






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


__snake_case				   =    logging.get_logger(__name__)

__snake_case				   =    {
    """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      UpperCAmelCase_		:



      """simple docstring"""
      def __init__(							self       ,							SCREAMING_SNAKE_CASE_=None       ,							**SCREAMING_SNAKE_CASE_					)							-> Optional[int]:
          logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''					)
          UpperCamelCase			:Any      				=  model
          UpperCamelCase			:str      				=  kwargs.get('''model_save_dir'''       ,							__lowercase					)
          UpperCamelCase			:Optional[int]      				=  kwargs.get('''latest_model_name'''       ,							__lowercase					)
      def __call__(							self       ,							**SCREAMING_SNAKE_CASE_					)							-> List[str]:
          UpperCamelCase			:Dict      				=  {k: np.array(__lowercase					) for k, v in kwargs.items()}
          return self.model.run(__lowercase       ,							__lowercase					)
      @staticmethod
      def        UpperCAmelCase		(							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None					)							-> Tuple:







          if provider is None:
              logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''					)
              UpperCamelCase			:str      				=  '''CPUExecutionProvider'''

          return ort.InferenceSession(__lowercase       ,							providers=[provider]       ,							sess_options=__lowercase					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_					)							-> Dict:
          UpperCamelCase			:Any      				=  file_name if file_name is not None else ONNX_WEIGHTS_NAME

          UpperCamelCase			:Optional[Any]      				=  self.model_save_dir.joinpath(self.latest_model_name					)
          UpperCamelCase			:str      				=  Path(__lowercase					).joinpath(__lowercase					)
          try:
              shutil.copyfile(__lowercase       ,							__lowercase					)
          except shutil.SameFileError:
              pass

          # copy external weights (for models >2GB)
          UpperCamelCase			:Optional[int]      				=  self.model_save_dir.joinpath(__lowercase					)
          if src_path.exists():
              UpperCamelCase			:Optional[Any]      				=  Path(__lowercase					).joinpath(__lowercase					)
              try:
                  shutil.copyfile(__lowercase       ,							__lowercase					)
              except shutil.SameFileError:
                  pass
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> Any:







          if os.path.isfile(__lowercase					):
              logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''					)
              return

          os.makedirs(__lowercase       ,							exist_ok=__lowercase					)

          # saving model weights/files
          self._save_pretrained(__lowercase       ,							**__lowercase					)
      @classmethod
      def        UpperCAmelCase		(							cls       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = False       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> Tuple:
          UpperCamelCase			:List[Any]      				=  file_name if file_name is not None else ONNX_WEIGHTS_NAME
          # load model from local directory
          if os.path.isdir(__lowercase					):
              UpperCamelCase			:int      				=  OnnxRuntimeModel.load_model(
                  os.path.join(__lowercase       ,							__lowercase					)       ,							provider=__lowercase       ,							sess_options=__lowercase					)
              UpperCamelCase			:str      				=  Path(__lowercase					)
          # load model from hub
          else:
              # download model
              UpperCamelCase			:Optional[int]      				=  hf_hub_download(
                  repo_id=__lowercase       ,							filename=__lowercase       ,							use_auth_token=__lowercase       ,							revision=__lowercase       ,							cache_dir=__lowercase       ,							force_download=__lowercase       ,							)
              UpperCamelCase			:str      				=  Path(__lowercase					).parent
              UpperCamelCase			:str      				=  Path(__lowercase					).name
              UpperCamelCase			:Union[str, Any]      				=  OnnxRuntimeModel.load_model(__lowercase       ,							provider=__lowercase       ,							sess_options=__lowercase					)
          return cls(model=__lowercase       ,							**__lowercase					)




      @classmethod
      def        UpperCAmelCase		(							cls       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_ = True       ,							SCREAMING_SNAKE_CASE_ = None       ,							SCREAMING_SNAKE_CASE_ = None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> Dict:
          UpperCamelCase			:Tuple      				=  None
          if len(str(__lowercase					).split('''@'''					)					) == 2:
              UpperCamelCase				,  UpperCamelCase			:Tuple      				=  model_id.split('''@'''					)

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








'''simple docstring'''

from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate


@dataclass(frozen=A )
class 	lowerCAmelCase  (   A ):
       # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
       lowerCAmelCase_												=			field(default="summarization" ,      metadata={"include_in_asdict_even_if_is_default": True} )
       lowerCAmelCase_												=			Features({"text": Value("string" )} )
       lowerCAmelCase_												=			Features({"summary": Value("string" )} )
       lowerCAmelCase_												=			"text"
       lowerCAmelCase_												=			"summary"







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            return {self.text_column: "text", self.summary_column: "summary"}


