
"""simple docstring"""






from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
 import timm


if is_torch_available():
 from torch import Tensor





class 				a__					(						A__   ,							A__       ):
     UpperCAmelCase__         =      '''pixel_values'''
     UpperCAmelCase__         =      False
     UpperCAmelCase__         =      TimmBackboneConfig



     def __init__(							self :int      ,  _lowerCamelCase :int      ,  **_lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      requires_backends(self      ,  'timm'   )
      super().__init__(_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=config

      if config.backbone is None:
       raise ValueError('backbone is not set in the config. Please set it to a timm model name.'   )

      if config.backbone not in timm.list_models():
       raise ValueError(f'''backbone {config.backbone} is not supported by timm.'''   )

      if hasattr(_lowerCamelCase      ,  'out_features'   ) and config.out_features is not None:
       raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'   )

      UpperCamelCase_     :					List[str]   	=getattr(_lowerCamelCase      ,  'use_pretrained_backbone'      ,  _lowerCamelCase   )
      if pretrained is None:
       raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'   )

      # We just take the final layer by default. This matches the default for the transformers models.
      UpperCamelCase_     :					List[str]   	=config.out_indices if getattr(_lowerCamelCase      ,  'out_indices'      ,  _lowerCamelCase   ) is not None else (-1,)

      UpperCamelCase_     :					List[Any]   	=timm.create_model(
          config.backbone      ,  pretrained=_lowerCamelCase      ,  features_only=config.features_only      ,  in_chans=config.num_channels      ,  out_indices=_lowerCamelCase      ,  **_lowerCamelCase      ,  )
      # These are used to control the output of the model when called. If output_hidden_states is True, then
      # return_layers is modified to include all layers.
      UpperCamelCase_     :					Optional[Any]   	=self._backbone.return_layers
      UpperCamelCase_     :					int   	={layer['module']: str(_lowerCamelCase   ) for i, layer in enumerate(self._backbone.feature_info.info   )}
      super()._init_backbone(_lowerCamelCase   )



     @classmethod
     def  lowerCamelCase_						(							cls :Dict      ,  _lowerCamelCase :List[str]      ,  *_lowerCamelCase :List[Any]      ,  **_lowerCamelCase :Union[str, Any]   ):







      '''simple docstring'''



      requires_backends(cls      ,  ['vision', 'timm']   )
      from ...models.timm_backbone import TimmBackboneConfig

      UpperCamelCase_     :					List[str]   	=kwargs.pop('config'      ,  TimmBackboneConfig()   )

      UpperCamelCase_     :					Union[str, Any]   	=kwargs.pop('use_timm_backbone'      ,  _lowerCamelCase   )
      if not use_timm:
       raise ValueError('use_timm_backbone must be True for timm backbones'   )

      UpperCamelCase_     :					Union[str, Any]   	=kwargs.pop('num_channels'      ,  config.num_channels   )
      UpperCamelCase_     :					List[str]   	=kwargs.pop('features_only'      ,  config.features_only   )
      UpperCamelCase_     :					Union[str, Any]   	=kwargs.pop('use_pretrained_backbone'      ,  config.use_pretrained_backbone   )
      UpperCamelCase_     :					Union[str, Any]   	=kwargs.pop('out_indices'      ,  config.out_indices   )
      UpperCamelCase_     :					Tuple   	=TimmBackboneConfig(
          backbone=_lowerCamelCase      ,  num_channels=_lowerCamelCase      ,  features_only=_lowerCamelCase      ,  use_pretrained_backbone=_lowerCamelCase      ,  out_indices=_lowerCamelCase      ,  )
      return super()._from_config(_lowerCamelCase      ,  **_lowerCamelCase   )



     def  lowerCamelCase_						(							self :Any      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      pass




     def  lowerCamelCase_						(							self :Dict      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :Tuple=None      ,  _lowerCamelCase :Any=None      ,  _lowerCamelCase :Optional[Any]=None      ,  **_lowerCamelCase :Dict   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=return_dict if return_dict is not None else self.config.use_return_dict
      UpperCamelCase_     :					Dict   	=(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      UpperCamelCase_     :					List[Any]   	=output_attentions if output_attentions is not None else self.config.output_attentions

      if output_attentions:
       raise ValueError('Cannot output attentions for timm backbones at the moment'   )

      if output_hidden_states:
       # We modify the return layers to include all the stages of the backbone
       UpperCamelCase_     :					str   	=self._all_layers
       UpperCamelCase_     :					Optional[int]   	=self._backbone(_lowerCamelCase      ,  **_lowerCamelCase   )
       UpperCamelCase_     :					Tuple   	=self._return_layers
       UpperCamelCase_     :					Optional[Any]   	=tuple(hidden_states[i] for i in self.out_indices   )
      else:
       UpperCamelCase_     :					List[str]   	=self._backbone(_lowerCamelCase      ,  **_lowerCamelCase   )
       UpperCamelCase_     :					int   	=None

      UpperCamelCase_     :					Optional[int]   	=tuple(_lowerCamelCase   )
      UpperCamelCase_     :					Union[str, Any]   	=tuple(_lowerCamelCase   ) if hidden_states is not None else None

      if not return_dict:
       UpperCamelCase_     :					Dict   	=(feature_maps,)
       if output_hidden_states:
        UpperCamelCase_     :					Dict   	=output + (hidden_states,)
       return output

      return BackboneOutput(feature_maps=_lowerCamelCase      ,  hidden_states=_lowerCamelCase      ,  attentions=_lowerCamelCase   )


"""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   )
