
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 						__magic_name__				(  snake_case      ):





		def __init__(							self      :		List[str]  ,					lowerCamelCase__      :		AutoencoderKL  ,					lowerCamelCase__      :		CLIPTextModel  ,					lowerCamelCase__      :		CLIPTokenizer  ,					lowerCamelCase__      :		UNetaDConditionModel  ,					lowerCamelCase__      :		Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]  ,					):
							super().__init__()

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





		def  _A							(							self      :		Tuple  ,					lowerCamelCase__      :		Optional[Any]			):
							with torch.no_grad():
												lowerCAmelCase						:   Dict							=       self.vae.encode(tfms.ToTensor()(lowerCamelCase__			).unsqueeze(0			).to(self.device			) * 2 - 1			)
												lowerCAmelCase						:   List[Any]							=       0.1_8_2_1_5 * latent.latent_dist.sample()
							return latent





		def  _A							(							self      :		Any  ,					lowerCamelCase__      :		List[Any]			):
							lowerCAmelCase						:   Optional[Any]							=       (1 / 0.1_8_2_1_5) * latent
							with torch.no_grad():
												lowerCAmelCase						:   str							=       self.vae.decode(lowerCamelCase__			).sample
							lowerCAmelCase						:   Optional[Any]							=       (img / 2 + 0.5).clamp(0  ,					1			)
							lowerCAmelCase						:   Dict							=       img.detach().cpu().permute(0  ,					2  ,					3  ,					1			).numpy()
							lowerCAmelCase						:   List[str]							=       (img * 2_5_5).round().astype('''uint8'''			)
							return Image.fromarray(img[0]			)





		def  _A							(							self      :		Union[str, Any]  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   Dict							=       self.tokenizer(
							    lowerCamelCase__  ,					padding='''max_length'''  ,					max_length=self.tokenizer.model_max_length  ,					truncation=lowerCamelCase__  ,					return_tensors='''pt'''  ,					)

							lowerCAmelCase						:   Any							=       self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

							lowerCAmelCase						:   Dict							=       self.tokenizer(
							    ''''''  ,					padding='''max_length'''  ,					max_length=self.tokenizer.model_max_length  ,					truncation=lowerCamelCase__  ,					return_tensors='''pt'''  ,					)

							lowerCAmelCase						:   Union[str, Any]							=       self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

							return torch.cat([uncond_embedding, text_embedding]			)





		def __call__(							self      :		Optional[int]  ,					lowerCamelCase__      :		Image.Image  ,					lowerCamelCase__      :		str  ,					lowerCamelCase__      :		float = 0.3  ,					lowerCamelCase__      :		float = 0.6  ,					lowerCamelCase__      :		float = 0.5  ,					lowerCamelCase__      :		int = 4_2  ,					lowerCamelCase__      :		int = 5_0  ,					lowerCamelCase__      :		float = 7.5  ,					):
							lowerCAmelCase						:   Optional[Any]							=       steps - int(kmin * steps			)
							lowerCAmelCase						:   Optional[Any]							=       steps - int(kmax * steps			)

							lowerCAmelCase						:   Optional[Any]							=       self.prep_text(lowerCamelCase__			)

							self.scheduler.set_timesteps(lowerCamelCase__			)

							lowerCAmelCase		,							lowerCAmelCase						:   Optional[int]							=       img.size
							lowerCAmelCase						:   Optional[int]							=       self.encode(lowerCamelCase__			)

							torch.manual_seed(lowerCamelCase__			)
							lowerCAmelCase						:   List[Any]							=       torch.randn(
							    (1, self.unet.config.in_channels, height // 8, width // 8)  ,					).to(self.device			)

							lowerCAmelCase						:   Tuple							=       self.scheduler.add_noise(
							    lowerCamelCase__  ,					lowerCamelCase__  ,					timesteps=self.scheduler.timesteps[tmax]  ,					)

							lowerCAmelCase						:   Optional[int]							=       torch.cat([latents] * 2			)

							lowerCAmelCase						:   Optional[int]							=       self.scheduler.scale_model_input(lowerCamelCase__  ,					self.scheduler.timesteps[tmax]			)

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

							lowerCAmelCase		,							lowerCAmelCase						:   Optional[Any]							=       pred.chunk(2			)
							lowerCAmelCase						:   int							=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

							lowerCAmelCase						:   str							=       self.scheduler.step(lowerCamelCase__  ,					self.scheduler.timesteps[tmax]  ,					lowerCamelCase__			).prev_sample

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

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

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

																	lowerCAmelCase						:   Tuple							=       self.scheduler.scale_model_input(lowerCamelCase__  ,					lowerCamelCase__			)

																	with torch.no_grad():
																						lowerCAmelCase						:   Optional[Any]							=       self.unet(
																						    lowerCamelCase__  ,					lowerCamelCase__  ,					encoder_hidden_states=lowerCamelCase__  ,					).sample

																	lowerCAmelCase		,							lowerCAmelCase						:   str							=       pred.chunk(2			)
																	lowerCAmelCase						:   List[Any]							=       pred_uncond + guidance_scale * (pred_text - pred_uncond)

																	lowerCAmelCase						:   List[Any]							=       self.scheduler.step(lowerCamelCase__  ,					lowerCamelCase__  ,					lowerCamelCase__			).prev_sample

							return self.decode(lowerCamelCase__			)


from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def       UpperCAmelCase__							(    __magic_name__  :       str=10					):


					'''simple docstring'''

					for _ in tqdm(range(0							,					args.n_examples							,					__magic_name__					)					):
										yield [next(__magic_name__					)[args.text_column] for _ in range(__magic_name__					)]


# Configuration
__SCREAMING_SNAKE_CASE     :				Dict             =       HfArgumentParser(TokenizerTrainingArguments)
__SCREAMING_SNAKE_CASE     :				str             =       parser.parse_args()

# Base tokenizer
__SCREAMING_SNAKE_CASE     :				Tuple             =       AutoTokenizer.from_pretrained(args.base_tokenizer)
__SCREAMING_SNAKE_CASE     :				List[str]             =       list(bytes_to_unicode().values())

# Load dataset
__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       load_dataset(args.dataset_name, split='train', streaming=True)
__SCREAMING_SNAKE_CASE     :				Any             =       iter(dataset)


# Training and saving
__SCREAMING_SNAKE_CASE     :				Optional[int]             =       tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
