




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

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save







def 			lowerCamelCase_		(       UpperCamelCase__				:      Dict ,     UpperCamelCase__				:      Union[str, Any] ,     UpperCamelCase__				:      Any=1024 ,     UpperCamelCase__				:      Union[str, Any]=1024 ,     UpperCamelCase__				:      int=False ,     **UpperCamelCase__				:      List[str]      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    AutoTokenizer.from_pretrained(_lowerCamelCase      )
				__lowerCamelCase		       =    SeqaSeqDataset(_lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase ,     type_path='train' ,     **_lowerCamelCase      )
				__lowerCamelCase		       =    tok.pad_token_id

				def get_lens(UpperCamelCase__				:      Tuple      ):
								__lowerCamelCase		       =    tqdm(
								    DataLoader(_lowerCamelCase ,     batch_size=512 ,     num_workers=8 ,     shuffle=_lowerCamelCase ,     collate_fn=ds.collate_fn      ) ,     desc=str(ds.len_file      ) ,     )
								__lowerCamelCase		       =    []
								for batch in dl:
												__lowerCamelCase		       =    batch["input_ids"].ne(_lowerCamelCase      ).sum(1      ).tolist()
												__lowerCamelCase		       =    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

				__lowerCamelCase		       =    get_lens(_lowerCamelCase      )
				__lowerCamelCase		       =    SeqaSeqDataset(_lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase ,     _lowerCamelCase ,     type_path='val' ,     **_lowerCamelCase      )
				__lowerCamelCase		       =    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)







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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

							lowerCamelCase__   			=		{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase     :							Any    =    image_size
														_lowerCAmelCase     :							Union[str, Any]    =    patch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embed_dim
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Optional[Any]    =    len(__a)
														_lowerCAmelCase     :							int    =    num_heads
														_lowerCAmelCase     :							int    =    window_size
														_lowerCAmelCase     :							int    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    qkv_bias
														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							Union[str, Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    drop_path_rate
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    use_absolute_embeddings
														_lowerCAmelCase     :							Optional[int]    =    layer_norm_eps
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Tuple    =    encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_lowerCAmelCase     :							List[str]    =    int(embed_dim * 2 ** (len(__a) - 1))
														_lowerCAmelCase     :							List[Any]    =    ["stem"] + [f"stage{idx}" for idx in range(1,			len(__a) + 1)]
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_aligned_output_features_output_indices(
														    out_features=__a,			out_indices=__a,			stage_names=self.stage_names)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
