



'''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	:       Union[str, Any]      	=	"\\n\n"

_lowerCamelCase	:       List[str]      	=	"\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n"

_lowerCamelCase	:       Dict      	=	"\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to 'cuda' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        78.22\n        >>> print(round(results[\"perplexities\"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric(\"perplexity\")\n        >>> input_texts = datasets.load_dataset(\"wikitext\",\n        ...                                     \"wikitext-2-raw-v1\",\n        ...                                     split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!='']\n        >>> results = perplexity.compute(model_id='gpt2',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        ['perplexities', 'mean_perplexity']\n        >>> print(round(results[\"mean_perplexity\"], 2))\n        60.35\n        >>> print(round(results[\"perplexities\"][0], 2))\n        81.12\n"

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






    """simple docstring"""





    def  A       (  self      :					Tuple ):


       """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  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int = 1_6  ,						UpperCamelCase__      :					bool = True  ,						UpperCamelCase__      :					List[Any]=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					=  'cuda'
       else:
          UpperCamelCase					=  'cuda' if torch.cuda.is_available() else 'cpu'

       UpperCamelCase					=  AutoModelForCausalLM.from_pretrained(UpperCamelCase__ )
       UpperCamelCase					=  model.to(UpperCamelCase__ )

       UpperCamelCase					=  AutoTokenizer.from_pretrained(UpperCamelCase__ )

       # 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					=  list(tokenizer.special_tokens_map_extended.values() )
          # check that the model already has at least one special token defined
          assert (
              len(UpperCamelCase__ ) > 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					=  model.config.max_length - 1
       else:
          UpperCamelCase					=  model.config.max_length

       UpperCamelCase					=  tokenizer(
           UpperCamelCase__  ,						add_special_tokens=UpperCamelCase__  ,						padding=UpperCamelCase__  ,						truncation=UpperCamelCase__  ,						max_length=UpperCamelCase__  ,						return_tensors='pt'  ,						return_attention_mask=UpperCamelCase__  ,						).to(UpperCamelCase__ )

       UpperCamelCase					=  encodings['input_ids']
       UpperCamelCase					=  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					=  []
       UpperCamelCase					=  CrossEntropyLoss(reduction='none' )

       for start_index in logging.tqdm(range(0  ,						len(UpperCamelCase__ )  ,						UpperCamelCase__ ) ):
          UpperCamelCase					=  min(start_index + batch_size  ,						len(UpperCamelCase__ ) )
          UpperCamelCase					=  encoded_texts[start_index:end_index]
          UpperCamelCase					=  attn_masks[start_index:end_index]

          if add_start_token:
             UpperCamelCase					=  torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(UpperCamelCase__ )
             UpperCamelCase					=  torch.cat([bos_tokens_tensor, encoded_batch]  ,						dim=1 )
             UpperCamelCase					=  torch.cat(
                 [torch.ones(bos_tokens_tensor.size()  ,						dtype=torch.intaa ).to(UpperCamelCase__ ), attn_mask]  ,						dim=1 )

          UpperCamelCase					=  encoded_batch

          with torch.no_grad():
             UpperCamelCase					=  model(UpperCamelCase__  ,						attention_mask=UpperCamelCase__ ).logits

          UpperCamelCase					=  out_logits[..., :-1, :].contiguous()
          UpperCamelCase					=  labels[..., 1:].contiguous()
          UpperCamelCase					=  attn_mask[..., 1:].contiguous()

          UpperCamelCase					=  torch.expa(
              (loss_fct(shift_logits.transpose(1  ,						2 )  ,						UpperCamelCase__ ) * shift_attention_mask_batch).sum(1 )
              / shift_attention_mask_batch.sum(1 ) )

          ppls += perplexity_batch.tolist()

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






'''simple docstring'''


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class  SCREAMING_SNAKE_CASE (  _a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  42
    _SCREAMING_SNAKE_CASE	  =  None

class  SCREAMING_SNAKE_CASE (  _a   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  2






    @register_to_config
    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					float = 0.0_2  ,						UpperCamelCase__      :					float = 1_0_0  ,						UpperCamelCase__      :					float = 1.0_0_7  ,						UpperCamelCase__      :					float = 8_0  ,						UpperCamelCase__      :					float = 0.0_5  ,						UpperCamelCase__      :					float = 5_0  ,						):


       """simple docstring"""







       UpperCamelCase					=  sigma_max

       # setable values
       UpperCamelCase					=  None
       UpperCamelCase					=  None
       UpperCamelCase					=  None  # sigma(t_i)






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					Optional[int] = None ):


       """simple docstring"""







       return sample






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					Union[str, torch.device] = None ):


       """simple docstring"""







       UpperCamelCase					=  num_inference_steps
       UpperCamelCase					=  np.arange(0  ,						self.num_inference_steps )[::-1].copy()
       UpperCamelCase					=  torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
       UpperCamelCase					=  [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       UpperCamelCase					=  torch.tensor(UpperCamelCase__  ,						dtype=torch.floataa  ,						device=UpperCamelCase__ )






    def  A       (  self      :					Dict  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					Optional[torch.Generator] = None ):


       """simple docstring"""







       if self.config.s_min <= sigma <= self.config.s_max:
          UpperCamelCase					=  min(self.config.s_churn / self.num_inference_steps  ,						2**0.5 - 1 )
       else:
          UpperCamelCase					=  0

       # sample eps ~ N(0, S_noise^2 * I)
       UpperCamelCase					=  self.config.s_noise * randn_tensor(sample.shape  ,						generator=UpperCamelCase__ ).to(sample.device )
       UpperCamelCase					=  sigma + gamma * sigma
       UpperCamelCase					=  sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat






    def  A       (  self      :					str  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_hat + sigma_hat * model_output
       UpperCamelCase					=  (sample_hat - pred_original_sample) / sigma_hat
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					List[Any]  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					float  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					torch.FloatTensor  ,						UpperCamelCase__      :					bool = True  ,						):


       """simple docstring"""







       UpperCamelCase					=  sample_prev + sigma_prev * model_output
       UpperCamelCase					=  (sample_prev - pred_original_sample) / sigma_prev
       UpperCamelCase					=  sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
          return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=UpperCamelCase__  ,						derivative=UpperCamelCase__  ,						pred_original_sample=UpperCamelCase__ )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					int  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       raise NotImplementedError()

