



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



class        lowerCAmelCase__					(  nn.Module       ):
				A_   :     int
				A_   :     jnp.dtype								=     jnp.floataa


				def __UpperCamelCase						(							self      :  str       )		->     Any:
											A      						=    nn.Conv(
											    self.out_channels						, kernel_size=(3, 3)						, strides=(1, 1)						, padding=((1, 1), (1, 1))						, dtype=self.dtype						, )


				def __call__(							self      :  Tuple						, __UpperCamelCase      :  Dict       )		->     List[Any]:
											A						,   A						,   A						,   A      						=    hidden_states.shape
											A      						=    jax.image.resize(
											    __UpperCamelCase						, shape=(batch, height * 2, width * 2, channels)						, method='nearest'						, )
											A      						=    self.conv(__UpperCamelCase       )
											return hidden_states





class        lowerCAmelCase__					(  nn.Module       ):
				A_   :     int
				A_   :     jnp.dtype								=     jnp.floataa


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    nn.Conv(
											    self.out_channels						, kernel_size=(3, 3)						, strides=(2, 2)						, padding=((1, 1), (1, 1))						, dtype=self.dtype						, )


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





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


				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    self.in_channels if self.out_channels is None else self.out_channels

											A      						=    nn.GroupNorm(num_groups=32						, epsilon=1e-5       )
											A      						=    nn.Conv(
											    __UpperCamelCase						, kernel_size=(3, 3)						, strides=(1, 1)						, padding=((1, 1), (1, 1))						, dtype=self.dtype						, )

											A      						=    nn.Dense(__UpperCamelCase						, dtype=self.dtype       )

											A      						=    nn.GroupNorm(num_groups=32						, epsilon=1e-5       )
											A      						=    nn.Dropout(self.dropout_prob       )
											A      						=    nn.Conv(
											    __UpperCamelCase						, kernel_size=(3, 3)						, strides=(1, 1)						, padding=((1, 1), (1, 1))						, dtype=self.dtype						, )

											A      						=    self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

											A      						=    None
											if use_nin_shortcut:
																		A      						=    nn.Conv(
																		    __UpperCamelCase						, kernel_size=(1, 1)						, strides=(1, 1)						, padding='VALID'						, dtype=self.dtype						, )


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

											A      						=    self.time_emb_proj(nn.swish(__UpperCamelCase       )       )
											A      						=    jnp.expand_dims(jnp.expand_dims(__UpperCamelCase						, 1       )						, 1       )
											A      						=    hidden_states + temb

											A      						=    self.norma(__UpperCamelCase       )
											A      						=    nn.swish(__UpperCamelCase       )
											A      						=    self.dropout(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    self.conva(__UpperCamelCase       )

											if self.conv_shortcut is not None:
																		A      						=    self.conv_shortcut(__UpperCamelCase       )

											return hidden_states + residual




from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class        lowerCAmelCase__					(  _lowerCamelCase       ):


				def __init__(							self      :  int						, __UpperCamelCase      :  Callable						, __UpperCamelCase      :  Optional[Features] = None						, __UpperCamelCase      :  str = None						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  bool = False						, __UpperCamelCase      :  Optional[dict] = None						, __UpperCamelCase      :  Optional[int] = None						, **__UpperCamelCase      :  str						, )		->     List[str]:
											super().__init__(
											    features=__UpperCamelCase						, cache_dir=__UpperCamelCase						, keep_in_memory=__UpperCamelCase						, streaming=__UpperCamelCase						, num_proc=__UpperCamelCase						, **__UpperCamelCase						, )
											A      						=    Generator(
											    cache_dir=__UpperCamelCase						, features=__UpperCamelCase						, generator=__UpperCamelCase						, gen_kwargs=__UpperCamelCase						, **__UpperCamelCase						, )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											# Build iterable dataset
											if self.streaming:
																		A      						=    self.builder.as_streaming_dataset(split='train'       )
											# Build regular (map-style) dataset
											else:
																		A      						=    None
																		A      						=    None
																		A      						=    None
																		A      						=    None

																		self.builder.download_and_prepare(
																		    download_config=__UpperCamelCase						, download_mode=__UpperCamelCase						, verification_mode=__UpperCamelCase						, base_path=__UpperCamelCase						, num_proc=self.num_proc						, )
																		A      						=    self.builder.as_dataset(
																		    split='train'						, verification_mode=__UpperCamelCase						, in_memory=self.keep_in_memory       )
											return dataset