'''simple docstring'''

import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


A_     :						Dict    							=pytest.mark.integration



@pytest.mark.parametrize('''path'''	,   ['''paws''', '''csv'''])
def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							Dict)			->					List[Any]:
   inspect_dataset(__snake_case	,   __snake_case)
   lowerCAmelCase_			=      path + '''.py'''
   assert script_name in os.listdir(__snake_case)
   assert "__pycache__" not in os.listdir(__snake_case)



@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning''')
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning''')
@pytest.mark.parametrize('''path'''	,   ['''accuracy'''])
def snake_case_      (					__snake_case  :							int	,   __snake_case  :							Any)			->					Optional[Any]:
   inspect_metric(__snake_case	,   __snake_case)
   lowerCAmelCase_			=      path + '''.py'''
   assert script_name in os.listdir(__snake_case)
   assert "__pycache__" not in os.listdir(__snake_case)



@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''	,   [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]	,   )
def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Tuple	,   __snake_case  :							int)			->					Any:
   lowerCAmelCase_			=      get_dataset_config_info(__snake_case	,   config_name=__snake_case)
   assert info.config_name == config_name
   assert list(info.splits.keys()) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''	,   [
        ('''paws''', None, ValueError),
    ]	,   )
def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Tuple	,   __snake_case  :							Tuple)			->					Tuple:
   with pytest.raises(__snake_case):
      get_dataset_config_info(__snake_case	,   config_name=__snake_case)



@pytest.mark.parametrize(
    '''path, expected'''	,   [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]	,   )
def snake_case_      (					__snake_case  :							Tuple	,   __snake_case  :							Dict)			->					Dict:
   lowerCAmelCase_			=      get_dataset_config_names(__snake_case)
   assert expected in config_names



@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''	,   [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]	,   )
def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Dict	,   __snake_case  :							List[Any])			->					Tuple:
   lowerCAmelCase_			=      get_dataset_infos(__snake_case)
   assert list(infos.keys()) == expected_configs
   lowerCAmelCase_			=      expected_configs[0]
   assert expected_config in infos
   lowerCAmelCase_			=      infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits_in_first_config



@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''	,   [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]	,   )
def snake_case_      (					__snake_case  :							Optional[int]	,   __snake_case  :							Optional[Any]	,   __snake_case  :							Tuple)			->					int:
   lowerCAmelCase_			=      get_dataset_infos(__snake_case)
   assert expected_config in infos
   lowerCAmelCase_			=      infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''	,   [
        ('''paws''', None, ValueError),
    ]	,   )
def snake_case_      (					__snake_case  :							Any	,   __snake_case  :							Union[str, Any]	,   __snake_case  :							Any)			->					Optional[Any]:
   with pytest.raises(__snake_case):
      get_dataset_split_names(__snake_case	,   config_name=__snake_case)






'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      None
   gc.collect()
   if is_xpu_available():
      torch.xpu.empty_cache()
   elif is_npu_available():
      torch.npu.empty_cache()
   else:
      torch.cuda.empty_cache()
   return objects



def snake_case_      (					__snake_case  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      nonlocal batch_size
      gc.collect()
      if is_xpu_available():
         torch.xpu.empty_cache()
      elif is_npu_available():
         torch.npu.empty_cache()
      else:
         torch.cuda.empty_cache()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]	,   args[1:])])
         raise TypeError(
             F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
             F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`''')
      while True:
         if batch_size == 0:
            raise RuntimeError('''No executable batch size found, reached zero.''')
         try:
            return function(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__snake_case):
               gc.collect()
               if is_xpu_available():
                  torch.xpu.empty_cache()
               elif is_npu_available():
                  torch.npu.empty_cache()
               else:
                  torch.cuda.empty_cache()
               batch_size //= 2
            else:
               raise

   return decorator





