
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__lowerCamelCase								=					logging.get_logger(__name__)

__lowerCamelCase								=					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCamelCase								=					{
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__lowerCamelCase								=					{
    'openai-gpt': 5_1_2,
}
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Union[str, Any]				   =      VOCAB_FILES_NAMES
						__A:						Optional[Any]				   =      PRETRAINED_VOCAB_FILES_MAP
						__A:						int				   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__A:						Optional[Any]				   =      ["""input_ids""", """attention_mask"""]
						__A:						int				   =      OpenAIGPTTokenizer

						def __init__(					self		:			Dict	,						_lowerCamelCase		:			Any=None	,						_lowerCamelCase		:			Optional[int]=None	,						_lowerCamelCase		:			Tuple=None	,						_lowerCamelCase		:			Union[str, Any]="<unk>"	,						**_lowerCamelCase		:			List[Any]			):
													super().__init__(_lowerCamelCase	,						_lowerCamelCase	,						tokenizer_file=_lowerCamelCase	,						unk_token=_lowerCamelCase	,						**_lowerCamelCase			)

						@property
						def 						a__ (					self		:			List[Any]			):
													return True






						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			Optional[str] = None			):
													_UpperCAmelCase  :			List[Any]	       =		self._tokenizer.model.save(_lowerCamelCase	,						name=_lowerCamelCase			)
													return tuple(_lowerCamelCase			)






import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

						def __enter__(					self		:			str			):
													if self.enabled:
																				_UpperCAmelCase  :			int	       =		self.model.no_sync()
																				self.model_sync_obj.__enter__()

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[Any]			):
													if self.enabled:
																				# Average all models on exit
																				self._sync_and_avg_model_params()
																				self.model_sync_obj.__exit__(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase			)

						def __init__(					self		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = True			):
													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)"			)
													_UpperCAmelCase  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[int]			):
													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 						a__ (					self		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)




