

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 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				0
			SCREAMING_SNAKE_CASE     :   bool									=				False
			SCREAMING_SNAKE_CASE     :   float									=				3.0






class 	snake_case_   (		unittest.TestCase					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[Any]							)      ->Optional[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=_UpperCamelCase							).to_kwargs()							,   {'''a''': 2, '''b''': True}							)
									self.assertDictEqual(MockClass(a=2							,   c=2.25							).to_kwargs()							,   {'''a''': 2, '''c''': 2.25}							)







			@require_cuda
			def 				snake_case__(     self :				Optional[int]							)      ->List[Any]:
									# If no defaults are changed, `to_kwargs` returns an empty dict.
									snake_case_   =	GradScalerKwargs(init_scale=1_0_2_4							,   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							,   1024.0							)
									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							,   2_0_0_0							)
									self.assertEqual(scaler._enabled							,   _UpperCamelCase							)







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


if __name__ == "__main__":
					lowerCAmelCase_       	=	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
					lowerCAmelCase_       	=	Accelerator(kwargs_handlers=[ddp_scaler])
					lowerCAmelCase_       	=	torch.nn.Linear(1_00, 2_00)
					lowerCAmelCase_       	=	accelerator.prepare(model)

					# Check the values changed in kwargs
					lowerCAmelCase_       	=	''''''
					lowerCAmelCase_       	=	model.bucket_bytes_cap // (10_24 * 10_24)
					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)
from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


__a  :     Optional[Any]       =  logging.get_logger(__name__)


__a  :     List[str]       =  TypeVar("""DatasetType""", Dataset, IterableDataset)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = None							,			lowercase = "first_exhausted"							,			):







				"""simple docstring"""



				from .arrow_dataset import Dataset
				from .iterable_dataset import IterableDataset

				if not datasets:
								raise ValueError('''Unable to interleave an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
								raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."							)
				if dataset_type is Dataset:
								return _interleave_map_style_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)
				else:
								return _interleave_iterable_datasets(
								    lowercase							,			lowercase							,			lowercase							,			info=lowercase							,			split=lowercase							,			stopping_strategy=lowercase							)





def 				UpperCAmelCase						(    lowercase							,			lowercase = None							,			lowercase = None							,			lowercase = 0							,			):







				"""simple docstring"""



				if not dsets:
								raise ValueError('''Unable to concatenate an empty list of datasets.'''							)
				for i, dataset in enumerate(lowercase							):
								if not isinstance(lowercase							,			(Dataset, IterableDataset)							):
												if isinstance(lowercase							,			(DatasetDict, IterableDatasetDict)							):
																if not dataset:
																				raise ValueError(
																				    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
																				    '''is an empty dataset dictionary.'''							)
																raise ValueError(
																    F"Dataset at position {i} has at least one split: {list(lowercase							)}\n"
																    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(lowercase							)							)}']"							)
												raise ValueError(
												    F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowercase							).__name__}."							)
								if i == 0:
												__lowercase			,     __lowercase 			=							(
												    (Dataset, IterableDataset) if isinstance(lowercase							,			lowercase							) else (IterableDataset, Dataset)
												)
								elif not isinstance(lowercase							,			lowercase							):
												raise ValueError(
												    F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."							)
				if dataset_type is Dataset:
								return _concatenate_map_style_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)
				else:
								return _concatenate_iterable_datasets(lowercase							,			info=lowercase							,			split=lowercase							,			axis=lowercase							)