



import math

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






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





				'''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__  :       Tuple				=  float(embedding_dim // 2)
				lowerCAmelCase__  :       Tuple				=  math.log(max_timescale / min_timescale) / (num_timescales - freq_shift)
				lowerCAmelCase__  :       Any				=  min_timescale * jnp.exp(jnp.arange(lowerCamelCase_ ,dtype=jnp.floataa) * -log_timescale_increment)
				lowerCAmelCase__  :       List[str]				=  jnp.expand_dims(lowerCamelCase_ ,1) * jnp.expand_dims(lowerCamelCase_ ,0)

				# scale embeddings
				lowerCAmelCase__  :       Dict				=  scale * emb

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



class  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



		snake_case_			=32
		snake_case_			=jnp.floataa



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  nn.Dense(self.time_embed_dim	,dtype=self.dtype	,name='''linear_1'''				)(__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  nn.silu(__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  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				)  ->    int:

						"""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				)






import warnings

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


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

__snake_case       :	str            ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

						super().__init__(
						    pad_token_id=__lowerCamelCase	,bos_token_id=__lowerCamelCase	,eos_token_id=__lowerCamelCase	,is_encoder_decoder=__lowerCamelCase	,decoder_start_token_id=__lowerCamelCase	,forced_eos_token_id=__lowerCamelCase	,**__lowerCamelCase	,)

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  self.bos_token_id
										warnings.warn(
										    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''				)

