



"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

a		:				Dict  			=    time.time()
a		:				Tuple  			=    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''')

a		:				Tuple  			=    time.time()
ds.push_to_hub(args.tokenized_data_repo)







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









"""simple docstring"""




import flax.linen as nn
import jax
import jax.numpy as jnp


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Tuple:
								a   :	str			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a, a, a, a   :	List[str]			=       hidden_states.shape
								a   :	List[Any]			=       jax.image.resize(
								    lowerCAmelCase__   ,      shape=(batch, height * 2, width * 2, channels)   ,      method="nearest"   ,      )
								a   :	List[str]			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       nn.Conv(
								    self.out_channels   ,      kernel_size=(3, 3)   ,      strides=(2, 2)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   )			->				Tuple:
								# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
								# hidden_states = jnp.pad(hidden_states, pad_width=pad)
								a   :	Tuple			=       self.conv(lowerCAmelCase__   )
								return hidden_states


class  __UpperCamelCase       (		nn.Module		):
							lowerCamelCase     :      int
							lowerCamelCase     :      int         =None
							lowerCamelCase     :      float         =0.0
							lowerCamelCase     :      bool         =None
							lowerCamelCase     :      jnp.dtype         =jnp.floataa






							def 					__a     (     self   )			->				int:
								a   :	Dict			=       self.in_channels if self.out_channels is None else self.out_channels

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	List[Any]			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	List[Any]			=       nn.Dense(lowerCAmelCase__   ,      dtype=self.dtype   )

								a   :	Union[str, Any]			=       nn.GroupNorm(num_groups=32   ,      epsilon=1E-5   )
								a   :	Optional[int]			=       nn.Dropout(self.dropout_prob   )
								a   :	Dict			=       nn.Conv(
								    lowerCAmelCase__   ,      kernel_size=(3, 3)   ,      strides=(1, 1)   ,      padding=((1, 1), (1, 1))   ,      dtype=self.dtype   ,      )

								a   :	Union[str, Any]			=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

								a   :	List[str]			=       None
								if use_nin_shortcut:
									a   :	Optional[Any]			=       nn.Conv(
									    lowerCAmelCase__   ,      kernel_size=(1, 1)   ,      strides=(1, 1)   ,      padding="VALID"   ,      dtype=self.dtype   ,      )






							def __call__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=True   )			->				str:
								a   :	int			=       hidden_states
								a   :	Tuple			=       self.norma(lowerCAmelCase__   )
								a   :	Any			=       nn.swish(lowerCAmelCase__   )
								a   :	int			=       self.conva(lowerCAmelCase__   )

								a   :	int			=       self.time_emb_proj(nn.swish(lowerCAmelCase__   )   )
								a   :	Tuple			=       jnp.expand_dims(jnp.expand_dims(lowerCAmelCase__   ,      1   )   ,      1   )
								a   :	Dict			=       hidden_states + temb

								a   :	str			=       self.norma(lowerCAmelCase__   )
								a   :	List[Any]			=       nn.swish(lowerCAmelCase__   )
								a   :	List[str]			=       self.dropout(lowerCAmelCase__   ,      lowerCAmelCase__   )
								a   :	List[str]			=       self.conva(lowerCAmelCase__   )

								if self.conv_shortcut is not None:
									a   :	Tuple			=       self.conv_shortcut(lowerCAmelCase__   )

								return hidden_states + residual


