




import math

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

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

      # scale embeddings
      SCREAMING_SNAKE_CASE_     						=      scale * emb

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





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 lowercase_       =   32
 lowercase_       =   jnp.floataa



 @nn.compact
 def __call__(							self  :		str    ,    _lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      nn.Dense(self.time_embed_dim    ,    dtype=self.dtype    ,    name='linear_1'						)(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      nn.silu(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      nn.Dense(self.time_embed_dim    ,    dtype=self.dtype    ,    name='linear_2'						)(_lowerCAmelCase						)
       return temb





class 	lowerCamelCase_							(				nn.Module ):


 '''simple docstring'''







 lowercase_       =   32
 lowercase_       =   False
 lowercase_       =   1



 @nn.compact
 def __call__(							self  :		List[str]    ,    _lowerCAmelCase  :		int						):
       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 ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


lowerCamelCase__			:       Dict         =							logging.get_logger(__name__)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


 '''simple docstring'''







 def __init__(							self  :		List[Any]    ,    *_lowerCAmelCase  :		List[str]    ,    **_lowerCAmelCase  :		Optional[int]						):
       warnings.warn(
           'The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use LayoutLMv2ImageProcessor instead.'    ,    _lowerCAmelCase    ,    )
       super().__init__(*_lowerCAmelCase    ,    **_lowerCAmelCase						)