'''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


snake_case__					:      str				      =  logging.get_logger(__name__)  # pylint: disable=invalid-name






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_				,		):




       '''simple docstring'''

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


  def _UpperCamelCase					(			self				,		snake_case_ = "auto"						):




       '''simple docstring'''

       if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            UpperCAmelCase_							:       Tuple       =				self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(snake_case_						)


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       self.enable_attention_slicing(snake_case_						)






  @torch.no_grad()
  def __call__(			self				,		snake_case_				,		snake_case_ = 5_1_2				,		snake_case_ = 5_1_2				,		snake_case_ = 5_0				,		snake_case_ = 7.5				,		snake_case_ = None				,		snake_case_ = 1				,		snake_case_ = 0.0				,		snake_case_ = None				,		snake_case_ = None				,		snake_case_ = "pil"				,		snake_case_ = True				,		snake_case_ = None				,		snake_case_ = 1				,		snake_case_ = None				,		**snake_case_				,		):




       '''simple docstring'''

       if isinstance(snake_case_				,		snake_case_						):
            UpperCAmelCase_							:       Any       =				1
       elif isinstance(snake_case_				,		snake_case_						):
            UpperCAmelCase_							:       List[Any]       =				len(snake_case_						)
       else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case_						)}'''						)

       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(snake_case_				,		snake_case_						) or callback_steps <= 0)
       ):
            raise ValueError(
                F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                F''' {type(snake_case_						)}.'''						)

       # get prompt text embeddings
       UpperCAmelCase_							:       Union[str, Any]       =				self.tokenizer(
           snake_case_				,		padding='max_length'				,		max_length=self.tokenizer.model_max_length				,		return_tensors='pt'				,		)
       UpperCAmelCase_							:       Union[str, Any]       =				text_inputs.input_ids

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

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

       # duplicate text embeddings for each generation per prompt, using mps friendly method
       UpperCAmelCase_							:       int       =				text_embeddings.shape
       UpperCAmelCase_							:       str       =				text_embeddings.repeat(1				,		snake_case_				,		1						)
       UpperCAmelCase_							:       List[Any]       =				text_embeddings.view(bs_embed * num_images_per_prompt				,		snake_case_				,		-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.
       UpperCAmelCase_							:       List[str]       =				guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
            UpperCAmelCase_							:       List[str]
            if negative_prompt is None:
                 UpperCAmelCase_							:       Any       =				['']
            elif type(snake_case_						) is not type(snake_case_						):
                 raise TypeError(
                     F'''`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_						)} !='''
                     F''' {type(snake_case_						)}.'''						)
            elif isinstance(snake_case_				,		snake_case_						):
                 UpperCAmelCase_							:       Optional[int]       =				[negative_prompt]
            elif batch_size != len(snake_case_						):
                 raise ValueError(
                     F'''`negative_prompt`: {negative_prompt} has batch size {len(snake_case_						)}, but `prompt`:'''
                     F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                     ' the batch size of `prompt`.'						)
            else:
                 UpperCAmelCase_							:       Dict       =				negative_prompt

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

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

       # set timesteps
       self.scheduler.set_timesteps(snake_case_						)

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

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

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

            # predict the noise residual
            UpperCAmelCase_							:       Any       =				self.unet(snake_case_				,		snake_case_				,		encoder_hidden_states=snake_case_						).sample

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

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase_							:       List[str]       =				self.scheduler.step(snake_case_				,		snake_case_				,		snake_case_				,		**snake_case_						).prev_sample

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

       UpperCAmelCase_							:       Optional[int]       =				1 / 0.1_82_15 * latents
       UpperCAmelCase_							:       List[Any]       =				self.vae.decode(snake_case_						).sample

       UpperCAmelCase_							:       int       =				(image / 2 + 0.5).clamp(0				,		1						)

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

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

       if output_type == "pil":
            UpperCAmelCase_							:       Optional[Any]       =				self.numpy_to_pil(snake_case_						)

       if not return_dict:
            return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=snake_case_				,		nsfw_content_detected=snake_case_						)







'''simple docstring'''


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

import datasets
from datasets import logging


snake_case__					:      str				      =  '''\

'''

snake_case__					:      Union[str, Any]				      =  '''
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
'''

snake_case__					:      str				      =  '''
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 					__SCREAMING_SNAKE_CASE						(	datasets.Metric	):







  '''simple docstring'''






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_ = 1_6				,		snake_case_ = True				,		snake_case_=None						):




       '''simple docstring'''

       if device is not None:
            assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
            if device == "gpu":
                 UpperCAmelCase_							:       int       =				'cuda'
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				'cuda' if torch.cuda.is_available() else 'cpu'

       UpperCAmelCase_							:       Tuple       =				AutoModelForCausalLM.from_pretrained(snake_case_						)
       UpperCAmelCase_							:       Dict       =				model.to(snake_case_						)

       UpperCAmelCase_							:       Dict       =				AutoTokenizer.from_pretrained(snake_case_						)

       # 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:
            UpperCAmelCase_							:       Any       =				list(tokenizer.special_tokens_map_extended.values()						)
            # check that the model already has at least one special token defined
            assert (
                len(snake_case_						) > 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"
            UpperCAmelCase_							:       Union[str, Any]       =				model.config.max_length - 1
       else:
            UpperCAmelCase_							:       int       =				model.config.max_length

       UpperCAmelCase_							:       List[str]       =				tokenizer(
           snake_case_				,		add_special_tokens=snake_case_				,		padding=snake_case_				,		truncation=snake_case_				,		max_length=snake_case_				,		return_tensors='pt'				,		return_attention_mask=snake_case_				,		).to(snake_case_						)

       UpperCAmelCase_							:       Tuple       =				encodings['input_ids']
       UpperCAmelCase_							:       Optional[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."

       UpperCAmelCase_							:       Union[str, Any]       =				[]
       UpperCAmelCase_							:       Optional[int]       =				CrossEntropyLoss(reduction='none'						)

       for start_index in logging.tqdm(range(0				,		len(snake_case_						)				,		snake_case_						)						):
            UpperCAmelCase_							:       Union[str, Any]       =				min(start_index + batch_size				,		len(snake_case_						)						)
            UpperCAmelCase_							:       List[Any]       =				encoded_texts[start_index:end_index]
            UpperCAmelCase_							:       Tuple       =				attn_masks[start_index:end_index]

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

            UpperCAmelCase_							:       List[Any]       =				encoded_batch

            with torch.no_grad():
                 UpperCAmelCase_							:       int       =				model(snake_case_				,		attention_mask=snake_case_						).logits

            UpperCAmelCase_							:       Any       =				out_logits[..., :-1, :].contiguous()
            UpperCAmelCase_							:       Dict       =				labels[..., 1:].contiguous()
            UpperCAmelCase_							:       Union[str, Any]       =				attn_mask[..., 1:].contiguous()

            UpperCAmelCase_							:       List[Any]       =				torch.expa(
                (loss_fct(shift_logits.transpose(1				,		2						)				,		snake_case_						) * shift_attention_mask_batch).sum(1						)
                / shift_attention_mask_batch.sum(1						)						)

            ppls += perplexity_batch.tolist()

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






