



import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=13	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=99	,				__UpperCAmelCase=32	,				__UpperCAmelCase=5	,				__UpperCAmelCase=4	,				__UpperCAmelCase=37	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=16	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=3	,				__UpperCAmelCase=4	,				__UpperCAmelCase=None	,				):



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    is_training
    __lowerCamelCase													=    use_input_mask
    __lowerCamelCase													=    use_token_type_ids
    __lowerCamelCase													=    use_labels
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    type_sequence_label_size
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    num_labels
    __lowerCamelCase													=    num_choices
    __lowerCamelCase													=    scope




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size				)

    __lowerCamelCase													=    None
    if self.use_input_mask:
       __lowerCamelCase													=    random_attention_mask([self.batch_size, self.seq_length]				)

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

    __lowerCamelCase													=    None
    __lowerCamelCase													=    None
    __lowerCamelCase													=    None
    if self.use_labels:
       __lowerCamelCase													=    ids_tensor([self.batch_size]	,				self.type_sequence_label_size				)
       __lowerCamelCase													=    ids_tensor([self.batch_size, self.seq_length]	,				self.num_labels				)
       __lowerCamelCase													=    ids_tensor([self.batch_size]	,				self.num_choices				)

    __lowerCamelCase													=    self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return OpenLlamaConfig(
        vocab_size=self.vocab_size	,				hidden_size=self.hidden_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	,				is_decoder=__UpperCAmelCase	,				initializer_range=self.initializer_range	,				use_stable_embedding=__UpperCAmelCase	,				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    OpenLlamaModel(config=__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase				)
    __lowerCamelCase													=    model(__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    True
    __lowerCamelCase													=    OpenLlamaModel(__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				encoder_hidden_states=__UpperCAmelCase	,				encoder_attention_mask=__UpperCAmelCase	,				)
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				encoder_hidden_states=__UpperCAmelCase	,				)
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase				)
    self.parent.assertEqual(result.last_hidden_state.shape	,				(self.batch_size, self.seq_length, self.hidden_size)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    OpenLlamaForCausalLM(config=__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				labels=__UpperCAmelCase				)
    self.parent.assertEqual(result.logits.shape	,				(self.batch_size, self.seq_length, self.vocab_size)				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    True
    __lowerCamelCase													=    True
    __lowerCamelCase													=    OpenLlamaForCausalLM(config=__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()

    # first forward pass
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				encoder_hidden_states=__UpperCAmelCase	,				encoder_attention_mask=__UpperCAmelCase	,				use_cache=__UpperCAmelCase	,				)
    __lowerCamelCase													=    outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    __lowerCamelCase													=    ids_tensor((self.batch_size, 3)	,				config.vocab_size				)
    __lowerCamelCase													=    ids_tensor((self.batch_size, 3)	,				vocab_size=2				)

    # append to next input_ids and
    __lowerCamelCase													=    torch.cat([input_ids, next_tokens]	,				dim=-1				)
    __lowerCamelCase													=    torch.cat([input_mask, next_mask]	,				dim=-1				)

    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				encoder_hidden_states=__UpperCAmelCase	,				encoder_attention_mask=__UpperCAmelCase	,				output_hidden_states=__UpperCAmelCase	,				)['''hidden_states'''][0]
    __lowerCamelCase													=    model(
        __UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				encoder_hidden_states=__UpperCAmelCase	,				encoder_attention_mask=__UpperCAmelCase	,				past_key_values=__UpperCAmelCase	,				output_hidden_states=__UpperCAmelCase	,				)['''hidden_states'''][0]

    # select random slice
    __lowerCamelCase													=    ids_tensor((1,)	,				output_from_past.shape[-1]				).item()
    __lowerCamelCase													=    output_from_no_past[:, -3:, random_slice_idx].detach()
    __lowerCamelCase													=    output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(__UpperCAmelCase	,				__UpperCAmelCase	,				atol=1E-3				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.prepare_config_and_inputs()
    (
        (
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,(
        __lowerCamelCase
    )							,
    )													=    config_and_inputs
    __lowerCamelCase													=    {'''input_ids''': input_ids, '''attention_mask''': input_mask}
    return config, inputs_dict



@require_torch
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	lowerCAmelCase__  ,	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  (
     (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
 )
 lowerCAmelCase__       					=  (OpenLlamaForCausalLM,) if is_torch_available() else ()
 lowerCAmelCase__       					=  (
     {
         """feature-extraction""": OpenLlamaModel,
         """text-classification""": OpenLlamaForSequenceClassification,
         """text-generation""": OpenLlamaForCausalLM,
         """zero-shot""": OpenLlamaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowerCAmelCase__       					=  False
 lowerCAmelCase__       					=  False




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    OpenLlamaModelTester(self				)
    __lowerCamelCase													=    ConfigTester(self	,				config_class=__UpperCAmelCase	,				hidden_size=37				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.config_tester.run_common_tests()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    for type in ["absolute", "relative_key", "relative_key_query"]:
       __lowerCamelCase													=    type
       self.model_tester.create_and_check_model(*__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase													=    3
    __lowerCamelCase													=    input_dict['''input_ids''']
    __lowerCamelCase													=    input_ids.ne(1				).to(__UpperCAmelCase				)
    __lowerCamelCase													=    ids_tensor([self.model_tester.batch_size]	,				self.model_tester.type_sequence_label_size				)
    __lowerCamelCase													=    OpenLlamaForSequenceClassification(__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				labels=__UpperCAmelCase				)
    self.assertEqual(result.logits.shape	,				(self.model_tester.batch_size, self.model_tester.num_labels)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase													=    3
    __lowerCamelCase													=    '''single_label_classification'''
    __lowerCamelCase													=    input_dict['''input_ids''']
    __lowerCamelCase													=    input_ids.ne(1				).to(__UpperCAmelCase				)
    __lowerCamelCase													=    ids_tensor([self.model_tester.batch_size]	,				self.model_tester.type_sequence_label_size				)
    __lowerCamelCase													=    OpenLlamaForSequenceClassification(__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				labels=__UpperCAmelCase				)
    self.assertEqual(result.logits.shape	,				(self.model_tester.batch_size, self.model_tester.num_labels)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase													=    3
    __lowerCamelCase													=    '''multi_label_classification'''
    __lowerCamelCase													=    input_dict['''input_ids''']
    __lowerCamelCase													=    input_ids.ne(1				).to(__UpperCAmelCase				)
    __lowerCamelCase													=    ids_tensor(
        [self.model_tester.batch_size, config.num_labels]	,				self.model_tester.type_sequence_label_size				).to(torch.float				)
    __lowerCamelCase													=    OpenLlamaForSequenceClassification(__UpperCAmelCase				)
    model.to(__UpperCAmelCase				)
    model.eval()
    __lowerCamelCase													=    model(__UpperCAmelCase	,				attention_mask=__UpperCAmelCase	,				labels=__UpperCAmelCase				)
    self.assertEqual(result.logits.shape	,				(self.model_tester.batch_size, self.model_tester.num_labels)				)




 @unittest.skip('''Open-Llama buffers include complex numbers, which breaks this test'''				)
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 @parameterized.expand([('''linear''',), ('''dynamic''',)]				)
 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase													=    ids_tensor([1, 10]	,				config.vocab_size				)
    __lowerCamelCase													=    ids_tensor([1, int(config.max_position_embeddings * 1.5				)]	,				config.vocab_size				)

    set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
    __lowerCamelCase													=    OpenLlamaModel(__UpperCAmelCase				)
    original_model.to(__UpperCAmelCase				)
    original_model.eval()
    __lowerCamelCase													=    original_model(__UpperCAmelCase				).last_hidden_state
    __lowerCamelCase													=    original_model(__UpperCAmelCase				).last_hidden_state

    set_seed(42				)  # Fixed seed at init time so the two models get the same random weights
    __lowerCamelCase													=    {'''type''': scaling_type, '''factor''': 10.0}
    __lowerCamelCase													=    OpenLlamaModel(__UpperCAmelCase				)
    scaled_model.to(__UpperCAmelCase				)
    scaled_model.eval()
    __lowerCamelCase													=    scaled_model(__UpperCAmelCase				).last_hidden_state
    __lowerCamelCase													=    scaled_model(__UpperCAmelCase				).last_hidden_state

    # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
    # maximum sequence length, so the outputs for the short input should match.
    if scaling_type == "dynamic":
       self.assertTrue(torch.allclose(__UpperCAmelCase	,				__UpperCAmelCase	,				atol=1E-5				)				)
    else:
       self.assertFalse(torch.allclose(__UpperCAmelCase	,				__UpperCAmelCase	,				atol=1E-5				)				)

    # The output should be different for long inputs
    self.assertFalse(torch.allclose(__UpperCAmelCase	,				__UpperCAmelCase	,				atol=1E-5				)				)











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


a_			=					getLogger(__name__)


a_			=					"""cuda""" if torch.cuda.is_available() else """cpu"""





def 		a__			(		_UpperCamelCase			: List[str] ,_UpperCamelCase			: str ,_UpperCamelCase			: str ,_UpperCamelCase			: int = 8 ,_UpperCamelCase			: str = DEFAULT_DEVICE ,_UpperCamelCase			: Dict=False ,_UpperCamelCase			: Dict="summarization" ,_UpperCamelCase			: Optional[int]=None ,**_UpperCamelCase			: Dict ,):
   __lowerCamelCase													=    Path(_UpperCamelCase							).open('''w''' ,encoding='''utf-8'''							)
   __lowerCamelCase													=    str(_UpperCamelCase							)
   __lowerCamelCase													=    AutoModelForSeqaSeqLM.from_pretrained(_UpperCamelCase							).to(_UpperCamelCase							)
   if fpaa:
      __lowerCamelCase													=    model.half()

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

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





def 		a__			(		):
   return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''							)





def 		a__			(		_UpperCamelCase			: Union[str, Any]=True							):
   __lowerCamelCase													=    argparse.ArgumentParser()
   parser.add_argument('''model_name''' ,type=_UpperCamelCase ,help='''like facebook/bart-large-cnn,t5-base, etc.'''							)
   parser.add_argument('''input_path''' ,type=_UpperCamelCase ,help='''like cnn_dm/test.source'''							)
   parser.add_argument('''save_path''' ,type=_UpperCamelCase ,help='''where to save summaries'''							)
   parser.add_argument('''--reference_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''like cnn_dm/test.target'''							)
   parser.add_argument('''--score_path''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default='''metrics.json''' ,help='''where to save metrics'''							)
   parser.add_argument('''--device''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''cuda, cuda:1, cpu etc.'''							)
   parser.add_argument(
       '''--prefix''' ,type=_UpperCamelCase ,required=_UpperCamelCase ,default=_UpperCamelCase ,help='''will be added to the begininng of src examples'''							)
   parser.add_argument('''--task''' ,type=_UpperCamelCase ,default='''summarization''' ,help='''used for task_specific_params + metrics'''							)
   parser.add_argument('''--bs''' ,type=_UpperCamelCase ,default=8 ,required=_UpperCamelCase ,help='''batch size'''							)
   parser.add_argument(
       '''--n_obs''' ,type=_UpperCamelCase ,default=-1 ,required=_UpperCamelCase ,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=_UpperCamelCase ,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
   __lowerCamelCase							,__lowerCamelCase													=    parser.parse_known_args()
   __lowerCamelCase													=    parse_numeric_n_bool_cl_kwargs(_UpperCamelCase							)
   if parsed_args and verbose:
      print(F"""parsed the following generate kwargs: {parsed_args}"""							)
   __lowerCamelCase													=    [''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path							).readlines()]
   if args.n_obs > 0:
      __lowerCamelCase													=    examples[: args.n_obs]
   Path(args.save_path							).parent.mkdir(exist_ok=_UpperCamelCase							)

   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'''							)

   __lowerCamelCase													=    generate_summaries_or_translations(
       _UpperCamelCase ,args.save_path ,args.model_name ,batch_size=args.bs ,device=args.device ,fpaa=args.fpaa ,task=args.task ,prefix=args.prefix ,**_UpperCamelCase ,)

   if args.reference_path is None:
      return {}

   # Compute scores
   __lowerCamelCase													=    calculate_bleu if '''translation''' in args.task else calculate_rouge
   __lowerCamelCase													=    [x.rstrip() for x in open(args.save_path							).readlines()]
   __lowerCamelCase													=    [x.rstrip() for x in open(args.reference_path							).readlines()][: len(_UpperCamelCase							)]
   __lowerCamelCase													=    score_fn(_UpperCamelCase ,_UpperCamelCase							)
   scores.update(_UpperCamelCase							)

   if args.dump_args:
      scores.update(_UpperCamelCase							)
   if args.info:
      __lowerCamelCase													=    args.info

   if verbose:
      print(_UpperCamelCase							)

   if args.score_path is not None:
      json.dump(_UpperCamelCase ,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)






