






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


__lowerCamelCase	:		Any										=   getLogger(__name__)


__lowerCamelCase	:		Optional[Any]										=   """cuda""" if torch.cuda.is_available() else """cpu"""







def 			A__							(      _a  :					List[str]		,  _a  :					str		,  _a  :					str		,  _a  :					int = 8		,  _a  :					str = DEFAULT_DEVICE		,  _a  :					Dict=False		,  _a  :					Tuple="summarization"		,  _a  :					List[str]=None		,  **_a  :					List[Any]		,  ):





 '''simple docstring'''







 snake_case__  :    int							       =Path(_a	).open("""w"""		,  encoding="""utf-8"""	)
 snake_case__  :    Any							       =str(_a	)
 snake_case__  :    str							       =AutoModelForSeqaSeqLM.from_pretrained(_a	).to(_a	)
 if fpaa:
  snake_case__  :    List[str]							       =model.half()

 snake_case__  :    Optional[Any]							       =AutoTokenizer.from_pretrained(_a	)
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"	)  # if this is wrong, check config.model_type.

 snake_case__  :    Tuple							       =time.time()
 # update config with task specific params
 use_task_specific_params(_a		,  _a	)
 if prefix is None:
  snake_case__  :    List[str]							       =prefix or getattr(model.config		,  """prefix"""		,  """"""	) or """"""
 for examples_chunk in tqdm(list(chunks(_a		,  _a	)	)	):
  snake_case__  :    List[Any]							       =[prefix + text for text in examples_chunk]
  snake_case__  :    str							       =tokenizer(_a		,  return_tensors="""pt"""		,  truncation=_a		,  padding="""longest"""	).to(_a	)
  snake_case__  :    int							       =model.generate(
      input_ids=batch.input_ids		,  attention_mask=batch.attention_mask		,  **_a		,  )
  snake_case__  :    Union[str, Any]							       =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__  :    str							       =int(time.time() - start_time	)  # seconds
 snake_case__  :    str							       =len(_a	)
 return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,  4	)}







def 			A__							(      ):





 '''simple docstring'''







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







def 			A__							(      _a  :					int=True	):





 '''simple docstring'''







 snake_case__  :    Optional[Any]							       =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__  :    List[str]							       =parser.parse_known_args()
 snake_case__  :    List[Any]							       =parse_numeric_n_bool_cl_kwargs(_a	)
 if parsed_args and verbose:
  print(f"parsed the following generate kwargs: {parsed_args}"	)
 snake_case__  :    Tuple							       =[""" """ + 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__  :    Optional[int]							       =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__  :    Tuple							       =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__  :    Optional[Any]							       =calculate_bleu if """translation""" in args.task else calculate_rouge
 snake_case__  :    str							       =[x.rstrip() for x in open(args.save_path	).readlines()]
 snake_case__  :    List[Any]							       =[x.rstrip() for x in open(args.reference_path	).readlines()][: len(_a	)]
 snake_case__  :    dict							       =score_fn(_a		,  _a	)
 scores.update(_a	)

 if args.dump_args:
  scores.update(_a	)
 if args.info:
  snake_case__  :    Dict							       =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)













from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =resample if resample is not None else self.resample

       if do_resize and size_divisor is None:
        raise ValueError("""size_divisor is required for resizing"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

       if do_rescale:
        snake_case__  :    Union[str, Any]							       =[self.rescale(a				,			scale=1 / 2_5_5   ) for image in images]

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





