

'''simple docstring'''
import math

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


def        _lowerCAmelCase					(						_UpperCamelCase						:     jnp.ndarray							, _UpperCamelCase						:     int							, _UpperCamelCase						:     float = 1							, _UpperCamelCase						:     float = 1							, _UpperCamelCase						:     float = 1.0E4							, _UpperCamelCase						:     bool = False							, _UpperCamelCase						:     float = 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							    =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 							A__    (       nn.Module  ):
  A__		 =				32
  A__		 =				jnp.floataa





  @nn.compact
  def __call__(		self      :	int							,					_a      :	Union[str, Any] )	->    str:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =nn.Dense(self.time_embed_dim							,					dtype=self.dtype							,					name='linear_1' )(_a )
       _SCREAMING_SNAKE_CASE							    =nn.silu(_a )
       _SCREAMING_SNAKE_CASE							    =nn.Dense(self.time_embed_dim							,					dtype=self.dtype							,					name='linear_2' )(_a )
       return temb







class 							A__    (       nn.Module  ):
  A__		 =				32
  A__		 =				False
  A__		 =				1





  @nn.compact
  def __call__(		self      :	Tuple							,					_a      :	Dict )	->    List[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'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCamelCase :      int					 =     {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      int					 =     ["GLPNFeatureExtractor"]
     lowerCamelCase :      Optional[int]					 =     ["GLPNImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase :      Union[str, Any]					 =     [
         "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "GLPNForDepthEstimation",
         "GLPNLayer",
         "GLPNModel",
         "GLPNPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_glpn import GLPNFeatureExtractor
          from .image_processing_glpn import GLPNImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_glpn import (
              GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
              GLPNForDepthEstimation,
              GLPNLayer,
              GLPNModel,
              GLPNPreTrainedModel,
          )


else:
     import sys

     lowerCamelCase :      List[Any]					 =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
