



"""simple docstring"""





from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class       lowercase__   (    __snake_case      ):







       '''simple docstring'''


       def __init__(							self			:  Any		,				_UpperCAmelCase			:  str		,				_UpperCAmelCase			:  Tuple		,				_UpperCAmelCase			:  Optional[Any]		,				_UpperCAmelCase			:  List[str]		,				_UpperCAmelCase			:  List[Any]		,				) ->						List[Any]:


             '''simple docstring'''




             super().__init__()

             self.register_modules(vae=lowercase_		,				text_encoder=lowercase_		,				tokenizer=lowercase_		,				unet=lowercase_		,				scheduler=lowercase_    )



       def      lowercase__				(							self			:  List[str]		,				_UpperCAmelCase			:  List[Any]    ) ->						List[str]:


             '''simple docstring'''




             with torch.no_grad():
                   UpperCAmelCase_			 =							self.vae.encode(tfms.ToTensor()(lowercase_    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
                   UpperCAmelCase_			 =							0.1_8215 * latent.latent_dist.sample()
             return latent



       def      lowercase__				(							self			:  Optional[int]		,				_UpperCAmelCase			:  int    ) ->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_			 =							(1 / 0.1_8215) * latent
             with torch.no_grad():
                   UpperCAmelCase_			 =							self.vae.decode(lowercase_    ).sample
             UpperCAmelCase_			 =							(img / 2 + 0.5).clamp(0		,				1    )
             UpperCAmelCase_			 =							img.detach().cpu().permute(0		,				2		,				3		,				1    ).numpy()
             UpperCAmelCase_			 =							(img * 255).round().astype("uint8"    )
             return Image.fromarray(img[0]    )



       def      lowercase__				(							self			:  Optional[int]		,				_UpperCAmelCase			:  Tuple    ) ->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_			 =							self.tokenizer(
                 lowercase_		,				padding="max_length"		,				max_length=self.tokenizer.model_max_length		,				truncation=lowercase_		,				return_tensors="pt"		,				)

             UpperCAmelCase_			 =							self.text_encoder(text_input.input_ids.to(self.device    )    )[0]

             UpperCAmelCase_			 =							self.tokenizer(
                 ""		,				padding="max_length"		,				max_length=self.tokenizer.model_max_length		,				truncation=lowercase_		,				return_tensors="pt"		,				)

             UpperCAmelCase_			 =							self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

             return torch.cat([uncond_embedding, text_embedding]    )







       def __call__(							self			:  Tuple		,				_UpperCAmelCase			:  Dict		,				_UpperCAmelCase			:  str		,				_UpperCAmelCase			:  Dict = 0.3		,				_UpperCAmelCase			:  List[Any] = 0.6		,				_UpperCAmelCase			:  List[str] = 0.5		,				_UpperCAmelCase			:  Union[str, Any] = 42		,				_UpperCAmelCase			:  Any = 50		,				_UpperCAmelCase			:  int = 7.5		,				) ->						Dict:


             '''simple docstring'''




             UpperCAmelCase_			 =							steps - int(kmin * steps    )
             UpperCAmelCase_			 =							steps - int(kmax * steps    )

             UpperCAmelCase_			 =							self.prep_text(lowercase_    )

             self.scheduler.set_timesteps(lowercase_    )

             UpperCAmelCase_			 =							img.size
             UpperCAmelCase_			 =							self.encode(lowercase_    )

             torch.manual_seed(lowercase_    )
             UpperCAmelCase_			 =							torch.randn(
                 (1, self.unet.config.in_channels, height // 8, width // 8)		,				).to(self.device    )

             UpperCAmelCase_			 =							self.scheduler.add_noise(
                 lowercase_		,				lowercase_		,				timesteps=self.scheduler.timesteps[tmax]		,				)

             UpperCAmelCase_			 =							torch.cat([latents] * 2    )

             UpperCAmelCase_			 =							self.scheduler.scale_model_input(lowercase_		,				self.scheduler.timesteps[tmax]    )

             with torch.no_grad():
                   UpperCAmelCase_			 =							self.unet(
                       lowercase_		,				self.scheduler.timesteps[tmax]		,				encoder_hidden_states=lowercase_		,				).sample

             UpperCAmelCase_			 =							pred.chunk(2    )
             UpperCAmelCase_			 =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

             UpperCAmelCase_			 =							self.scheduler.step(lowercase_		,				self.scheduler.timesteps[tmax]		,				lowercase_    ).prev_sample

             for i, t in enumerate(tqdm(self.scheduler.timesteps    )    ):
                   if i > tmax:
                         if i < tmin:  # layout generation phase
                               UpperCAmelCase_			 =							self.scheduler.add_noise(
                                   lowercase_		,				lowercase_		,				timesteps=lowercase_		,				)

                               UpperCAmelCase_			 =							(mix_factor * latents) + (
                                   1 - mix_factor
                               ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                               UpperCAmelCase_			 =							torch.cat([input] * 2    )

                         else:  # content generation phase
                               UpperCAmelCase_			 =							torch.cat([latents] * 2    )

                         UpperCAmelCase_			 =							self.scheduler.scale_model_input(lowercase_		,				lowercase_    )

                         with torch.no_grad():
                               UpperCAmelCase_			 =							self.unet(
                                   lowercase_		,				lowercase_		,				encoder_hidden_states=lowercase_		,				).sample

                         UpperCAmelCase_			 =							pred.chunk(2    )
                         UpperCAmelCase_			 =							pred_uncond + guidance_scale * (pred_text - pred_uncond)

                         UpperCAmelCase_			 =							self.scheduler.step(lowercase_		,				lowercase_		,				lowercase_    ).prev_sample

             return self.decode(lowercase_    )





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)