






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

import datasets
from datasets import logging


_SCREAMING_SNAKE_CASE   =  "\\n\n"

_SCREAMING_SNAKE_CASE   =  "\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"

_SCREAMING_SNAKE_CASE   =  "\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 				UpperCamelCase__						(				self	: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 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:Any,      snake_case	:int = 16,      snake_case	:bool = True,      snake_case	: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":
                _lowercase              ='cuda'
          else:
             _lowercase              ='cuda' if torch.cuda.is_available() else 'cpu'

          _lowercase              =AutoModelForCausalLM.from_pretrained(snake_case)
          _lowercase              =model.to(snake_case)

          _lowercase              =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:
             _lowercase              =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"
             _lowercase              =model.config.max_length - 1
          else:
             _lowercase              =model.config.max_length

          _lowercase              =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)

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

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

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

             _lowercase              =encoded_batch

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

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

             _lowercase              =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)}










import warnings

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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		List[str]					='''mvp'''
       __lowerCAmelCase    :		List[Any]					=['''past_key_values''']
       __lowerCAmelCase    :		Optional[Any]					={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





       def __init__(				self	:Any,      snake_case	:Any=5_0267,      snake_case	:List[str]=1024,      snake_case	:List[str]=12,      snake_case	:List[Any]=4096,      snake_case	:List[Any]=16,      snake_case	:Optional[int]=12,      snake_case	:Optional[int]=4096,      snake_case	:List[str]=16,      snake_case	:Optional[Any]=0.0,      snake_case	:Tuple=0.0,      snake_case	:str="gelu",      snake_case	:int=1024,      snake_case	:Any=0.1,      snake_case	:Dict=0.0,      snake_case	:Optional[Any]=0.0,      snake_case	:int=0.0_2,      snake_case	:Union[str, Any]=0.0,      snake_case	:Tuple=False,      snake_case	:Optional[int]=True,      snake_case	:Any=1,      snake_case	:Union[str, Any]=0,      snake_case	:Optional[Any]=2,      snake_case	:Union[str, Any]=True,      snake_case	:Tuple=2,      snake_case	:Any=2,      snake_case	:List[Any]=False,      snake_case	:Optional[int]=100,      snake_case	:Union[str, Any]=800,      **snake_case	:Tuple,      ):



          """simple docstring"""

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =d_model
          _lowercase              =encoder_ffn_dim
          _lowercase              =encoder_layers
          _lowercase              =encoder_attention_heads
          _lowercase              =decoder_ffn_dim
          _lowercase              =decoder_layers
          _lowercase              =decoder_attention_heads
          _lowercase              =dropout
          _lowercase              =attention_dropout
          _lowercase              =activation_dropout
          _lowercase              =activation_function
          _lowercase              =init_std
          _lowercase              =encoder_layerdrop
          _lowercase              =decoder_layerdrop
          _lowercase              =classifier_dropout
          _lowercase              =use_cache
          _lowercase              =encoder_layers
          _lowercase              =scale_embedding  # scale factor will be sqrt(d_model) if True
          _lowercase              =use_prompt
          _lowercase              =prompt_length
          _lowercase              =prompt_mid_dim

          super().__init__(
              pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      is_encoder_decoder=snake_case,      decoder_start_token_id=snake_case,      forced_eos_token_id=snake_case,      **snake_case,      )

          if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',      snake_case):
             _lowercase              =self.bos_token_id
             warnings.warn(
                 f'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
                 'The config can simply be saved and uploaded again to be fixed.')


