




import math

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







def      lowerCamelCase_						(			UpperCamelCase__							: jnp.ndarray					,	UpperCamelCase__							: int					,	UpperCamelCase__							: float = 1					,	UpperCamelCase__							: float = 1					,	UpperCamelCase__							: float = 1.0E4					,	UpperCamelCase__							: bool = False					,	UpperCamelCase__							: float = 1.0					,	)	->			jnp.ndarray:





				"""simple docstring"""


				assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
				assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
				__lowerCamelCase									=       float(embedding_dim // 2 )
				__lowerCamelCase									=       math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
				__lowerCamelCase									=       min_timescale * jnp.exp(jnp.arange(UpperCamelCase__					,	dtype=jnp.floataa ) * -log_timescale_increment )
				__lowerCamelCase									=       jnp.expand_dims(UpperCamelCase__					,	1 ) * jnp.expand_dims(UpperCamelCase__					,	0 )

				# scale embeddings
				__lowerCamelCase									=       scale * emb

				if flip_sin_to_cos:
								__lowerCamelCase									=       jnp.concatenate([jnp.cos(UpperCamelCase__ ), jnp.sin(UpperCamelCase__ )]					,	axis=1 )
				else:
								__lowerCamelCase									=       jnp.concatenate([jnp.sin(UpperCamelCase__ ), jnp.cos(UpperCamelCase__ )]					,	axis=1 )
				__lowerCamelCase									=       jnp.reshape(UpperCamelCase__					,	[jnp.shape(UpperCamelCase__ )[0], embedding_dim] )
				return signal







class        __lowerCAmelCase					(			nn.Module					):

			"""simple docstring"""







			snake_case_  						=			32
			snake_case_  						=			jnp.floataa


			@nn.compact
			def __call__( self     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       nn.Dense(self.time_embed_dim     ,		dtype=self.dtype     ,		name='linear_1'				)(lowerCamelCase__				)
							__lowerCamelCase									=       nn.silu(lowerCamelCase__				)
							__lowerCamelCase									=       nn.Dense(self.time_embed_dim     ,		dtype=self.dtype     ,		name='linear_2'				)(lowerCamelCase__				)
							return temb







class        __lowerCAmelCase					(			nn.Module					):

			"""simple docstring"""







			snake_case_  						=			32
			snake_case_  						=			False
			snake_case_  						=			1


			@nn.compact
			def __call__( self     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							return get_sinusoidal_embeddings(
							    lowerCamelCase__     ,		embedding_dim=self.dim     ,		flip_sin_to_cos=self.flip_sin_to_cos     ,		freq_shift=self.freq_shift				)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''vit_mae'''


			def __init__( self     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=12     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.0     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=224     ,		lowerCamelCase__=16     ,		lowerCamelCase__=3     ,		lowerCamelCase__=True     ,		lowerCamelCase__=16     ,		lowerCamelCase__=512     ,		lowerCamelCase__=8     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=0.75     ,		lowerCamelCase__=False     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__				)

							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_size
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       qkv_bias
							__lowerCamelCase									=       decoder_num_attention_heads
							__lowerCamelCase									=       decoder_hidden_size
							__lowerCamelCase									=       decoder_num_hidden_layers
							__lowerCamelCase									=       decoder_intermediate_size
							__lowerCamelCase									=       mask_ratio
							__lowerCamelCase									=       norm_pix_loss


