

import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_SCREAMING_SNAKE_CASE          = {
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}





def    _snake_case		(snake_case_				:    Optional[Any]     )       ->		List[str]:
  __UpperCAmelCase  :     str		=    search.split()
  __UpperCAmelCase  :     int		=    dict((g.split('='     ) for g in groups)     )
  __UpperCAmelCase  :     Tuple		=    list(entries.keys()     )
  __UpperCAmelCase  :     str		=    [[F"--{k} {v}" for v in vs.split(':'     )] for k, vs in entries.items()]
  __UpperCAmelCase  :     Union[str, Any]		=    [list(snake_case_     ) for x in itertools.product(*snake_case_     )]
  return matrix, entry_names





def    _snake_case		()       ->		int:
  __UpperCAmelCase  :     List[str]		=    sys.argv[0]

  __UpperCAmelCase  :     Dict		=    argparse.ArgumentParser(
      usage=(
          '\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'
          ' refer to `run_eval.py -h` for the complete list.'
      )     )
  parser.add_argument(
      '--search'						,					type=snake_case_						,					required=snake_case_						,					help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'						,					)
  parser.add_argument(
      '--bs'						,					type=snake_case_						,					default=8						,					required=snake_case_						,					help='initial batch size (may get reduced if it\'s too big)'     )
  parser.add_argument('--task'						,					type=snake_case_						,					help='used for task_specific_params + metrics'     )
  parser.add_argument(
      '--info'						,					nargs='?'						,					type=snake_case_						,					const=datetime_now()						,					help=(
          'add custom notes to be printed before the results table. If no value is passed, the current datetime'
          ' string will be used.'
      )						,					)
  __UpperCAmelCase, __UpperCAmelCase  :     Any		=    parser.parse_known_args()
  # we share some of the args
  args_main.extend(['--task', args.task]     )
  __UpperCAmelCase  :     str		=    [prog] + args_main

  # to support variations like translation_en_to_de"
  __UpperCAmelCase  :     str		=    'translation' if 'translation' in args.task else 'summarization'

  __UpperCAmelCase, __UpperCAmelCase  :     List[str]		=    parse_search_arg(args.search     )
  __UpperCAmelCase  :     Optional[Any]		=    task_score_names[task]  # score cols first
  __UpperCAmelCase  :     Tuple		=    {col: len(str(snake_case_     )     ) for col in col_names}
  __UpperCAmelCase  :     List[str]		=    []
  for r in matrix:
    __UpperCAmelCase  :     List[Any]		=    dict((x.replace('--'						,					''     ).split() for x in r)     )
    __UpperCAmelCase  :     Dict		=    ' '.join(snake_case_     ).split()
    args_exp.extend(['--bs', str(args.bs     )]     )  # in case we need to reduce its size due to CUDA OOM
    __UpperCAmelCase  :     Union[str, Any]		=    args_normal + args_exp

    # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

    __UpperCAmelCase  :     Optional[Any]		=    run_generate(verbose=snake_case_     )
    # make sure scores are first in the table
    __UpperCAmelCase  :     List[str]		=    OrderedDict()
    for score in task_score_names[task]:
      __UpperCAmelCase  :     Union[str, Any]		=    scores[score]
    result.update(snake_case_     )
    results.append(snake_case_     )

    # find widest entries
    for k, v in result.items():
      __UpperCAmelCase  :     Optional[int]		=    len(str(snake_case_     )     )
      if l > col_widths[k]:
        __UpperCAmelCase  :     int		=    l

  __UpperCAmelCase  :     Dict		=    sorted(snake_case_						,					key=operator.itemgetter(*task_score_names[task]     )						,					reverse=snake_case_     )
  print(' | '.join([F"{col:{col_widths[col]}}" for col in col_names]     )     )
  print(' | '.join([F"{'-'*col_widths[col]}" for col in col_names]     )     )
  for row in results_sorted:
    print(' | '.join([F"{row[col]:{col_widths[col]}}" for col in col_names]     )     )

  __UpperCAmelCase  :     Tuple		=    results_sorted[0]
  for score in task_score_names[task]:
    del best[score]
  __UpperCAmelCase  :     Any		=    [F"--{k} {v}" for k, v in best.items()]
  __UpperCAmelCase  :     List[str]		=    ['--bs', str(args.bs     )]
  if args.info:
    print(F"\nInfo: {args.info}"     )
  print('\nBest score args:'     )
  print(' '.join(args_main + best_args + dyn_args     )     )

  return results_sorted


if __name__ == "__main__":
      # Usage:
      # [normal-run_eval_search.py cmd plus] \
      # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
      #
      # Example:
      # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
      # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
      # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
      # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
      run_search()








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_SCREAMING_SNAKE_CASE          = logging.get_logger(__name__)


class     lowerCamelCase_   ( _lowerCAmelCase):
 _lowerCAmelCase       		=  '''mt5'''
 _lowerCAmelCase       		=  ['''past_key_values''']




 def __init__( self     ,     _UpperCAmelCase=250_112     ,     _UpperCAmelCase=512     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=1_024     ,     _UpperCAmelCase=8     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=6     ,     _UpperCAmelCase=32     ,     _UpperCAmelCase=128     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=1E-6     ,     _UpperCAmelCase=1.0     ,     _UpperCAmelCase="gated-gelu"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="T5Tokenizer"     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase=0     ,     _UpperCAmelCase=1     ,     _UpperCAmelCase=0     ,     **_UpperCAmelCase     ,     ):
   super().__init__(
       is_encoder_decoder=_UpperCAmelCase     ,     tokenizer_class=_UpperCAmelCase     ,     tie_word_embeddings=_UpperCAmelCase     ,     pad_token_id=_UpperCAmelCase     ,     eos_token_id=_UpperCAmelCase     ,     decoder_start_token_id=_UpperCAmelCase     ,     **_UpperCAmelCase     ,     )
   __UpperCAmelCase  :     Dict		=    vocab_size
   __UpperCAmelCase  :     List[str]		=    d_model
   __UpperCAmelCase  :     str		=    d_kv
   __UpperCAmelCase  :     List[str]		=    d_ff
   __UpperCAmelCase  :     int		=    num_layers
   __UpperCAmelCase  :     List[Any]		=    (
       num_decoder_layers if num_decoder_layers is not None else self.num_layers
   )  # default = symmetry
   __UpperCAmelCase  :     List[Any]		=    num_heads
   __UpperCAmelCase  :     Tuple		=    relative_attention_num_buckets
   __UpperCAmelCase  :     Optional[int]		=    relative_attention_max_distance
   __UpperCAmelCase  :     Dict		=    dropout_rate
   __UpperCAmelCase  :     Union[str, Any]		=    layer_norm_epsilon
   __UpperCAmelCase  :     Tuple		=    initializer_factor
   __UpperCAmelCase  :     Union[str, Any]		=    feed_forward_proj
   __UpperCAmelCase  :     Optional[Any]		=    use_cache

   __UpperCAmelCase  :     Any		=    self.feed_forward_proj.split('-'       )
   __UpperCAmelCase  :     Dict		=    act_info[-1]
   __UpperCAmelCase  :     Any		=    act_info[0] == 'gated'

   if len(_UpperCAmelCase       ) > 1 and act_info[0] != "gated" or len(_UpperCAmelCase       ) > 2:
     raise ValueError(
         F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
         'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
         '\'gated-gelu\' or \'relu\''       )

   # for backwards compatibility
   if feed_forward_proj == "gated-gelu":
     __UpperCAmelCase  :     List[str]		=    'gelu_new'




 @property
 def 						a					( self       ):
   return self.d_model




 @property
 def 						a					( self       ):
   return self.num_heads




 @property
 def 						a					( self       ):
   return self.num_layers



class     lowerCamelCase_   ( _lowerCAmelCase):




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[int]		=    {
       'input_ids': {0: 'batch', 1: 'encoder_sequence'},
       'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
   }
   if self.use_past:
     __UpperCAmelCase  :     Optional[Any]		=    'past_encoder_sequence + sequence'
     __UpperCAmelCase  :     Tuple		=    {0: 'batch'}
     __UpperCAmelCase  :     List[Any]		=    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
   else:
     __UpperCAmelCase  :     Tuple		=    {0: 'batch', 1: 'decoder_sequence'}
     __UpperCAmelCase  :     int		=    {0: 'batch', 1: 'decoder_sequence'}

   if self.use_past:
     self.fill_with_past_key_values_(_UpperCAmelCase     ,     direction='inputs'       )

   return common_inputs




 @property
 # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
 def 						a					( self       ):
   return 13




 @property
 def 						a					( self       ):
   return 5E-4





