






import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  assert isinstance(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"    ,  [False, True]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE							:   List[str]						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__    ,  keep_in_memory=SCREAMING_SNAKE_CASE__					).read()
  _check_json_dataset(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)


@pytest.mark.parametrize(
    "features"    ,  [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]    ,  )
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   int						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   Tuple						    =     features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE							:   str						    =     (
      Features({feature: Value(SCREAMING_SNAKE_CASE__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  SCREAMING_SNAKE_CASE							:   Any						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  features=SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__					).read()
  _check_json_dataset(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)


@pytest.mark.parametrize(
    "features"    ,  [
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]    ,  )
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     {"col_3": "float64", "col_1": "string", "col_2": "int64"}
  SCREAMING_SNAKE_CASE							:   Any						    =     features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE							:   Any						    =     (
      Features({feature: Value(SCREAMING_SNAKE_CASE__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  features=SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__					).read()
  assert isinstance(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)
  assert dataset.num_rows == 2
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_3", "col_1", "col_2"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


def 	lowerCamelCase__      (  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   str						    =     {"col_2": "int64", "col_3": "float64", "col_1": "string"}
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     features.copy()
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     (
      Features({feature: Value(SCREAMING_SNAKE_CASE__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  SCREAMING_SNAKE_CASE							:   List[str]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   Tuple						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  features=SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__					).read()
  assert isinstance(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)
  assert dataset.num_rows == 2
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_2", "col_3", "col_1"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("split"    ,  [None, NamedSplit("train"					), "train", "test"]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   List[str]						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__    ,  split=SCREAMING_SNAKE_CASE__					).read()
  _check_json_dataset(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"    ,  [str, list]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  if issubclass(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					):
    SCREAMING_SNAKE_CASE							:   str						    =     jsonl_path
  elif issubclass(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					):
    SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [jsonl_path]
  SCREAMING_SNAKE_CASE							:   List[str]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   Dict						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__					).read()
  _check_json_dataset(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)


def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase=("train",)					):

  """simple docstring"""






  assert isinstance(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)
  for split in splits:
    SCREAMING_SNAKE_CASE							:   Dict						    =     dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"    ,  [False, True]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   str						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE							:   int						    =     JsonDatasetReader({"train": jsonl_path}    ,  cache_dir=SCREAMING_SNAKE_CASE__    ,  keep_in_memory=SCREAMING_SNAKE_CASE__					).read()
  _check_json_datasetdict(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)


@pytest.mark.parametrize(
    "features"    ,  [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]    ,  )
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   str						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   str						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   Optional[int]						    =     features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     (
      Features({feature: Value(SCREAMING_SNAKE_CASE__					) for feature, dtype in features.items()}					) if features is not None else None
  )
  SCREAMING_SNAKE_CASE							:   List[Any]						    =     JsonDatasetReader({"train": jsonl_path}    ,  features=SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__					).read()
  _check_json_datasetdict(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__					)


@pytest.mark.parametrize("split"    ,  [None, NamedSplit("train"					), "train", "test"]					)
def 	lowerCamelCase__      (  lowercase    ,  lowercase    ,  lowercase					):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {split: jsonl_path}
  else:
    SCREAMING_SNAKE_CASE							:   List[str]						    =     "train"
    SCREAMING_SNAKE_CASE							:   List[Any]						    =     {"train": jsonl_path, "test": jsonl_path}
  SCREAMING_SNAKE_CASE							:   int						    =     tmp_path / "cache"
  SCREAMING_SNAKE_CASE							:   str						    =     {"col_1": "string", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE							:   List[str]						    =     JsonDatasetReader(SCREAMING_SNAKE_CASE__    ,  cache_dir=SCREAMING_SNAKE_CASE__					).read()
  _check_json_datasetdict(SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__    ,  splits=list(path.keys()					)					)
  assert all(dataset[split].split == split for split in path.keys()					)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  return json.load(SCREAMING_SNAKE_CASE__					)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  return [json.loads(SCREAMING_SNAKE_CASE__					) for line in buffer]


class 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     @pytest.mark.parametrize("lines, load_json_function"       ,			[(True, load_json_lines), (False, load_json)]   )
     def 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Tuple   ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(UpperCAmelCase_       ,			UpperCAmelCase_       ,			lines=UpperCAmelCase_   ).write()
         buffer.seek(0   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     load_json_function(UpperCAmelCase_   )
       assert isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   )
       assert isinstance(exported_content[0]       ,			UpperCAmelCase_   )
       assert len(UpperCAmelCase_   ) == 10


     @pytest.mark.parametrize(
         "orient, container, keys, len_at"       ,			[
             ("records", list, {"tokens", "labels", "answers", "id"}, None),
             ("split", dict, {"columns", "data"}, "data"),
             ("index", dict, set("0123456789"   ), None),
             ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
             ("values", list, None, None),
             ("table", dict, {"schema", "data"}, "data"),
         ]       ,			)
     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Optional[int]   ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(UpperCAmelCase_       ,			UpperCAmelCase_       ,			lines=UpperCAmelCase_       ,			orient=UpperCAmelCase_   ).write()
         buffer.seek(0   )
         SCREAMING_SNAKE_CASE							:   str						    =     load_json(UpperCAmelCase_   )
       assert isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   )
       if keys:
         if container is dict:
           assert exported_content.keys() == keys
         else:
           assert exported_content[0].keys() == keys
       else:
         assert not hasattr(UpperCAmelCase_       ,			"keys"   ) and not hasattr(exported_content[0]       ,			"keys"   )
       if len_at:
         assert len(exported_content[len_at]   ) == 10
       else:
         assert len(UpperCAmelCase_   ) == 10


     @pytest.mark.parametrize("lines, load_json_function"       ,			[(True, load_json_lines), (False, load_json)]   )
     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						int       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						str   ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(UpperCAmelCase_       ,			UpperCAmelCase_       ,			lines=UpperCAmelCase_       ,			num_proc=2   ).write()
         buffer.seek(0   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     load_json_function(UpperCAmelCase_   )
       assert isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   )
       assert isinstance(exported_content[0]       ,			UpperCAmelCase_   )
       assert len(UpperCAmelCase_   ) == 10


     @pytest.mark.parametrize(
         "orient, container, keys, len_at"       ,			[
             ("records", list, {"tokens", "labels", "answers", "id"}, None),
             ("split", dict, {"columns", "data"}, "data"),
             ("index", dict, set("0123456789"   ), None),
             ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
             ("values", list, None, None),
             ("table", dict, {"schema", "data"}, "data"),
         ]       ,			)
     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Optional[int]   ):
       with io.BytesIO() as buffer:
         JsonDatasetWriter(UpperCAmelCase_       ,			UpperCAmelCase_       ,			lines=UpperCAmelCase_       ,			orient=UpperCAmelCase_       ,			num_proc=2   ).write()
         buffer.seek(0   )
         SCREAMING_SNAKE_CASE							:   int						    =     load_json(UpperCAmelCase_   )
       assert isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   )
       if keys:
         if container is dict:
           assert exported_content.keys() == keys
         else:
           assert exported_content[0].keys() == keys
       else:
         assert not hasattr(UpperCAmelCase_       ,			"keys"   ) and not hasattr(exported_content[0]       ,			"keys"   )
       if len_at:
         assert len(exported_content[len_at]   ) == 10
       else:
         assert len(UpperCAmelCase_   ) == 10


     def 					_A							(					self							:						Any       ,			UpperCAmelCase_							:						Optional[int]   ):
       with pytest.raises(UpperCAmelCase_   ):
         with io.BytesIO() as buffer:
           JsonDatasetWriter(UpperCAmelCase_       ,			UpperCAmelCase_       ,			num_proc=0   )






     @pytest.mark.parametrize("compression, extension"       ,			[("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]   )
     def 					_A							(					self							:						List[str]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						int   ):
       SCREAMING_SNAKE_CASE							:   List[str]						    =     tmp_path_factory.mktemp("data"   ) / f'''test.json.{extension}'''
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     str(shared_datadir / f'''test_file.json.{extension}'''   )
       JsonDatasetWriter(UpperCAmelCase_       ,			UpperCAmelCase_       ,			compression=UpperCAmelCase_   ).write()

       with fsspec.open(UpperCAmelCase_       ,			"rb"       ,			compression="infer"   ) as f:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     f.read()
       with fsspec.open(UpperCAmelCase_       ,			"rb"       ,			compression="infer"   ) as f:
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     f.read()
       assert exported_content == original_content














'''simple docstring'''



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


__UpperCamelCase   :							int         =   getLogger(__name__)


__UpperCamelCase   :							int         =   """cuda""" if torch.cuda.is_available() else """cpu"""



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int = 8,       SCREAMING_SNAKE_CASE__:						str = DEFAULT_DEVICE,       SCREAMING_SNAKE_CASE__:						Any=False,       SCREAMING_SNAKE_CASE__:						Tuple="summarization",       SCREAMING_SNAKE_CASE__:						List[Any]=None,       **SCREAMING_SNAKE_CASE__:						int,       )	->      Dict:

   """simple docstring"""
   __a					  =     Path(SCREAMING_SNAKE_CASE__     ).open('w',       encoding='utf-8'     )
   __a					  =     str(SCREAMING_SNAKE_CASE__     )
   __a					  =     AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE__     ).to(SCREAMING_SNAKE_CASE__     )
   if fpaa:
      __a					  =     model.half()

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

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



def  __UpperCAmelCase						(					)	->      List[str]:

   """simple docstring"""
   return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[int]=True     )	->      List[Any]:

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

   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'     )

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

   if args.reference_path is None:
      return {}

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

   if args.dump_args:
      scores.update(SCREAMING_SNAKE_CASE__     )
   if args.info:
      __a					  =     args.info

   if verbose:
      print(SCREAMING_SNAKE_CASE__     )

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