






from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info






def 							A     (       a_					)     ->						int:
					return {key.lstrip('-'					): value for key, value in zip(unknown_args[::2]							,unknown_args[1::2]					)}






def 							A     (       )     ->						Dict:
					__UpperCamelCase      :					Optional[Any]       		=ArgumentParser(
					    'HuggingFace Datasets CLI tool'							,usage='datasets-cli <command> [<args>]'							,allow_abbrev=_UpperCAmelCase					)
					__UpperCamelCase      :					List[Any]       		=parser.add_subparsers(help='datasets-cli command helpers'					)
					set_verbosity_info()

					# Register commands
					ConvertCommand.register_subcommand(_UpperCAmelCase					)
					EnvironmentCommand.register_subcommand(_UpperCAmelCase					)
					TestCommand.register_subcommand(_UpperCAmelCase					)
					RunBeamCommand.register_subcommand(_UpperCAmelCase					)
					DummyDataCommand.register_subcommand(_UpperCAmelCase					)

					# Parse args
					__UpperCamelCase      :					Optional[int]       		=parser.parse_known_args()
					if not hasattr(_UpperCAmelCase							,'func'					):
										parser.print_help()
										exit(1					)
					__UpperCamelCase      :					Optional[Any]       		=parse_unknown_args(_UpperCAmelCase					)

					# Run
					__UpperCamelCase      :					Optional[int]       		=args.func(_UpperCAmelCase							,**_UpperCAmelCase					)
					service.run()


if __name__ == "__main__":
							main()








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

class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      Any	)			->	Any:
     lowerCamelCase__    :      str					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      int			,     UpperCAmelCase  :      Dict	)			->	Optional[Any]:
     lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		hidden_states.shape
     lowerCamelCase__    :      Union[str, Any]					 =		jax.image.resize(
         UpperCAmelCase			,     shape=(batch, height * 2, width * 2, channels)			,     method='nearest'			,     )
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	int:
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         self.out_channels			,     kernel_size=(3, 3)			,     strides=(2, 2)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )







 def __call__(      self  :      str			,     UpperCAmelCase  :      Union[str, Any]	)			->	Optional[Any]:
     # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
     # hidden_states = jnp.pad(hidden_states, pad_width=pad)
     lowerCamelCase__    :      Optional[Any]					 =		self.conv(UpperCAmelCase	)
     return hidden_states


class    lowerCAmelCase							(					nn.Module			):
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		0.0
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		jnp.floataa
 def      A_	(      self  :      List[str]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels if self.out_channels is None else self.out_channels

     lowerCamelCase__    :      Tuple					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      int					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Union[str, Any]					 =		nn.Dense(UpperCAmelCase			,     dtype=self.dtype	)

     lowerCamelCase__    :      Union[str, Any]					 =		nn.GroupNorm(num_groups=32			,     epsilon=1e-5	)
     lowerCamelCase__    :      List[Any]					 =		nn.Dropout(self.dropout_prob	)
     lowerCamelCase__    :      Tuple					 =		nn.Conv(
         UpperCAmelCase			,     kernel_size=(3, 3)			,     strides=(1, 1)			,     padding=((1, 1), (1, 1))			,     dtype=self.dtype			,     )

     lowerCamelCase__    :      Optional[Any]					 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

     lowerCamelCase__    :      Union[str, Any]					 =		None
     if use_nin_shortcut:
         lowerCamelCase__    :      Dict					 =		nn.Conv(
             UpperCAmelCase			,     kernel_size=(1, 1)			,     strides=(1, 1)			,     padding='VALID'			,     dtype=self.dtype			,     )







 def __call__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      int=True	)			->	Optional[int]:
     lowerCamelCase__    :      Union[str, Any]					 =		hidden_states
     lowerCamelCase__    :      List[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[Any]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Any					 =		self.conva(UpperCAmelCase	)

     lowerCamelCase__    :      Optional[Any]					 =		self.time_emb_proj(nn.swish(UpperCAmelCase	)	)
     lowerCamelCase__    :      List[str]					 =		jnp.expand_dims(jnp.expand_dims(UpperCAmelCase			,     1	)			,     1	)
     lowerCamelCase__    :      List[str]					 =		hidden_states + temb

     lowerCamelCase__    :      Optional[Any]					 =		self.norma(UpperCAmelCase	)
     lowerCamelCase__    :      List[str]					 =		nn.swish(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[int]					 =		self.dropout(UpperCAmelCase			,     UpperCAmelCase	)
     lowerCamelCase__    :      str					 =		self.conva(UpperCAmelCase	)

     if self.conv_shortcut is not None:
         lowerCamelCase__    :      Dict					 =		self.conv_shortcut(UpperCAmelCase	)

     return hidden_states + residual





