
"""simple docstring"""
import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def    _lowerCAmelCase    (     lowerCamelCase__							:							Optional[int]				)  ->    Tuple:
  return EnvironmentCommand()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict				)  ->    List[str]:
  return EnvironmentCommand(args.accelerate_config_file				)





class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = parser.add_parser("env"				)
       download_parser.set_defaults(func=snake_case__				)
       download_parser.add_argument(
           "--accelerate-config_file"  ,     default=snake_case__  ,     help="The accelerate config file to use for the default values in the launching script."  ,     )
       download_parser.set_defaults(func=snake_case__				)
     def __init__(     self  ,     snake_case__  ,     *snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = accelerate_config_file
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "not installed"
       if is_safetensors_available():
         import safetensors

         _SCREAMING_SNAKE_CASE   :				Any							    = safetensors.__version__
       elif importlib.util.find_spec("safetensors"				) is not None:
         import safetensors

         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

       _SCREAMING_SNAKE_CASE   :				Tuple							    = "not installed"
       _SCREAMING_SNAKE_CASE   :				Any							    = "not found"
       if is_accelerate_available():
         import accelerate
         from accelerate.commands.config import default_config_file, load_config_from_file

         _SCREAMING_SNAKE_CASE   :				Tuple							    = accelerate.__version__
         # Get the default from the config file.
         if self._accelerate_config_file is not None or os.path.isfile(snake_case__				):
           _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = load_config_from_file(self._accelerate_config_file				).to_dict()

         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = (
             "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]				)
             if isinstance(snake_case__  ,     snake_case__				)
             else F'''\t{accelerate_config}'''
         )

       _SCREAMING_SNAKE_CASE   :				str							    = "not installed"
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = "NA"
       if is_torch_available():
         import torch

         _SCREAMING_SNAKE_CASE   :				Any							    = torch.__version__
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = torch.cuda.is_available()

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = "not installed"
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = "NA"
       if is_tf_available():
         import tensorflow as tf

         _SCREAMING_SNAKE_CASE   :				List[Any]							    = tf.__version__
         try:
           # deprecated in v2.1
           _SCREAMING_SNAKE_CASE   :				Dict							    = tf.test.is_gpu_available()
         except AttributeError:
           # returns list of devices, convert to bool
           _SCREAMING_SNAKE_CASE   :				List[str]							    = bool(tf.config.list_physical_devices("GPU"				)				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = "not installed"
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = "not installed"
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = "not installed"
       _SCREAMING_SNAKE_CASE   :				Any							    = "NA"
       if is_flax_available():
         import flax
         import jax
         import jaxlib

         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = flax.__version__
         _SCREAMING_SNAKE_CASE   :				Dict							    = jax.__version__
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = jaxlib.__version__
         _SCREAMING_SNAKE_CASE   :				Dict							    = jax.lib.xla_bridge.get_backend().platform

       _SCREAMING_SNAKE_CASE   :				Any							    = {
           "`transformers` version": version,
           "Platform": platform.platform(),
           "Python version": platform.python_version(),
           "Huggingface_hub version": huggingface_hub.__version__,
           "Safetensors version": F'''{safetensors_version}''',
           "Accelerate version": F'''{accelerate_version}''',
           "Accelerate config": F'''{accelerate_config_str}''',
           "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
           "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
           "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
           "Jax version": F'''{jax_version}''',
           "JaxLib version": F'''{jaxlib_version}''',
           "Using GPU in script?": "<fill in>",
           "Using distributed or parallel set-up in script?": "<fill in>",
       }

       print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"				)
       print(self.format_dict(snake_case__				)				)

       return info




     @staticmethod
     def 				__SCREAMING_SNAKE_CASE							(     snake_case__				):




       """simple docstring"""


       return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]				) + "\n"





"""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"				)				)



