





"""simple docstring"""






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


A			      =	logging.get_logger(__name__)

A			      =	{
    '''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 					__lowercase      (      _UpperCamelCase				):







					'''simple docstring'''




					__lowerCAmelCase	=	'''glpn'''







					def __init__(    self						,      _UpperCAmelCase=3						,      _UpperCAmelCase=4						,      _UpperCAmelCase=[2, 2, 2, 2]						,      _UpperCAmelCase=[8, 4, 2, 1]						,      _UpperCAmelCase=[32, 64, 160, 256]						,      _UpperCAmelCase=[7, 3, 3, 3]						,      _UpperCAmelCase=[4, 2, 2, 2]						,      _UpperCAmelCase=[1, 2, 5, 8]						,      _UpperCAmelCase=[4, 4, 4, 4]						,      _UpperCAmelCase="gelu"						,      _UpperCAmelCase=0.0						,      _UpperCAmelCase=0.0						,      _UpperCAmelCase=0.0_2						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=1e-6						,      _UpperCAmelCase=64						,      _UpperCAmelCase=10						,      _UpperCAmelCase=-1						,      **_UpperCAmelCase						,      ):
									super().__init__(**_UpperCAmelCase  )

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






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class     UpperCamelCase__					(     lowerCamelCase__				, lowerCamelCase__				, lowerCamelCase__				):






		'''simple docstring'''






		@register_to_config
		def __init__(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = False,   )       -> Any:






									"""simple docstring"""
									super().__init__()

									lowercase_	:   str  				=     nn.Embedding(snake_case__,   snake_case__  )

									lowercase_	:   Dict  				=     nn.Embedding(snake_case__,   snake_case__  )
									lowercase_	:   List[str]  				=     False

									lowercase_	:   Optional[int]  				=     nn.Dropout(p=snake_case__  )

									lowercase_	:   Any  				=     TaConfig(
									    vocab_size=snake_case__,   d_model=snake_case__,   num_heads=snake_case__,   d_kv=snake_case__,   d_ff=snake_case__,   dropout_rate=snake_case__,   feed_forward_proj=snake_case__,   is_decoder=snake_case__,   is_encoder_decoder=snake_case__,   )

									lowercase_	:   List[Any]  				=     nn.ModuleList()
									for lyr_num in range(snake_case__  ):
																lowercase_	:   List[str]  				=     TaBlock(snake_case__  )
																self.encoders.append(snake_case__  )

									lowercase_	:   Tuple  				=     TaLayerNorm(snake_case__  )
									lowercase_	:   Dict  				=     nn.Dropout(p=snake_case__  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> Union[str, Any]:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     self.token_embedder(snake_case__  )

									lowercase_	:   Tuple  				=     encoder_input_tokens.shape[1]
									lowercase_	:   List[str]  				=     torch.arange(snake_case__,   device=encoder_input_tokens.device  )
									x += self.position_encoding(snake_case__  )

									lowercase_	:   Union[str, Any]  				=     self.dropout_pre(snake_case__  )

									# inverted the attention mask
									lowercase_	:   List[str]  				=     encoder_input_tokens.size()
									lowercase_	:   Union[str, Any]  				=     self.get_extended_attention_mask(snake_case__,   snake_case__  )

									for lyr in self.encoders:
																lowercase_	:   Optional[Any]  				=     lyr(snake_case__,   snake_case__  )[0]
									lowercase_	:   Dict  				=     self.layer_norm(snake_case__  )

									return self.dropout_post(snake_case__  ), encoder_inputs_mask