# -*- coding: utf-8 -*-

"""BLEU metric implementation.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import os
import re
import subprocess
import tempfile
import numpy as np

from six.moves import urllib
import tensorflow as tf

def moses_multi_bleu(hypotheses,
                     references,
                     lowercase=False):
  """Calculate the bleu score for hypotheses and references
  using the MOSES ulti-bleu.perl script.

  Args:
    hypotheses: A numpy array of strings where each string is a single example.
    references: A numpy array of strings where each string is a single example.
    lowercase: If true, pass the "-lc" flag to the multi-bleu script

  Returns:
    The BLEU score as a float32 value.
  """

  if np.size(hypotheses) == 0:
    return np.float32(0.0)

  # Get MOSES multi-bleu script
  multi_bleu_path, _ = urllib.request.urlretrieve(
      "https://raw.githubusercontent.com/moses-smt/mosesdecoder/"
      "master/scripts/generic/multi-bleu.perl")
  os.chmod(multi_bleu_path, 0o755)

  # Alternatively, get file locally
  # training_dir = os.path.dirname(os.path.realpath(__file__))
  # bin_dir = os.path.abspath(os.path.join(training_dir, "..", "..", "bin"))
  # multi_bleu_path = os.path.join(bin_dir, "tools/multi-bleu.perl")

  # Dump hypotheses and references to tempfiles
  hypothesis_file = tempfile.NamedTemporaryFile()
  hypothesis_file.write("\n".join(hypotheses).encode("utf-8"))
  hypothesis_file.write(b"\n")
  hypothesis_file.flush()
  reference_file = tempfile.NamedTemporaryFile()
  reference_file.write("\n".join(references).encode("utf-8"))
  reference_file.write(b"\n")
  reference_file.flush()

  # Calculate BLEU using multi-bleu script
  with open(hypothesis_file.name, "r") as read_pred:
    bleu_cmd = [multi_bleu_path]
    if lowercase:
      bleu_cmd += ["-lc"]
    bleu_cmd += [reference_file.name]
    try:
      bleu_out = subprocess.check_output(
          bleu_cmd, stdin=read_pred, stderr=subprocess.STDOUT)
      bleu_out = bleu_out.decode("utf-8")
      bleu_score = re.search(r"BLEU = (.+?),", bleu_out).group(1)
      bleu_score = float(bleu_score)
    except subprocess.CalledProcessError as error:
      if error.output is not None:
        tf.logging.warning("multi-bleu.perl script returned non-zero exit code")
        tf.logging.warning(error.output)
      return float(0.0)

  # Close temp files
  hypothesis_file.close()
  reference_file.close()

  return np.float32(bleu_score)

# -*- coding: utf-8 -*-

"""BLEU metric implementation.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import os
import re
import subprocess
import tempfile
import numpy as np

from six.moves import urllib
import tensorflow as tf

def moses_multi_bleu(hypotheses,
                     references,
                     lowercase=False):
  """Calculate the bleu score for hypotheses and references
  using the MOSES ulti-bleu.perl script.

  Args:
    hypotheses: A numpy array of strings where each string is a single example.
    references: A numpy array of strings where each string is a single example.
    lowercase: If true, pass the "-lc" flag to the multi-bleu script

  Returns:
    The BLEU score as a float32 value.
  """

  if np.size(hypotheses) == 0:
    return np.float32(0.0)

  # Get MOSES multi-bleu script
  multi_bleu_path, _ = urllib.request.urlretrieve(
      "https://raw.githubusercontent.com/moses-smt/mosesdecoder/"
      "master/scripts/generic/multi-bleu.perl")
  os.chmod(multi_bleu_path, 0o755)

  # Alternatively, get file locally
  # training_dir = os.path.dirname(os.path.realpath(__file__))
  # bin_dir = os.path.abspath(os.path.join(training_dir, "..", "..", "bin"))
  # multi_bleu_path = os.path.join(bin_dir, "tools/multi-bleu.perl")

  # Dump hypotheses and references to tempfiles
  hypothesis_file = tempfile.NamedTemporaryFile()
  hypothesis_file.write("\n".join(hypotheses).encode("utf-8"))
  hypothesis_file.write(b"\n")
  hypothesis_file.flush()
  reference_file = tempfile.NamedTemporaryFile()
  reference_file.write("\n".join(references).encode("utf-8"))
  reference_file.write(b"\n")
  reference_file.flush()

  # Calculate BLEU using multi-bleu script
  with open(hypothesis_file.name, "r") as read_pred:
    bleu_cmd = [multi_bleu_path]
    if lowercase:
      bleu_cmd += ["-lc"]
    bleu_cmd += [reference_file.name]
    try:
      bleu_out = subprocess.check_output(
          bleu_cmd, stdin=read_pred, stderr=subprocess.STDOUT)
      bleu_out = bleu_out.decode("utf-8")
      bleu_score = re.search(r"BLEU = (.+?),", bleu_out).group(1)
      bleu_score = float(bleu_score)
    except subprocess.CalledProcessError as error:
      if error.output is not None:
        tf.logging.warning("multi-bleu.perl script returned non-zero exit code")
        tf.logging.warning(error.output)
      bleu_score = np.float32(0.0)

  # Close temp files
  hypothesis_file.close()
  reference_file.close()

  return np.float32(bleu_score)

Return np.float from pyfunc