






'''simple docstring'''
import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
						__lowerCAmelCase							=argparse.ArgumentParser()

						# Hyperparameters sent by the client are passed as command-line arguments to the script.
						parser.add_argument("--epochs", type=int, default=1)
						parser.add_argument("--per_device_train_batch_size", type=int, default=16)
						parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
						parser.add_argument("--model_name_or_path", type=str)
						parser.add_argument("--learning_rate", type=str, default=5e-5)
						parser.add_argument("--do_train", type=bool, default=True)
						parser.add_argument("--do_eval", type=bool, default=True)
						parser.add_argument("--output_dir", type=str)

						__lowerCAmelCase							,				__lowerCAmelCase							=parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						__lowerCAmelCase							=16
						__lowerCAmelCase							=16

						# Set up logging
						__lowerCAmelCase							=logging.getLogger(__name__)

						logging.basicConfig(
						    level=logging.getLevelName("INFO"),
						    handlers=[logging.StreamHandler(sys.stdout)],
						    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
						)

						# Load model and tokenizer
						__lowerCAmelCase							=TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						__lowerCAmelCase							=AutoTokenizer.from_pretrained(args.model_name_or_path)

						# Load dataset
						__lowerCAmelCase							,				__lowerCAmelCase							=load_dataset("imdb", split=["train", "test"])
						__lowerCAmelCase							=train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
						__lowerCAmelCase							=test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

						# Preprocess train dataset
						__lowerCAmelCase							=train_dataset.map(
						    lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
						)
						train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

						__lowerCAmelCase							={
						    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["input_ids", "attention_mask"]
						}
						__lowerCAmelCase							=tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
						    args.per_device_train_batch_size
						)

						# Preprocess test dataset
						__lowerCAmelCase							=test_dataset.map(
						    lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
						)
						test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

						__lowerCAmelCase							={
						    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
						    for x in ["input_ids", "attention_mask"]
						}
						__lowerCAmelCase							=tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])).batch(
						    args.per_device_eval_batch_size
						)

						# fine optimizer and loss
						__lowerCAmelCase							=tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
						__lowerCAmelCase							=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
						__lowerCAmelCase							=[tf.keras.metrics.SparseCategoricalAccuracy()]
						model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

						__lowerCAmelCase							=time.time()
						__lowerCAmelCase							=model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						__lowerCAmelCase							=time.time() - start_train_time

						logger.info("*** Train ***")
						logger.info(f'''train_runtime = {end_train_time}''')
						for key, value in train_results.history.items():
												logger.info(f'''  {key} = {value}''')









'''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    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

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




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

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

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

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

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

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

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

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

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

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

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

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

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

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

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

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

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

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

