'''simple docstring'''





import math

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


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_ = 1       ,      a_ = 1       ,      a_ = 1.0e4       ,      a_ = False       ,      a_ = 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					:			Dict    	=       float(embedding_dim // 2		)
     SCREAMING_SNAKE_CASE					:			Dict    	=       math.log(max_timescale / min_timescale		) / (num_timescales - freq_shift)
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       min_timescale * jnp.exp(jnp.arange(a_       ,      dtype=jnp.floataa		) * -log_timescale_increment		)
     SCREAMING_SNAKE_CASE					:			int    	=       jnp.expand_dims(a_       ,      1		) * jnp.expand_dims(a_       ,      0		)

     # scale embeddings
     SCREAMING_SNAKE_CASE					:			Any    	=       scale * emb

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






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    snake_case__							:   int							=						3_2
    snake_case__							:   jnp.dtype							=						jnp.floataa






    @nn.compact
    def __call__(       self    ,							lowercase__ )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Any    	=       nn.Dense(self.time_embed_dim    ,							dtype=self.dtype    ,							name='linear_1' )(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       nn.silu(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       nn.Dense(self.time_embed_dim    ,							dtype=self.dtype    ,							name='linear_2' )(lowercase__ )
         return temb






class 				UpperCAmelCase (       nn.Module ):




    '''simple docstring'''







    snake_case__							:   int							=						3_2
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						1






    @nn.compact
    def __call__(       self    ,							lowercase__ )							-> str:
         return get_sinusoidal_embeddings(
             lowercase__    ,							embedding_dim=self.dim    ,							flip_sin_to_cos=self.flip_sin_to_cos    ,							freq_shift=self.freq_shift )




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase  :int  				=    {
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :str  				=    ["""AlbertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    ["""AlbertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :List[str]  				=    [
     """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """AlbertForMaskedLM""",
     """AlbertForMultipleChoice""",
     """AlbertForPreTraining""",
     """AlbertForQuestionAnswering""",
     """AlbertForSequenceClassification""",
     """AlbertForTokenClassification""",
     """AlbertModel""",
     """AlbertPreTrainedModel""",
     """load_tf_weights_in_albert""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Any  				=    [
     """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFAlbertForMaskedLM""",
     """TFAlbertForMultipleChoice""",
     """TFAlbertForPreTraining""",
     """TFAlbertForQuestionAnswering""",
     """TFAlbertForSequenceClassification""",
     """TFAlbertForTokenClassification""",
     """TFAlbertMainLayer""",
     """TFAlbertModel""",
     """TFAlbertPreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :Optional[int]  				=    [
     """FlaxAlbertForMaskedLM""",
     """FlaxAlbertForMultipleChoice""",
     """FlaxAlbertForPreTraining""",
     """FlaxAlbertForQuestionAnswering""",
     """FlaxAlbertForSequenceClassification""",
     """FlaxAlbertForTokenClassification""",
     """FlaxAlbertModel""",
     """FlaxAlbertPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert import AlbertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_albert_fast import AlbertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlbertForMaskedLM,
      AlbertForMultipleChoice,
      AlbertForPreTraining,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertForTokenClassification,
      AlbertModel,
      AlbertPreTrainedModel,
      load_tf_weights_in_albert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_albert import (
      TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAlbertForMaskedLM,
      TFAlbertForMultipleChoice,
      TFAlbertForPreTraining,
      TFAlbertForQuestionAnswering,
      TFAlbertForSequenceClassification,
      TFAlbertForTokenClassification,
      TFAlbertMainLayer,
      TFAlbertModel,
      TFAlbertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
      FlaxAlbertPreTrainedModel,
  )
else:
 import sys

 _lowerCAmelCase  :Union[str, Any]  				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



