




'''simple docstring'''


import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a					,							_a     					=    image.size
							_a					,							_a     					=    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
							_a     					=    image.resize((w, h)			,						resample=PIL_INTERPOLATION['''lanczos''']				)
							_a     					=    np.array(lowerCAmelCase__				).astype(np.floataa				) / 255.0
							_a     					=    image[None].transpose(0			,						3			,						1			,						2				)
							_a     					=    torch.from_numpy(lowerCAmelCase__				)
							return 2.0 * image - 1.0

class       A   (		__SCREAMING_SNAKE_CASE   ):







							def __init__(  self	:  List[Any]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Tuple							,      )      ->							Optional[Any]:


														"""simple docstring"""


														super().__init__()
														self.register_modules(vqvae=_a							,      unet=_a							,      scheduler=_a )







							@torch.no_grad()
							def __call__(  self	:  List[str]							,      lowerCAmelCase_	:  Optional[int] = None							,      lowerCAmelCase_	:  List[Any] = 1							,      lowerCAmelCase_	:  Union[str, Any] = 1_00							,      lowerCAmelCase_	:  List[Any] = 0.0							,      lowerCAmelCase_	:  Optional[int] = None							,      lowerCAmelCase_	:  Tuple = "pil"							,      lowerCAmelCase_	:  int = True							,      )      ->							Union[str, Any]:


														"""simple docstring"""


														if isinstance(_a							,      PIL.Image.Image ):
																					_a     					=    1
														elif isinstance(_a							,      torch.Tensor ):
																					_a     					=    image.shape[0]
														else:
																					raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a )}' )

														if isinstance(_a							,      PIL.Image.Image ):
																					_a     					=    preprocess(_a )

														_a					,							_a     					=    image.shape[-2:]

														# in_channels should be 6: 3 for latents, 3 for low resolution image
														_a     					=    (batch_size, self.unet.config.in_channels // 2, height, width)
														_a     					=    next(self.unet.parameters() ).dtype

														_a     					=    randn_tensor(_a							,      generator=_a							,      device=self.device							,      dtype=_a )

														_a     					=    image.to(device=self.device							,      dtype=_a )

														# set timesteps and move to the correct device
														self.scheduler.set_timesteps(_a							,      device=self.device )
														_a     					=    self.scheduler.timesteps

														# scale the initial noise by the standard deviation required by the scheduler
														_a     					=    latents * self.scheduler.init_noise_sigma

														# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
														# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
														# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
														# and should be between [0, 1]
														_a     					=    '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
														_a     					=    {}
														if accepts_eta:
																					_a     					=    eta

														for t in self.progress_bar(_a ):
																					# concat latents and low resolution image in the channel dimension.
																					_a     					=    torch.cat([latents, image]							,      dim=1 )
																					_a     					=    self.scheduler.scale_model_input(_a							,      _a )
																					# predict the noise residual
																					_a     					=    self.unet(_a							,      _a ).sample
																					# compute the previous noisy sample x_t -> x_t-1
																					_a     					=    self.scheduler.step(_a							,      _a							,      _a							,      **_a ).prev_sample

														# decode the image latents with the VQVAE
														_a     					=    self.vqvae.decode(_a ).sample
														_a     					=    torch.clamp(_a							,      -1.0							,      1.0 )
														_a     					=    image / 2 + 0.5
														_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,)

														return ImagePipelineOutput(images=_a )







'''simple docstring'''


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       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    iter(self.dataset )
														_a     					=    True
														while more_examples:
																					_a					,							_a     					=    [], 0
																					while True:
																												if buffer_len >= self.input_characters:
																																			break
																												try:
																																			buffer.append(next(lowerCAmelCase_ )['''content'''] )
																																			buffer_len += len(buffer[-1] )
																												except StopIteration:
																																			_a     					=    False
																																			break
																					_a     					=    tokenizer(lowerCAmelCase_							,      truncation=lowerCAmelCase_ )['''input_ids''']
																					_a     					=    []
																					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 ):
																												_a     					=    all_token_ids[i : i + self.seq_length]
																												if len(lowerCAmelCase_ ) == self.seq_length:
																																			yield torch.tensor(lowerCAmelCase_ )



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    {'''streaming''': True}
							_a     					=    load_dataset(args.dataset_name			,						split='''train'''			,						**UpperCamelCase				)
							_a     					=    ConstantLengthDataset(UpperCamelCase			,						UpperCamelCase			,						seq_length=args.seq_length				)
							_a     					=    DataLoader(UpperCamelCase			,						batch_size=args.batch_size				)
							return eval_dataloader
def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    outputs.loss.repeat(args.batch_size				)
														losses.append(accelerator.gather(UpperCamelCase				)				)

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							_a     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

# Parse configuration
_snake_case				:				List[str]       							=		HfArgumentParser(EvaluationArguments)
_snake_case				:				Optional[int]       							=		parser.parse_args()
set_seed(args.seed)

# Logging
_snake_case				:				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
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

# Prepare everything with our `accelerator`.
_snake_case							,    _snake_case				:				Optional[int]       							=		accelerator.prepare(model, eval_dataloader)

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

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

