



'''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
lowercase__  :	Tuple            =      {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}





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





def     snake_case__			():
    UpperCamelCase__	     =      sys.argv[0]

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

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

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

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

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

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

    UpperCamelCase__	     =      sorted(lowerCAmelCase_       ,	key=operator.itemgetter(*task_score_names[task]						)       ,	reverse=lowerCAmelCase_						)
    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__	     =      results_sorted[0]
    for score in task_score_names[task]:
        del best[score]
    UpperCamelCase__	     =      [F'--{k} {v}' for k, v in best.items()]
    UpperCamelCase__	     =      ["--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    lowerCamelCase_				(						unittest.TestCase	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=13  ,    __UpperCamelCase=7  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=99  ,    __UpperCamelCase=24  ,    __UpperCamelCase=32  ,    __UpperCamelCase=5  ,    __UpperCamelCase=4  ,    __UpperCamelCase=37  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=5_12  ,    __UpperCamelCase=16  ,    __UpperCamelCase=2  ,    __UpperCamelCase=0.02  ,    __UpperCamelCase=4  ,    ):
       UpperCamelCase__	     =      parent
       UpperCamelCase__	     =      batch_size
       UpperCamelCase__	     =      seq_length
       UpperCamelCase__	     =      is_training
       UpperCamelCase__	     =      use_attention_mask
       UpperCamelCase__	     =      use_token_type_ids
       UpperCamelCase__	     =      use_labels
       UpperCamelCase__	     =      vocab_size
       UpperCamelCase__	     =      hidden_size
       UpperCamelCase__	     =      embedding_size
       UpperCamelCase__	     =      num_hidden_layers
       UpperCamelCase__	     =      num_attention_heads
       UpperCamelCase__	     =      intermediate_size
       UpperCamelCase__	     =      hidden_act
       UpperCamelCase__	     =      hidden_dropout_prob
       UpperCamelCase__	     =      attention_probs_dropout_prob
       UpperCamelCase__	     =      max_position_embeddings
       UpperCamelCase__	     =      type_vocab_size
       UpperCamelCase__	     =      type_sequence_label_size
       UpperCamelCase__	     =      initializer_range
       UpperCamelCase__	     =      num_choices


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size			)

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

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

       UpperCamelCase__	     =      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 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.prepare_config_and_inputs()
       UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__	     =      config_and_inputs
       UpperCamelCase__	     =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
       return config, inputs_dict

@require_flax
class    lowerCamelCase_				(						lowerCAmelCase_       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  Dict					=						True

   A			:  Optional[Any]					=						(
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      FlaxElectraModelTester(self			)







   @slow
   def 						_UpperCamelCase							(	self			):
       for model_class_name in self.all_model_classes:
           if model_class_name == FlaxElectraForMaskedLM:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-generator"			)
           else:
               UpperCamelCase__	     =      model_class_name.from_pretrained("google/electra-small-discriminator"			)
           UpperCamelCase__	     =      model(np.ones((1, 1)			)			)
           self.assertIsNotNone(__UpperCamelCase			)




