




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 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Optional[int]		,     A__     :      AutoencoderKL		,     A__     :      CLIPTextModel		,     A__     :      CLIPTokenizer		,     A__     :      UNetaDConditionModel		,     A__     :      Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]		,     ) ->				Dict:
           super().__init__()

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




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      int	) ->				int:
           with torch.no_grad():
                 lowerCamelCase__					     = self.vae.encode(tfms.ToTensor()(A__	).unsqueeze(0	).to(self.device	) * 2 - 1	)
                 lowerCamelCase__					     = 0.18215 * latent.latent_dist.sample()
           return latent




     def        __snake_case      (			self     :      Any		,     A__     :      Dict	) ->				List[str]:
           lowerCamelCase__					     = (1 / 0.18215) * latent
           with torch.no_grad():
                 lowerCamelCase__					     = self.vae.decode(A__	).sample
           lowerCamelCase__					     = (img / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = img.detach().cpu().permute(0		,     2		,     3		,     1	).numpy()
           lowerCamelCase__					     = (img * 2_5_5).round().astype('uint8'	)
           return Image.fromarray(img[0]	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      Optional[Any]	) ->				Dict:
           lowerCamelCase__					     = self.tokenizer(
               A__		,     padding='max_length'		,     max_length=self.tokenizer.model_max_length		,     truncation=A__		,     return_tensors='pt'		,     )

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

           lowerCamelCase__					     = self.tokenizer(
               ''		,     padding='max_length'		,     max_length=self.tokenizer.model_max_length		,     truncation=A__		,     return_tensors='pt'		,     )

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

           return torch.cat([uncond_embedding, text_embedding]	)




     def __call__(			self     :      int		,     A__     :      Image.Image		,     A__     :      str		,     A__     :      float = 0.3		,     A__     :      float = 0.6		,     A__     :      float = 0.5		,     A__     :      int = 4_2		,     A__     :      int = 5_0		,     A__     :      float = 7.5		,     ) ->				Image.Image:
           lowerCamelCase__					     = steps - int(kmin * steps	)
           lowerCamelCase__					     = steps - int(kmax * steps	)

           lowerCamelCase__					     = self.prep_text(A__	)

           self.scheduler.set_timesteps(A__	)

           lowerCamelCase__     ,						lowerCamelCase__					     = img.size
           lowerCamelCase__					     = self.encode(A__	)

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

           lowerCamelCase__					     = self.scheduler.add_noise(
               A__		,     A__		,     timesteps=self.scheduler.timesteps[tmax]		,     )

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

           lowerCamelCase__					     = self.scheduler.scale_model_input(A__		,     self.scheduler.timesteps[tmax]	)

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

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

           lowerCamelCase__					     = self.scheduler.step(A__		,     self.scheduler.timesteps[tmax]		,     A__	).prev_sample

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

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

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

                       lowerCamelCase__					     = self.scheduler.scale_model_input(A__		,     A__	)

                       with torch.no_grad():
                             lowerCamelCase__					     = self.unet(
                                 A__		,     A__		,     encoder_hidden_states=A__		,     ).sample

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

                       lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__	).prev_sample

           return self.decode(A__	)

"""simple docstring"""




import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


snake_case    =		1_0_0_0_0_0_0_0_0_0_0_0
snake_case    =		1_0_0_0_0

snake_case   ,			snake_case    =		os.path.split(__file__)
snake_case    =		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	pa.Table.from_pydict({'col': [0] * chunk_size}			)
					SCREAMING_SNAKE_CASE				=	pa.concat_tables([table] * (num_examples // chunk_size)			)
					return datasets.Dataset(SCREAMING_SNAKE_CASE_,     fingerprint='table_100B'			)




@dataclass
class      UpperCamelCase	:

				"""simple docstring"""



				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int
				UpperCAmelCase_				:      int


				def 							A					(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	np.random.default_rng(self.seed			)
									SCREAMING_SNAKE_CASE				=	rng.integers(low=self.low		,	high=self.high		,	size=self.size			).tolist()


				def __iter__(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									return iter(self._sampled_values			)




				def __len__(				self			)    ->    Optional[Any]:
									"""simple docstring"""







									return self.size






@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[0]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[-1]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[range(len(SCREAMING_SNAKE_CASE_			) // 2,     len(SCREAMING_SNAKE_CASE_			) // 2 + 1_0_2_4			)]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	dataset[RandIter(0,     len(SCREAMING_SNAKE_CASE_			),     1_0_2_4,     seed=4_2			)]

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE				=	{'num examples': SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=	(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('generating dataset'			)
					SCREAMING_SNAKE_CASE				=	generate_100B_dataset(num_examples=SCREAMING_SNAKE_CASE_,     chunk_size=SCREAMING_SNAKE_CASE_			)
					print('Functions'			)
					for func in functions:
										print(func.__name__			)
										SCREAMING_SNAKE_CASE				=	func(SCREAMING_SNAKE_CASE_			)

					with open(SCREAMING_SNAKE_CASE_,     'wb'			) as f:
										f.write(json.dumps(SCREAMING_SNAKE_CASE_			).encode('utf-8'			)			)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_table_100B()
