

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


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







class 					__lowercase  (						__A		,       __A				):





							"""simple docstring"""

							UpperCAmelCase_						:		str								=	'maskformer-swin'

							UpperCAmelCase_						:		int								=	{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}




							def __init__(					self    ,						__UpperCAmelCase=2_24    ,						__UpperCAmelCase=4    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=96    ,						__UpperCAmelCase=[2, 2, 6, 2]    ,						__UpperCAmelCase=[3, 6, 12, 24]    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=4.0    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=0.0    ,						__UpperCAmelCase=0.0    ,						__UpperCAmelCase=0.1    ,						__UpperCAmelCase="gelu"    ,						__UpperCAmelCase=False    ,						__UpperCAmelCase=0.0_2    ,						__UpperCAmelCase=1E-5    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       List[Any]:
														super().__init__(**snake_case_						)

														A      :					Tuple      		=					image_size
														A      :					Tuple      		=					patch_size
														A      :					Union[str, Any]      		=					num_channels
														A      :					Optional[Any]      		=					embed_dim
														A      :					Dict      		=					depths
														A      :					List[str]      		=					len(snake_case_						)
														A      :					Optional[Any]      		=					num_heads
														A      :					Optional[Any]      		=					window_size
														A      :					List[Any]      		=					mlp_ratio
														A      :					Dict      		=					qkv_bias
														A      :					Any      		=					hidden_dropout_prob
														A      :					Any      		=					attention_probs_dropout_prob
														A      :					Dict      		=					drop_path_rate
														A      :					str      		=					hidden_act
														A      :					str      		=					use_absolute_embeddings
														A      :					Dict      		=					layer_norm_eps
														A      :					int      		=					initializer_range
														# 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
														A      :					int      		=					int(embed_dim * 2 ** (len(snake_case_						) - 1)						)
														A      :					Optional[Any]      		=					["""stem"""] + [f'stage{idx}' for idx in range(1    ,						len(snake_case_						) + 1						)]
														A      :					Optional[int]      		=					get_aligned_output_features_output_indices(
														    out_features=snake_case_    ,						out_indices=snake_case_    ,						stage_names=self.stage_names						)







"""simple docstring"""



import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase=1024							):
							A__				,							A__       :     Any								=			[], []

							A__       :     List[Any]								=			list(zip(lowerCAmelCase			,  lowerCAmelCase							)							)
							A__				,							A__       :     Optional[int]								=			sorted_examples[0]

							def is_too_big(lowerCAmelCase							):
														return tok(lowerCAmelCase			,  return_tensors="""pt"""							).input_ids.shape[1] > max_tokens

							for src, tgt in tqdm(sorted_examples[1:]							):
														A__       :     List[Any]								=			new_src + """ """ + src
														A__       :     List[str]								=			new_tgt + """ """ + tgt
														if is_too_big(lowerCAmelCase							) or is_too_big(lowerCAmelCase							):  # cant fit, finalize example
																					finished_src.append(lowerCAmelCase							)
																					finished_tgt.append(lowerCAmelCase							)
																					A__				,							A__       :     str								=			src, tgt
														else:  # can fit, keep adding
																					A__				,							A__       :     Union[str, Any]								=			cand_src, cand_tgt

    # cleanup
							if new_src:
														assert new_tgt
														finished_src.append(lowerCAmelCase							)
														finished_tgt.append(lowerCAmelCase							)
							return finished_src, finished_tgt

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Optional[int]								=			Path(lowerCAmelCase							)
							save_path.mkdir(exist_ok=lowerCAmelCase							)
							for split in ["train"]:
														A__				,							A__       :     List[Any]								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														A__       :     Tuple								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__       :     Union[str, Any]								=			[x.rstrip() for x in Path(lowerCAmelCase							).open().readlines()]
														A__				,							A__       :     Tuple								=			pack_examples(lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							)
														print(F'''packed {split} split from {len(lowerCAmelCase							)} examples -> {len(lowerCAmelCase							)}.'''							)
														Path(save_path / F'''{split}.source'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
														Path(save_path / F'''{split}.target'''							).open("""w"""							).write("""\n""".join(lowerCAmelCase							)							)
							for split in ["val", "test"]:
														A__				,							A__       :     Dict								=			data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.source'''							)
														shutil.copyfile(lowerCAmelCase			,  save_path / F'''{split}.target'''							)

def  _A(		):
							A__       :     str								=			argparse.ArgumentParser()
							parser.add_argument("""--tok_name"""			,  type=lowerCAmelCase			,  help="""like facebook/bart-large-cnn,t5-base, etc."""							)
							parser.add_argument("""--max_seq_len"""			,  type=lowerCAmelCase			,  default=128							)
							parser.add_argument("""--data_dir"""			,  type=lowerCAmelCase							)
							parser.add_argument("""--save_path"""			,  type=lowerCAmelCase							)
							A__       :     str								=			parser.parse_args()
							A__       :     Optional[Any]								=			AutoTokenizer.from_pretrained(args.tok_name							)
							return pack_data_dir(lowerCAmelCase			,  Path(args.data_dir							)			,  args.max_seq_len			,  args.save_path							)


if __name__ == "__main__":
				packer_cli()





