




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


__lowerCAmelCase	:       List[str]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       List[Any]     =	{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[Any]											=      """gpt_neox"""





				def __init__(   self       :  Dict      ,   __lowerCamelCase       :  Dict=5_04_32      ,   __lowerCamelCase       :  Any=61_44      ,   __lowerCamelCase       :  Dict=44      ,   __lowerCamelCase       :  List[str]=64      ,   __lowerCamelCase       :  List[str]=2_45_76      ,   __lowerCamelCase       :  Union[str, Any]="gelu"      ,   __lowerCamelCase       :  str=0.25      ,   __lowerCamelCase       :  int=1_00_00      ,   __lowerCamelCase       :  Union[str, Any]=0.0      ,   __lowerCamelCase       :  Union[str, Any]=0.0      ,   __lowerCamelCase       :  str=0.1      ,   __lowerCamelCase       :  List[str]=20_48      ,   __lowerCamelCase       :  List[Any]=0.02      ,   __lowerCamelCase       :  str=1e-5      ,   __lowerCamelCase       :  List[str]=True      ,   __lowerCamelCase       :  int=0      ,   __lowerCamelCase       :  Union[str, Any]=2      ,   __lowerCamelCase       :  List[str]=False      ,   __lowerCamelCase       :  List[str]=True      ,   __lowerCamelCase       :  Dict=None      ,   **__lowerCamelCase       :  Dict      ,   )      ->  Optional[Any]:
							super().__init__(bos_token_id=__lowerCamelCase      ,   eos_token_id=__lowerCamelCase      ,   **__lowerCamelCase							)
							a					=    vocab_size
							a					=    max_position_embeddings
							a					=    hidden_size
							a					=    num_hidden_layers
							a					=    num_attention_heads
							a					=    intermediate_size
							a					=    hidden_act
							a					=    rotary_pct
							a					=    rotary_emb_base
							a					=    attention_dropout
							a					=    hidden_dropout
							a					=    classifier_dropout
							a					=    initializer_range
							a					=    layer_norm_eps
							a					=    use_cache
							a					=    tie_word_embeddings
							a					=    use_parallel_residual
							a					=    rope_scaling
							self._rope_scaling_validation()

							if self.hidden_size % self.num_attention_heads != 0:
										raise ValueError(
										    "The hidden size is not divisble by the number of attention heads! Make sure to update them!"							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Optional[Any]:
							if self.rope_scaling is None:
										return

							if not isinstance(self.rope_scaling      ,   __lowerCamelCase							) or len(self.rope_scaling							) != 2:
										raise ValueError(
										    "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
										    f"""got {self.rope_scaling}"""							)
							a					=    self.rope_scaling.get("type"      ,   __lowerCamelCase							)
							a					=    self.rope_scaling.get("factor"      ,   __lowerCamelCase							)
							if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
										raise ValueError(
										    f"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""							)
							if rope_scaling_factor is None or not isinstance(__lowerCamelCase      ,   __lowerCamelCase							) or rope_scaling_factor <= 1.0:
										raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""							)












import math

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




def     __magic_name__       (				A	:      jnp.ndarray,						A	:      int,						A	:      float = 1,						A	:      float = 1,						A	:      float = 1.0E4,						A	:      bool = False,						A	:      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"""
			a					=    float(embedding_dim // 2     )
			a					=    math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
			a					=    min_timescale * jnp.exp(jnp.arange(A,						dtype=jnp.floataa     ) * -log_timescale_increment     )
			a					=    jnp.expand_dims(A,						1     ) * jnp.expand_dims(A,						0     )

			# scale embeddings
			a					=    scale * emb

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




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							jnp.dtype											=      jnp.floataa





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]							)      ->  List[Any]:
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_1"							)(__lowerCamelCase							)
							a					=    nn.silu(__lowerCamelCase							)
							a					=    nn.Dense(self.time_embed_dim      ,   dtype=self.dtype      ,   name="linear_2"							)(__lowerCamelCase							)
							return temb




class 					snake_case__							(nn.Module     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							int											=      32
				SCREAMING_SNAKE_CASE_ :							bool											=      False
				SCREAMING_SNAKE_CASE_ :							float											=      1





				@nn.compact
				def __call__(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							return get_sinusoidal_embeddings(
							    __lowerCamelCase      ,   embedding_dim=self.dim      ,   flip_sin_to_cos=self.flip_sin_to_cos      ,   freq_shift=self.freq_shift							)






