





"""simple docstring"""




def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  list(_UpperCAmelCase )
  A_  :	str    						=  list(_UpperCAmelCase )
  A_  :	List[str]    						=  len(_UpperCAmelCase )
  A_  :	Union[str, Any]    						=  len(_UpperCAmelCase )

  A_  :	Optional[int]    						=  [
      [0 for _ in range(len_destination_seq + 1 )] for _ in range(len_source_seq + 1 )
  ]
  A_  :	Optional[Any]    						=  [
      ['0' for _ in range(len_destination_seq + 1 )] for _ in range(len_source_seq + 1 )
  ]

  for i in range(1			, len_source_seq + 1 ):
    A_  :	Union[str, Any]    						=  i * delete_cost
    A_  :	Any    						=  f"""D{source_seq[i - 1]:c}"""

  for i in range(1			, len_destination_seq + 1 ):
    A_  :	Optional[int]    						=  i * insert_cost
    A_  :	Dict    						=  f"""I{destination_seq[i - 1]:c}"""

  for i in range(1			, len_source_seq + 1 ):
    for j in range(1			, len_destination_seq + 1 ):
      if source_seq[i - 1] == destination_seq[j - 1]:
        A_  :	Dict    						=  costs[i - 1][j - 1] + copy_cost
        A_  :	int    						=  f"""C{source_seq[i - 1]:c}"""
      else:
        A_  :	Any    						=  costs[i - 1][j - 1] + replace_cost
        A_  :	Any    						=  f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1] )

      if costs[i - 1][j] + delete_cost < costs[i][j]:
        A_  :	Tuple    						=  costs[i - 1][j] + delete_cost
        A_  :	int    						=  f"""D{source_seq[i - 1]:c}"""

      if costs[i][j - 1] + insert_cost < costs[i][j]:
        A_  :	Tuple    						=  costs[i][j - 1] + insert_cost
        A_  :	Any    						=  f"""I{destination_seq[j - 1]:c}"""

  return costs, ops





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if i == 0 and j == 0:
    return []
  else:
    if ops[i][j][0] == "C" or ops[i][j][0] == "R":
      A_  :	Dict    						=  assemble_transformation(_UpperCAmelCase			, i - 1			, j - 1 )
      seq.append(ops[i][j] )
      return seq
    elif ops[i][j][0] == "D":
      A_  :	List[str]    						=  assemble_transformation(_UpperCAmelCase			, i - 1			, _UpperCAmelCase )
      seq.append(ops[i][j] )
      return seq
    else:
      A_  :	Optional[int]    						=  assemble_transformation(_UpperCAmelCase			, _UpperCAmelCase			, j - 1 )
      seq.append(ops[i][j] )
      return seq


if __name__ == "__main__":
   lowerCamelCase_ , lowerCamelCase_					:						List[str]      = compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

   lowerCamelCase_					:						int      = len(operations)
   lowerCamelCase_					:						int      = len(operations[0])
   lowerCamelCase_					:						str      = assemble_transformation(operations, m - 1, n - 1)

   lowerCamelCase_					:						int      = list('Python')
   lowerCamelCase_					:						str      = 0
   lowerCamelCase_					:						List[str]      = 0

   with open('min_cost.txt', 'w') as file:
      for op in sequence:
         print(''.join(string))

         if op[0] == "C":
            file.write('%-16s' % 'Copy %c' % op[1])
            file.write('\t\t\t' + ''.join(string))
            file.write('\r\n')

            cost -= 1
         elif op[0] == "R":
            lowerCamelCase_					:						int      = op[2]

            file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
            file.write('\t\t' + ''.join(string))
            file.write('\r\n')

            cost += 1
         elif op[0] == "D":
            string.pop(i)

            file.write('%-16s' % 'Delete %c' % op[1])
            file.write('\t\t\t' + ''.join(string))
            file.write('\r\n')

            cost += 2
         else:
            string.insert(i, op[1])

            file.write('%-16s' % 'Insert %c' % op[1])
            file.write('\t\t\t' + ''.join(string))
            file.write('\r\n')

            cost += 2

         i += 1

      print(''.join(string))
      print('Cost: ', cost)

      file.write('\r\nMinimum cost: ' + str(cost))






"""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


lowerCamelCase_					:						List[str]      = getLogger(__name__)


lowerCamelCase_					:						Optional[Any]      = 'cuda' if torch.cuda.is_available() else 'cpu'





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 8			, _UpperCAmelCase = DEFAULT_DEVICE			, _UpperCAmelCase=False			, _UpperCAmelCase="summarization"			, _UpperCAmelCase=None			, **_UpperCAmelCase			, ):






  """simple docstring"""





  A_  :	Tuple    						=  Path(_UpperCAmelCase ).open('w'			, encoding='utf-8' )
  A_  :	List[str]    						=  str(_UpperCAmelCase )
  A_  :	List[str]    						=  AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase ).to(_UpperCAmelCase )
  if fpaa:
    A_  :	List[str]    						=  model.half()

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

  A_  :	Optional[int]    						=  time.time()
  # update config with task specific params
  use_task_specific_params(_UpperCAmelCase			, _UpperCAmelCase )
  if prefix is None:
    A_  :	Optional[int]    						=  prefix or getattr(model.config			, 'prefix'			, '' ) or ''
  for examples_chunk in tqdm(list(chunks(_UpperCAmelCase			, _UpperCAmelCase ) ) ):
    A_  :	Any    						=  [prefix + text for text in examples_chunk]
    A_  :	List[Any]    						=  tokenizer(_UpperCAmelCase			, return_tensors='pt'			, truncation=_UpperCAmelCase			, padding='longest' ).to(_UpperCAmelCase )
    A_  :	Dict    						=  model.generate(
        input_ids=batch.input_ids			, attention_mask=batch.attention_mask			, **_UpperCAmelCase			, )
    A_  :	Dict    						=  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()
  A_  :	Dict    						=  int(time.time() - start_time )  # seconds
  A_  :	Any    						=  len(_UpperCAmelCase )
  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__		(					_UpperCAmelCase=True ):






  """simple docstring"""





  A_  :	int    						=  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
  A_						,	A_  :	int    						=  parser.parse_known_args()
  A_  :	List[Any]    						=  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:
    A_  :	List[str]    						=  [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
  if args.n_obs > 0:
    A_  :	int    						=  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.""" )
  A_  :	Optional[Any]    						=  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
  A_  :	Union[str, Any]    						=  calculate_bleu if 'translation' in args.task else calculate_rouge
  A_  :	List[Any]    						=  [x.rstrip() for x in open(args.save_path ).readlines()]
  A_  :	Dict    						=  [x.rstrip() for x in open(args.reference_path ).readlines()][: len(_UpperCAmelCase )]
  A_  :	dict    						=  score_fn(_UpperCAmelCase			, _UpperCAmelCase )
  scores.update(_UpperCAmelCase )

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