


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_      : str  					=       logging.get_logger(__name__)

UpperCAmelCase_      : List[str]  					=       {
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Dict        =						'''glpn'''
      def __init__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Dict=3     ,							SCREAMING_SNAKE_CASE__				:       int=4     ,							SCREAMING_SNAKE_CASE__				:       int=[2, 2, 2, 2]     ,							SCREAMING_SNAKE_CASE__				:       str=[8, 4, 2, 1]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=[7, 3, 3, 3]     ,							SCREAMING_SNAKE_CASE__				:       List[str]=[4, 2, 2, 2]     ,							SCREAMING_SNAKE_CASE__				:       Dict=[1, 2, 5, 8]     ,							SCREAMING_SNAKE_CASE__				:       List[str]=[4, 4, 4, 4]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]="gelu"     ,							SCREAMING_SNAKE_CASE__				:       int=0.0     ,							SCREAMING_SNAKE_CASE__				:       List[str]=0.0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=0.02     ,							SCREAMING_SNAKE_CASE__				:       str=0.1     ,							SCREAMING_SNAKE_CASE__				:       str=1E-6     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=6_4     ,							SCREAMING_SNAKE_CASE__				:       str=1_0     ,							SCREAMING_SNAKE_CASE__				:       Tuple=-1     ,							**SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							)		->			int:
             super().__init__(**SCREAMING_SNAKE_CASE__     )

             a_					:		str							=  num_channels
             a_					:		Tuple							=  num_encoder_blocks
             a_					:		Union[str, Any]							=  depths
             a_					:		Any							=  sr_ratios
             a_					:		Optional[Any]							=  hidden_sizes
             a_					:		Union[str, Any]							=  patch_sizes
             a_					:		List[str]							=  strides
             a_					:		List[Any]							=  mlp_ratios
             a_					:		Optional[int]							=  num_attention_heads
             a_					:		Optional[Any]							=  hidden_act
             a_					:		List[Any]							=  hidden_dropout_prob
             a_					:		Tuple							=  attention_probs_dropout_prob
             a_					:		str							=  initializer_range
             a_					:		Tuple							=  drop_path_rate
             a_					:		Dict							=  layer_norm_eps
             a_					:		Dict							=  decoder_hidden_size
             a_					:		int							=  max_depth
             a_					:		Union[str, Any]							=  head_in_index






import flax.linen as nn
import jax
import jax.numpy as jnp
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			int:
             a_					:		Dict							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			Optional[int]:
             a_							,		a_							,		a_							,		a_					:		Union[str, Any]							=  hidden_states.shape
             a_					:		List[str]							=  jax.image.resize(
                 SCREAMING_SNAKE_CASE__     ,							shape=(batch, height * 2, width * 2, channels)     ,							method='nearest'     ,							)
             a_					:		Any							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[int]:
             a_					:		Optional[int]							=  nn.Conv(
                 self.out_channels     ,							kernel_size=(3, 3)     ,							strides=(2, 2)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Optional[int]:
             # pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
             # hidden_states = jnp.pad(hidden_states, pad_width=pad)
             a_					:		str							=  self.conv(SCREAMING_SNAKE_CASE__     )
             return hidden_states
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int
      snake_case__				:     int        =						None
      snake_case__				:     float        =						0.0
      snake_case__				:     bool        =						None
      snake_case__				:     jnp.dtype        =						jnp.floataa
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             a_					:		List[str]							=  self.in_channels if self.out_channels is None else self.out_channels

             a_					:		Optional[int]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		Any							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		Optional[int]							=  nn.Dense(SCREAMING_SNAKE_CASE__     ,							dtype=self.dtype     )

             a_					:		Union[str, Any]							=  nn.GroupNorm(num_groups=3_2     ,							epsilon=1E-5     )
             a_					:		int							=  nn.Dropout(self.dropout_prob     )
             a_					:		Optional[Any]							=  nn.Conv(
                 SCREAMING_SNAKE_CASE__     ,							kernel_size=(3, 3)     ,							strides=(1, 1)     ,							padding=((1, 1), (1, 1))     ,							dtype=self.dtype     ,							)

             a_					:		List[str]							=  self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

             a_					:		List[Any]							=  None
             if use_nin_shortcut:
                    a_					:		Union[str, Any]							=  nn.Conv(
                        SCREAMING_SNAKE_CASE__     ,							kernel_size=(1, 1)     ,							strides=(1, 1)     ,							padding='VALID'     ,							dtype=self.dtype     ,							)

      def __call__(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       List[Any]=True     )		->			int:
             a_					:		List[Any]							=  hidden_states
             a_					:		Any							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             a_					:		int							=  self.time_emb_proj(nn.swish(SCREAMING_SNAKE_CASE__     )     )
             a_					:		List[str]							=  jnp.expand_dims(jnp.expand_dims(SCREAMING_SNAKE_CASE__     ,							1     )     ,							1     )
             a_					:		Optional[int]							=  hidden_states + temb

             a_					:		List[str]							=  self.norma(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.swish(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[int]							=  self.dropout(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  self.conva(SCREAMING_SNAKE_CASE__     )

             if self.conv_shortcut is not None:
                    a_					:		List[str]							=  self.conv_shortcut(SCREAMING_SNAKE_CASE__     )

             return hidden_states + residual


