       UpperCamelCase_: bool = False    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional["ort.SessionOptions"] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						file_name if file_name is not None else ONNX_WEIGHTS_NAME
             # load model from local directory
             if os.path.isdir(UpperCamelCase_   ):
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(
                        os.path.join(UpperCamelCase_    ,       UpperCamelCase_   )    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
                    __lowerCamelCase   =						Path(UpperCamelCase_   )
             # load model from hub
             else:
                    # download model
                    __lowerCamelCase   =						hf_hub_download(
                        repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       )
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).parent
                    __lowerCamelCase   =						Path(UpperCamelCase_   ).name
                    __lowerCamelCase   =						OnnxRuntimeModel.load_model(UpperCamelCase_    ,       provider=UpperCamelCase_    ,       sess_options=UpperCamelCase_   )
             return cls(model=UpperCamelCase_    ,       **UpperCamelCase_   )




      @classmethod
      def 			lowerCAmelCase__						(				cls: Optional[int]    ,       UpperCamelCase_: Union[str, Path]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Optional[str] = None    ,       UpperCamelCase_: Optional[str] = None    ,       **UpperCamelCase_: int    ,       ):
             __lowerCamelCase   =						None
             if len(str(UpperCamelCase_   ).split("""@"""   )   ) == 2:
                    __lowerCamelCase,   __lowerCamelCase   =						model_id.split("""@"""   )

             return cls._from_pretrained(
                 model_id=UpperCamelCase_    ,       revision=UpperCamelCase_    ,       cache_dir=UpperCamelCase_    ,       force_download=UpperCamelCase_    ,       use_auth_token=UpperCamelCase_    ,       **UpperCamelCase_    ,       )

