





import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
   pass

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


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    'kakaobrain/align-base': 'https://huggingface.co/kakaobrain/align-base/resolve/main/config.json',
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Optional[int]     	=	"align_text_model"




   def __init__(					self,	__magic_name__=30522,	__magic_name__=768,	__magic_name__=12,	__magic_name__=12,	__magic_name__=3072,	__magic_name__="gelu",	__magic_name__=0.1,	__magic_name__=0.1,	__magic_name__=512,	__magic_name__=2,	__magic_name__=0.02,	__magic_name__=1E-12,	__magic_name__=0,	__magic_name__="absolute",	__magic_name__=True,	**__magic_name__,	)				->   Union[str, Any]:






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

       UpperCamelCase__							:       Optional[int]      =   vocab_size
       UpperCamelCase__							:       Union[str, Any]      =   hidden_size
       UpperCamelCase__							:       List[Any]      =   num_hidden_layers
       UpperCamelCase__							:       List[str]      =   num_attention_heads
       UpperCamelCase__							:       Union[str, Any]      =   hidden_act
       UpperCamelCase__							:       Tuple      =   intermediate_size
       UpperCamelCase__							:       Dict      =   hidden_dropout_prob
       UpperCamelCase__							:       Tuple      =   attention_probs_dropout_prob
       UpperCamelCase__							:       int      =   max_position_embeddings
       UpperCamelCase__							:       List[Any]      =   type_vocab_size
       UpperCamelCase__							:       Tuple      =   initializer_range
       UpperCamelCase__							:       Any      =   layer_norm_eps
       UpperCamelCase__							:       List[str]      =   position_embedding_type
       UpperCamelCase__							:       Optional[Any]      =   use_cache
       UpperCamelCase__							:       int      =   pad_token_id




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	**__magic_name__							)				->   "PretrainedConfig":






       """simple docstring"""
       cls._set_token_in_kwargs(__A							)

       UpperCamelCase__     ,UpperCamelCase__							:       List[Any]      =   cls.get_config_dict(__A,	**__A							)

       # get the text config dict if we are loading from AlignConfig
       if config_dict.get('''model_type'''							) == "align":
           UpperCamelCase__							:       Optional[Any]      =   config_dict['''text_config''']

       if "model_type" in config_dict and hasattr(cls,	'''model_type'''							) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
               f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

       return cls.from_dict(__A,	**__A							)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							int     	=	"align_vision_model"




   def __init__(					self,	__magic_name__ = 3,	__magic_name__ = 600,	__magic_name__ = 2.0,	__magic_name__ = 3.1,	__magic_name__ = 8,	__magic_name__ = [3, 3, 5, 3, 5, 5, 3],	__magic_name__ = [32, 16, 24, 40, 80, 112, 192],	__magic_name__ = [16, 24, 40, 80, 112, 192, 320],	__magic_name__ = [],	__magic_name__ = [1, 2, 2, 2, 1, 2, 1],	__magic_name__ = [1, 2, 2, 3, 3, 4, 1],	__magic_name__ = [1, 6, 6, 6, 6, 6, 6],	__magic_name__ = 0.25,	__magic_name__ = "swish",	__magic_name__ = 2560,	__magic_name__ = "mean",	__magic_name__ = 0.02,	__magic_name__ = 0.001,	__magic_name__ = 0.99,	__magic_name__ = 0.2,	**__magic_name__,	)				->   str:






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

       UpperCamelCase__							:       Union[str, Any]      =   num_channels
       UpperCamelCase__							:       Any      =   image_size
       UpperCamelCase__							:       List[Any]      =   width_coefficient
       UpperCamelCase__							:       Tuple      =   depth_coefficient
       UpperCamelCase__							:       Tuple      =   depth_divisor
       UpperCamelCase__							:       Optional[int]      =   kernel_sizes
       UpperCamelCase__							:       Optional[Any]      =   in_channels
       UpperCamelCase__							:       List[str]      =   out_channels
       UpperCamelCase__							:       str      =   depthwise_padding
       UpperCamelCase__							:       List[str]      =   strides
       UpperCamelCase__							:       List[Any]      =   num_block_repeats
       UpperCamelCase__							:       Optional[int]      =   expand_ratios
       UpperCamelCase__							:       Any      =   squeeze_expansion_ratio
       UpperCamelCase__							:       List[Any]      =   hidden_act
       UpperCamelCase__							:       Union[str, Any]      =   hidden_dim
       UpperCamelCase__							:       str      =   pooling_type
       UpperCamelCase__							:       List[Any]      =   initializer_range
       UpperCamelCase__							:       str      =   batch_norm_eps
       UpperCamelCase__							:       Optional[Any]      =   batch_norm_momentum
       UpperCamelCase__							:       Tuple      =   drop_connect_rate
       UpperCamelCase__							:       Dict      =   sum(__A							) * 4




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	**__magic_name__							)				->   "PretrainedConfig":






       """simple docstring"""
       cls._set_token_in_kwargs(__A							)

       UpperCamelCase__     ,UpperCamelCase__							:       int      =   cls.get_config_dict(__A,	**__A							)

       # get the vision config dict if we are loading from AlignConfig
       if config_dict.get('''model_type'''							) == "align":
           UpperCamelCase__							:       Union[str, Any]      =   config_dict['''vision_config''']

       if "model_type" in config_dict and hasattr(cls,	'''model_type'''							) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
               f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

       return cls.from_dict(__A,	**__A							)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Optional[Any]     	=	"align"
   a		:							List[str]     	=	True




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=640,	__magic_name__=1.0,	__magic_name__=0.02,	**__magic_name__,	)				->   str:






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

       if text_config is None:
           UpperCamelCase__							:       Optional[int]      =   {}
           logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''							)

       if vision_config is None:
           UpperCamelCase__							:       Union[str, Any]      =   {}
           logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''							)

       UpperCamelCase__							:       Any      =   AlignTextConfig(**__A							)
       UpperCamelCase__							:       Optional[Any]      =   AlignVisionConfig(**__A							)

       UpperCamelCase__							:       Optional[int]      =   projection_dim
       UpperCamelCase__							:       int      =   temperature_init_value
       UpperCamelCase__							:       Dict      =   initializer_range




   @classmethod
   def    UpperCamelCase__						(					cls,	__magic_name__,	__magic_name__,	**__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       return cls(text_config=text_config.to_dict(),	vision_config=vision_config.to_dict(),	**__A							)




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       str      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       List[str]      =   self.text_config.to_dict()
       UpperCamelCase__							:       Union[str, Any]      =   self.vision_config.to_dict()
       UpperCamelCase__							:       int      =   self.__class__.model_type
       return output








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       UpperCamelCase__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''							)

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       UpperCamelCase__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




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






       """simple docstring"""
       return self.config.num_train_timesteps

