






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


lowercase    				=   logging.get_logger(__name__)


lowercase    				=   "▁"

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

lowercase    				=   {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowercase    				=   {
    "google/reformer-crime-and-punishment": 524288,
}




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					['''input_ids''', '''attention_mask''']




	def __init__(			self			,     a			,     a="</s>"			,     a="<unk>"			,     a=[]			,     a = None			,     **a			,     )			->       None:
								snake_case_           =				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=a			,     unk_token=a			,     additional_special_tokens=a			,     sp_model_kwargs=self.sp_model_kwargs			,     **a			,     )

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




	@property
	def 				_UpperCamelCase     (			self   )			->       str:
								return self.sp_model.get_piece_size()




	def 				_UpperCamelCase     (			self   )			->       Dict[str, int]:
								snake_case_           =				{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   )			->       List[Any]:
								snake_case_           =				self.__dict__.copy()
								snake_case_           =				None
								return state




	def __setstate__(			self			,     a   )			->       Any:
								snake_case_           =				d

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

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




	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								return self.sp_model.encode(a			,     out_type=a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       Tuple:
								return self.sp_model.piece_to_id(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       str:
								if index < self.sp_model.get_piece_size():
															snake_case_           =				self.sp_model.IdToPiece(a   )
								return token




	def 				_UpperCamelCase     (			self			,     a   )			->       Optional[int]:
								snake_case_           =				[]
								snake_case_           =				''
								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
																						snake_case_           =				[]
															else:
																						current_sub_tokens.append(a   )
								out_string += self.sp_model.decode(a   )
								return out_string.strip()






	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       Tuple[str]:
								if not os.path.isdir(a   ):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
															return
								snake_case_           =				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:
																						snake_case_           =				self.sp_model.serialized_model_proto()
																						fi.write(a   )

								return (out_vocab_file,)













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)





