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


lowercase_ 				=			logging.get_logger(__name__)

lowercase_ 				=			{
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			'''mgp-str'''



     def __init__(				self							:							Optional[Any]     ,						_A							:							Tuple=[32, 128]     ,						_A							:							List[Any]=4     ,						_A							:							str=3     ,						_A							:							Union[str, Any]=27     ,						_A							:							Optional[int]=38     ,						_A							:							Optional[int]=5_0257     ,						_A							:							Optional[int]=3_0522     ,						_A							:							Tuple=768     ,						_A							:							Any=12     ,						_A							:							str=12     ,						_A							:							int=4.0     ,						_A							:							int=True     ,						_A							:							List[Any]=False     ,						_A							:							Optional[Any]=1e-5     ,						_A							:							str=0.0     ,						_A							:							List[Any]=0.0     ,						_A							:							Optional[int]=0.0     ,						_A							:							Any=False     ,						_A							:							Optional[Any]=0.02     ,						**_A							:							List[Any]     ,						):







       """simple docstring"""




       super().__init__(**_A )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		image_size
       __SCREAMING_SNAKE_CASE      :	str					=		patch_size
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_channels
       __SCREAMING_SNAKE_CASE      :	Tuple					=		max_token_length
       __SCREAMING_SNAKE_CASE      :	Dict					=		num_character_labels
       __SCREAMING_SNAKE_CASE      :	Any					=		num_bpe_labels
       __SCREAMING_SNAKE_CASE      :	Any					=		num_wordpiece_labels
       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		hidden_size
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_hidden_layers
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		num_attention_heads
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		mlp_ratio
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		distilled
       __SCREAMING_SNAKE_CASE      :	Tuple					=		layer_norm_eps
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		drop_rate
       __SCREAMING_SNAKE_CASE      :	Dict					=		qkv_bias
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		attn_drop_rate
       __SCREAMING_SNAKE_CASE      :	Tuple					=		drop_path_rate
       __SCREAMING_SNAKE_CASE      :	Any					=		output_aa_attentions
       __SCREAMING_SNAKE_CASE      :	Tuple					=		initializer_range



'''simple docstring'''





import math

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






def 				_lowerCAmelCase			(   __magic_name__						:     jnp.ndarray   , __magic_name__						:     int   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1   , __magic_name__						:     float = 1.0E4   , __magic_name__						:     bool = False   , __magic_name__						:     float = 1.0   , )				->  jnp.ndarray:
						assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
						assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
						lowercase :							int    		=float(embedding_dim // 2						)
						lowercase :							Optional[int]    		=math.log(max_timescale / min_timescale						) / (num_timescales - freq_shift)
						lowercase :							Any    		=min_timescale * jnp.exp(jnp.arange(__magic_name__   , dtype=jnp.floataa						) * -log_timescale_increment						)
						lowercase :							List[Any]    		=jnp.expand_dims(__magic_name__   , 1						) * jnp.expand_dims(__magic_name__   , 0						)

						# scale embeddings
						lowercase :							Tuple    		=scale * emb

						if flip_sin_to_cos:
												lowercase :							Dict    		=jnp.concatenate([jnp.cos(__magic_name__						), jnp.sin(__magic_name__						)]   , axis=1						)
						else:
												lowercase :							Any    		=jnp.concatenate([jnp.sin(__magic_name__						), jnp.cos(__magic_name__						)]   , axis=1						)
						lowercase :							List[str]    		=jnp.reshape(__magic_name__   , [jnp.shape(__magic_name__						)[0], embedding_dim]						)
						return signal





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    jnp.floataa







				@nn.compact
				def __call__(       self     :		Tuple		,					UpperCAmelCase__     :		int   ):
										'''simple docstring'''
										lowercase :							List[Any]    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_1'''   )(UpperCAmelCase__   )
										lowercase :							Any    		=nn.silu(UpperCAmelCase__   )
										lowercase :							int    		=nn.Dense(self.time_embed_dim		,					dtype=self.dtype		,					name='''linear_2'''   )(UpperCAmelCase__   )
										return temb





class 		__SCREAMING_SNAKE_CASE							(			nn.Module      ):
				lowerCamelCase_		       =    32
				lowerCamelCase_		       =    False
				lowerCamelCase_		       =    1







				@nn.compact
				def __call__(       self     :		int		,					UpperCAmelCase__     :		str   ):
										'''simple docstring'''
										return get_sinusoidal_embeddings(
										    UpperCAmelCase__		,					embedding_dim=self.dim		,					flip_sin_to_cos=self.flip_sin_to_cos		,					freq_shift=self.freq_shift   )





