


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        super().__init__()

        if hasattr(scheduler.config  ,       """steps_offset"""    ) and scheduler.config.steps_offset != 1:
           __a					=  (
               f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
               f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
               """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
               """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
               """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
               """ file"""
           )
           deprecate("""steps_offset!=1"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           __a					=  dict(scheduler.config    )
           __a					=  1
           __a					=  FrozenDict(__A    )

        if hasattr(scheduler.config  ,       """skip_prk_steps"""    ) and scheduler.config.skip_prk_steps is False:
           __a					=  (
               f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
               """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
               """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
               """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
               """ Hub, it would be very nice if you could open a Pull request for the"""
               """ `scheduler/scheduler_config.json` file"""
           )
           deprecate("""skip_prk_steps not set"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           __a					=  dict(scheduler.config    )
           __a					=  True
           __a					=  FrozenDict(__A    )

        if safety_checker is None:
           logger.warning(
               f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
               """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
               """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
               """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
               """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
               """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

        self.register_modules(
            segmentation_model=__A  ,       segmentation_processor=__A  ,       vae=__A  ,       text_encoder=__A  ,       tokenizer=__A  ,       unet=__A  ,       scheduler=__A  ,       safety_checker=__A  ,       feature_extractor=__A  ,       )


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


     def 	snake_case_  (	self    ):
        self.enable_attention_slicing(__A    )


     def 	snake_case_  (	self    ):
        if is_accelerate_available():
           from accelerate import cpu_offload
        else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

        __a					=  torch.device("""cuda"""    )

        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
           if cpu_offloaded_model is not None:
              cpu_offload(__A  ,       __A    )


     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 	snake_case_  (	self    ):
        if self.device != torch.device("""meta"""    ) or not hasattr(self.unet  ,       """_hf_hook"""    ):
           return self.device
        for module in self.unet.modules():
           if (
               hasattr(__A  ,       """_hf_hook"""    )
               and hasattr(module._hf_hook  ,       """execution_device"""    )
               and module._hf_hook.execution_device is not None
           ):
              return torch.device(module._hf_hook.execution_device    )
        return self.device


     @torch.no_grad()
     def __call__(	self  ,       __A  ,       __A  ,       __A  ,       __A = 512  ,       __A = 512  ,       __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  ,       ):
        __a					=  self.segmentation_processor(
            text=[text]  ,       images=[image]  ,       padding="""max_length"""  ,       return_tensors="""pt"""    ).to(self.device    )
        __a					=  self.segmentation_model(**__A    )
        __a					=  torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
        __a					=  self.numpy_to_pil(__A    )[0].resize(image.size    )

        # Run inpainting pipeline with the generated mask
        __a					=  StableDiffusionInpaintPipeline(
            vae=self.vae  ,       text_encoder=self.text_encoder  ,       tokenizer=self.tokenizer  ,       unet=self.unet  ,       scheduler=self.scheduler  ,       safety_checker=self.safety_checker  ,       feature_extractor=self.feature_extractor  ,       )
        return inpainting_pipeline(
            prompt=__A  ,       image=__A  ,       mask_image=__A  ,       height=__A  ,       width=__A  ,       num_inference_steps=__A  ,       guidance_scale=__A  ,       negative_prompt=__A  ,       num_images_per_prompt=__A  ,       eta=__A  ,       generator=__A  ,       latents=__A  ,       output_type=__A  ,       return_dict=__A  ,       callback=__A  ,       callback_steps=__A  ,       )








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

import datasets
from datasets import logging


_lowerCamelCase    :		List[str]		      =    """\

"""

_lowerCamelCase    :		Optional[int]		      =    """
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    :		List[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 						lowercase    (							datasets.Metric):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''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       lowerCamelCase_				(  self    :				List[Any]       ,	snake_case    :				Optional[int]       ,	snake_case    :				int       ,	snake_case    :				int = 16       ,	snake_case    :				bool = True       ,	snake_case    :				Dict=None	):



											'''simple docstring'''


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

											SCREAMING_SNAKE_CASE							:     Any						=				AutoModelForCausalLM.from_pretrained(snake_case	)
											SCREAMING_SNAKE_CASE							:     Dict						=				model.to(snake_case	)

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				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:
																SCREAMING_SNAKE_CASE							:     str						=				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"
																SCREAMING_SNAKE_CASE							:     List[str]						=				model.config.max_length - 1
											else:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				model.config.max_length

											SCREAMING_SNAKE_CASE							:     Tuple						=				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	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				encodings['input_ids']
											SCREAMING_SNAKE_CASE							:     Dict						=				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."

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

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

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

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				encoded_batch

																with torch.no_grad():
																					SCREAMING_SNAKE_CASE							:     str						=				model(snake_case       ,	attention_mask=snake_case	).logits

																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				out_logits[..., :-1, :].contiguous()
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				labels[..., 1:].contiguous()
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				attn_mask[..., 1:].contiguous()

																SCREAMING_SNAKE_CASE							:     Optional[int]						=				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	)}