




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    :						int							,							_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    :						Union[str, Any]							,							_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 torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "M-CLIP"



 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						List[str]=1_024							,							_lowerCAmelCase    :						str=768							,							**_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     transformerDimSize
       SCREAMING_SNAKE_CASE_      		=     imageDimSize
       super().__init__(**_lowerCAmelCase		)





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       MCLIPConfig



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]							,							*_lowerCAmelCase    :						str							,							**_lowerCAmelCase    :						str		):
       super().__init__(_lowerCAmelCase							,							*_lowerCAmelCase							,							**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaModel(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Linear(
           in_features=config.transformerDimensions							,							out_features=config.numDims		)




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.transformer(input_ids=_lowerCAmelCase							,							attention_mask=_lowerCAmelCase		)[0]
       SCREAMING_SNAKE_CASE_      		=     (embs * attention_mask.unsqueeze(2		)).sum(dim=1		) / attention_mask.sum(dim=1		)[:, None]
       return self.LinearTransformation(_lowerCAmelCase		), embs