

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import (
      TF_MODEL_FOR_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_MASKED_LM_MAPPING,
      TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
      TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
      TF_MODEL_FOR_PRETRAINING_MAPPING,
      TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
      TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
      TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
      TFSharedEmbeddings,
  )

  if _tf_gpu_memory_limit is not None:
    __UpperCAmelCase     					=  tf.config.list_physical_devices('GPU')
    for gpu in gpus:
      # Restrict TensorFlow to only allocate x GB of memory on the GPUs
      try:
        tf.config.set_logical_device_configuration(
            gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
        )
        __UpperCAmelCase     					=  tf.config.list_logical_devices('GPU')
        print('Logical GPUs', logical_gpus)
      except RuntimeError as e:
        # Virtual devices must be set before GPUs have been initialized
        print(e)





@require_tf
class A__     :



 """simple docstring"""







 _lowercase  :       Optional[int]  			=    None
 _lowercase  :       Union[str, Any]  			=    ()
 _lowercase  :       Dict  			=    ()
 _lowercase  :       Tuple  			=    True
 _lowercase  :       Any  			=    True
 _lowercase  :       Union[str, Any]  			=    True
 _lowercase  :       Tuple  			=    False

 def  __magic_name__							(		self			:						int  ,    A_			:						Dict  ,    A_			:						Any  ,    A_			:						Tuple=False							):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     copy.deepcopy(A_							)

      if model_class in get_values(A_							):
           _lowerCAmelCase :       List[Any]	   =     {
               k: tf.tile(tf.expand_dims(A_  ,    1							)  ,    (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
               if isinstance(A_  ,    tf.Tensor							) and v.ndim > 0
               else v
               for k, v in inputs_dict.items()
           }

      if return_labels:
           if model_class in get_values(A_							):
                _lowerCAmelCase :       List[Any]	   =     tf.ones(self.model_tester.batch_size  ,    dtype=tf.intaa							)
           elif model_class in get_values(A_							):
                _lowerCAmelCase :       List[str]	   =     tf.zeros(self.model_tester.batch_size  ,    dtype=tf.intaa							)
                _lowerCAmelCase :       Any	   =     tf.zeros(self.model_tester.batch_size  ,    dtype=tf.intaa							)
           elif model_class in [
               *get_values(A_							),
               *get_values(A_							),
           ]:
                _lowerCAmelCase :       Dict	   =     tf.zeros(self.model_tester.batch_size  ,    dtype=tf.intaa							)
           elif model_class in get_values(A_							):
                _lowerCAmelCase :       List[Any]	   =     tf.zeros(self.model_tester.batch_size  ,    dtype=tf.intaa							)
           elif model_class in [
               *get_values(A_							),
               *get_values(A_							),
               *get_values(A_							),
               *get_values(A_							),
               *get_values(A_							),
           ]:
                _lowerCAmelCase :       List[str]	   =     tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length)  ,    dtype=tf.intaa							)
      return inputs_dict

 @slow
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
           _lowerCAmelCase :       Optional[Any]	   =     self._prepare_for_class(A_  ,    A_							)
           _lowerCAmelCase :       Optional[int]	   =     model_class(A_							)

           @tf.function
           def run_in_graph_mode():
                return model(A_							)

           _lowerCAmelCase :       List[str]	   =     run_in_graph_mode()
           self.assertIsNotNone(A_							)

 @slow
 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       str	   =     self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
           _lowerCAmelCase :       Union[str, Any]	   =     self._prepare_for_class(A_  ,    A_							)
           _lowerCAmelCase :       Optional[Any]	   =     model_class(A_							)

           @tf.function(experimental_compile=A_							)
           def run_in_graph_mode():
                return model(A_							)

           _lowerCAmelCase :       Optional[Any]	   =     run_in_graph_mode()
           self.assertIsNotNone(A_							)

 @slow
 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes[:2]:
           _lowerCAmelCase :       str	   =     model_class(A_							)
           if getattr(A_  ,    "hf_compute_loss"  ,    A_							):
                # Test that model correctly compute the loss with kwargs
                _lowerCAmelCase :       Dict	   =     self._prepare_for_class(inputs_dict.copy()  ,    A_  ,    return_labels=A_							)
                # Is there a better way to remove these decoder inputs?
                _lowerCAmelCase :       Any	   =     {
                    key: val
                    for key, val in prepared_for_class.items()
                    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                }

                _lowerCAmelCase :       List[Any]	   =     {
                    "labels",
                    "label",
                    "label_ids",
                    "start_positions",
                    "start_position",
                    "end_positions",
                    "end_position",
                    "next_sentence_label",
                }
                _lowerCAmelCase :       int	   =     possible_label_cols.intersection(set(A_							)							)
                self.assertGreater(len(A_							)  ,    0  ,    msg="No matching label names found!"							)
                _lowerCAmelCase :       int	   =     {key: val for key, val in prepared_for_class.items() if key in label_names}
                _lowerCAmelCase :       Optional[int]	   =     {key: val for key, val in prepared_for_class.items() if key not in label_names}
                self.assertGreater(len(A_							)  ,    0							)

                # Make sure it works with XLA!
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,    jit_compile=A_							)
                # Make sure the model fits without crashing regardless of where we pass the labels
                _lowerCAmelCase :       Optional[int]	   =     model.fit(
                    A_  ,    validation_data=A_  ,    steps_per_epoch=1  ,    validation_steps=1  ,    shuffle=A_  ,    verbose=0  ,    )
                _lowerCAmelCase :       List[str]	   =     history.history["loss"][0]
                self.assertTrue(not isnan(A_							)							)
                _lowerCAmelCase :       str	   =     history.history["val_loss"][0]
                self.assertTrue(not isnan(A_							)							)

                # Now test it with separate labels, to make sure that path works in XLA too.
                _lowerCAmelCase :       int	   =     model_class(A_							)
                model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)  ,    jit_compile=A_							)
                _lowerCAmelCase :       str	   =     model.fit(
                    A_  ,    A_  ,    validation_data=(inputs_minus_labels, labels)  ,    steps_per_epoch=1  ,    validation_steps=1  ,    shuffle=A_  ,    verbose=0  ,    )

                _lowerCAmelCase :       Union[str, Any]	   =     history.history["loss"][0]
                self.assertTrue(not isnan(A_							)							)
                _lowerCAmelCase :       Any	   =     history.history["val_loss"][0]
                self.assertTrue(not isnan(A_							)							)

 @slow
 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Union[str, Any]	   =     self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCAmelCase :       Optional[int]	   =     True
      _lowerCAmelCase :       Dict	   =     True

      if hasattr(A_  ,    "use_cache"							):
           _lowerCAmelCase :       Union[str, Any]	   =     True

      _lowerCAmelCase :       str	   =     getattr(self.model_tester  ,    "encoder_seq_length"  ,    self.model_tester.seq_length							)
      _lowerCAmelCase :       Optional[int]	   =     getattr(self.model_tester  ,    "key_length"  ,    A_							)

      for model_class in self.all_model_classes[:2]:
           _lowerCAmelCase :       Dict	   =     self._prepare_for_class(A_  ,    A_							)
           _lowerCAmelCase :       Optional[Any]	   =     model_class(A_							)
           model.build()
           _lowerCAmelCase :       Optional[int]	   =     len(model(A_							)							)

           for key in list(class_inputs_dict.keys()							):
                # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                if key not in model.input_signature:
                     del class_inputs_dict[key]
                # Check it's a tensor, in case the inputs dict has some bools in it too
                elif isinstance(class_inputs_dict[key]  ,    tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
                     _lowerCAmelCase :       Tuple	   =     tf.cast(class_inputs_dict[key]  ,    tf.intaa							)

           if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
                continue  # Some models have inputs that the preparation functions don't create, we skip those

           with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(A_  ,    saved_model=A_							)
                _lowerCAmelCase :       Any	   =     os.path.join(A_  ,    "saved_model"  ,    "1"							)
                _lowerCAmelCase :       Any	   =     tf.keras.models.load_model(A_							)
                _lowerCAmelCase :       List[Any]	   =     model(A_							)

                if self.is_encoder_decoder:
                     _lowerCAmelCase :       Any	   =     outputs["encoder_hidden_states"]
                     _lowerCAmelCase :       List[Any]	   =     outputs["encoder_attentions"]
                else:
                     _lowerCAmelCase :       Any	   =     outputs["hidden_states"]
                     _lowerCAmelCase :       Union[str, Any]	   =     outputs["attentions"]

                self.assertEqual(len(A_							)  ,    A_							)

                _lowerCAmelCase :       Dict	   =     getattr(
                    self.model_tester  ,    "expected_num_hidden_layers"  ,    self.model_tester.num_hidden_layers + 1							)

                self.assertEqual(len(A_							)  ,    A_							)
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]							)  ,    [self.model_tester.seq_length, self.model_tester.hidden_size]  ,    )

                self.assertEqual(len(A_							)  ,    self.model_tester.num_hidden_layers							)
                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]							)  ,    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]  ,    )

 @slow
 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      tf.keras.mixed_precision.set_global_policy("mixed_float16"							)

      # try/finally block to ensure subsequent tests run in float32
      try:
           _lowerCAmelCase   ,      _lowerCAmelCase :       Dict	   =     self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes[:2]:
                _lowerCAmelCase :       List[Any]	   =     self._prepare_for_class(A_  ,    A_							)
                _lowerCAmelCase :       Union[str, Any]	   =     model_class(A_							)
                _lowerCAmelCase :       int	   =     model(A_							)

                self.assertIsNotNone(A_							)
      finally:
           tf.keras.mixed_precision.set_global_policy("float32"							)

 @slow
 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       Any	   =     self.model_tester.prepare_config_and_inputs_for_common()
      # head_mask and decoder_head_mask has different shapes than other input args
      if "head_mask" in inputs_dict:
           del inputs_dict["head_mask"]
      if "decoder_head_mask" in inputs_dict:
           del inputs_dict["decoder_head_mask"]
      if "cross_attn_head_mask" in inputs_dict:
           del inputs_dict["cross_attn_head_mask"]
      _lowerCAmelCase :       Optional[Any]	   =     {
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__							),)
          for module_member_name in dir(A_							)
          if module_member_name.endswith("MainLayer"							)
          for module_member in (getattr(A_  ,    A_							),)
          if isinstance(A_  ,    A_							)
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(A_  ,    "_keras_serializable"  ,    A_							)
      }

      for main_layer_class in tf_main_layer_classes:
           # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
           if "T5" in main_layer_class.__name__:
                # Take the same values than in TFT5ModelTester for this shared layer
                _lowerCAmelCase :       str	   =     TFSharedEmbeddings(self.model_tester.vocab_size  ,    self.model_tester.hidden_size  ,    name="shared"							)
                _lowerCAmelCase :       Dict	   =     False
                _lowerCAmelCase :       List[Any]	   =     main_layer_class(A_  ,    embed_tokens=A_							)
           else:
                _lowerCAmelCase :       List[Any]	   =     main_layer_class(A_							)

           _lowerCAmelCase :       Union[str, Any]	   =     {
               name: tf.keras.Input(tensor.shape[1:]  ,    dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
           }

           if hasattr(self.model_tester  ,    "num_labels"							):
                _lowerCAmelCase :       Union[str, Any]	   =     self.model_tester.num_labels
           else:
                _lowerCAmelCase :       Optional[int]	   =     2

           _lowerCAmelCase :       int	   =     tf.data.Dataset.from_tensor_slices(
               (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

           _lowerCAmelCase :       Union[str, Any]	   =     main_layer(A_							)[0]
           _lowerCAmelCase :       str	   =     tf.keras.layers.Dense(A_  ,    activation="softmax"  ,    name="outputs"							)(A_							)
           _lowerCAmelCase :       str	   =     tf.keras.models.Model(inputs=A_  ,    outputs=[outputs]							)

           model.compile(loss="binary_crossentropy"  ,    optimizer="adam"  ,    metrics=["binary_accuracy"]							)
           model.fit(A_  ,    epochs=1							)

           with tempfile.TemporaryDirectory() as tmpdirname:
                _lowerCAmelCase :       Any	   =     os.path.join(A_  ,    "keras_model.h5"							)
                model.save(A_							)
                if "T5" in main_layer_class.__name__:
                     _lowerCAmelCase :       Dict	   =     tf.keras.models.load_model(
                         A_  ,    custom_objects={
                             main_layer_class.__name__: main_layer_class,
                             "TFSharedEmbeddings": TFSharedEmbeddings,
                         }  ,    )
                else:
                     _lowerCAmelCase :       str	   =     tf.keras.models.load_model(
                         A_  ,    custom_objects={main_layer_class.__name__: main_layer_class}							)
                assert isinstance(A_  ,    tf.keras.Model							)
                model(A_							)

 @slow
 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:2]:
           _lowerCAmelCase :       Dict	   =     model_class(A_							)

           _lowerCAmelCase :       Optional[int]	   =     copy.deepcopy(A_							)

           if not self.is_encoder_decoder:
                _lowerCAmelCase :       Dict	   =     inputs["input_ids"]
                del inputs["input_ids"]
           else:
                _lowerCAmelCase :       Optional[int]	   =     inputs["input_ids"]
                _lowerCAmelCase :       Optional[Any]	   =     inputs.get("decoder_input_ids"  ,    A_							)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids"  ,    A_							)

           if not self.is_encoder_decoder:
                _lowerCAmelCase :       List[str]	   =     model.get_input_embeddings()(A_							)
           else:
                _lowerCAmelCase :       Union[str, Any]	   =     model.get_input_embeddings()(A_							)
                _lowerCAmelCase :       Tuple	   =     model.get_input_embeddings()(A_							)

           _lowerCAmelCase :       Dict	   =     self._prepare_for_class(A_  ,    A_							)

           @tf.function
           def run_in_graph_mode():
                return model(A_							)

           _lowerCAmelCase :       int	   =     run_in_graph_mode()
           self.assertIsNotNone(A_							)

 def  __magic_name__							(		self			:						int  ,    A_			:						Union[str, Any]  ,    A_			:						str							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     []
      if model.config.bos_token_id is not None:
           special_tokens.append(model.config.bos_token_id							)
      if model.config.pad_token_id is not None:
           special_tokens.append(model.config.pad_token_id							)
      if model.config.eos_token_id is not None:
           special_tokens.append(model.config.eos_token_id							)

      # create random bad tokens that are not special tokens
      _lowerCAmelCase :       Optional[int]	   =     []
      while len(A_							) < num_bad_tokens:
           _lowerCAmelCase :       Any	   =     tf.squeeze(ids_tensor((1, 1)  ,    self.model_tester.vocab_size							)  ,    0							).numpy()[0]
           if token not in special_tokens:
                bad_tokens.append(A_							)
      return bad_tokens

 def  __magic_name__							(		self			:						str  ,    A_			:						str							):






      '''simple docstring'''




      for token_id in output_ids[0].numpy().tolist():
           self.assertGreaterEqual(A_  ,    0							)
           self.assertLess(A_  ,    self.model_tester.vocab_size							)


 def  __magic_name__							(		self			:						Union[str, Any]  ,    A_			:						Any  ,    A_			:						Any							):






      '''simple docstring'''




      for bad_word_ids in bad_words_ids:
           # for all slices in batch
           for generated_ids_slice in generated_ids:
                # for all word idx
                for i in range(len(A_							)  ,    len(A_							)							):
                     # if tokens match
                     if generated_ids_slice[i - len(A_							) : i] == bad_word_ids:
                          return True
      return False









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







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     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=SCREAMING_SNAKE_CASE		,				required=SCREAMING_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=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_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."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

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

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

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

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

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

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_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]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--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()






