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	( _SCREAMING_SNAKE_CASE		):





      '''simple docstring'''






      _snake_case    		=       0
      _snake_case    		=       False
      _snake_case    		=       3.0





class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[Any]:


          """simple docstring"""


          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=2.2_5).to_kwargs()    ,  {"""a""": 2, """c""": 2.2_5})



      @require_cuda
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						GradScalerKwargs(init_scale=1024    ,  growth_factor=2)
          AcceleratorState._reset_state()
          _lowerCamelCase     :		Dict 						=						Accelerator(mixed_precision="""fp16"""    ,  kwargs_handlers=[scaler_handler])
          print(accelerator.use_fpaa)
          _lowerCamelCase     :		Union[str, Any] 						=						accelerator.scaler

          # Check the kwargs have been applied
          self.assertEqual(scaler._init_scale    ,  1_0_2_4.0)
          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    ,  2000)
          self.assertEqual(scaler._enabled    ,  A_)




      @require_multi_gpu
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__)]
          execute_subprocess_async(A_    ,  env=os.environ.copy())


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

    # Check the values changed in kwargs
    lowerCAmelCase			:							Dict     =""
    lowerCAmelCase			:							Any     =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)





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				NestedDataStructureLike[PathLike]    ,  _UpperCamelCase     :				Optional[NamedSplit] = None    ,  _UpperCamelCase     :				Optional[Features] = None    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				Optional[int] = None    ,  **_UpperCamelCase     :				Tuple    ,  )				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              _UpperCamelCase    ,  split=_UpperCamelCase    ,  features=_UpperCamelCase    ,  cache_dir=_UpperCamelCase    ,  keep_in_memory=_UpperCamelCase    ,  streaming=_UpperCamelCase    ,  num_proc=_UpperCamelCase    ,  **_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						path_or_paths if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else {self.split: path_or_paths}
          _lowerCamelCase     :		Any 						=						Text(
              cache_dir=_UpperCamelCase    ,  data_files=_UpperCamelCase    ,  features=_UpperCamelCase    ,  **_UpperCamelCase    ,  )




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          if self.streaming:
              _lowerCamelCase     :		Tuple 						=						self.builder.as_streaming_dataset(split=self.split)
          # Build regular (map-style) dataset
          else:
              _lowerCamelCase     :		List[Any] 						=						None
              _lowerCamelCase     :		Any 						=						None
              _lowerCamelCase     :		List[str] 						=						None
              _lowerCamelCase     :		Dict 						=						None

              self.builder.download_and_prepare(
                  download_config=_UpperCamelCase    ,  download_mode=_UpperCamelCase    ,  verification_mode=_UpperCamelCase    ,  base_path=_UpperCamelCase    ,  num_proc=self.num_proc    ,  )
              _lowerCamelCase     :		Optional[int] 						=						self.builder.as_dataset(
                  split=self.split    ,  verification_mode=_UpperCamelCase    ,  in_memory=self.keep_in_memory)
          return dataset




