




'''simple docstring'''





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
UpperCamelCase_		:    Optional[int]									=		{
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}





def 			__a			(     _UpperCamelCase:					List[str]					)		->				Tuple:

     """simple docstring"""




     _snake_case							=   search.split()
     _snake_case							=   dict((g.split("="					) for g in groups)					)
     _snake_case							=   list(entries.keys()					)
     _snake_case							=   [[F"""--{k} {v}""" for v in vs.split(":"					)] for k, vs in entries.items()]
     _snake_case							=   [list(_UpperCamelCase					) for x in itertools.product(*_UpperCamelCase					)]
     return matrix, entry_names





def 			__a			(     )		->				str:

     """simple docstring"""




     _snake_case							=   sys.argv[0]

     _snake_case							=   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=_UpperCamelCase      ,		required=_UpperCamelCase      ,		help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""      ,		)
     parser.add_argument(
         "--bs"      ,		type=_UpperCamelCase      ,		default=8      ,		required=_UpperCamelCase      ,		help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"      ,		type=_UpperCamelCase      ,		help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"      ,		nargs="?"      ,		type=_UpperCamelCase      ,		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."
         )      ,		)
     _snake_case							,      _snake_case							=   parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _snake_case							=   [prog] + args_main

     # to support variations like translation_en_to_de"
     _snake_case							=   "translation" if "translation" in args.task else "summarization"

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

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

          _snake_case							=   run_generate(verbose=_UpperCamelCase					)
          # make sure scores are first in the table
          _snake_case							=   OrderedDict()
          for score in task_score_names[task]:
               _snake_case							=   scores[score]
          result.update(_UpperCamelCase					)
          results.append(_UpperCamelCase					)

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

     _snake_case							=   sorted(_UpperCamelCase      ,		key=operator.itemgetter(*task_score_names[task]					)      ,		reverse=_UpperCamelCase					)
     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]					)					)

     _snake_case							=   results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _snake_case							=   [F"""--{k} {v}""" for k, v in best.items()]
     _snake_case							=   ["--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()








'''simple docstring'''





import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   from transformers.models.electra.modeling_flax_electra import (
       FlaxElectraForCausalLM,
       FlaxElectraForMaskedLM,
       FlaxElectraForMultipleChoice,
       FlaxElectraForPreTraining,
       FlaxElectraForQuestionAnswering,
       FlaxElectraForSequenceClassification,
       FlaxElectraForTokenClassification,
       FlaxElectraModel,
   )

class      _a       (		unittest.TestCase							):






     def __init__(				self      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE=13      ,_SCREAMING_SNAKE_CASE=7      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=True      ,_SCREAMING_SNAKE_CASE=99      ,_SCREAMING_SNAKE_CASE=24      ,_SCREAMING_SNAKE_CASE=32      ,_SCREAMING_SNAKE_CASE=5      ,_SCREAMING_SNAKE_CASE=4      ,_SCREAMING_SNAKE_CASE=37      ,_SCREAMING_SNAKE_CASE="gelu"      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=0.1      ,_SCREAMING_SNAKE_CASE=512      ,_SCREAMING_SNAKE_CASE=16      ,_SCREAMING_SNAKE_CASE=2      ,_SCREAMING_SNAKE_CASE=0.0_2      ,_SCREAMING_SNAKE_CASE=4      ,)							->    List[str]:
          _snake_case							=   parent
          _snake_case							=   batch_size
          _snake_case							=   seq_length
          _snake_case							=   is_training
          _snake_case							=   use_attention_mask
          _snake_case							=   use_token_type_ids
          _snake_case							=   use_labels
          _snake_case							=   vocab_size
          _snake_case							=   hidden_size
          _snake_case							=   embedding_size
          _snake_case							=   num_hidden_layers
          _snake_case							=   num_attention_heads
          _snake_case							=   intermediate_size
          _snake_case							=   hidden_act
          _snake_case							=   hidden_dropout_prob
          _snake_case							=   attention_probs_dropout_prob
          _snake_case							=   max_position_embeddings
          _snake_case							=   type_vocab_size
          _snake_case							=   type_sequence_label_size
          _snake_case							=   initializer_range
          _snake_case							=   num_choices






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size					)

          _snake_case							=   None
          if self.use_attention_mask:
               _snake_case							=   random_attention_mask([self.batch_size, self.seq_length]					)

          _snake_case							=   None
          if self.use_token_type_ids:
               _snake_case							=   ids_tensor([self.batch_size, self.seq_length]      ,self.type_vocab_size					)

          _snake_case							=   ElectraConfig(
              vocab_size=self.vocab_size      ,hidden_size=self.hidden_size      ,embedding_size=self.embedding_size      ,num_hidden_layers=self.num_hidden_layers      ,num_attention_heads=self.num_attention_heads      ,intermediate_size=self.intermediate_size      ,hidden_act=self.hidden_act      ,hidden_dropout_prob=self.hidden_dropout_prob      ,attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,type_vocab_size=self.type_vocab_size      ,initializer_range=self.initializer_range      ,)

          return config, input_ids, token_type_ids, attention_mask






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   self.prepare_config_and_inputs()
          _snake_case							,      _snake_case							,      _snake_case							,      _snake_case							=   config_and_inputs
          _snake_case							=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict







@require_flax
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		True

     SCREAMING_SNAKE_CASE_							:					Any					       =		(
         (
             FlaxElectraModel,
             FlaxElectraForCausalLM,
             FlaxElectraForMaskedLM,
             FlaxElectraForPreTraining,
             FlaxElectraForTokenClassification,
             FlaxElectraForQuestionAnswering,
             FlaxElectraForMultipleChoice,
             FlaxElectraForSequenceClassification,
         )
         if is_flax_available()
         else ()
     )






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   FlaxElectraModelTester(self					)






     @slow
     def        _lowercase			(				self					)							->    List[Any]:
          for model_class_name in self.all_model_classes:
               if model_class_name == FlaxElectraForMaskedLM:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-generator"					)
               else:
                    _snake_case							=   model_class_name.from_pretrained("google/electra-small-discriminator"					)
               _snake_case							=   model(np.ones((1, 1)					)					)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE					)


