





'''simple docstring'''







import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_lowercase       						=			getLogger(__name__)


_lowercase       						=			'cuda' if torch.cuda.is_available() else 'cpu'

def    __UpperCamelCase	(	a    : List[str]					,   a    : str					,   a    : str					,   a    : int = 8					,   a    : str = DEFAULT_DEVICE					,   a    : Union[str, Any]=False					,   a    : int="summarization"					,   a    : Optional[int]=None					,   **a    : int					,   ) ->Dict:
 snake_case				=			Path(a     ).open('''w'''					,   encoding='''utf-8'''     )
 snake_case				=			str(a     )
 snake_case				=			AutoModelForSeqaSeqLM.from_pretrained(a     ).to(a     )
 if fpaa:
  snake_case				=			model.half()

 snake_case				=			AutoTokenizer.from_pretrained(a     )
 logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

 snake_case				=			time.time()
 # update config with task specific params
 use_task_specific_params(a					,   a     )
 if prefix is None:
  snake_case				=			prefix or getattr(model.config					,   '''prefix'''					,   ''''''     ) or ''''''
 for examples_chunk in tqdm(list(chunks(a					,   a     )     )     ):
  snake_case				=			[prefix + text for text in examples_chunk]
  snake_case				=			tokenizer(a					,   return_tensors='''pt'''					,   truncation=a					,   padding='''longest'''     ).to(a     )
  snake_case				=			model.generate(
      input_ids=batch.input_ids					,   attention_mask=batch.attention_mask					,   **a					,   )
  snake_case				=			tokenizer.batch_decode(a					,   skip_special_tokens=a					,   clean_up_tokenization_spaces=a     )
  for hypothesis in dec:
   fout.write(hypothesis + '''\n'''     )
   fout.flush()
 fout.close()
 snake_case				=			int(time.time() - start_time     )  # seconds
 snake_case				=			len(a     )
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs					,   4     )}

def    __UpperCamelCase	(	) ->int:
 return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''     )

def    __UpperCamelCase	(	a    : Optional[Any]=True     ) ->Optional[Any]:
 snake_case				=			argparse.ArgumentParser()
 parser.add_argument('''model_name'''					,   type=a					,   help='''like facebook/bart-large-cnn,t5-base, etc.'''     )
 parser.add_argument('''input_path'''					,   type=a					,   help='''like cnn_dm/test.source'''     )
 parser.add_argument('''save_path'''					,   type=a					,   help='''where to save summaries'''     )
 parser.add_argument('''--reference_path'''					,   type=a					,   required=a					,   help='''like cnn_dm/test.target'''     )
 parser.add_argument('''--score_path'''					,   type=a					,   required=a					,   default='''metrics.json'''					,   help='''where to save metrics'''     )
 parser.add_argument('''--device'''					,   type=a					,   required=a					,   default=a					,   help='''cuda, cuda:1, cpu etc.'''     )
 parser.add_argument(
     '''--prefix'''					,   type=a					,   required=a					,   default=a					,   help='''will be added to the begininng of src examples'''     )
 parser.add_argument('''--task'''					,   type=a					,   default='''summarization'''					,   help='''used for task_specific_params + metrics'''     )
 parser.add_argument('''--bs'''					,   type=a					,   default=8					,   required=a					,   help='''batch size'''     )
 parser.add_argument(
     '''--n_obs'''					,   type=a					,   default=-1					,   required=a					,   help='''How many observations. Defaults to all.'''     )
 parser.add_argument('''--fp16'''					,   action='''store_true'''     )
 parser.add_argument('''--dump-args'''					,   action='''store_true'''					,   help='''print the custom hparams with the results'''     )
 parser.add_argument(
     '''--info'''					,   nargs='''?'''					,   type=a					,   const=datetime_now()					,   help=(
         '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
         ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
     )					,   )
 # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
 snake_case				,		snake_case				=			parser.parse_known_args()
 snake_case				=			parse_numeric_n_bool_cl_kwargs(a     )
 if parsed_args and verbose:
  print(f"""parsed the following generate kwargs: {parsed_args}"""     )
 snake_case				=			[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
 if args.n_obs > 0:
  snake_case				=			examples[: args.n_obs]
 Path(args.save_path     ).parent.mkdir(exist_ok=a     )

 if args.reference_path is None and Path(args.score_path     ).exists():
  warnings.warn(f"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""     )

 if args.device == "cpu" and args.fpaa:
  # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
  raise ValueError('''Can\'t mix --fp16 and --device cpu'''     )

 snake_case				=			generate_summaries_or_translations(
     a					,   args.save_path					,   args.model_name					,   batch_size=args.bs					,   device=args.device					,   fpaa=args.fpaa					,   task=args.task					,   prefix=args.prefix					,   **a					,   )

 if args.reference_path is None:
  return {}

 # Compute scores
 snake_case				=			calculate_bleu if '''translation''' in args.task else calculate_rouge
 snake_case				=			[x.rstrip() for x in open(args.save_path     ).readlines()]
 snake_case				=			[x.rstrip() for x in open(args.reference_path     ).readlines()][: len(a     )]
 snake_case				=			score_fn(a					,   a     )
 scores.update(a     )

 if args.dump_args:
  scores.update(a     )
 if args.info:
  snake_case				=			args.info

 if verbose:
  print(a     )

 if args.score_path is not None:
  json.dump(a					,   open(args.score_path					,   '''w'''     )     )

 return scores


if __name__ == "__main__":
   # Usage for MT:
   # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
   run_generate(verbose=True)








'''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)

