





"""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 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  Union[str, Any]						)       ->Dict:



				'''simple docstring'''







				__A   :						Union[str, Any]									=					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   :						Union[str, Any]									=					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 		__lowercase					(       snake_case_  :  Dict    ,snake_case_  :  DatasetInfo						)       ->Any:



				'''simple docstring'''







				__A   :						int									=					str(snake_case_						)
				dataset_info.write_to_directory(snake_case_						)
				__A   :						Optional[int]									=					DatasetInfo.from_directory(snake_case_						)
				assert dataset_info == reloaded
				assert os.path.exists(os.path.join(snake_case_    ,'''dataset_info.json'''						)						)







def 		__lowercase					(       )       ->List[str]:



				'''simple docstring'''







				__A   :						Tuple									=					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=1337    ,post_processing_size=442    ,dataset_size=1234    ,size_in_bytes=1337 + 442 + 1234    ,)
				__A   :						List[str]									=					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   :						Any									=					yaml.safe_dump(snake_case_						)
				__A   :						List[str]									=					yaml.safe_load(snake_case_						)
				assert dataset_info_yaml_dict == reloaded







def 		__lowercase					(       )       ->Union[str, Any]:



				'''simple docstring'''







				__A   :						int									=					DatasetInfo()
				__A   :						str									=					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=1337						),
            }						),
    ]    ,)
def 		__lowercase					(       snake_case_  :  int    ,snake_case_  :  DatasetInfosDict						)       ->Any:



				'''simple docstring'''







				__A   :						Optional[int]									=					str(snake_case_						)
				dataset_infos_dict.write_to_directory(snake_case_						)
				__A   :						Union[str, Any]									=					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   :						Union[str, 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
								__A   :						Tuple									=					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"""



from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




