



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'''






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




class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							self		):
											# 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 _a      (							self		):
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											a__				      =		GradScalerKwargs(init_scale=1_024  ,  growth_factor=2		)
											AcceleratorState._reset_state()
											a__				      =		Accelerator(mixed_precision="""fp16"""  ,  kwargs_handlers=[scaler_handler]		)
											print(accelerator.use_fpaa		)
											a__				      =		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 _a      (							self		):
											a__				      =		["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
											execute_subprocess_async(a_  ,  env=os.environ.copy()		)


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

	# Check the values changed in kwargs
	UpperCAmelCase														=					""""""
	UpperCAmelCase														=					model.bucket_bytes_cap // (1_024 * 1_024)
	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)






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{"""vocab_file""": """spiece.model"""}

UpperCAmelCase							 =					{
    """vocab_file""": {
        """bert_for_seq_generation""": (
            """https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"""
        ),
    }
}

UpperCAmelCase							 =					{"""bert_for_seq_generation""": 512}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      Optional[Any]         =					VOCAB_FILES_NAMES
							UpperCamelCase__				:      Any         =					PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase__				:      List[str]         =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase__				:      List[int]         =					[]
							UpperCamelCase__				:      Optional[int]         =					["""input_ids""", """attention_mask"""]


							def __init__(							self  ,  a_  ,  a_="<s>"  ,  a_="</s>"  ,  a_="<unk>"  ,  a_="<pad>"  ,  a_="<::::>"  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Add extra_ids to the special token list
											super().__init__(
											    bos_token=a_  ,  eos_token=a_  ,  unk_token=a_  ,  pad_token=a_  ,  sep_token=a_  ,  sp_model_kwargs=self.sp_model_kwargs  ,  **a_  ,  )

											a__				      =		vocab_file

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(a_		)


							@property
							def _a      (							self		):
											return self.sp_model.get_piece_size()


							def _a      (							self		):
											a__				      =		{self.convert_ids_to_tokens(a_		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab


							def __getstate__(							self		):
											a__				      =		self.__dict__.copy()
											a__				      =		None
											return state


							def __setstate__(							self  ,  a_		):
											a__				      =		d

											# for backward compatibility
											if not hasattr(self  ,  """sp_model_kwargs"""		):
															a__				      =		{}

											a__				      =		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
											self.sp_model.Load(self.vocab_file		)


							def _a      (							self  ,  a_		):
											return self.sp_model.encode(a_  ,  out_type=a_		)


							def _a      (							self  ,  a_		):
											return self.sp_model.piece_to_id(a_		)


							def _a      (							self  ,  a_		):
											a__				      =		self.sp_model.IdToPiece(a_		)
											return token


							def _a      (							self  ,  a_		):
											a__				      =		[]
											a__				      =		""""""
											for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(a_		) + token
																			a__				      =		[]
															else:
																			current_sub_tokens.append(a_		)
											out_string += self.sp_model.decode(a_		)
											return out_string.strip()







							def _a      (							self  ,  a_  ,  a_ = None		):
											if not os.path.isdir(a_		):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
															return
											a__				      =		os.path.join(
											    a_  ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(a_		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file  ,  a_		)
											elif not os.path.isfile(self.vocab_file		):
															with open(a_  ,  """wb"""		) as fi:
																			a__				      =		self.sp_model.serialized_model_proto()
																			fi.write(a_		)

											return (out_vocab_file,)

