


"""simple docstring"""




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

import datasets
from datasets import logging


_A			=						'\\n\n'

_A			=						'\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'

_A			=						'\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    UpperCAmelCase__	( datasets.Metric		):



    """simple docstring"""






    def _a (      self   )  ->	List[Any]:
         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_ = 16 ,       A_ = True ,       A_=None   )  ->	Optional[int]:

         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(lowercase_   )
         __UpperCamelCase   							=model.to(lowercase_   )

         __UpperCamelCase   							=AutoTokenizer.from_pretrained(lowercase_   )

         # 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(lowercase_   ) > 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(
             lowercase_ ,       add_special_tokens=lowercase_ ,       padding=lowercase_ ,       truncation=lowercase_ ,       max_length=lowercase_ ,       return_tensors='pt' ,       return_attention_mask=lowercase_ ,       ).to(lowercase_   )

         __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(lowercase_   ) ,       lowercase_   )   ):
              __UpperCamelCase   							=min(start_index + batch_size ,       len(lowercase_   )   )
              __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(lowercase_   )
                   __UpperCamelCase   							=torch.cat([bos_tokens_tensor, encoded_batch] ,       dim=1   )
                   __UpperCamelCase   							=torch.cat(
                       [torch.ones(bos_tokens_tensor.size() ,       dtype=torch.intaa   ).to(lowercase_   ), attn_mask] ,       dim=1   )

              __UpperCamelCase   							=encoded_batch

              with torch.no_grad():
                   __UpperCamelCase   							=model(lowercase_ ,       attention_mask=lowercase_   ).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   ) ,       lowercase_   ) * shift_attention_mask_batch).sum(1   )
                  / shift_attention_mask_batch.sum(1   )   )

              ppls += perplexity_batch.tolist()

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A			=						logging.get_logger(__name__)

_A			=						{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		str              =  "realm"






    def __init__(      self ,       A_=30522 ,       A_=768 ,       A_=128 ,       A_=12 ,       A_=12 ,       A_=8 ,       A_=3072 ,       A_="gelu_new" ,       A_=0.1 ,       A_=0.1 ,       A_=512 ,       A_=2 ,       A_=0.02 ,       A_=1E-12 ,       A_=256 ,       A_=10 ,       A_=1E-3 ,       A_=5 ,       A_=320 ,       A_=13353718 ,       A_=5000 ,       A_=1 ,       A_=0 ,       A_=2 ,       **A_ ,       )  ->	Dict:
         super().__init__(pad_token_id=A_ ,       bos_token_id=A_ ,       eos_token_id=A_ ,       **A_   )

         # Common config
         __UpperCamelCase   							=vocab_size
         __UpperCamelCase   							=max_position_embeddings
         __UpperCamelCase   							=hidden_size
         __UpperCamelCase   							=retriever_proj_size
         __UpperCamelCase   							=num_hidden_layers
         __UpperCamelCase   							=num_attention_heads
         __UpperCamelCase   							=num_candidates
         __UpperCamelCase   							=intermediate_size
         __UpperCamelCase   							=hidden_act
         __UpperCamelCase   							=hidden_dropout_prob
         __UpperCamelCase   							=attention_probs_dropout_prob
         __UpperCamelCase   							=initializer_range
         __UpperCamelCase   							=type_vocab_size
         __UpperCamelCase   							=layer_norm_eps

         # Reader config
         __UpperCamelCase   							=span_hidden_size
         __UpperCamelCase   							=max_span_width
         __UpperCamelCase   							=reader_layer_norm_eps
         __UpperCamelCase   							=reader_beam_size
         __UpperCamelCase   							=reader_seq_len

         # Retrieval config
         __UpperCamelCase   							=num_block_records
         __UpperCamelCase   							=searcher_beam_size





