


import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def        UpperCamelCase_(				_A   :Optional[Any]  )->		str:

			'''simple docstring'''





			UpperCamelCase__					=       {}
			UpperCamelCase__					=       tokenizer(example["content"]				,     truncation=_A  )["input_ids"]
			UpperCamelCase__					=       len(example["content"]  ) / len(output["input_ids"]  )
			return output


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

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

__UpperCamelCase    		=       time.time()
__UpperCamelCase    		=       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''')

__UpperCamelCase    		=       time.time()
ds.push_to_hub(args.tokenized_data_repo)







print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


