





"""simple docstring"""




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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_xglm import XGLMTokenizer
else:
						UpperCamelCase_	 =None


UpperCamelCase_	 =logging.get_logger(__name__)

UpperCamelCase_	 ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_	 ={
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase_	 ={
    """facebook/xglm-564M""": 2_048,
}




class 					_a		( __lowercase							):
				UpperCamelCase								=       VOCAB_FILES_NAMES
				UpperCamelCase								=       PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=       ['''input_ids''', '''attention_mask''']
				UpperCamelCase								=       XGLMTokenizer






				def __init__(					self						:       Optional[Any],					lowerCAmelCase__						:       Optional[Any]=None,					lowerCAmelCase__						:       List[str]=None,					lowerCAmelCase__						:       Any="<s>",					lowerCAmelCase__						:       Any="</s>",					lowerCAmelCase__						:       Union[str, Any]="</s>",					lowerCAmelCase__						:       List[str]="<s>",					lowerCAmelCase__						:       Optional[Any]="<unk>",					lowerCAmelCase__						:       Optional[Any]="<pad>",					**lowerCAmelCase__						:       List[str],					)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       7
										_UpperCamelCase     :   Tuple      =       [f"""<madeupword{i}>""" for i in range(self.num_madeup_words				)]

										_UpperCamelCase     :   List[Any]      =       kwargs.get('''additional_special_tokens''',					[]				)
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    A_,					tokenizer_file=A_,					bos_token=A_,					eos_token=A_,					sep_token=A_,					cls_token=A_,					unk_token=A_,					pad_token=A_,					**A_,					)

										_UpperCamelCase     :   List[Any]      =       vocab_file
										_UpperCamelCase     :   Optional[Any]      =       False if not self.vocab_file else True






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       str = None				)      -> List[int]:




										'''simple docstring'''



										if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
										_UpperCamelCase     :   List[Any]      =       [self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a






				def 							snake_case      (					self						:       Dict,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       Union[str, Any] = None				)      -> List[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       [self.sep_token_id]

										if token_ids_a is None:
																return len(sep + token_ids_a				) * [0]
										return len(sep + token_ids_a + sep + sep + token_ids_a				) * [0]






				def 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any] = None				)      -> Tuple[str]:




										'''simple docstring'''



										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																    '''tokenizer.'''				)

										if not os.path.isdir(A_				):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""				)
																return
										_UpperCamelCase     :   str      =       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_				):
																copyfile(self.vocab_file,					A_				)

										return (out_vocab_file,)




import torch

from accelerate import Accelerator, DistributedType



class 			UpperCamelCase					:

     '''simple docstring'''

     def __enter__(       self    )			->					int:






           """simple docstring"""
           if self.enabled:
                 _lowerCamelCase           =					self.model.no_sync()
                 self.model_sync_obj.__enter__()

           return self



     def __exit__(       self					,					A_					,					A_					,					A_    )			->					Union[str, Any]:






           """simple docstring"""
           if self.enabled:
                 # Average all models on exit
                 self._sync_and_avg_model_params()
                 self.model_sync_obj.__exit__(A_					,					A_					,					A_    )



     def __init__(       self					,					A_					,					A_					,					A_					,					A_ = True    )			->					List[str]:






           """simple docstring"""
           if accelerator.distributed_type not in [
               DistributedType.NO,
               DistributedType.MULTI_CPU,
               DistributedType.MULTI_GPU,
           ]:
                 raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''    )
           _lowerCamelCase           =					enabled and accelerator.distributed_type != DistributedType.NO
           _lowerCamelCase           =					0
           if self.enabled:
                 _lowerCamelCase           =					accelerator
                 _lowerCamelCase           =					model
                 _lowerCamelCase           =					local_sgd_steps



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






           """simple docstring"""
           self.num_steps += 1
           if not self.enabled:
                 return

           if self.num_steps % self.local_sgd_steps == 0:
                 self._sync_and_avg_model_params()





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






           """simple docstring"""
           self.accelerator.wait_for_everyone()
           with self.accelerator.autocast():
                 for param in self.model.parameters():
                       _lowerCamelCase           =					self.accelerator.reduce(param.data					,					reduction='''mean'''    )