
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

A     :	List[str]    = {
    'google/vit-base-patch16-224': 'https://huggingface.co/vit-base-patch16-224/resolve/main/config.json',
    # See all ViT models at https://huggingface.co/models?filter=vit
}







class 			__A( a			):
				snake_case_						   =    '''vit'''




				def __init__(     self		,     _snake_case=768		,     _snake_case=12		,     _snake_case=12		,     _snake_case=3_072		,     _snake_case="gelu"		,     _snake_case=0.0		,     _snake_case=0.0		,     _snake_case=0.02		,     _snake_case=1E-12		,     _snake_case=224		,     _snake_case=16		,     _snake_case=3		,     _snake_case=True		,     _snake_case=16		,     **_snake_case		,     )    ->      Optional[int]:


								'''simple docstring'''




								super().__init__(**_snake_case				)

								__a										=    hidden_size
								__a										=    num_hidden_layers
								__a										=    num_attention_heads
								__a										=    intermediate_size
								__a										=    hidden_act
								__a										=    hidden_dropout_prob
								__a										=    attention_probs_dropout_prob
								__a										=    initializer_range
								__a										=    layer_norm_eps
								__a										=    image_size
								__a										=    patch_size
								__a										=    num_channels
								__a										=    qkv_bias
								__a										=    encoder_stride







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4

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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

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

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


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