

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__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase					,					lowercase=1024					,					lowercase=1024					,					lowercase=3.6				)     ->  Tuple:
							'''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				)     ->  Tuple:
							'''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(lowercase				)["content"]				)
																			buffer_len += len(buffer[-1]				)
															except StopIteration:
																			A__						=	False
																			break
											A__						=	tokenizer(lowercase					,					truncation=lowercase				)["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(lowercase				)					,					self.seq_length				):
															A__						=	all_token_ids[i : i + self.seq_length]
															if len(lowercase				) == self.seq_length:
																			yield torch.tensor(lowercase				)



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[str]					)	->					List[str]:




				'''simple docstring'''





				A__						=	{"streaming": True}
				A__						=	load_dataset(args.dataset_name		,				split="train"		,				**SCREAMING_SNAKE_CASE_					)
				A__						=	ConstantLengthDataset(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				seq_length=args.seq_length					)
				A__						=	DataLoader(SCREAMING_SNAKE_CASE_		,				batch_size=args.batch_size					)
				return eval_dataloader



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Optional[Any]					)	->					int:




				'''simple docstring'''





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

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


# Setup Accelerator
lowerCAmelCase__		      =       Accelerator()

# Parse configuration
lowerCAmelCase__		      =       HfArgumentParser(EvaluationArguments)
lowerCAmelCase__		      =       parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase__		      =       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__		      =       AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase__		      =       AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowerCAmelCase__		      =       create_dataloader(args)

# Prepare everything with our `accelerator`.
lowerCAmelCase__      ,   lowerCAmelCase__		      =       accelerator.prepare(model, eval_dataloader)

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





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





"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


SCREAMING_SNAKE_CASE :    List[str]									=				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       a_				,       ):





			'''simple docstring'''

			super().__init__()
			self.register_modules(
			    vae=a_				,       text_encoder=a_				,       tokenizer=a_				,       unet=a_				,       scheduler=a_				,       safety_checker=a_				,       feature_extractor=a_				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_ = "auto"					):





			'''simple docstring'''

			if slice_size == "auto":
				# half the attention head size is usually a good trade-off between
				# speed and memory
				__snake_case	:						Optional[int]						=				self.unet.config.attention_head_dim // 2
			self.unet.set_attention_slice(a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.enable_attention_slicing(a_					)



		@torch.no_grad()
		def __call__(self				,       a_				,       a_ = 5_12				,       a_ = 5_12				,       a_ = 50				,       a_ = 7.5				,       a_ = None				,       a_ = 1				,       a_ = 0.0				,       a_ = None				,       a_ = None				,       a_ = "pil"				,       a_ = True				,       a_ = None				,       a_ = 1				,       a_ = None				,       **a_				,       ):





			'''simple docstring'''

			if isinstance(a_				,       a_					):
				__snake_case	:						Any						=				1
			elif isinstance(a_				,       a_					):
				__snake_case	:						Any						=				len(a_					)
			else:
				raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(a_					)}"""					)

			if height % 8 != 0 or width % 8 != 0:
				raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""					)

			if (callback_steps is None) or (
			    callback_steps is not None and (not isinstance(a_				,       a_					) or callback_steps <= 0)
			):
				raise ValueError(
				    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
				    f""" {type(a_					)}."""					)

			# get prompt text embeddings
			__snake_case	:						int						=				self.tokenizer(
			    a_				,       padding='''max_length'''				,       max_length=self.tokenizer.model_max_length				,       return_tensors='''pt'''				,       )
			__snake_case	:						int						=				text_inputs.input_ids

			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
				__snake_case	:						List[Any]						=				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
				logger.warning(
				    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
				    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)
				__snake_case	:						Optional[int]						=				text_input_ids[:, : self.tokenizer.model_max_length]

			if text_embeddings is None:
				__snake_case	:						Optional[int]						=				self.text_encoder(text_input_ids.to(self.device					)					)[0]

			# duplicate text embeddings for each generation per prompt, using mps friendly method
			__snake_case	,							__snake_case	,							__snake_case	:						Union[str, Any]						=				text_embeddings.shape
			__snake_case	:						Optional[int]						=				text_embeddings.repeat(1				,       a_				,       1					)
			__snake_case	:						Dict						=				text_embeddings.view(bs_embed * num_images_per_prompt				,       a_				,       -1					)

			# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
			# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
			# corresponds to doing no classifier free guidance.
			__snake_case	:						Dict						=				guidance_scale > 1.0
			# get unconditional embeddings for classifier free guidance
			if do_classifier_free_guidance:
				__snake_case	:						List[str]
				if negative_prompt is None:
					__snake_case	:						List[Any]						=				['''''']
				elif type(a_					) is not type(a_					):
					raise TypeError(
					    f"""`negative_prompt` should be the same type to `prompt`, but got {type(a_					)} !="""
					    f""" {type(a_					)}."""					)
				elif isinstance(a_				,       a_					):
					__snake_case	:						List[str]						=				[negative_prompt]
				elif batch_size != len(a_					):
					raise ValueError(
					    f"""`negative_prompt`: {negative_prompt} has batch size {len(a_					)}, but `prompt`:"""
					    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
					    ''' the batch size of `prompt`.'''					)
				else:
					__snake_case	:						Optional[int]						=				negative_prompt

				__snake_case	:						Optional[int]						=				text_input_ids.shape[-1]
				__snake_case	:						List[Any]						=				self.tokenizer(
				    a_				,       padding='''max_length'''				,       max_length=a_				,       truncation=a_				,       return_tensors='''pt'''				,       )
				__snake_case	:						str						=				self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]

				# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
				__snake_case	:						str						=				uncond_embeddings.shape[1]
				__snake_case	:						int						=				uncond_embeddings.repeat(a_				,       a_				,       1					)
				__snake_case	:						int						=				uncond_embeddings.view(batch_size * num_images_per_prompt				,       a_				,       -1					)

				# For classifier free guidance, we need to do two forward passes.
				# Here we concatenate the unconditional and text embeddings into a single batch
				# to avoid doing two forward passes
				__snake_case	:						Any						=				torch.cat([uncond_embeddings, text_embeddings]					)

			# get the initial random noise unless the user supplied it

			# Unlike in other pipelines, latents need to be generated in the target device
			# for 1-to-1 results reproducibility with the CompVis implementation.
			# However this currently doesn't work in `mps`.
			__snake_case	:						Any						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
			__snake_case	:						Dict						=				(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
			__snake_case	:						Dict						=				text_embeddings.dtype
			if latents is None:
				if self.device.type == "mps":
					# randn does not exist on mps
					__snake_case	:						Union[str, Any]						=				torch.randn(
					    a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(self.device					)
					__snake_case	:						Tuple						=				torch.randn(a_				,       generator=a_				,       device='''cpu'''				,       dtype=a_					).to(
					    self.device					)
				else:
					__snake_case	:						Dict						=				torch.randn(
					    a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
					__snake_case	:						Dict						=				torch.randn(a_				,       generator=a_				,       device=self.device				,       dtype=a_					)
			else:
				if latents_reference.shape != latents_shape:
					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""					)
				__snake_case	:						Union[str, Any]						=				latents_reference.to(self.device					)
				__snake_case	:						Dict						=				latents.to(self.device					)

			# This is the key part of the pipeline where we
			# try to ensure that the generated images w/ the same seed
			# but different sizes actually result in similar images
			__snake_case	:						int						=				(latents_shape[3] - latents_shape_reference[3]) // 2
			__snake_case	:						Tuple						=				(latents_shape[2] - latents_shape_reference[2]) // 2
			__snake_case	:						Optional[int]						=				latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
			__snake_case	:						Union[str, Any]						=				latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
			__snake_case	:						int						=				0 if dx < 0 else dx
			__snake_case	:						Union[str, Any]						=				0 if dy < 0 else dy
			__snake_case	:						str						=				max(-dx				,       0					)
			__snake_case	:						Tuple						=				max(-dy				,       0					)
			# import pdb
			# pdb.set_trace()
			__snake_case	:						Any						=				latents_reference[:, :, dy : dy + h, dx : dx + w]

			# set timesteps
			self.scheduler.set_timesteps(a_					)

			# Some schedulers like PNDM have timesteps as arrays
			# It's more optimized to move all timesteps to correct device beforehand
			__snake_case	:						Optional[Any]						=				self.scheduler.timesteps.to(self.device					)

			# scale the initial noise by the standard deviation required by the scheduler
			__snake_case	:						List[Any]						=				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]
			__snake_case	:						Any						=				'''eta''' in set(inspect.signature(self.scheduler.step					).parameters.keys()					)
			__snake_case	:						Tuple						=				{}
			if accepts_eta:
				__snake_case	:						List[str]						=				eta

			for i, t in enumerate(self.progress_bar(a_					)					):
				# expand the latents if we are doing classifier free guidance
				__snake_case	:						str						=				torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
				__snake_case	:						Tuple						=				self.scheduler.scale_model_input(a_				,       a_					)

				# predict the noise residual
				__snake_case	:						int						=				self.unet(a_				,       a_				,       encoder_hidden_states=a_					).sample

				# perform guidance
				if do_classifier_free_guidance:
					__snake_case	,							__snake_case	:						Tuple						=				noise_pred.chunk(2					)
					__snake_case	:						str						=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				# compute the previous noisy sample x_t -> x_t-1
				__snake_case	:						Optional[Any]						=				self.scheduler.step(a_				,       a_				,       a_				,       **a_					).prev_sample

				# call the callback, if provided
				if callback is not None and i % callback_steps == 0:
					callback(a_				,       a_				,       a_					)

			__snake_case	:						Union[str, Any]						=				1 / 0.1_8215 * latents
			__snake_case	:						Optional[Any]						=				self.vae.decode(a_					).sample

			__snake_case	:						Union[str, Any]						=				(image / 2 + 0.5).clamp(0				,       1					)

			# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
			__snake_case	:						Union[str, Any]						=				image.cpu().permute(0				,       2				,       3				,       1					).float().numpy()

			if self.safety_checker is not None:
				__snake_case	:						Optional[int]						=				self.feature_extractor(self.numpy_to_pil(a_					)				,       return_tensors='''pt'''					).to(
				    self.device					)
				__snake_case	,							__snake_case	:						List[Any]						=				self.safety_checker(
				    images=a_				,       clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype					)					)
			else:
				__snake_case	:						Union[str, Any]						=				None

			if output_type == "pil":
				__snake_case	:						Union[str, Any]						=				self.numpy_to_pil(a_					)

			if not return_dict:
				return (image, has_nsfw_concept)

			return StableDiffusionPipelineOutput(images=a_				,       nsfw_content_detected=a_					)





