


'''simple docstring'''






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


a_					:    Tuple          =    logging.get_logger(__name__)

a_					:    int          =    {
    "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  a     :
 def __init__( self      ,				__magic_name__=None      ,				**__magic_name__						)					->							str:
      logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
      _a			      =							model
      _a			      =							kwargs.get('model_save_dir'      ,				__magic_name__						)
      _a			      =							kwargs.get('latest_model_name'      ,				__magic_name__						)
 def __call__( self      ,				**__magic_name__						)					->							str:
      _a			      =							{k: np.array(__magic_name__						) for k, v in kwargs.items()}
      return self.model.run(__magic_name__      ,				__magic_name__						)
 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__      ,				__magic_name__=None      ,				__magic_name__=None						)					->							Optional[Any]:
      if provider is None:
           logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
           _a			      =							'CPUExecutionProvider'

      return ort.InferenceSession(__magic_name__      ,				providers=[provider]      ,				sess_options=__magic_name__						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__ = None      ,				**__magic_name__						)					->							Tuple:
      _a			      =							file_name if file_name is not None else ONNX_WEIGHTS_NAME

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

      # copy external weights (for models >2GB)
      _a			      =							self.model_save_dir.joinpath(__magic_name__						)
      if src_path.exists():
           _a			      =							Path(__magic_name__						).joinpath(__magic_name__						)
           try:
                shutil.copyfile(__magic_name__      ,				__magic_name__						)
           except shutil.SameFileError:
                pass
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				**__magic_name__      ,				)					->							Optional[int]:
      if os.path.isfile(__magic_name__						):
           logger.error(f'Provided path ({save_directory}) should be a directory, not a file'						)
           return

      os.makedirs(__magic_name__      ,				exist_ok=__magic_name__						)

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




 @classmethod
 def 							__UpperCAmelCase		( cls      ,				__magic_name__      ,				__magic_name__ = True      ,				__magic_name__ = None      ,				__magic_name__ = None      ,				**__magic_name__      ,				)					->							Any:
      _a			      =							None
      if len(str(__magic_name__						).split('@'						)						) == 2:
           _a      ,   _a			      =							model_id.split('@'						)

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





'''simple docstring'''






import datasets


a_					:    List[Any]          =    "\\n@InProceedings{conneau2018xnli,\n  author = \"Conneau, Alexis\n                 and Rinott, Ruty\n                 and Lample, Guillaume\n                 and Williams, Adina\n                 and Bowman, Samuel R.\n                 and Schwenk, Holger\n                 and Stoyanov, Veselin\",\n  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",\n  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods\n               in Natural Language Processing\",\n  year = \"2018\",\n  publisher = \"Association for Computational Linguistics\",\n  location = \"Brussels, Belgium\",\n}\n"

a_					:    Optional[Any]          =    "\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

a_					:    Optional[Any]          =    "\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"






def 	_A				(lowerCAmelCase__  :List[Any]				,						lowerCAmelCase__  :Tuple		)   ->  Dict:




     '''simple docstring'''



     return (preds == labels).mean()






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION       )
class  a     (     datasets.Metric       ):
 def 							__UpperCAmelCase		( self						)					->							Dict:
      return datasets.MetricInfo(
          description=_DESCRIPTION      ,				citation=_CITATION      ,				inputs_description=_KWARGS_DESCRIPTION      ,				features=datasets.Features(
              {
                  'predictions': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'						),
                  'references': datasets.Value('int64' if self.config_name != 'sts-b' else 'float32'						),
              }						)      ,				codebase_urls=[]      ,				reference_urls=[]      ,				format='numpy'      ,				)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							Dict:
      return {"accuracy": simple_accuracy(__magic_name__      ,				__magic_name__						)}

