




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


if TYPE_CHECKING:
  pass

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


_lowerCamelCase      :						Any							=  logging.get_logger(__name__)

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


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''align_text_model'''


    def __init__(     self :      Optional[int]   ,					UpperCAmelCase__ :      List[Any]=30_522   ,					UpperCAmelCase__ :      Optional[Any]=768   ,					UpperCAmelCase__ :      List[str]=12   ,					UpperCAmelCase__ :      Optional[int]=12   ,					UpperCAmelCase__ :      Optional[Any]=3_072   ,					UpperCAmelCase__ :      Union[str, Any]="gelu"   ,					UpperCAmelCase__ :      Any=0.1   ,					UpperCAmelCase__ :      Union[str, Any]=0.1   ,					UpperCAmelCase__ :      Any=512   ,					UpperCAmelCase__ :      Tuple=2   ,					UpperCAmelCase__ :      List[Any]=0.02   ,					UpperCAmelCase__ :      Optional[int]=1e-12   ,					UpperCAmelCase__ :      str=0   ,					UpperCAmelCase__ :      Optional[int]="absolute"   ,					UpperCAmelCase__ :      Union[str, Any]=True   ,					**UpperCAmelCase__ :      Optional[Any]   ,					)		->Union[str, Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           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 			SCREAMING_SNAKE_CASE				(     cls :      Any   ,					UpperCAmelCase__ :      Union[str, os.PathLike]   ,					**UpperCAmelCase__ :      Optional[Any])		->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__					,				A__ 					=       cls.get_config_dict(UpperCAmelCase__   ,					**UpperCAmelCase__)

           # 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(UpperCAmelCase__   ,					**UpperCAmelCase__)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''align_vision_model'''


    def __init__(     self :      int   ,					UpperCAmelCase__ :      int = 3   ,					UpperCAmelCase__ :      int = 600   ,					UpperCAmelCase__ :      float = 2.0   ,					UpperCAmelCase__ :      float = 3.1   ,					UpperCAmelCase__ :      int = 8   ,					UpperCAmelCase__ :      List[int] = [3, 3, 5, 3, 5, 5, 3]   ,					UpperCAmelCase__ :      List[int] = [32, 16, 24, 40, 80, 112, 192]   ,					UpperCAmelCase__ :      List[int] = [16, 24, 40, 80, 112, 192, 320]   ,					UpperCAmelCase__ :      List[int] = []   ,					UpperCAmelCase__ :      List[int] = [1, 2, 2, 2, 1, 2, 1]   ,					UpperCAmelCase__ :      List[int] = [1, 2, 2, 3, 3, 4, 1]   ,					UpperCAmelCase__ :      List[int] = [1, 6, 6, 6, 6, 6, 6]   ,					UpperCAmelCase__ :      float = 0.25   ,					UpperCAmelCase__ :      str = "swish"   ,					UpperCAmelCase__ :      int = 2_560   ,					UpperCAmelCase__ :      str = "mean"   ,					UpperCAmelCase__ :      float = 0.02   ,					UpperCAmelCase__ :      float = 0.001   ,					UpperCAmelCase__ :      float = 0.99   ,					UpperCAmelCase__ :      float = 0.2   ,					**UpperCAmelCase__ :      int   ,					)		->Union[str, Any]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           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(UpperCAmelCase__) * 4





    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Optional[Any]   ,					UpperCAmelCase__ :      Union[str, os.PathLike]   ,					**UpperCAmelCase__ :      Dict)		->"PretrainedConfig":





           '''simple docstring'''

           cls._set_token_in_kwargs(UpperCAmelCase__)

           A__					,				A__ 					=       cls.get_config_dict(UpperCAmelCase__   ,					**UpperCAmelCase__)

           # 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(UpperCAmelCase__   ,					**UpperCAmelCase__)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''align'''
    UpperCAmelCase__				    = True


    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[Any]=None   ,					UpperCAmelCase__ :      Optional[Any]=None   ,					UpperCAmelCase__ :      List[Any]=640   ,					UpperCAmelCase__ :      List[Any]=1.0   ,					UpperCAmelCase__ :      Dict=0.02   ,					**UpperCAmelCase__ :      Any   ,					)		->Dict:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           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(**UpperCAmelCase__)
           A__ 					=       AlignVisionConfig(**UpperCAmelCase__)

           A__ 					=       projection_dim
           A__ 					=       temperature_init_value
           A__ 					=       initializer_range


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Dict   ,					UpperCAmelCase__ :      AlignTextConfig   ,					UpperCAmelCase__ :      AlignVisionConfig   ,					**UpperCAmelCase__ :      Tuple)		->Tuple:





           '''simple docstring'''

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





    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Optional[int]:





           '''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










from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A__      :     Union[str, Any]       =					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return image
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [image]

				lowercase__  					=     [trans(img.convert('''RGB'''							)							) for img in image]
				lowercase__  					=     torch.stack(lowerCamelCase_							)
				return image




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								lowercase__  					=     DDIMScheduler.from_config(scheduler.config    )

								self.register_modules(unet=lowerCamelCase,    scheduler=lowerCamelCase    )




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								if strength < 0 or strength > 1:
												raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								# get the original timestep using init_timestep
								lowercase__  					=     min(int(num_inference_steps * strength    ),    lowerCamelCase    )

								lowercase__  					=     max(num_inference_steps - init_timestep,    0    )
								lowercase__  					=     self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any=None    ):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,    (torch.Tensor, PIL.Image.Image, list)    ):
												raise ValueError(
												    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase    )}"""    )

								lowercase__  					=     image.to(device=lowerCamelCase,    dtype=lowerCamelCase    )

								if isinstance(lowerCamelCase,    lowerCamelCase    ) and len(lowerCamelCase    ) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(lowerCamelCase    )}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

								lowercase__  					=     init_latents.shape
								lowercase__  					=     randn_tensor(lowerCamelCase,    generator=lowerCamelCase,    device=lowerCamelCase,    dtype=lowerCamelCase    )

								# get latents
								print('''add noise to latents at timestep''',    lowerCamelCase    )
								lowercase__  					=     self.scheduler.add_noise(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     init_latents

								return latents




				@torch.no_grad()
				def __call__(	self		:       Dict,    lowerCamelCase		:       Union[torch.FloatTensor, PIL.Image.Image] = None,    lowerCamelCase		:       float = 0.8,    lowerCamelCase		:       int = 1,    lowerCamelCase		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       int = 50,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[str] = "pil",    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								self.check_inputs(lowerCamelCase    )

								# 2. Preprocess image
								lowercase__  					=     preprocess(lowerCamelCase    )

								# 3. set timesteps
								self.scheduler.set_timesteps(lowerCamelCase,    device=self.device    )
								lowercase__					,		lowercase__  					=     self.get_timesteps(lowerCamelCase,    lowerCamelCase,    self.device    )
								lowercase__  					=     timesteps[:1].repeat(lowerCamelCase    )

								# 4. Prepare latent variables
								lowercase__  					=     self.prepare_latents(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    self.unet.dtype,    self.device,    lowerCamelCase    )
								lowercase__  					=     latents

								# 5. Denoising loop
								for t in self.progress_bar(lowerCamelCase    ):
												# 1. predict noise model_output
												lowercase__  					=     self.unet(lowerCamelCase,    lowerCamelCase    ).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												lowercase__  					=     self.scheduler.step(
												    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    eta=lowerCamelCase,    use_clipped_model_output=lowerCamelCase,    generator=lowerCamelCase,    ).prev_sample

								lowercase__  					=     (image / 2 + 0.5).clamp(0,    1    )
								lowercase__  					=     image.cpu().permute(0,    2,    3,    1    ).numpy()
								if output_type == "pil":
												lowercase__  					=     self.numpy_to_pil(lowerCamelCase    )

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowerCamelCase    )




