


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer




def 	A_ (							snake_case							:   Any ,      snake_case							:   Union[str, Any] ,      snake_case							:   int ,      snake_case							:   Optional[int]=1024 )      ->    Any:



						'''simple docstring'''

						__UpperCamelCase       =			[], []

						__UpperCamelCase       =			list(zip(UpperCamelCase__ ,      UpperCamelCase__ ) )
						__UpperCamelCase       =			sorted_examples[0]

						def is_too_big(snake_case							:   Tuple ):
												return tok(UpperCamelCase__ ,      return_tensors='''pt''' ).input_ids.shape[1] > max_tokens

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

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




def 	A_ (							snake_case							:   Any ,      snake_case							:   Path ,      snake_case							:   Any ,      snake_case							:   List[Any] )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			Path(UpperCamelCase__ )
						save_path.mkdir(exist_ok=UpperCamelCase__ )
						for split in ["train"]:
												__UpperCamelCase       =			data_dir / f"{split}.source", data_dir / f"{split}.target"
												__UpperCamelCase       =			[x.rstrip() for x in Path(UpperCamelCase__ ).open().readlines()]
												__UpperCamelCase       =			[x.rstrip() for x in Path(UpperCamelCase__ ).open().readlines()]
												__UpperCamelCase       =			pack_examples(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ )
												print(f"packed {split} split from {len(UpperCamelCase__ )} examples -> {len(UpperCamelCase__ )}." )
												Path(save_path / f"{split}.source" ).open('''w''' ).write('''\n'''.join(UpperCamelCase__ ) )
												Path(save_path / f"{split}.target" ).open('''w''' ).write('''\n'''.join(UpperCamelCase__ ) )
						for split in ["val", "test"]:
												__UpperCamelCase       =			data_dir / f"{split}.source", data_dir / f"{split}.target"
												shutil.copyfile(UpperCamelCase__ ,      save_path / f"{split}.source" )
												shutil.copyfile(UpperCamelCase__ ,      save_path / f"{split}.target" )




def 	A_ (							)      ->    Any:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser()
						parser.add_argument('''--tok_name''' ,      type=UpperCamelCase__ ,      help='''like facebook/bart-large-cnn,t5-base, etc.''' )
						parser.add_argument('''--max_seq_len''' ,      type=UpperCamelCase__ ,      default=128 )
						parser.add_argument('''--data_dir''' ,      type=UpperCamelCase__ )
						parser.add_argument('''--save_path''' ,      type=UpperCamelCase__ )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			AutoTokenizer.from_pretrained(args.tok_name )
						return pack_data_dir(UpperCamelCase__ ,      Path(args.data_dir ) ,      args.max_seq_len ,      args.save_path )


if __name__ == "__main__":
				packer_cli()






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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		unittest.TestCase    ):

							"""simple docstring"""






							_snake_case     		=  FlaxAutoencoderKL



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			4
													__UpperCamelCase       =			3
													__UpperCamelCase       =			(32, 32)

													__UpperCamelCase       =			jax.random.PRNGKey(0			)
													__UpperCamelCase       =			jax.random.uniform(SCREAMING_SNAKE_CASE_     ,	((batch_size, num_channels) + sizes)			)

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



							def     A__		(  self			)->   List[Any]:





													'''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




