

'''simple docstring'''







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


__lowerCamelCase										=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	str:
       if isinstance(UpperCAmelCase__,				torch.Tensor ):
              return image
       elif isinstance(UpperCAmelCase__,				PIL.Image.Image ):
              A_		    =					[image]

       A_		    =					[trans(img.convert("""RGB""" ) ) for img in image]
       A_		    =					torch.stack(UpperCAmelCase__ )
       return image







class     A__					(					lowerCamelCase_	):



    def __init__(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					List[Any]:





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

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

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



    def     snake_case_	(    self						, UpperCamelCase__				)  ->					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     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__				)  ->					List[Any]:





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

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

           return timesteps, num_inference_steps - t_start



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__=None				)  ->					List[str]:





           '''simple docstring'''
           if not isinstance(__a						, (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(__a				)}'''				)

           A_		    =					image.to(device=__a						, dtype=__a				)

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

           A_		    =					init_latents.shape
           A_		    =					randn_tensor(__a						, generator=__a						, device=__a						, dtype=__a				)

           # get latents
           print("""add noise to latents at timestep"""						, __a				)
           A_		    =					self.scheduler.add_noise(__a						, __a						, __a				)
           A_		    =					init_latents

           return latents







    @torch.no_grad()
    def __call__(    self						, UpperCamelCase__ = None						, UpperCamelCase__ = 0.8						, UpperCamelCase__ = 1						, UpperCamelCase__ = None						, UpperCamelCase__ = 0.0						, UpperCamelCase__ = 50						, UpperCamelCase__ = None						, UpperCamelCase__ = "pil"						, UpperCamelCase__ = True						, )  ->					Optional[int]:





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

           # 2. Preprocess image
           A_		    =					preprocess(__a				)

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

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

           # 5. Denoising loop
           for t in self.progress_bar(__a				):
                  # 1. predict noise model_output
                  A_		    =					self.unet(__a						, __a				).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
                  A_		    =					self.scheduler.step(
                      __a						, __a						, __a						, eta=__a						, use_clipped_model_output=__a						, generator=__a						, ).prev_sample

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

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

           return ImagePipelineOutput(images=__a				)







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
