


import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=3.6       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					tokenizer
										SCREAMING_SNAKE_CASE_		:     Any					=					tokenizer.bos_token_id
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					dataset
										SCREAMING_SNAKE_CASE_		:     str					=					seq_length
										SCREAMING_SNAKE_CASE_		:     int					=					seq_length * chars_per_token * num_of_sequences






							def __iter__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					iter(self.dataset       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					True
										while more_examples:
													SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Dict					=					[], 0
													while True:
																if buffer_len >= self.input_characters:
																			break
																try:
																			buffer.append(next(lowerCAmelCase__       )['content']       )
																			buffer_len += len(buffer[-1]       )
																except StopIteration:
																			SCREAMING_SNAKE_CASE_		:     Dict					=					False
																			break
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					tokenizer(lowerCAmelCase__			,						truncation=lowerCAmelCase__       )['input_ids']
													SCREAMING_SNAKE_CASE_		:     int					=					[]
													for tokenized_input in tokenized_inputs:
																all_token_ids.extend(tokenized_input + [self.concat_token_id]       )
													for i in range(0			,						len(lowerCAmelCase__       )			,						self.seq_length       ):
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					all_token_ids[i : i + self.seq_length]
																if len(lowerCAmelCase__       ) == self.seq_length:
																			yield torch.tensor(lowerCAmelCase__       )







def 						a__			(				A__				):
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					{'streaming': True}
			SCREAMING_SNAKE_CASE_		:     List[str]					=					load_dataset(args.dataset_name,      split='train',      **A__				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					ConstantLengthDataset(A__,      A__,      seq_length=args.seq_length				)
			SCREAMING_SNAKE_CASE_		:     List[str]					=					DataLoader(A__,      batch_size=args.batch_size				)
			return eval_dataloader






def 						a__			(				A__				):
			model.eval()
			SCREAMING_SNAKE_CASE_		:     int					=					[]
			for step, batch in enumerate(A__				):
						with torch.no_grad():
									SCREAMING_SNAKE_CASE_		:     str					=					model(A__,      labels=A__				)
						SCREAMING_SNAKE_CASE_		:     Any					=					outputs.loss.repeat(args.batch_size				)
						losses.append(accelerator.gather(A__				)				)

						if args.max_eval_steps > 0 and step >= args.max_eval_steps:
									break
			SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.mean(torch.cat(A__				)				)
			try:
						SCREAMING_SNAKE_CASE_		:     str					=					torch.exp(A__				)
			except OverflowError:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					float('inf'				)
			return loss.item(), perplexity.item()


# Setup Accelerator
lowerCAmelCase__			:      Tuple								=Accelerator()

# Parse configuration
lowerCAmelCase__			:      Optional[Any]								=HfArgumentParser(EvaluationArguments)
lowerCAmelCase__			:      Optional[int]								=parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase__			:      Optional[Any]								=logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
lowerCAmelCase__			:      Union[str, Any]								=AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase__			:      Tuple								=AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowerCAmelCase__			:      Dict								=create_dataloader(args)

# Prepare everything with our `accelerator`.
lowerCAmelCase__							,lowerCAmelCase__			:      List[str]								=accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
lowerCAmelCase__							,lowerCAmelCase__			:      Tuple								=evaluate(args)



logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")









import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
lowerCAmelCase__			:      Optional[int]								=0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
lowerCAmelCase__			:      str								=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					WATERMARK_BITS
										SCREAMING_SNAKE_CASE_		:     int					=					WatermarkEncoder()

										self.encoder.set_watermark('bits'			,						self.watermark       )






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										if images.shape[-1] < 2_5_6:
													return images

										SCREAMING_SNAKE_CASE_		:     Tuple					=					(2_5_5 * (images / 2 + 0.5)).cpu().permute(0			,						2			,						3			,						1       ).float().numpy()

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					[self.encoder.encode(lowerCAmelCase__			,						'dwtDct'       ) for image in images]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.from_numpy(np.array(lowerCAmelCase__       )       ).permute(0			,						3			,						1			,						2       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.clamp(2 * (images / 2_5_5 - 0.5)			,						min=-1.0			,						max=1.0       )
										return images





