






"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =42
							_SCREAMING_SNAKE_CASE    =42




							def __init__(   self:     Union[str, Any]				,		__A:     UNetaDModel				,		__A:     KarrasVeScheduler    ):

												'''simple docstring'''

												super().__init__()
												self.register_modules(unet=__A				,		scheduler=__A    )





							@torch.no_grad()
							def __call__(   self:     List[str]				,		__A:     int = 1				,		__A:     int = 50				,		__A:     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,		__A:     Optional[str] = "pil"				,		__A:     bool = True				,		**__A:     Union[str, Any]				,		):

												'''simple docstring'''

												a__          =  self.unet.config.sample_size
												a__          =  (batch_size, 3, img_size, img_size)

												a__          =  self.unet

												# sample x_0 ~ N(0, sigma_0^2 * I)
												a__          =  randn_tensor(__A				,		generator=__A				,		device=self.device    ) * self.scheduler.init_noise_sigma

												self.scheduler.set_timesteps(__A    )

												for t in self.progress_bar(self.scheduler.timesteps    ):
																	# here sigma_t == t_i from the paper
																	a__          =  self.scheduler.schedule[t]
																	a__          =  self.scheduler.schedule[t - 1] if t > 0 else 0

																	# 1. Select temporarily increased noise level sigma_hat
																	# 2. Add new noise to move from sample_i to sample_hat
																	a__       ,a__          =  self.scheduler.add_noise_to_input(__A				,		__A				,		generator=__A    )

																	# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																	# The model inputs and output are adjusted by following eq. (213) in [1].
																	a__          =  (sigma_hat / 2) * model((sample_hat + 1) / 2				,		sigma_hat / 2    ).sample

																	# 4. Evaluate dx/dt at sigma_hat
																	# 5. Take Euler step from sigma to sigma_prev
																	a__          =  self.scheduler.step(__A				,		__A				,		__A				,		__A    )

																	if sigma_prev != 0:
																						# 6. Apply 2nd order correction
																						# The model inputs and output are adjusted by following eq. (213) in [1].
																						a__          =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,		sigma_prev / 2    ).sample
																						a__          =  self.scheduler.step_correct(
																						    __A				,		__A				,		__A				,		__A				,		step_output.prev_sample				,		step_output['''derivative''']				,		)
																	a__          =  step_output.prev_sample

												a__          =  (sample / 2 + 0.5).clamp(0				,		1    )
												a__          =  sample.cpu().permute(0				,		2				,		3				,		1    ).numpy()
												if output_type == "pil":
																	a__          =  self.numpy_to_pil(__A    )

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=__A    )










"""simple docstring"""







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


if TYPE_CHECKING:
	pass

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


__a       :	List[Any]             = logging.get_logger(__name__)

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







class        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_text_model'




							def __init__(   self:     Tuple				,		__A:     Optional[int]=30522				,		__A:     Tuple=768				,		__A:     Any=12				,		__A:     Any=12				,		__A:     Dict=3072				,		__A:     Tuple="gelu"				,		__A:     Union[str, Any]=0.1				,		__A:     List[str]=0.1				,		__A:     Optional[int]=512				,		__A:     Tuple=2				,		__A:     str=0.0_2				,		__A:     int=1e-12				,		__A:     Optional[int]=0				,		__A:     Optional[int]="absolute"				,		__A:     List[Any]=True				,		**__A:     Tuple				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  hidden_act
												a__          =  intermediate_size
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  initializer_range
												a__          =  layer_norm_eps
												a__          =  position_embedding_type
												a__          =  use_cache
												a__          =  pad_token_id





							@classmethod
							def  lowercase		(   cls:     Any				,		__A:     Union[str, os.PathLike]				,		**__A:     Tuple    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the text config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  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        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align_vision_model'




							def __init__(   self:     Dict				,		__A:     int = 3				,		__A:     int = 600				,		__A:     float = 2.0				,		__A:     float = 3.1				,		__A:     int = 8				,		__A:     List[int] = [3, 3, 5, 3, 5, 5, 3]				,		__A:     List[int] = [32, 16, 24, 40, 80, 112, 192]				,		__A:     List[int] = [16, 24, 40, 80, 112, 192, 320]				,		__A:     List[int] = []				,		__A:     List[int] = [1, 2, 2, 2, 1, 2, 1]				,		__A:     List[int] = [1, 2, 2, 3, 3, 4, 1]				,		__A:     List[int] = [1, 6, 6, 6, 6, 6, 6]				,		__A:     float = 0.2_5				,		__A:     str = "swish"				,		__A:     int = 2560				,		__A:     str = "mean"				,		__A:     float = 0.0_2				,		__A:     float = 0.0_0_1				,		__A:     float = 0.9_9				,		__A:     float = 0.2				,		**__A:     List[Any]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												a__          =  num_channels
												a__          =  image_size
												a__          =  width_coefficient
												a__          =  depth_coefficient
												a__          =  depth_divisor
												a__          =  kernel_sizes
												a__          =  in_channels
												a__          =  out_channels
												a__          =  depthwise_padding
												a__          =  strides
												a__          =  num_block_repeats
												a__          =  expand_ratios
												a__          =  squeeze_expansion_ratio
												a__          =  hidden_act
												a__          =  hidden_dim
												a__          =  pooling_type
												a__          =  initializer_range
												a__          =  batch_norm_eps
												a__          =  batch_norm_momentum
												a__          =  drop_connect_rate
												a__          =  sum(__A    ) * 4





							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     Union[str, os.PathLike]				,		**__A:     List[Any]    ):

												'''simple docstring'''

												cls._set_token_in_kwargs(__A    )

												a__       ,a__          =  cls.get_config_dict(__A				,		**__A    )

												# get the vision config dict if we are loading from AlignConfig
												if config_dict.get('''model_type'''    ) == "align":
																	a__          =  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        _SCREAMING_SNAKE_CASE						(  __snake_case							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    ='align'
							_SCREAMING_SNAKE_CASE    =True




							def __init__(   self:     Optional[int]				,		__A:     Optional[int]=None				,		__A:     Dict=None				,		__A:     List[str]=640				,		__A:     Optional[int]=1.0				,		__A:     str=0.0_2				,		**__A:     List[str]				,		):

												'''simple docstring'''

												super().__init__(**__A    )

												if text_config is None:
																	a__          =  {}
																	logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''    )

												if vision_config is None:
																	a__          =  {}
																	logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''    )

												a__          =  AlignTextConfig(**__A    )
												a__          =  AlignVisionConfig(**__A    )

												a__          =  projection_dim
												a__          =  temperature_init_value
												a__          =  initializer_range




							@classmethod
							def  lowercase		(   cls:     Dict				,		__A:     AlignTextConfig				,		__A:     AlignVisionConfig				,		**__A:     str    ):

												'''simple docstring'''

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





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  copy.deepcopy(self.__dict__    )
												a__          =  self.text_config.to_dict()
												a__          =  self.vision_config.to_dict()
												a__          =  self.__class__.model_type
												return output


