


"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


a__				:							Union[str, Any]						=     logging.get_logger(__name__)

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

a__				:							Dict						=     {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

a__				:							Dict						=     {
    '''gpt-neox-20b''': 2_0_4_8,
}




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  List[Any]    					=			VOCAB_FILES_NAMES
	snake_case__				:  Dict    					=			PRETRAINED_VOCAB_FILES_MAP
	snake_case__				:  Union[str, Any]    					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__				:  Union[str, Any]    					=			["input_ids", "attention_mask"]




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				int=None			,			UpperCAmelCase__			:				List[str]=None			,			UpperCAmelCase__			:				str=None			,			UpperCAmelCase__			:				Union[str, Any]="<|endoftext|>"			,			UpperCAmelCase__			:				Optional[Any]="<|endoftext|>"			,			UpperCAmelCase__			:				List[str]="<|endoftext|>"			,			UpperCAmelCase__			:				List[Any]=False			,			**UpperCAmelCase__			:				Optional[int]			,			)       ->	Optional[Any]:
						super().__init__(
						    UpperCAmelCase__			,			UpperCAmelCase__			,			tokenizer_file=UpperCAmelCase__			,			unk_token=UpperCAmelCase__			,			bos_token=UpperCAmelCase__			,			eos_token=UpperCAmelCase__			,			add_prefix_space=UpperCAmelCase__			,			**UpperCAmelCase__			,			)

						__SCREAMING_SNAKE_CASE						   =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
						if pre_tok_state.get("add_prefix_space"			,			UpperCAmelCase__   ) != add_prefix_space:
											__SCREAMING_SNAKE_CASE						   =				getattr(UpperCAmelCase__			,			pre_tok_state.pop("type"   )   )
											__SCREAMING_SNAKE_CASE						   =				add_prefix_space
											__SCREAMING_SNAKE_CASE						   =				pre_tok_class(**UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				add_prefix_space




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[str] = None   )       ->	Tuple[str]:
						__SCREAMING_SNAKE_CASE						   =				self._tokenizer.model.save(UpperCAmelCase__			,			name=UpperCAmelCase__   )
						return tuple(UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				"Conversation"   )       ->	List[int]:
						__SCREAMING_SNAKE_CASE						   =				[]
						for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(UpperCAmelCase__			,			add_special_tokens=UpperCAmelCase__   ) + [self.eos_token_id]   )

						if len(UpperCAmelCase__   ) > self.model_max_length:
											__SCREAMING_SNAKE_CASE						   =				input_ids[-self.model_max_length :]
						return input_ids







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
