




"""simple docstring"""

import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict






@pytest.mark.parametrize(
    'files'							,    [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]							,    )
def 		_snake_case	(   _snake_case				:   List[Any]							,    _snake_case				:   Tuple				)       ->   Tuple:






      '''simple docstring'''


      _A     =  tmp_path_factory.mktemp('dset_infos_dir'				)
      if "full:README.md" in files:
            with open(dataset_infos_dir / 'README.md'							,    'w'				) as f:
                  f.write('---\ndataset_info:\n  dataset_size: 42\n---'				)
      if "empty:README.md" in files:
            with open(dataset_infos_dir / 'README.md'							,    'w'				) as f:
                  f.write(''				)
    # we want to support dataset_infos.json for backward compatibility
      if "dataset_infos.json" in files:
            with open(dataset_infos_dir / 'dataset_infos.json'							,    'w'				) as f:
                  f.write('{"default": {"dataset_size": 42}}'				)
      _A     =  DatasetInfosDict.from_directory(_snake_case				)
      assert dataset_infos
      assert dataset_infos["default"].dataset_size == 42






@pytest.mark.parametrize(
    'dataset_info'							,    [
        DatasetInfo(),
        DatasetInfo(
            description='foo'							,    features=Features({'a': Value('int32'				)}				)							,    builder_name='builder'							,    config_name='config'							,    version='1.0.0'							,    splits=[{'name': 'train'}]							,    download_size=42							,    ),
    ]							,    )
def 		_snake_case	(   _snake_case				:   Optional[Any]							,    _snake_case				:   DatasetInfo				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  str(_snake_case				)
      dataset_info.write_to_directory(_snake_case				)
      _A     =  DatasetInfo.from_directory(_snake_case				)
      assert dataset_info == reloaded
      assert os.path.exists(os.path.join(_snake_case							,    'dataset_info.json'				)				)






def 		_snake_case	(   )       ->   Dict:






      '''simple docstring'''


      _A     =  DatasetInfo(
          description='foo'							,    citation='bar'							,    homepage='https://foo.bar'							,    license='CC0'							,    features=Features({'a': Value('int32'				)}				)							,    post_processed={}							,    supervised_keys=()							,    task_templates=[]							,    builder_name='builder'							,    config_name='config'							,    version='1.0.0'							,    splits=[{'name': 'train', 'num_examples': 42}]							,    download_checksums={}							,    download_size=13_37							,    post_processing_size=4_42							,    dataset_size=12_34							,    size_in_bytes=13_37 + 4_42 + 12_34							,    )
      _A     =  dataset_info._to_yaml_dict()
      assert sorted(_snake_case				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
      for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
            assert key in dataset_info_yaml_dict
            assert isinstance(dataset_info_yaml_dict[key]							,    (list, dict, int, str)				)
      _A     =  yaml.safe_dump(_snake_case				)
      _A     =  yaml.safe_load(_snake_case				)
      assert dataset_info_yaml_dict == reloaded






def 		_snake_case	(   )       ->   str:






      '''simple docstring'''


      _A     =  DatasetInfo()
      _A     =  dataset_info._to_yaml_dict()
      assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    'dataset_infos_dict'							,    [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}				),
        DatasetInfosDict({'my_config_name': DatasetInfo()}				),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'							,    features=Features({'a': Value('int32'				)}				)							,    builder_name='builder'							,    config_name='config'							,    version='1.0.0'							,    splits=[{'name': 'train'}]							,    download_size=42							,    )
            }				),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42				),
                'v2': DatasetInfo(dataset_size=13_37				),
            }				),
    ]							,    )
def 		_snake_case	(   _snake_case				:   Dict							,    _snake_case				:   DatasetInfosDict				)       ->   Union[str, Any]:






      '''simple docstring'''


      _A     =  str(_snake_case				)
      dataset_infos_dict.write_to_directory(_snake_case				)
      _A     =  DatasetInfosDict.from_directory(_snake_case				)

      # the config_name of the dataset_infos_dict take over the attribute
      for config_name, dataset_info in dataset_infos_dict.items():
            _A     =  config_name
            # the yaml representation doesn't include fields like description or citation
            # so we just test that we can recover what we can from the yaml
            _A     =  DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
      assert dataset_infos_dict == reloaded

      if dataset_infos_dict:
            assert os.path.exists(os.path.join(_snake_case							,    'README.md'				)				)







"""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							      =     logging.get_logger(__name__)

a							      =     {
    '''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 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict=None       ,				**_UpperCAmelCase			:			Optional[Any]						):
       logger.info('`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'						)
       _A     =  model
       _A     =  kwargs.get('model_save_dir'       ,				_UpperCAmelCase						)
       _A     =  kwargs.get('latest_model_name'       ,				_UpperCAmelCase						)

 def __call__(					self			:			Dict       ,				**_UpperCAmelCase			:			List[Any]						):
       _A     =  {k: np.array(_UpperCAmelCase						) for k, v in kwargs.items()}
       return self.model.run(_UpperCAmelCase       ,				_UpperCAmelCase						)

 @staticmethod
 def      lowerCAmelCase_				(					_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			List[Any]=None						):
       if provider is None:
             logger.info('No onnxruntime provider specified, using CPUExecutionProvider'						)
             _A     =  'CPUExecutionProvider'

       return ort.InferenceSession(_UpperCAmelCase       ,				providers=[provider]       ,				sess_options=_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			List[Any]						):
       _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(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
       try:
             shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
       except shutil.SameFileError:
             pass

       # copy external weights (for models >2GB)
       _A     =  self.model_save_dir.joinpath(_UpperCAmelCase						)
       if src_path.exists():
             _A     =  Path(_UpperCAmelCase						).joinpath(_UpperCAmelCase						)
             try:
                   shutil.copyfile(_UpperCAmelCase       ,				_UpperCAmelCase						)
             except shutil.SameFileError:
                   pass

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, os.PathLike]       ,				**_UpperCAmelCase			:			List[str]       ,				):
       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			:			Tuple       ,				_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			:			Union[str, Any]       ,				):
       _A     =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
       # load model from local directory
       if os.path.isdir(_UpperCAmelCase						):
             _A     =  OnnxRuntimeModel.load_model(
                 os.path.join(_UpperCAmelCase       ,				_UpperCAmelCase						)       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
             _A     =  Path(_UpperCAmelCase						)
       # load model from hub
       else:
             # download model
             _A     =  hf_hub_download(
                 repo_id=_UpperCAmelCase       ,				filename=_UpperCAmelCase       ,				use_auth_token=_UpperCAmelCase       ,				revision=_UpperCAmelCase       ,				cache_dir=_UpperCAmelCase       ,				force_download=_UpperCAmelCase       ,				)
             _A     =  Path(_UpperCAmelCase						).parent
             _A     =  Path(_UpperCAmelCase						).name
             _A     =  OnnxRuntimeModel.load_model(_UpperCAmelCase       ,				provider=_UpperCAmelCase       ,				sess_options=_UpperCAmelCase						)
       return cls(model=_UpperCAmelCase       ,				**_UpperCAmelCase						)



 @classmethod
 def      lowerCAmelCase_				(					cls			:			List[Any]       ,				_UpperCAmelCase			:			Union[str, Path]       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Optional[str] = None       ,				_UpperCAmelCase			:			Optional[str] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  None
       if len(str(_UpperCAmelCase						).split('@'						)						) == 2:
             _A  ,       _A     =  model_id.split('@'						)

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

