






"""simple docstring"""


import math

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




def       snake_case  (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					:      Optional[int]    =       float(embedding_dim // 2				)
  a					:      str    =       math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
  a					:      Optional[Any]    =       min_timescale * jnp.exp(jnp.arange(A_     ,      dtype=jnp.floataa				) * -log_timescale_increment				)
  a					:      Union[str, Any]    =       jnp.expand_dims(A_     ,      1				) * jnp.expand_dims(A_     ,      0				)

  # scale embeddings
  a					:      str    =       scale * emb

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






class 						snake_case  (  nn.Module	):
    __magic_name__   					=					32
    __magic_name__   					=					jnp.floataa





    @nn.compact
    def __call__(							self     : List[Any]    ,		A     : Any      ):


      '''simple docstring'''




      a					:      List[str]    =       nn.Dense(self.time_embed_dim    ,		dtype=self.dtype    ,		name='linear_1'      )(A      )
      a					:      Any    =       nn.silu(A      )
      a					:      Optional[int]    =       nn.Dense(self.time_embed_dim    ,		dtype=self.dtype    ,		name='linear_2'      )(A      )
      return temb






class 						snake_case  (  nn.Module	):
    __magic_name__   					=					32
    __magic_name__   					=					False
    __magic_name__   					=					1





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


      '''simple docstring'''




      return get_sinusoidal_embeddings(
          A    ,		embedding_dim=self.dim    ,		flip_sin_to_cos=self.flip_sin_to_cos    ,		freq_shift=self.freq_shift      )












"""simple docstring"""


import os
import sys

_UpperCamelCase    :   str   			=						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,
)


_UpperCamelCase    :   int   			=						[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]




@add_start_docstrings(AutoConfig.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:int				):







  '''simple docstring'''

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




@add_start_docstrings(AutoTokenizer.__doc__				)
def       snake_case  (*A_				:Optional[int]     ,      **A_				:List[Any]				):







  '''simple docstring'''

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




@add_start_docstrings(AutoModel.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Tuple				):







  '''simple docstring'''

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




@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def       snake_case  (*A_				:str     ,      **A_				:Optional[int]				):







  '''simple docstring'''

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




@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def       snake_case  (*A_				:Optional[Any]     ,      **A_				:Dict				):







  '''simple docstring'''

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




@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:str				):







  '''simple docstring'''

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






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def       snake_case  (*A_				:Dict     ,      **A_				:List[Any]				):







  '''simple docstring'''

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




