


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


snake_case      :   List[Any]							= logging.get_logger(__name__)

snake_case      :   str							= {
    'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}



class  __lowercase	(							lowercase__				):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE :       Optional[Any]	   =    "switch_transformers"
							SCREAMING_SNAKE_CASE :       int	   =    ["past_key_values"]
							SCREAMING_SNAKE_CASE :       Any	   =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





							def __init__(     self					,			A_=32128					,			A_=768					,			A_=64					,			A_=2048					,			A_=64					,			A_=12					,			A_=3					,			A_=12					,			A_=3					,			A_=12					,			A_=8					,			A_=False					,			A_=0.01					,			A_="float32"					,			A_=False					,			A_=32					,			A_=128					,			A_=0.1					,			A_=1e-6					,			A_=0.001					,			A_=0.001					,			A_=1.0					,			A_="relu"					,			A_=True					,			A_=False					,			A_=True					,			A_=0					,			A_=1					,			**A_					,			)->						Optional[Any]:
											_SCREAMING_SNAKE_CASE   =			vocab_size
											_SCREAMING_SNAKE_CASE   =			d_model
											_SCREAMING_SNAKE_CASE   =			d_kv
											_SCREAMING_SNAKE_CASE   =			d_ff

											_SCREAMING_SNAKE_CASE   =			num_sparse_encoder_layers

											_SCREAMING_SNAKE_CASE   =			num_layers
											_SCREAMING_SNAKE_CASE   =			(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											_SCREAMING_SNAKE_CASE   =			num_sparse_decoder_layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_encoder_layers > 0:
															_SCREAMING_SNAKE_CASE   =			self.num_layers // self.num_sparse_encoder_layers
											else:
															_SCREAMING_SNAKE_CASE   =			self.num_layers  # HACK: this will create 0 sparse layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_decoder_layers > 0:
															_SCREAMING_SNAKE_CASE   =			self.num_decoder_layers // self.num_sparse_decoder_layers
											else:
															_SCREAMING_SNAKE_CASE   =			self.num_decoder_layers  # HACK: this will create 0 sparse layers

											_SCREAMING_SNAKE_CASE   =			num_heads
											_SCREAMING_SNAKE_CASE   =			num_experts
											_SCREAMING_SNAKE_CASE   =			expert_capacity
											_SCREAMING_SNAKE_CASE   =			router_bias
											_SCREAMING_SNAKE_CASE   =			router_jitter_noise
											if router_dtype not in ["float32", "float16", "bfloat16"]:
															raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''						)
											_SCREAMING_SNAKE_CASE   =			router_dtype

											_SCREAMING_SNAKE_CASE   =			router_ignore_padding_tokens
											_SCREAMING_SNAKE_CASE   =			relative_attention_num_buckets
											_SCREAMING_SNAKE_CASE   =			relative_attention_max_distance

											_SCREAMING_SNAKE_CASE   =			dropout_rate
											_SCREAMING_SNAKE_CASE   =			layer_norm_epsilon
											_SCREAMING_SNAKE_CASE   =			initializer_factor
											_SCREAMING_SNAKE_CASE   =			feed_forward_proj
											_SCREAMING_SNAKE_CASE   =			use_cache
											_SCREAMING_SNAKE_CASE   =			add_router_probs

											_SCREAMING_SNAKE_CASE   =			router_z_loss_coef
											_SCREAMING_SNAKE_CASE   =			router_aux_loss_coef

											_SCREAMING_SNAKE_CASE   =			self.feed_forward_proj.split('-'						)
											_SCREAMING_SNAKE_CASE   =			act_info[-1]
											_SCREAMING_SNAKE_CASE   =			act_info[0] == '''gated'''

											if len(UpperCAmelCase__						) > 1 and act_info[0] != "gated" or len(UpperCAmelCase__						) > 2:
															raise ValueError(
															    F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
															    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
															    '\'gated-gelu\' or \'relu\''						)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
															_SCREAMING_SNAKE_CASE   =			'''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase__					,			eos_token_id=UpperCAmelCase__					,			is_encoder_decoder=UpperCAmelCase__					,			**UpperCAmelCase__					,			)



'''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   )





