
"""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


lowercase_       :						Dict     					=	logging.get_logger(__name__)

lowercase_       :						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 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future."				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = model
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = kwargs.get("model_save_dir"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = kwargs.get("latest_model_name"  ,     snake_case__				)
     def __call__(     self  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = {k: np.array(snake_case__				) for k, v in kwargs.items()}
       return self.model.run(snake_case__  ,     snake_case__				)
     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     snake_case__  ,     snake_case__=None  ,     snake_case__=None				):




       """simple docstring"""


       if provider is None:
         logger.info("No onnxruntime provider specified, using CPUExecutionProvider"				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = "CPUExecutionProvider"

       return ort.InferenceSession(snake_case__  ,     providers=[provider]  ,     sess_options=snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = file_name if file_name is not None else ONNX_WEIGHTS_NAME

       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.model_save_dir.joinpath(self.latest_model_name				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = Path(snake_case__				).joinpath(snake_case__				)
       try:
         shutil.copyfile(snake_case__  ,     snake_case__				)
       except shutil.SameFileError:
         pass

       # copy external weights (for models >2GB)
       _SCREAMING_SNAKE_CASE   :				Any							    = self.model_save_dir.joinpath(snake_case__				)
       if src_path.exists():
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = Path(snake_case__				).joinpath(snake_case__				)
         try:
           shutil.copyfile(snake_case__  ,     snake_case__				)
         except shutil.SameFileError:
           pass
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     **snake_case__  ,     ):




       """simple docstring"""


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

       os.makedirs(snake_case__  ,     exist_ok=snake_case__				)

       # saving model weights/files
       self._save_pretrained(snake_case__  ,     **snake_case__				)
     @classmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = False  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


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




     @classmethod
     def 				__SCREAMING_SNAKE_CASE							(     cls  ,     snake_case__  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = None
       if len(str(snake_case__				).split("@"				)				) == 2:
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Tuple							    = model_id.split("@"				)

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





"""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    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = 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}}"				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = DatasetInfosDict.from_directory(lowerCamelCase__				)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 4_2





@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=4_2,						),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Union[str, Any],						lowerCamelCase__							:							DatasetInfo				)  ->    List[Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = str(lowerCamelCase__				)
  dataset_info.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				int							    = DatasetInfo.from_directory(lowerCamelCase__				)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(lowerCamelCase__,						"dataset_info.json"				)				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				str							    = 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": 4_2}],						download_checksums={},						download_size=1_3_3_7,						post_processing_size=4_4_2,						dataset_size=1_2_3_4,						size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4,						)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset_info._to_yaml_dict()
  assert sorted(lowerCamelCase__				) == 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)				)
  _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = yaml.safe_dump(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = yaml.safe_load(lowerCamelCase__				)
  assert dataset_info_yaml_dict == reloaded





def    _lowerCAmelCase    (     )  ->    Tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = DatasetInfo()
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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=4_2,						)
            }				),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=4_2				),
                "v2": DatasetInfo(dataset_size=1_3_3_7				),
            }				),
    ],						)
def    _lowerCAmelCase    (     lowerCamelCase__							:							Any,						lowerCamelCase__							:							DatasetInfosDict				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = str(lowerCamelCase__				)
  dataset_infos_dict.write_to_directory(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DatasetInfosDict.from_directory(lowerCamelCase__				)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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
    _SCREAMING_SNAKE_CASE   :				Dict							    = 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(lowerCamelCase__,						"README.md"				)				)



