


"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					torch.FloatTensor
     snake_case__	:					torch.FloatTensor
     snake_case__	:					Optional[torch.FloatTensor]													=None


class 			A__	(						__lowercase ,     __lowercase):


     """simple docstring"""
     snake_case__	:					Any													=2

     @register_to_config
     def __init__(	self:						Any     ,		__a:						float = 0.02     ,		__a:						float = 100     ,		__a:						float = 1.0_07     ,		__a:						float = 80     ,		__a:						float = 0.05     ,		__a:						float = 50     ,		)->    Optional[int]:
           # standard deviation of the initial noise distribution
           lowerCamelCase					:					Tuple        =	sigma_max

           # setable values
           lowerCamelCase					:					int        =	None
           lowerCamelCase					:					np.IntTensor        =	None
           lowerCamelCase					:					torch.FloatTensor        =	None  # sigma(t_i)

     def 		a__						(	self:						List[str]     ,		__a:						torch.FloatTensor     ,		__a:						Optional[int] = None	)->    torch.FloatTensor:
           return sample

     def 		a__						(	self:						Tuple     ,		__a:						int     ,		__a:						Union[str, torch.device] = None	)->    Any:
           lowerCamelCase					:					Optional[Any]        =	num_inference_steps
           lowerCamelCase					:					Union[str, Any]        =	np.arange(0     ,		self.num_inference_steps	)[::-1].copy()
           lowerCamelCase					:					Dict        =	torch.from_numpy(__a	).to(__a	)
           lowerCamelCase					:					int        =	[
               (
                   self.config.sigma_max**2
                   * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
               )
               for i in self.timesteps
           ]
           lowerCamelCase					:					List[Any]        =	torch.tensor(__a     ,		dtype=torch.floataa     ,		device=__a	)

     def 		a__						(	self:						int     ,		__a:						torch.FloatTensor     ,		__a:						float     ,		__a:						Optional[torch.Generator] = None	)->    Tuple[torch.FloatTensor, float]:
           if self.config.s_min <= sigma <= self.config.s_max:
                 lowerCamelCase					:					str        =	min(self.config.s_churn / self.num_inference_steps     ,		2**0.5 - 1	)
           else:
                 lowerCamelCase					:					Optional[Any]        =	0

           # sample eps ~ N(0, S_noise^2 * I)
           lowerCamelCase					:					str        =	self.config.s_noise * randn_tensor(sample.shape     ,		generator=__a	).to(sample.device	)
           lowerCamelCase					:					str        =	sigma + gamma * sigma
           lowerCamelCase					:					Dict        =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

           return sample_hat, sigma_hat

     def 		a__						(	self:						Optional[Any]     ,		__a:						torch.FloatTensor     ,		__a:						float     ,		__a:						float     ,		__a:						torch.FloatTensor     ,		__a:						bool = True     ,		)->    Union[KarrasVeOutput, Tuple]:
           lowerCamelCase					:					List[str]        =	sample_hat + sigma_hat * model_output
           lowerCamelCase					:					Tuple        =	(sample_hat - pred_original_sample) / sigma_hat
           lowerCamelCase					:					Optional[Any]        =	sample_hat + (sigma_prev - sigma_hat) * derivative

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=__a     ,		derivative=__a     ,		pred_original_sample=__a	)

     def 		a__						(	self:						Dict     ,		__a:						torch.FloatTensor     ,		__a:						float     ,		__a:						float     ,		__a:						torch.FloatTensor     ,		__a:						torch.FloatTensor     ,		__a:						torch.FloatTensor     ,		__a:						bool = True     ,		)->    Union[KarrasVeOutput, Tuple]:
           lowerCamelCase					:					List[str]        =	sample_prev + sigma_prev * model_output
           lowerCamelCase					:					List[str]        =	(sample_prev - pred_original_sample) / sigma_prev
           lowerCamelCase					:					Any        =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=__a     ,		derivative=__a     ,		pred_original_sample=__a	)







     def 		a__						(	self:						Dict     ,		__a:						Dict     ,		__a:						Dict     ,		__a:						Union[str, Any]	)->    str:
           raise NotImplementedError()








"""simple docstring"""


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


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    '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 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													='''glpn'''

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

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




