
"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)





class 				a__					(						A__   ,							A__       ):
     UpperCAmelCase__         =      '''maskformer-swin'''

     UpperCAmelCase__         =      {
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }



     def __init__(							self :List[Any]      ,  _lowerCamelCase :Optional[int]=224      ,  _lowerCamelCase :List[str]=4      ,  _lowerCamelCase :Optional[int]=3      ,  _lowerCamelCase :int=96      ,  _lowerCamelCase :Any=[2, 2, 6, 2]      ,  _lowerCamelCase :Any=[3, 6, 12, 24]      ,  _lowerCamelCase :List[Any]=7      ,  _lowerCamelCase :int=4.0      ,  _lowerCamelCase :int=True      ,  _lowerCamelCase :int=0.0      ,  _lowerCamelCase :int=0.0      ,  _lowerCamelCase :Union[str, Any]=0.1      ,  _lowerCamelCase :Tuple="gelu"      ,  _lowerCamelCase :List[Any]=False      ,  _lowerCamelCase :Optional[int]=0.02      ,  _lowerCamelCase :List[Any]=1E-5      ,  _lowerCamelCase :Any=None      ,  _lowerCamelCase :Dict=None      ,  **_lowerCamelCase :List[str]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Union[str, Any]   	=image_size
      UpperCamelCase_     :					str   	=patch_size
      UpperCamelCase_     :					Union[str, Any]   	=num_channels
      UpperCamelCase_     :					Tuple   	=embed_dim
      UpperCamelCase_     :					str   	=depths
      UpperCamelCase_     :					Optional[Any]   	=len(_lowerCamelCase   )
      UpperCamelCase_     :					Optional[int]   	=num_heads
      UpperCamelCase_     :					Dict   	=window_size
      UpperCamelCase_     :					Tuple   	=mlp_ratio
      UpperCamelCase_     :					Tuple   	=qkv_bias
      UpperCamelCase_     :					Optional[int]   	=hidden_dropout_prob
      UpperCamelCase_     :					Dict   	=attention_probs_dropout_prob
      UpperCamelCase_     :					List[Any]   	=drop_path_rate
      UpperCamelCase_     :					int   	=hidden_act
      UpperCamelCase_     :					Any   	=use_absolute_embeddings
      UpperCamelCase_     :					Optional[int]   	=layer_norm_eps
      UpperCamelCase_     :					Any   	=initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      UpperCamelCase_     :					int   	=int(embed_dim * 2 ** (len(_lowerCamelCase   ) - 1)   )
      UpperCamelCase_     :					Union[str, Any]   	=['stem'] + [f'''stage{idx}''' for idx in range(1      ,  len(_lowerCamelCase   ) + 1   )]
      UpperCamelCase_	,     UpperCamelCase_     :					Any   	=get_aligned_output_features_output_indices(
          out_features=_lowerCamelCase      ,  out_indices=_lowerCamelCase      ,  stage_names=self.stage_names   )


"""simple docstring"""






import math

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




def       A_  (				__lowercase						,	__lowercase						,	__lowercase = 1						,	__lowercase = 1						,	__lowercase = 1.0e4						,	__lowercase = False						,	__lowercase = 1.0						,	):
 assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
 assert embedding_dim % 2 == 0, F'''Embedding dimension {embedding_dim} should be even'''
 UpperCamelCase_     :					Optional[int]   	=float(embedding_dim // 2	)
 UpperCamelCase_     :					Optional[Any]   	=math.log(max_timescale / min_timescale	) / (num_timescales - freq_shift)
 UpperCamelCase_     :					List[Any]   	=min_timescale * jnp.exp(jnp.arange(__lowercase						,	dtype=jnp.floataa	) * -log_timescale_increment	)
 UpperCamelCase_     :					int   	=jnp.expand_dims(__lowercase						,	1	) * jnp.expand_dims(__lowercase						,	0	)

 # scale embeddings
 UpperCamelCase_     :					List[str]   	=scale * emb

 if flip_sin_to_cos:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.cos(__lowercase	), jnp.sin(__lowercase	)]						,	axis=1	)
 else:
  UpperCamelCase_     :					Tuple   	=jnp.concatenate([jnp.sin(__lowercase	), jnp.cos(__lowercase	)]						,	axis=1	)
 UpperCamelCase_     :					List[Any]   	=jnp.reshape(__lowercase						,	[jnp.shape(__lowercase	)[0], embedding_dim]	)
 return signal





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      jnp.floataa



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







      '''simple docstring'''



      UpperCamelCase_     :					Dict   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_1'   )(_lowerCamelCase   )
      UpperCamelCase_     :					Any   	=nn.silu(_lowerCamelCase   )
      UpperCamelCase_     :					Tuple   	=nn.Dense(self.time_embed_dim      ,  dtype=self.dtype      ,  name='linear_2'   )(_lowerCamelCase   )
      return temb





class 				a__					(						nn.Module       ):
     UpperCAmelCase__         =      32
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      1



     @nn.compact
     def __call__(							self :Union[str, Any]      ,  _lowerCamelCase :Dict   ):







      '''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   )
