





"""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


__UpperCamelCase    :	Optional[Any]						=     getLogger(__name__)


__UpperCamelCase    :	Any						=     '''cuda''' if torch.cuda.is_available() else '''cpu'''

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						List[str]     ,    _UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						str     ,    _UpperCAmelCase				:						int = 8     ,    _UpperCAmelCase				:						str = DEFAULT_DEVICE     ,    _UpperCAmelCase				:						str=False     ,    _UpperCAmelCase				:						List[str]="summarization"     ,    _UpperCAmelCase				:						str=None     ,    **_UpperCAmelCase				:						str     ,    ):
 lowerCAmelCase													=   Path(_UpperCAmelCase ).open('w'     ,    encoding='utf-8' )
 lowerCAmelCase													=   str(_UpperCAmelCase )
 lowerCAmelCase													=   AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
 if fpaa:
  lowerCAmelCase													=   model.half()

 lowerCAmelCase													=   AutoTokenizer.from_pretrained(_UpperCAmelCase )
 logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.

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

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

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						Optional[Any]=True ):
 lowerCAmelCase													=   argparse.ArgumentParser()
 parser.add_argument('model_name'     ,    type=_UpperCAmelCase     ,    help='like facebook/bart-large-cnn,t5-base, etc.' )
 parser.add_argument('input_path'     ,    type=_UpperCAmelCase     ,    help='like cnn_dm/test.source' )
 parser.add_argument('save_path'     ,    type=_UpperCAmelCase     ,    help='where to save summaries' )
 parser.add_argument('--reference_path'     ,    type=_UpperCAmelCase     ,    required=_UpperCAmelCase     ,    help='like cnn_dm/test.target' )
 parser.add_argument('--score_path'     ,    type=_UpperCAmelCase     ,    required=_UpperCAmelCase     ,    default='metrics.json'     ,    help='where to save metrics' )
 parser.add_argument('--device'     ,    type=_UpperCAmelCase     ,    required=_UpperCAmelCase     ,    default=_UpperCAmelCase     ,    help='cuda, cuda:1, cpu etc.' )
 parser.add_argument(
     '--prefix'     ,    type=_UpperCAmelCase     ,    required=_UpperCAmelCase     ,    default=_UpperCAmelCase     ,    help='will be added to the begininng of src examples' )
 parser.add_argument('--task'     ,    type=_UpperCAmelCase     ,    default='summarization'     ,    help='used for task_specific_params + metrics' )
 parser.add_argument('--bs'     ,    type=_UpperCAmelCase     ,    default=8     ,    required=_UpperCAmelCase     ,    help='batch size' )
 parser.add_argument(
     '--n_obs'     ,    type=_UpperCAmelCase     ,    default=-1     ,    required=_UpperCAmelCase     ,    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=_UpperCAmelCase     ,    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
 lowerCAmelCase							,lowerCAmelCase													=   parser.parse_known_args()
 lowerCAmelCase													=   parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase )
 if parsed_args and verbose:
  print(F'parsed the following generate kwargs: {parsed_args}' )
 with open(args.input_path ) as f:
  lowerCAmelCase													=   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
 if args.n_obs > 0:
  lowerCAmelCase													=   examples[: args.n_obs]
 Path(args.save_path ).parent.mkdir(exist_ok=_UpperCAmelCase )
 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.' )
 lowerCAmelCase													=   generate_summaries_or_translations(
     _UpperCAmelCase     ,    args.save_path     ,    args.model_name     ,    batch_size=args.bs     ,    device=args.device     ,    fpaa=args.fpaa     ,    task=args.task     ,    prefix=args.prefix     ,    **_UpperCAmelCase     ,    )

 if args.reference_path is None:
  return {}

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

 if args.dump_args:
  scores.update(_UpperCAmelCase )
 if args.info:
  lowerCAmelCase													=   args.info

 if verbose:
  print(_UpperCAmelCase )

 if args.score_path is not None:
  json.dump(_UpperCAmelCase     ,    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 unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
       from PIL import Image







class 			a       (      unittest.TestCase      ,   a__     ):



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   load_tool('image-question-answering'    )
  self.tool.setup()
  lowerCAmelCase													=   load_tool('image-question-answering'							,					remote=_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
  lowerCAmelCase													=   self.tool(_snake_case							,					'How many cats are sleeping on the couch?'    )
  self.assertEqual(_snake_case							,					'2'    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
  lowerCAmelCase													=   self.remote_tool(_snake_case							,					'How many cats are sleeping on the couch?'    )
  self.assertEqual(_snake_case							,					'2'    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
  lowerCAmelCase													=   self.tool(image=_snake_case							,					question='How many cats are sleeping on the couch?'    )
  self.assertEqual(_snake_case							,					'2'    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'    )    ) / '000000039769.png'    )
  lowerCAmelCase													=   self.remote_tool(image=_snake_case							,					question='How many cats are sleeping on the couch?'    )
  self.assertEqual(_snake_case							,					'2'    )





