






from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin




@dataclass
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      42




class 							snake_case_       (    __UpperCamelCase		,							__UpperCamelCase      ):

						"""simple docstring"""



						@register_to_config
						def __init__(self:					Optional[Any] ,					__UpperCAmelCase:					int = 16 ,					__UpperCAmelCase:					int = 88 ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					int = 1 ,					__UpperCAmelCase:					float = 0.0 ,					__UpperCAmelCase:					int = 32 ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					Optional[int] = None ,					__UpperCAmelCase:					str = "geglu" ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					bool = True ,					)    ->     Tuple:




											'''simple docstring'''



											super().__init__()
											__a					:						Optional[int]      =					num_attention_heads
											__a					:						Dict      =					attention_head_dim
											__a					:						Union[str, Any]      =					num_attention_heads * attention_head_dim

											__a					:						Dict      =					in_channels

											__a					:						str      =					torch.nn.GroupNorm(num_groups=__UpperCAmelCase ,					num_channels=__UpperCAmelCase ,					eps=1E-6 ,					affine=__UpperCAmelCase   )
											__a					:						Any      =					nn.Linear(__UpperCAmelCase ,					__UpperCAmelCase   )

											# 3. Define transformers blocks
											__a					:						Tuple      =					nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					dropout=__UpperCAmelCase ,					cross_attention_dim=__UpperCAmelCase ,					activation_fn=__UpperCAmelCase ,					attention_bias=__UpperCAmelCase ,					double_self_attention=__UpperCAmelCase ,					norm_elementwise_affine=__UpperCAmelCase ,					)
											        for d in range(__UpperCAmelCase   )
											    ]   )

											__a					:						List[str]      =					nn.Linear(__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Tuple ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					List[Any]=None ,					__UpperCAmelCase:					List[Any]=None ,					__UpperCAmelCase:					Any=None ,					__UpperCAmelCase:					Union[str, Any]=1 ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					bool = True ,					)    ->     List[Any]:




											'''simple docstring'''



											__a      ,			__a      ,			__a      ,			__a					:						Dict      =					hidden_states.shape
											__a					:						List[str]      =					batch_frames // num_frames

											__a					:						int      =					hidden_states

											__a					:						str      =					hidden_states[None, :].reshape(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						int      =					hidden_states.permute(0 ,					2 ,					1 ,					3 ,					4   )

											__a					:						Any      =					self.norm(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					hidden_states.permute(0 ,					3 ,					4 ,					2 ,					1   ).reshape(batch_size * height * width ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[int]      =					self.proj_in(__UpperCAmelCase   )

											# 2. Blocks
											for block in self.transformer_blocks:
																__a					:						int      =					block(
																    __UpperCAmelCase ,					encoder_hidden_states=__UpperCAmelCase ,					timestep=__UpperCAmelCase ,					cross_attention_kwargs=__UpperCAmelCase ,					class_labels=__UpperCAmelCase ,					)

											# 3. Output
											__a					:						Union[str, Any]      =					self.proj_out(__UpperCAmelCase   )
											__a					:						List[Any]      =					(
											    hidden_states[None, None, :]
											    .reshape(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											    .permute(0 ,					3 ,					4 ,					1 ,					2   )
											    .contiguous()
											)
											__a					:						List[Any]      =					hidden_states.reshape(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Tuple      =					hidden_states + residual

											if not return_dict:
																return (output,)

											return TransformerTemporalModelOutput(sample=__UpperCAmelCase   )













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


UpperCAmelCase__      			=						logging.get_logger(__name__)

UpperCAmelCase__      			=						{
    '''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 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """glpn"""



						def __init__(self:					Tuple ,					__UpperCAmelCase:					Tuple=3 ,					__UpperCAmelCase:					Dict=4 ,					__UpperCAmelCase:					Any=[2, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[8, 4, 2, 1] ,					__UpperCAmelCase:					Dict=[32, 64, 160, 256] ,					__UpperCAmelCase:					List[str]=[7, 3, 3, 3] ,					__UpperCAmelCase:					Dict=[4, 2, 2, 2] ,					__UpperCAmelCase:					Optional[int]=[1, 2, 5, 8] ,					__UpperCAmelCase:					Dict=[4, 4, 4, 4] ,					__UpperCAmelCase:					List[str]="gelu" ,					__UpperCAmelCase:					str=0.0 ,					__UpperCAmelCase:					List[Any]=0.0 ,					__UpperCAmelCase:					Dict=0.02 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=1E-6 ,					__UpperCAmelCase:					Dict=64 ,					__UpperCAmelCase:					Dict=10 ,					__UpperCAmelCase:					Union[str, Any]=-1 ,					**__UpperCAmelCase:					Dict ,					)    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )

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





