



from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase							=       logging.get_logger(__name__)

class 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__					:   Tuple       =       ["""input_ids""", """attention_mask"""]


							def __init__(				self	,	a_="[PAD]"	,	a_="[BOS]"	,	a_="[EOS]"	,	a_="[MASK]"	,	a_="[CLS]"	,	a_="[SEP]"	,	a_=2_048	,	**a_	,	):
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else pad_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else bos_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else eos_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else mask_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else cls_token
											a__					   =		AddedToken(a_	,	lstrip=a_	,	rstrip=a_		) if isinstance(a_	,	a_		) else sep_token

											super().__init__(
											    pad_token=a_	,	bos_token=a_	,	eos_token=a_	,	mask_token=a_	,	cls_token=a_	,	sep_token=a_	,	model_max_length=a_	,	**a_	,	)

											a__					   =		2**8  # utf is 8 bits

											# define special tokens dict
											a__					   =		{
											    self.pad_token: 0,
											    self.bos_token: 1,
											    self.eos_token: 2,
											    self.mask_token: 3,
											    self.cls_token: 4,
											    self.sep_token: 5,
											}
											a__					   =		len(self.special_tokens_encoder		)
											a__					   =		{v: k for k, v in self.special_tokens_encoder.items()}


							def _a   (				self		):
											a__					   =		self.special_tokens_encoder.copy()
											vocab.update(self.added_tokens_encoder		)
											for i in range(self._utf_vocab_size		):
															a__					   =		chr(a_		)
															a__					   =		i + len(self.special_tokens_encoder		)
											return vocab


							@property
							def _a   (				self		):
											return self._utf_vocab_size + self._num_special_tokens


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=a_	,	token_ids_a=a_	,	already_has_special_tokens=a_		)

											# normal case: some special tokens
											if token_ids_a is None:
															return [1] + [0] * len(a_		) + [1]
											return [1] + ([0] * len(a_		)) + [1] + ([0] * len(a_		)) + [1]


							def _a   (				self	,	a_	,	a_ = None		):
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											else:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]


							def _a   (				self	,	a_		):
											a__					   =		[chr(a_		) for i in text.encode("""utf-8"""		)]
											return tokens


							def _a   (				self	,	a_		):
											if token in self.special_tokens_encoder:
															a__					   =		self.special_tokens_encoder[token]
											elif token in self.added_tokens_encoder:
															a__					   =		self.added_tokens_encoder[token]
											elif len(a_		) != 1:
															a__					   =		self.unk_token_id
											else:
															a__					   =		ord(a_		) + self._num_special_tokens
											return token_id


							def _a   (				self	,	a_		):
											if index in self.special_tokens_decoder:
															a__					   =		self.special_tokens_decoder[index]
											elif index in self.added_tokens_decoder:
															a__					   =		self.added_tokens_decoder[index]
											else:
															a__					   =		chr(index - self._num_special_tokens		)
											return token


							def _a   (				self	,	a_		):
											a__					   =		B""""""
											for token in tokens:
															if token in self.special_tokens_decoder:
																			a__					   =		self.special_tokens_decoder[token].encode("""utf-8"""		)
															elif token in self.added_tokens_decoder:
																			a__					   =		self.special_tokens_decoder[token].encode("""utf-8"""		)
															elif token in self.special_tokens_encoder:
																			a__					   =		token.encode("""utf-8"""		)
															elif token in self.added_tokens_encoder:
																			a__					   =		token.encode("""utf-8"""		)
															else:
																			a__					   =		bytes([ord(a_		)]		)
															bstring += tok_string
											a__					   =		bstring.decode("""utf-8"""	,	errors="""replace"""		)
											return string







							def _a   (				self	,	a_	,	a_ = None		):
											return ()







"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


