



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


lowerCAmelCase__					  =  logging.get_logger(__name__)

class 				snake_case__(_UpperCamelCase    ,      _UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		"""maskformer-swin"""

					lowercase_								=		{
					    """num_attention_heads""": """num_heads""",
					    """num_hidden_layers""": """num_layers""",
					}







					def __init__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Tuple=224				,       SCREAMING_SNAKE_CASE :     Optional[int]=4				,       SCREAMING_SNAKE_CASE :     int=3				,       SCREAMING_SNAKE_CASE :     List[str]=96				,       SCREAMING_SNAKE_CASE :     Tuple=[2, 2, 6, 2]				,       SCREAMING_SNAKE_CASE :     Tuple=[3, 6, 12, 24]				,       SCREAMING_SNAKE_CASE :     Tuple=7				,       SCREAMING_SNAKE_CASE :     Optional[Any]=4.0				,       SCREAMING_SNAKE_CASE :     List[str]=True				,       SCREAMING_SNAKE_CASE :     Optional[Any]=0.0				,       SCREAMING_SNAKE_CASE :     List[str]=0.0				,       SCREAMING_SNAKE_CASE :     List[Any]=0.1				,       SCREAMING_SNAKE_CASE :     int="gelu"				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=False				,       SCREAMING_SNAKE_CASE :     Any=0.02				,       SCREAMING_SNAKE_CASE :     Tuple=1E-5				,       SCREAMING_SNAKE_CASE :     List[Any]=None				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       **SCREAMING_SNAKE_CASE :     Any				,       ):
									super().__init__(**SCREAMING_SNAKE_CASE					)

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









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


lowerCAmelCase__					  =  HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__					  =  parser.parse_args()
if args.num_workers is None:
				lowerCAmelCase__					  =  multiprocessing.cpu_count()
lowerCAmelCase__					  =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




