





'''simple docstring'''







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

import datasets
from datasets import logging


_lowercase       						=			'\\n\n'

_lowercase       						=			'\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'

_lowercase       						=			'\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 			_lowercase						(       datasets.Metric					):







     def     UpperCamelCase			( self    ) ->	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     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__ = 16    ,   A__ = True    ,   A__=None    ) ->	Optional[Any]:
      if device is not None:
       assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
       if device == "gpu":
        snake_case				=			'''cuda'''
      else:
       snake_case				=			'''cuda''' if torch.cuda.is_available() else '''cpu'''

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

      snake_case				=			AutoTokenizer.from_pretrained(A__    )

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

      snake_case				=			tokenizer(
          A__    ,   add_special_tokens=A__    ,   padding=A__    ,   truncation=A__    ,   max_length=A__    ,   return_tensors='''pt'''    ,   return_attention_mask=A__    ,   ).to(A__    )

      snake_case				=			encodings['''input_ids''']
      snake_case				=			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."

      snake_case				=			[]
      snake_case				=			CrossEntropyLoss(reduction='''none'''    )

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

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

       snake_case				=			encoded_batch

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

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

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

       ppls += perplexity_batch.tolist()

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








'''simple docstring'''







import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase       						=			logging.get_logger(__name__)

_lowercase       						=			[
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

_lowercase       						=			[
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]

def    __UpperCamelCase	(	a    : List[str]     ) ->Optional[int]:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 return sd

def    __UpperCamelCase	(	a    : Optional[int]					,   a    : Union[str, Any]					,   a    : int=rename_keys_prefix     ) ->Tuple:
 snake_case				=			OrderedDict()
 snake_case				=			torch.arange(config.max_position_embeddings     ).expand((1, -1)     )
 # detector_d = OrderedDict()
 for key in d:
  if "detector" in key:
   # detector_d[key.replace('detector.','')] = d[key]
   continue
  snake_case				=			key
  for name_pair in rename_keys_prefix:
   snake_case				=			new_key.replace(name_pair[0]					,   name_pair[1]     )
  snake_case				=			d[key]
  if key == "bert.cls.predictions.decoder.weight":
   # Old bert code didn't have `decoder.bias`, but was added separately
   snake_case				=			new_d['''cls.predictions.bias''']
 return new_d

@torch.no_grad()
def    __UpperCamelCase	(	a    : Optional[int]					,   a    : int     ) ->Union[str, Any]:
 assert (
     checkpoint_path.split('''/'''     )[-1] in ACCEPTABLE_CHECKPOINTS
 ), f"""The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."""

 # Get Config
 if "pre" in checkpoint_path:
  snake_case				=			'''pretraining'''
  if "vcr" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 512}
  elif "vqa_advanced" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 2048}
  elif "vqa" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 2048}
  elif "nlvr" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 1024}
  else:
   raise NotImplementedError(f"""No implementation found for `{checkpoint_path}`."""     )
 else:
  if "vcr" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 512}
   snake_case				=			'''multichoice'''
  elif "vqa_advanced" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 2048}
   snake_case				=			'''vqa_advanced'''
  elif "vqa" in checkpoint_path:
   snake_case				=			{'''visual_embedding_dim''': 2048, '''num_labels''': 3129}
   snake_case				=			'''vqa'''
  elif "nlvr" in checkpoint_path:
   snake_case				=			{
       '''visual_embedding_dim''': 1024,
       '''num_labels''': 2,
   }
   snake_case				=			'''nlvr'''

 snake_case				=			VisualBertConfig(**a     )

 # Load State Dict
 snake_case				=			load_state_dict(a     )

 snake_case				=			get_new_dict(a					,   a     )

 if model_type == "pretraining":
  snake_case				=			VisualBertForPreTraining(a     )
 elif model_type == "vqa":
  snake_case				=			VisualBertForQuestionAnswering(a     )
 elif model_type == "nlvr":
  snake_case				=			VisualBertForVisualReasoning(a     )
 elif model_type == "multichoice":
  snake_case				=			VisualBertForMultipleChoice(a     )

 model.load_state_dict(a     )
 # Save Checkpoints
 Path(a     ).mkdir(exist_ok=a     )
 model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
   parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
   _lowercase       						=			parser.parse_args()
   convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)

