






'''simple docstring'''



# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

__UpperCamelCase   :							Union[str, Any]         =   """facebook/wmt19-en-de"""
__UpperCamelCase   :							Any         =   FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__UpperCamelCase   :							int         =   FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

__UpperCamelCase   :							Optional[Any]         =   FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__UpperCamelCase   :							Optional[int]         =   tokenizer(["""Making tiny model"""], return_tensors="""pt""")
__UpperCamelCase   :							List[str]         =   tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
__UpperCamelCase   :							Optional[Any]         =   """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-de







'''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


__UpperCamelCase   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

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

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



def  __UpperCAmelCase						(					)	->      List[str]:

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

   """simple docstring"""
   __a					  =     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
   __a  ,      __a					  =     parser.parse_known_args()
   __a					  =     parse_numeric_n_bool_cl_kwargs(SCREAMING_SNAKE_CASE__     )
   if parsed_args and verbose:
      print(f"""parsed the following generate kwargs: {parsed_args}"""     )
   __a					  =     [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
   if args.n_obs > 0:
      __a					  =     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'     )

   __a					  =     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
   __a					  =     calculate_bleu if 'translation' in args.task else calculate_rouge
   __a					  =     [x.rstrip() for x in open(args.save_path     ).readlines()]
   __a					  =     [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(SCREAMING_SNAKE_CASE__     )]
   __a					  =     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:
      __a					  =     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)