




"""simple docstring"""


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  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 0
 lowercase__			 = False
 lowercase__			 = 3.0







class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		Tuple		):




   '''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=2.25		).to_kwargs(),				{"""a""": 2, """c""": 2.25}		)


 @require_cuda
 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		int								=  GradScalerKwargs(init_scale=1_024,				growth_factor=2		)
   AcceleratorState._reset_state()
   _snake_case							:		List[Any]								=  Accelerator(mixed_precision="""fp16""",				kwargs_handlers=[scaler_handler]		)
   print(accelerator.use_fpaa		)
   _snake_case							:		List[str]								=  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,				2_000		)
   self.assertEqual(scaler._enabled,				a_		)


 @require_multi_gpu
 def 			UpperCamelCase_       (    self:		str		):




   '''simple docstring'''



   _snake_case							:		int								=  ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__		)]
   execute_subprocess_async(a_,				env=os.environ.copy()		)


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

     # Check the values changed in kwargs
     A_      =    ''''''
     A_      =    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)








"""simple docstring"""


import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Any		,       snake_case__							: List[str]		,       snake_case__							: int   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    _snake_case							:		List[Any]								=  np.full((len(snake_case__   ), sequence_length, 2)		,       snake_case__   )
  else:
    _snake_case							:		Any								=  np.full((len(snake_case__   ), sequence_length)		,       snake_case__   )

  for i, tensor in enumerate(snake_case__   ):
    if padding_side == "right":
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		Dict								=  tensor[:sequence_length]
      else:
        _snake_case							:		List[Any]								=  tensor[:sequence_length]
    else:
      if isinstance(snake_case__		,       snake_case__   ):
        _snake_case							:		str								=  tensor[:sequence_length]
      else:
        _snake_case							:		Tuple								=  tensor[:sequence_length]

  return out_tensor.tolist()


def  UpperCAmelCase__	(snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		str								=  ord(snake_case__   )
  if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
    return True
  _snake_case							:		Union[str, Any]								=  unicodedata.category(snake_case__   )
  if cat.startswith("""P"""   ):
    return True
  return False






@dataclass
class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = 42
 lowercase__			 = True
 lowercase__			 = None
 lowercase__			 = None
 lowercase__			 = -1_00
 lowercase__			 = "pt"


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   import torch

   _snake_case							:		Optional[Any]								=  """label""" if """label""" in features[0].keys() else """labels"""
   _snake_case							:		str								=  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
   _snake_case							:		Any								=  self.tokenizer.pad(
       a_,				padding=self.padding,				max_length=self.max_length,				pad_to_multiple_of=self.pad_to_multiple_of,				return_tensors="""pt""" if labels is None else None,				)

   if labels is None:
     return batch

   _snake_case							:		Optional[int]								=  torch.tensor(batch["""entity_ids"""]		).shape[1]
   _snake_case							:		Any								=  self.tokenizer.padding_side
   if padding_side == "right":
     _snake_case							:		Optional[int]								=  [
         list(a_		) + [self.label_pad_token_id] * (sequence_length - len(a_		)) for label in labels
     ]
   else:
     _snake_case							:		Union[str, Any]								=  [
         [self.label_pad_token_id] * (sequence_length - len(a_		)) + list(a_		) for label in labels
     ]

   _snake_case							:		List[Any]								=  [feature["""ner_tags"""] for feature in features]
   _snake_case							:		str								=  padding_tensor(a_,				-1,				a_,				a_		)
   _snake_case							:		Any								=  [feature["""original_entity_spans"""] for feature in features]
   _snake_case							:		int								=  padding_tensor(a_,				(-1, -1),				a_,				a_		)
   _snake_case							:		str								=  {k: torch.tensor(a_,				dtype=torch.intaa		) for k, v in batch.items()}

   return batch


