






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


lowerCamelCase      :				Optional[int]							=[
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.de'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.en'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.fr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.frr'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.it'''},
    {'''dataset''': '''wikipedia''', '''config_name''': '''20220301.simple'''},
    {'''dataset''': '''snli''', '''config_name''': '''plain_text'''},
    {'''dataset''': '''eli5''', '''config_name''': '''LFQA_reddit'''},
    {'''dataset''': '''wiki40b''', '''config_name''': '''en'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.compressed'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.nq.no_index'''},
    {'''dataset''': '''wiki_dpr''', '''config_name''': '''psgs_w100.multiset.no_index'''},
    {'''dataset''': '''natural_questions''', '''config_name''': '''default'''},
]







def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase=True     )					->      str:
     if with_config:
          return [
              {
                  "testcase_name": d["dataset"] + "/" + d["config_name"],
                  "dataset": d["dataset"],
                  "config_name": d["config_name"],
              }
              for d in DATASETS_ON_HF_GCP
          ]
     else:
          return [
              {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
          ]





@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case_			)			)
class      __a    (       snake_case_			):
 _lowerCAmelCase			:			Tuple								=					None
 _lowerCAmelCase			:			Any								=					None







 def    __lowercase   (						self   :  int				,  SCREAMING_SNAKE_CASE   :  str				,  SCREAMING_SNAKE_CASE   :  Any		):


      '''simple docstring'''






      with TemporaryDirectory() as tmp_dir:
           UpperCamelCase__     :  Tuple										=    dataset_module_factory(SCREAMING_SNAKE_CASE				,  cache_dir=SCREAMING_SNAKE_CASE		)

           UpperCamelCase__     :  Tuple										=    import_main_class(dataset_module.module_path				,  dataset=SCREAMING_SNAKE_CASE		)

           UpperCamelCase__     :  Tuple										=    builder_cls(
               cache_dir=SCREAMING_SNAKE_CASE				,  config_name=SCREAMING_SNAKE_CASE				,  hash=dataset_module.hash				,  )

           UpperCamelCase__     :  Tuple										=    "/".join(
               [
                   HF_GCP_BASE_URL,
                   builder_instance._relative_data_dir(with_hash=SCREAMING_SNAKE_CASE		).replace(os.sep				,  "/"		),
                   config.DATASET_INFO_FILENAME,
               ]		)
           UpperCamelCase__     :  Union[str, Any]										=    cached_path(SCREAMING_SNAKE_CASE				,  cache_dir=SCREAMING_SNAKE_CASE		)
           self.assertTrue(os.path.exists(SCREAMING_SNAKE_CASE		)		)







@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase     )					->      Union[str, Any]:
     UpperCamelCase__     :  Optional[Any]										=    tmp_path_factory.mktemp("test_hf_gcp"     ) / "test_wikipedia_simple"
     UpperCamelCase__     :  Union[str, Any]										=    dataset_module_factory("wikipedia"   ,       cache_dir=a_     )
     UpperCamelCase__     :  Any										=    import_main_class(dataset_module.module_path     )
     UpperCamelCase__     :  List[str]										=    builder_cls(
         cache_dir=a_   ,       config_name="20220301.frr"   ,       hash=dataset_module.hash   ,       )
     # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
     UpperCamelCase__     :  Optional[int]										=    None
     builder_instance.download_and_prepare()
     UpperCamelCase__     :  Any										=    builder_instance.as_dataset()
     assert ds







@pytest.mark.integration
def 						SCREAMING_SNAKE_CASE				(   __lowerCAmelCase     )					->      Tuple:
     UpperCamelCase__     :  Optional[int]										=    dataset_module_factory("wikipedia"   ,       cache_dir=a_     )
     UpperCamelCase__     :  Dict										=    import_main_class(dataset_module.module_path   ,       dataset=a_     )
     UpperCamelCase__     :  Any										=    builder_cls(
         cache_dir=a_   ,       config_name="20220301.frr"   ,       hash=dataset_module.hash   ,       )
     UpperCamelCase__     :  int										=    builder_instance.as_streaming_dataset()
     assert ds
     assert isinstance(a_   ,       a_     )
     assert "train" in ds
     assert isinstance(ds["train"]   ,       a_     )
     assert next(iter(ds["train"]     )     )







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								self.assertDictEqual(MockClass().to_kwargs()			,     {}   )
								self.assertDictEqual(MockClass(a=2   ).to_kwargs()			,     {'a': 2}   )
								self.assertDictEqual(MockClass(a=2			,     b=a   ).to_kwargs()			,     {'a': 2, 'b': True}   )
								self.assertDictEqual(MockClass(a=2			,     c=2.25   ).to_kwargs()			,     {'a': 2, 'c': 2.25}   )




	@require_cuda
	def 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				accelerator.scaler

								# Check the kwargs have been applied
								self.assertEqual(scaler._init_scale			,     1_024.0   )
								self.assertEqual(scaler._growth_factor			,     2.0   )

								# Check the other values are at the default
								self.assertEqual(scaler._backoff_factor			,     0.5   )
								self.assertEqual(scaler._growth_interval			,     20_00   )
								self.assertEqual(scaler._enabled			,     a   )






	@require_multi_gpu
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
								execute_subprocess_async(a			,     env=os.environ.copy()   )


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	if observed_bucket_cap_map != 15:
		error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
	if model.find_unused_parameters is not True:
		error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

	# Check the values of the defaults
	if model.dim != 0:
		error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
	if model.broadcast_buffers is not True:
		error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
	if model.gradient_as_bucket_view is not False:
		error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)





