


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


_snake_case             =     getLogger(__name__)


_snake_case             =     '''cuda''' if torch.cuda.is_available() else '''cpu'''






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = 8      ,						SCREAMING_SNAKE_CASE_ = DEFAULT_DEVICE      ,						SCREAMING_SNAKE_CASE_=False      ,						SCREAMING_SNAKE_CASE_="summarization"      ,						SCREAMING_SNAKE_CASE_=None      ,						**SCREAMING_SNAKE_CASE_      ,						):


  '''simple docstring'''

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

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

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






def 		lowercase_(  ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_=True	):


  '''simple docstring'''

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

  lowerCamelCase :					Dict    =				generate_summaries_or_translations(
      SCREAMING_SNAKE_CASE_      ,						args.save_path      ,						args.model_name      ,						batch_size=args.bs      ,						device=args.device      ,						fpaa=args.fpaa      ,						task=args.task      ,						prefix=args.prefix      ,						**SCREAMING_SNAKE_CASE_      ,						)

  if args.reference_path is None:
    return {}

  # Compute scores
  lowerCamelCase :					List[str]    =				calculate_bleu if "translation" in args.task else calculate_rouge
  lowerCamelCase :					List[str]    =				[x.rstrip() for x in open(args.save_path	).readlines()]
  lowerCamelCase :					Dict    =				[x.rstrip() for x in open(args.reference_path	).readlines()][: len(SCREAMING_SNAKE_CASE_	)]
  lowerCamelCase :					dict    =				score_fn(SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_	)
  scores.update(SCREAMING_SNAKE_CASE_	)

  if args.dump_args:
    scores.update(SCREAMING_SNAKE_CASE_	)
  if args.info:
    lowerCamelCase :					List[Any]    =				args.info

  if verbose:
    print(SCREAMING_SNAKE_CASE_	)

  if args.score_path is not None:
    json.dump(SCREAMING_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 json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class 	UpperCAmelCase_					(					UpperCamelCase		, unittest.TestCase    ):

   '''simple docstring'''




   __A :      List[Any]     		=		BioGptTokenizer
   __A :      Optional[int]     		=		False






   def _snake_case    (		self ):






     """simple docstring"""





     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     lowerCamelCase :					Union[str, Any]    =				[
         "l",
         "o",
         "w",
         "e",
         "r",
         "s",
         "t",
         "i",
         "d",
         "n",
         "w</w>",
         "r</w>",
         "t</w>",
         "lo",
         "low",
         "er</w>",
         "low</w>",
         "lowest</w>",
         "newer</w>",
         "wider</w>",
         "<unk>",
     ]
     lowerCamelCase :					str    =				dict(zip(__A						,				range(len(__A ) ) ) )
     lowerCamelCase :					Dict    =				["l o 123", "lo w 1456", "e r</w> 1789", ""]

     lowerCamelCase :					Dict    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["vocab_file"] )
     lowerCamelCase :					int    =				os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES["merges_file"] )
     with open(self.vocab_file						,				"w" ) as fp:
       fp.write(json.dumps(__A ) )
     with open(self.merges_file						,				"w" ) as fp:
       fp.write("\n".join(__A ) )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     lowerCamelCase :					Dict    =				"lower newer"
     lowerCamelCase :					Union[str, Any]    =				"lower newer"
     return input_text, output_text






   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer(self.vocab_file						,				self.merges_file )

     lowerCamelCase :					Optional[int]    =				"lower"
     lowerCamelCase :					Any    =				["low", "er</w>"]
     lowerCamelCase :					List[str]    =				tokenizer.tokenize(__A )
     self.assertListEqual(__A						,				__A )

     lowerCamelCase :					Union[str, Any]    =				tokens + ["<unk>"]
     lowerCamelCase :					List[str]    =				[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A )						,				__A )






   @slow
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				BioGptTokenizer.from_pretrained("microsoft/biogpt" )

     lowerCamelCase :					Optional[int]    =				tokenizer.encode("sequence builders"						,				add_special_tokens=__A )
     lowerCamelCase :					Tuple    =				tokenizer.encode("multi-sequence build"						,				add_special_tokens=__A )

     lowerCamelCase :					Tuple    =				tokenizer.build_inputs_with_special_tokens(__A )
     lowerCamelCase :					List[str]    =				tokenizer.build_inputs_with_special_tokens(__A						,				__A )

     self.assertTrue(encoded_sentence == [2] + text )
     self.assertTrue(encoded_pair == [2] + text + [2] + text_a )



