
"""simple docstring"""






import math

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





def __A						(   a_ :							jnp.ndarray		,		a_ :							int		,		a_ :							float = 1		,		a_ :							float = 1		,		a_ :							float = 1.0E4		,		a_ :							bool = False		,		a_ :							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"
		SCREAMING_SNAKE_CASE       :     List[Any]				= float(embedding_dim // 2     )
		SCREAMING_SNAKE_CASE       :     List[Any]				= math.log(max_timescale / min_timescale     ) / (num_timescales - freq_shift)
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= min_timescale * jnp.exp(jnp.arange(a_		,		dtype=jnp.floataa     ) * -log_timescale_increment     )
		SCREAMING_SNAKE_CASE       :     Tuple				= jnp.expand_dims(a_		,		1     ) * jnp.expand_dims(a_		,		0     )

		# scale embeddings
		SCREAMING_SNAKE_CASE       :     int				= scale * emb

		if flip_sin_to_cos:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= jnp.concatenate([jnp.cos(a_     ), jnp.sin(a_     )]		,		axis=1     )
		else:
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= jnp.concatenate([jnp.sin(a_     ), jnp.cos(a_     )]		,		axis=1     )
		SCREAMING_SNAKE_CASE       :     List[str]				= jnp.reshape(a_		,		[jnp.shape(a_     )[0], embedding_dim]     )
		return signal




class 				lowercase__(				nn.Module   ):
	'''simple docstring'''

	UpperCamelCase				=  32
	UpperCamelCase				=  jnp.floataa





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= nn.Dense(self.time_embed_dim		, dtype=self.dtype		, name='''linear_1'''					)(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     str				= nn.silu(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= nn.Dense(self.time_embed_dim		, dtype=self.dtype		, name='''linear_2'''					)(lowerCamelCase_					)
			return temb





class 				lowercase__(				nn.Module   ):
	'''simple docstring'''

	UpperCamelCase				=  32
	UpperCamelCase				=  False
	UpperCamelCase				=  1





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







			'''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					)







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__ :       str						     =			[
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






		return AutoConfig.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






		return AutoTokenizer.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






		return AutoModel.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






		return AutoModelForCausalLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForMaskedLM.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






		return AutoModelForSequenceClassification.from_pretrained(*a_		,		**a_     )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






		return AutoModelForQuestionAnswering.from_pretrained(*a_		,		**a_     )





