






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__       		=  getLogger(__name__)


lowerCamelCase__       		=  """cuda""" if torch.cuda.is_available() else """cpu"""






def 	UpperCamelCase	(							snake_case__  : List[str]						,snake_case__  : str						,snake_case__  : str						,snake_case__  : int = 8						,snake_case__  : str = DEFAULT_DEVICE						,snake_case__  : Optional[Any]=False						,snake_case__  : List[str]="summarization"						,snake_case__  : Optional[int]=None						,**snake_case__  : Optional[int]						,):




     '''simple docstring'''






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

     __snake_case      :Any 							=    AutoTokenizer.from_pretrained(snake_case__	)
     logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.

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






def 	UpperCamelCase	(							):




     '''simple docstring'''






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






def 	UpperCamelCase	(							snake_case__  : List[str]=True	):




     '''simple docstring'''






     __snake_case      :Optional[int] 							=    argparse.ArgumentParser()
     parser.add_argument("""model_name"""						,type=snake_case__						,help="""like facebook/bart-large-cnn,t5-base, etc."""	)
     parser.add_argument("""input_path"""						,type=snake_case__						,help="""like cnn_dm/test.source"""	)
     parser.add_argument("""save_path"""						,type=snake_case__						,help="""where to save summaries"""	)
     parser.add_argument("""--reference_path"""						,type=snake_case__						,required=snake_case__						,help="""like cnn_dm/test.target"""	)
     parser.add_argument("""--score_path"""						,type=snake_case__						,required=snake_case__						,default="""metrics.json"""						,help="""where to save metrics"""	)
     parser.add_argument("""--device"""						,type=snake_case__						,required=snake_case__						,default=snake_case__						,help="""cuda, cuda:1, cpu etc."""	)
     parser.add_argument(
         """--prefix"""						,type=snake_case__						,required=snake_case__						,default=snake_case__						,help="""will be added to the begininng of src examples"""	)
     parser.add_argument("""--task"""						,type=snake_case__						,default="""summarization"""						,help="""used for task_specific_params + metrics"""	)
     parser.add_argument("""--bs"""						,type=snake_case__						,default=8						,required=snake_case__						,help="""batch size"""	)
     parser.add_argument(
         """--n_obs"""						,type=snake_case__						,default=-1						,required=snake_case__						,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=snake_case__						,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[Any] 							=    parser.parse_known_args()
     __snake_case      :Dict 							=    parse_numeric_n_bool_cl_kwargs(snake_case__	)
     if parsed_args and verbose:
          print(f'''parsed the following generate kwargs: {parsed_args}'''	)
     __snake_case      :Any 							=    [""" """ + 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      :Any 							=    examples[: args.n_obs]
     Path(args.save_path	).parent.mkdir(exist_ok=snake_case__	)

     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      :Union[str, Any] 							=    generate_summaries_or_translations(
         snake_case__						,args.save_path						,args.model_name						,batch_size=args.bs						,device=args.device						,fpaa=args.fpaa						,task=args.task						,prefix=args.prefix						,**snake_case__						,)

     if args.reference_path is None:
          return {}

     # Compute scores
     __snake_case      :Any 							=    calculate_bleu if """translation""" in args.task else calculate_rouge
     __snake_case      :Optional[Any] 							=    [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(snake_case__	)]
     __snake_case      :dict 							=    score_fn(snake_case__						,snake_case__	)
     scores.update(snake_case__	)

     if args.dump_args:
          scores.update(snake_case__	)
     if args.info:
          __snake_case      :Tuple 							=    args.info

     if verbose:
          print(snake_case__	)

     if args.score_path is not None:
          json.dump(snake_case__						,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)










import colorsys

from PIL import Image  # type: ignore






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float						,snake_case__  : int	):




     '''simple docstring'''






     __snake_case      :List[Any] 							=    x
     __snake_case      :str 							=    y
     for step in range(snake_case__	):  # noqa: B007
          __snake_case      :Tuple 							=    a * a - b * b + x
          __snake_case      :Dict 							=    2 * a * b + y
          __snake_case      :str 							=    a_new

          # divergence happens for all complex number with an absolute value
          # greater than 4
          if a * a + b * b > 4:
               break
     return step / (max_step - 1)






def 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






     if distance == 1:
          return (0, 0, 0)
     else:
          return (255, 255, 255)






def 	UpperCamelCase	(							snake_case__  : float	):




     '''simple docstring'''






     if distance == 1:
          return (0, 0, 0)
     else:
          return tuple(round(i * 255	) for i in colorsys.hsv_to_rgb(snake_case__						,1						,1	)	)






def 	UpperCamelCase	(							snake_case__  : int = 800						,snake_case__  : int = 600						,snake_case__  : float = -0.6						,snake_case__  : float = 0						,snake_case__  : float = 3.2						,snake_case__  : int = 50						,snake_case__  : bool = True						,):




     '''simple docstring'''






     __snake_case      :Tuple 							=    Image.new("""RGB"""						,(image_width, image_height)	)
     __snake_case      :Dict 							=    img.load()

     # loop through the image-coordinates
     for image_x in range(snake_case__	):
          for image_y in range(snake_case__	):
               # determine the figure-coordinates based on the image-coordinates
               __snake_case      :Dict 							=    figure_width / image_width * image_height
               __snake_case      :Any 							=    figure_center_x + (image_x / image_width - 0.5) * figure_width
               __snake_case      :List[str] 							=    figure_center_y + (image_y / image_height - 0.5) * figure_height

               __snake_case      :Tuple 							=    get_distance(snake_case__						,snake_case__						,snake_case__	)

               # color the corresponding pixel based on the selected coloring-function
               if use_distance_color_coding:
                    __snake_case      :Optional[int] 							=    get_color_coded_rgb(snake_case__	)
               else:
                    __snake_case      :Optional[int] 							=    get_black_and_white_rgb(snake_case__	)

     return img


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # colored version, full figure
     lowerCamelCase__       		=  get_image()

     # uncomment for colored version, different section, zoomed in
     # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
     # figure_width = 0.8)

     # uncomment for black and white version, full figure
     # img = get_image(use_distance_color_coding = False)

     # uncomment to save the image
     # img.save("mandelbrot.png")

     img.show()


