






"""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       snake_case  (A_				:Optional[Any]     ,      A_				:Optional[Any]     ,      A_				:List[Any]=1_0_2_4     ,      A_				:str=1_0_2_4     ,      A_				:List[Any]=False     ,      **A_				:List[Any]				):







		'''simple docstring'''

		a					:      str    =       AutoTokenizer.from_pretrained(lowerCamelCase__				)
		a					:      str    =       SeqaSeqDataset(lowerCamelCase__     ,      lowerCamelCase__     ,      lowerCamelCase__     ,      lowerCamelCase__     ,      type_path='train'     ,      **lowerCamelCase__				)
		a					:      int    =       tok.pad_token_id

		def get_lens(A_				:Dict				):
				a					:      Optional[int]    =       tqdm(
				    DataLoader(lowerCamelCase__     ,      batch_size=5_1_2     ,      num_workers=8     ,      shuffle=lowerCamelCase__     ,      collate_fn=ds.collate_fn				)     ,      desc=str(ds.len_file				)     ,      )
				a					:      List[Any]    =       []
				for batch in dl:
						a					:      Any    =       batch['input_ids'].ne(lowerCamelCase__				).sum(1				).tolist()
						a					:      Dict    =       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					:      Tuple    =       get_lens(lowerCamelCase__				)
		a					:      Dict    =       SeqaSeqDataset(lowerCamelCase__     ,      lowerCamelCase__     ,      lowerCamelCase__     ,      lowerCamelCase__     ,      type_path='val'     ,      **lowerCamelCase__				)
		a					:      Optional[int]    =       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)












"""simple docstring"""


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


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

_UpperCamelCase    :   List[Any]   			=						{
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''beit'''





    def __init__(							self     : int    ,		A     : int=8_1_9_2    ,		A     : List[Any]=7_6_8    ,		A     : str=1_2    ,		A     : str=1_2    ,		A     : Dict=3_0_7_2    ,		A     : Optional[int]="gelu"    ,		A     : List[Any]=0.0    ,		A     : Union[str, Any]=0.0    ,		A     : Optional[Any]=0.02    ,		A     : Optional[int]=1E-12    ,		A     : Dict=2_2_4    ,		A     : str=1_6    ,		A     : Optional[Any]=3    ,		A     : List[Any]=False    ,		A     : Union[str, Any]=False    ,		A     : Optional[Any]=False    ,		A     : int=False    ,		A     : List[str]=0.1    ,		A     : Union[str, Any]=0.1    ,		A     : str=True    ,		A     : Tuple=[3, 5, 7, 1_1]    ,		A     : List[str]=[1, 2, 3, 6]    ,		A     : Optional[Any]=True    ,		A     : Union[str, Any]=0.4    ,		A     : Any=2_5_6    ,		A     : List[Any]=1    ,		A     : Optional[Any]=False    ,		A     : Any=2_5_5    ,		**A     : List[Any]    ,		):


      '''simple docstring'''




      super().__init__(**A      )

      a					:      Optional[int]    =       vocab_size
      a					:      Dict    =       hidden_size
      a					:      Optional[int]    =       num_hidden_layers
      a					:      Tuple    =       num_attention_heads
      a					:      Optional[int]    =       intermediate_size
      a					:      Optional[Any]    =       hidden_act
      a					:      Optional[int]    =       hidden_dropout_prob
      a					:      Optional[int]    =       attention_probs_dropout_prob
      a					:      Optional[Any]    =       initializer_range
      a					:      Union[str, Any]    =       layer_norm_eps

      a					:      Union[str, Any]    =       image_size
      a					:      str    =       patch_size
      a					:      Optional[Any]    =       num_channels
      a					:      List[str]    =       use_mask_token
      a					:      Optional[Any]    =       use_absolute_position_embeddings
      a					:      Any    =       use_relative_position_bias
      a					:      Any    =       use_shared_relative_position_bias
      a					:      Dict    =       layer_scale_init_value
      a					:      Optional[int]    =       drop_path_rate
      a					:      Dict    =       use_mean_pooling
      # decode head attributes (semantic segmentation)
      a					:      Optional[Any]    =       out_indices
      a					:      List[str]    =       pool_scales
      # auxiliary head attributes (semantic segmentation)
      a					:      Tuple    =       use_auxiliary_head
      a					:      Dict    =       auxiliary_loss_weight
      a					:      Any    =       auxiliary_channels
      a					:      Dict    =       auxiliary_num_convs
      a					:      List[str]    =       auxiliary_concat_input
      a					:      List[Any]    =       semantic_loss_ignore_index






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




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





    @property
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      return 1E-4




