




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				Dict       			=		{
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					"""camembert"""





				def __init__(      self				:     Any					,      __lowerCamelCase				:     Tuple=3_05_22					,      __lowerCamelCase				:     Optional[Any]=7_68					,      __lowerCamelCase				:     Tuple=12					,      __lowerCamelCase				:     Any=12					,      __lowerCamelCase				:     Any=30_72					,      __lowerCamelCase				:     str="gelu"					,      __lowerCamelCase				:     int=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Optional[Any]=5_12					,      __lowerCamelCase				:     Any=2					,      __lowerCamelCase				:     str=0.02					,      __lowerCamelCase				:     Optional[Any]=1e-12					,      __lowerCamelCase				:     int=1					,      __lowerCamelCase				:     Union[str, Any]=0					,      __lowerCamelCase				:     List[str]=2					,      __lowerCamelCase				:     Tuple="absolute"					,      __lowerCamelCase				:     List[str]=True					,      __lowerCamelCase				:     Dict=None					,      **__lowerCamelCase				:     Dict					,      )					->     Tuple:
							super().__init__(pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase							)

							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			hidden_act
							a    =			intermediate_size
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			initializer_range
							a    =			layer_norm_eps
							a    =			position_embedding_type
							a    =			use_cache
							a    =			classifier_dropout




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										a    =			{0: "batch", 1: "choice", 2: "sequence"}
							else:
										a    =			{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]							)











'''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 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__=10_24						,      lowerCamelCase__=10_24						,      lowerCamelCase__=False						,      **lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Any	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						Union[str, Any]	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""train"""						,      **lowerCamelCase__    )
			A_	:						Optional[Any]	=     tok.pad_token_id

			def get_lens(lowerCamelCase__    ):
						A_	:						int	=     tqdm(
						    DataLoader(lowerCamelCase__						,      batch_size=5_12						,      num_workers=8						,      shuffle=lowerCamelCase__						,      collate_fn=ds.collate_fn    )						,      desc=str(ds.len_file    )						,      )
						A_	:						int	=     []
						for batch in dl:
									A_	:						str	=     batch["""input_ids"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									A_	:						Tuple	=     batch["""labels"""].ne(lowerCamelCase__    ).sum(1    ).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase__						,      lowerCamelCase__    ):
															max_lens.append(max(lowerCamelCase__						,      lowerCamelCase__    )    )
									else:
												max_lens.extend(lowerCamelCase__    )
						return max_lens

			A_	:						str	=     get_lens(lowerCamelCase__    )
			A_	:						Dict	=     SeqaSeqDataset(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      type_path="""val"""						,      **lowerCamelCase__    )
			A_	:						List[Any]	=     get_lens(lowerCamelCase__    )
			pickle_save(lowerCamelCase__						,      train_ds.len_file    )
			pickle_save(lowerCamelCase__						,      val_ds.len_file    )


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