


'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


lowerCAmelCase   : Optional[int]             =				"""\

"""

lowerCAmelCase   : Tuple             =				"""
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.

For more information, see https://huggingface.co/docs/transformers/perplexity
"""

lowerCAmelCase   : Any             =				"""
Args:
    model_id (str): model used for calculating Perplexity
            NOTE: Perplexity can only be calculated for causal language models.
                    This includes models such as gpt2, causal variations of bert,
                    causal versions of t5, and more (the full list can be found
                    in the AutoModelForCausalLM documentation here:
                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )

    input_texts (list of str): input text, each separate text snippet
        is one list entry.
    batch_size (int): the batch size to run texts through the model. Defaults to 16.
    add_start_token (bool): whether to add the start token to the texts,
        so the perplexity can include the probability of the first word. Defaults to True.
    device (str): device to run on, defaults to 'cuda' when available
Returns:
    perplexity: dictionary containing the perplexity scores for the texts
        in the input list, as well as the mean perplexity. If one of the input texts is
        longer than the max input length of the model, then it is truncated to the
        max length for the perplexity computation.
Examples:
    Example 1:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              add_start_token=False,
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        78.22
        >>> print(round(results[\"perplexities\"][0], 2))
        11.11

    Example 2:
        >>> perplexity = datasets.load_metric(\"perplexity\")
        >>> input_texts = datasets.load_dataset(\"wikitext\",
        ...                                     \"wikitext-2-raw-v1\",
        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
        [...]
        >>> input_texts = [s for s in input_texts if s!='']
        >>> results = perplexity.compute(model_id='gpt2',
        ...                              input_texts=input_texts) # doctest:+ELLIPSIS
        >>> print(list(results.keys()))
        ['perplexities', 'mean_perplexity']
        >>> print(round(results[\"mean_perplexity\"], 2))
        60.35
        >>> print(round(results[\"perplexities\"][0], 2))
        81.12
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION)
class        _UpperCamelCase	(    datasets.Metric):


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[int]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION							,      citation=_CITATION							,      inputs_description=_KWARGS_DESCRIPTION							,      features=datasets.Features(
					        {
					            "input_texts": datasets.Value("string"							),
					        }							)							,      reference_urls=["https://huggingface.co/docs/transformers/perplexity"]							,      )


				def 	a__		(     self							,      a_							,      a_							,      a_ = 1_6							,      a_ = True							,      a_=None							)    ->  List[Any]:
					if device is not None:
						assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
						if device == "gpu":
							lowercase						:		int   					=		"cuda"
					else:
						lowercase						:		Tuple   					=		"cuda" if torch.cuda.is_available() else "cpu"

					lowercase						:		Tuple   					=		AutoModelForCausalLM.from_pretrained(a_							)
					lowercase						:		Dict   					=		model.to(a_							)

					lowercase						:		str   					=		AutoTokenizer.from_pretrained(a_							)

					# if batch_size > 1 (which generally leads to padding being required), and
					# if there is not an already assigned pad_token, assign an existing
					# special token to also be the padding token
					if tokenizer.pad_token is None and batch_size > 1:
						lowercase						:		List[str]   					=		list(tokenizer.special_tokens_map_extended.values()							)
						# check that the model already has at least one special token defined
						assert (
						    len(a_							) > 0
						), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
						# assign one of the special tokens to also be the pad token
						tokenizer.add_special_tokens({"pad_token": existing_special_tokens[0]}							)

					if add_start_token:
						# leave room for <BOS> token to be added:
						assert (
						    tokenizer.bos_token is not None
						), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
						lowercase						:		Optional[Any]   					=		model.config.max_length - 1
					else:
						lowercase						:		Union[str, Any]   					=		model.config.max_length

					lowercase						:		Any   					=		tokenizer(
					    a_							,      add_special_tokens=a_							,      padding=a_							,      truncation=a_							,      max_length=a_							,      return_tensors="pt"							,      return_attention_mask=a_							,      ).to(a_							)

					lowercase						:		Any   					=		encodings["input_ids"]
					lowercase						:		int   					=		encodings["attention_mask"]

					# check that each input is long enough:
					if add_start_token:
						assert torch.all(torch.ge(attn_masks.sum(1							)							,      1							)							), "Each input text must be at least one token long."
					else:
						assert torch.all(
						    torch.ge(attn_masks.sum(1							)							,      2							)							), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

					lowercase						:		Dict   					=		[]
					lowercase						:		Union[str, Any]   					=		CrossEntropyLoss(reduction="none"							)

					for start_index in logging.tqdm(range(0							,      len(a_							)							,      a_							)							):
						lowercase						:		str   					=		min(start_index + batch_size							,      len(a_							)							)
						lowercase						:		Dict   					=		encoded_texts[start_index:end_index]
						lowercase						:		List[str]   					=		attn_masks[start_index:end_index]

						if add_start_token:
							lowercase						:		Any   					=		torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0							)							).to(a_							)
							lowercase						:		int   					=		torch.cat([bos_tokens_tensor, encoded_batch]							,      dim=1							)
							lowercase						:		Union[str, Any]   					=		torch.cat(
							    [torch.ones(bos_tokens_tensor.size()							,      dtype=torch.intaa							).to(a_							), attn_mask]							,      dim=1							)

						lowercase						:		Optional[int]   					=		encoded_batch

						with torch.no_grad():
							lowercase						:		int   					=		model(a_							,      attention_mask=a_							).logits

						lowercase						:		List[str]   					=		out_logits[..., :-1, :].contiguous()
						lowercase						:		Optional[Any]   					=		labels[..., 1:].contiguous()
						lowercase						:		Any   					=		attn_mask[..., 1:].contiguous()

						lowercase						:		Dict   					=		torch.expa(
						    (loss_fct(shift_logits.transpose(1							,      2							)							,      a_							) * shift_attention_mask_batch).sum(1							)
						    / shift_attention_mask_batch.sum(1							)							)

						ppls += perplexity_batch.tolist()

					return {"perplexities": ppls, "mean_perplexity": np.mean(a_							)}






'''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


lowerCAmelCase   : Any             =				logging.get_logger(__name__)  # pylint: disable=invalid-name




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      a_							,      )    ->  Union[str, Any]:
					super().__init__()
					self.register_modules(
					    vae=a_							,      text_encoder=a_							,      tokenizer=a_							,      unet=a_							,      scheduler=a_							,      safety_checker=a_							,      feature_extractor=a_							,      )

				def 	a__		(     self							,      a_ = "auto"							)    ->  Dict:
					if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						lowercase						:		Union[str, Any]   					=		self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(a_							)

				def 	a__		(     self							)    ->  Dict:
					self.enable_attention_slicing(a_							)


				@torch.no_grad()
				def __call__(     self							,      a_							,      a_ = 5_1_2							,      a_ = 5_1_2							,      a_ = 5_0							,      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_							,      )    ->  Union[str, Any]:

					if isinstance(a_							,      a_							):
						lowercase						:		Tuple   					=		1
					elif isinstance(a_							,      a_							):
						lowercase						:		int   					=		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
					lowercase						:		int   					=		self.tokenizer(
					    a_							,      padding="max_length"							,      max_length=self.tokenizer.model_max_length							,      return_tensors="pt"							,      )
					lowercase						:		List[Any]   					=		text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
						lowercase						:		str   					=		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}'''							)
						lowercase						:		Dict   					=		text_input_ids[:, : self.tokenizer.model_max_length]

					if text_embeddings is None:
						lowercase						:		List[Any]   					=		self.text_encoder(text_input_ids.to(self.device							)							)[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					lowercase					,		lowercase					,		lowercase						:		List[Any]   					=		text_embeddings.shape
					lowercase						:		Tuple   					=		text_embeddings.repeat(1							,      a_							,      1							)
					lowercase						:		Optional[int]   					=		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.
					lowercase						:		str   					=		guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
						lowercase						:		List[str]
						if negative_prompt is None:
							lowercase						:		Optional[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_							):
							lowercase						:		Tuple   					=		[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:
							lowercase						:		Dict   					=		negative_prompt

						lowercase						:		Tuple   					=		text_input_ids.shape[-1]
						lowercase						:		Any   					=		self.tokenizer(
						    a_							,      padding="max_length"							,      max_length=a_							,      truncation=a_							,      return_tensors="pt"							,      )
						lowercase						:		Any   					=		self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						lowercase						:		Union[str, Any]   					=		uncond_embeddings.shape[1]
						lowercase						:		Tuple   					=		uncond_embeddings.repeat(a_							,      a_							,      1							)
						lowercase						:		Optional[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
						lowercase						:		Optional[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`.
					lowercase						:		List[str]   					=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					lowercase						:		Union[str, Any]   					=		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
					lowercase						:		Union[str, Any]   					=		text_embeddings.dtype
					if latents is None:
						if self.device.type == "mps":
							# randn does not exist on mps
							lowercase						:		List[str]   					=		torch.randn(
							    a_							,      generator=a_							,      device="cpu"							,      dtype=a_							).to(self.device							)
							lowercase						:		Any   					=		torch.randn(a_							,      generator=a_							,      device="cpu"							,      dtype=a_							).to(
							    self.device							)
						else:
							lowercase						:		List[Any]   					=		torch.randn(
							    a_							,      generator=a_							,      device=self.device							,      dtype=a_							)
							lowercase						:		Optional[Any]   					=		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}'''							)
						lowercase						:		Dict   					=		latents_reference.to(self.device							)
						lowercase						:		int   					=		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
					lowercase						:		int   					=		(latents_shape[3] - latents_shape_reference[3]) // 2
					lowercase						:		Union[str, Any]   					=		(latents_shape[2] - latents_shape_reference[2]) // 2
					lowercase						:		int   					=		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
					lowercase						:		Dict   					=		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
					lowercase						:		Optional[int]   					=		0 if dx < 0 else dx
					lowercase						:		Tuple   					=		0 if dy < 0 else dy
					lowercase						:		Optional[int]   					=		max(-dx							,      0							)
					lowercase						:		int   					=		max(-dy							,      0							)
					# import pdb
					# pdb.set_trace()
					lowercase						:		Union[str, 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
					lowercase						:		List[Any]   					=		self.scheduler.timesteps.to(self.device							)

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

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

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

						# perform guidance
						if do_classifier_free_guidance:
							lowercase					,		lowercase						:		Dict   					=		noise_pred.chunk(2							)
							lowercase						:		Optional[int]   					=		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# compute the previous noisy sample x_t -> x_t-1
						lowercase						:		int   					=		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_							)

					lowercase						:		List[Any]   					=		1 / 0.1_82_15 * latents
					lowercase						:		Tuple   					=		self.vae.decode(a_							).sample

					lowercase						:		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
					lowercase						:		Any   					=		image.cpu().permute(0							,      2							,      3							,      1							).float().numpy()

					if self.safety_checker is not None:
						lowercase						:		Tuple   					=		self.feature_extractor(self.numpy_to_pil(a_							)							,      return_tensors="pt"							).to(
						    self.device							)
						lowercase					,		lowercase						:		Tuple   					=		self.safety_checker(
						    images=a_							,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype							)							)
					else:
						lowercase						:		Union[str, Any]   					=		None

					if output_type == "pil":
						lowercase						:		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_							)


