


'''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


a_  :    int      =			getLogger(__name__)


a_  :    str      =			'cuda' if torch.cuda.is_available() else 'cpu'





def __snake_case (				UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						int ,    UpperCAmelCase_						:						Optional[Any] = 8 ,    UpperCAmelCase_						:						List[str] = DEFAULT_DEVICE ,    UpperCAmelCase_						:						Tuple=False ,    UpperCAmelCase_						:						Optional[int]="summarization" ,    UpperCAmelCase_						:						str=None ,    **UpperCAmelCase_						:						int ,    ):
 lowerCamelCase_								=  Path(lowerCamelCase_		).open("w" ,    encoding="utf-8"		)
 lowerCamelCase_								=  str(lowerCamelCase_		)
 lowerCamelCase_								=  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_		).to(lowerCamelCase_		)
 if fpaa:
  lowerCamelCase_								=  model.half()

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





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





def __snake_case (				UpperCAmelCase_						:						Optional[int]=True		):
 lowerCamelCase_								=  argparse.ArgumentParser()
 parser.add_argument("model_name" ,    type=lowerCamelCase_ ,    help="like facebook/bart-large-cnn,t5-base, etc."		)
 parser.add_argument("input_path" ,    type=lowerCamelCase_ ,    help="like cnn_dm/test.source"		)
 parser.add_argument("save_path" ,    type=lowerCamelCase_ ,    help="where to save summaries"		)
 parser.add_argument("--reference_path" ,    type=lowerCamelCase_ ,    required=lowerCamelCase_ ,    help="like cnn_dm/test.target"		)
 parser.add_argument("--score_path" ,    type=lowerCamelCase_ ,    required=lowerCamelCase_ ,    default="metrics.json" ,    help="where to save metrics"		)
 parser.add_argument("--device" ,    type=lowerCamelCase_ ,    required=lowerCamelCase_ ,    default=lowerCamelCase_ ,    help="cuda, cuda:1, cpu etc."		)
 parser.add_argument(
     "--prefix" ,    type=lowerCamelCase_ ,    required=lowerCamelCase_ ,    default=lowerCamelCase_ ,    help="will be added to the begininng of src examples"		)
 parser.add_argument("--task" ,    type=lowerCamelCase_ ,    default="summarization" ,    help="used for task_specific_params + metrics"		)
 parser.add_argument("--bs" ,    type=lowerCamelCase_ ,    default=8 ,    required=lowerCamelCase_ ,    help="batch size"		)
 parser.add_argument(
     "--n_obs" ,    type=lowerCamelCase_ ,    default=-1 ,    required=lowerCamelCase_ ,    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=lowerCamelCase_ ,    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(lowerCamelCase_		)
 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=lowerCamelCase_		)
 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(
     lowerCamelCase_ ,    args.save_path ,    args.model_name ,    batch_size=args.bs ,    device=args.device ,    fpaa=args.fpaa ,    task=args.task ,    prefix=args.prefix ,    **lowerCamelCase_ ,    )

 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(lowerCamelCase_		)]
 lowerCamelCase_								=  score_fn(lowerCamelCase_ ,    lowerCamelCase_		)
 scores.update(lowerCamelCase_		)

 if args.dump_args:
  scores.update(lowerCamelCase_		)
 if args.info:
  lowerCamelCase_								=  args.info

 if verbose:
  print(lowerCamelCase_		)

 if args.score_path is not None:
  json.dump(lowerCamelCase_ ,    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 numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



