





import comet  # From: unbabel-comet
import torch

import datasets


__lowerCamelCase	:					Union[str, Any]			  =					datasets.logging.get_logger(__name__)

__lowerCamelCase	:					Any			  =					"""\
@inproceedings{rei-EtAl:2020:WMT,
   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},
   title     = {Unbabel's Participation in the WMT20 Metrics Shared Task},
   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},
   month          = {November},
   year           = {2020},
   address        = {Online},
   publisher      = {Association for Computational Linguistics},
   pages     = {909--918},
}
@inproceedings{rei-etal-2020-comet,
   title = \"{COMET}: A Neural Framework for {MT} Evaluation\",
   author = \"Rei, Ricardo  and
      Stewart, Craig  and
      Farinha, Ana C  and
      Lavie, Alon\",
   booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",
   month = nov,
   year = \"2020\",
   address = \"Online\",
   publisher = \"Association for Computational Linguistics\",
   url = \"https://www.aclweb.org/anthology/2020.emnlp-main.213\",
   pages = \"2685--2702\",
}
"""

__lowerCamelCase	:					Optional[Any]			  =					"""\
Crosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA's or MQM).
With the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.

See the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.
"""

__lowerCamelCase	:					str			  =					"""
COMET score.

Args:

`sources` (list of str): Source sentences
`predictions` (list of str): candidate translations
`references` (list of str): reference translations
`cuda` (bool): If set to True, runs COMET using GPU
`show_progress` (bool): Shows progress
`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.

Returns:
    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.
    `scores`: List of scores.

Examples:

    >>> comet_metric = datasets.load_metric('comet')
    >>> # comet_metric = load_metric('comet', 'wmt20-comet-da')  # you can also choose which model to use
    >>> source = [\"Dem Feuer konnte Einhalt geboten werden\", \"Schulen und Kindergärten wurden eröffnet.\"]
    >>> hypothesis = [\"The fire could be stopped\", \"Schools and kindergartens were open\"]
    >>> reference = [\"They were able to control the fire.\", \"Schools and kindergartens opened\"]
    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [0.19, 0.92]
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,       _KWARGS_DESCRIPTION      )
class  A__       (	datasets.Metric      ):





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION	,							citation=_CITATION	,							homepage="https://unbabel.github.io/COMET/html/index.html"	,							inputs_description=_KWARGS_DESCRIPTION	,							features=datasets.Features(
               {
                   "sources": datasets.Value("string"	,							id="sequence"       ),
                   "predictions": datasets.Value("string"	,							id="sequence"       ),
                   "references": datasets.Value("string"	,							id="sequence"       ),
               }       )	,							codebase_urls=["https://github.com/Unbabel/COMET"]	,							reference_urls=[
               "https://github.com/Unbabel/COMET",
               "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
               "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
           ]	,							)





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       if self.config_name == "default":
        UpperCamelCase     : Union[str, Any]	     =       comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"       )       )
       else:
        UpperCamelCase     : Optional[Any]	     =       comet.load_from_checkpoint(comet.download_model(self.config_name       )       )





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_=None	,							A_=False       ):
       '''simple docstring'''




       if gpus is None:
        UpperCamelCase     : Union[str, Any]	     =       1 if torch.cuda.is_available() else 0
       UpperCamelCase     : Tuple	     =       {"src": sources, "mt": predictions, "ref": references}
       UpperCamelCase     : Dict	     =       [dict(zip(A_	,							A_       )       ) for t in zip(*data.values()       )]
       UpperCamelCase     : Any	     =       self.scorer.predict(A_	,							gpus=A_	,							progress_bar=A_       )
       return {"mean_score": mean_score, "scores": scores}











import collections
import gzip
import os
import urllib

import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated

__lowerCamelCase	:					Any			  =					collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__lowerCamelCase	:					str			  =					"""https://storage.googleapis.com/cvdf-datasets/mnist/"""



def 		A_    (					_lowerCAmelCase					)						->			Optional[Any]:
 UpperCamelCase     : Tuple	     =       numpy.dtype(numpy.uintaa					).newbyteorder(">"					)
 return numpy.frombuffer(bytestream.read(4					)							,       dtype=_lowerCAmelCase					)[0]



@deprecated(_lowerCAmelCase							,       "Please use tf.data to implement this functionality."					)
def 		A_    (					_lowerCAmelCase					)						->			int:
 print("Extracting"							,       f.name					)
 with gzip.GzipFile(fileobj=_lowerCAmelCase					) as bytestream:
  UpperCamelCase     : Dict	     =       _readaa(_lowerCAmelCase					)
  if magic != 2051:
   raise ValueError(
       "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)					)
  UpperCamelCase     : Optional[int]	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : int	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : Union[str, Any]	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : List[Any]	     =       bytestream.read(rows * cols * num_images					)
  UpperCamelCase     : List[str]	     =       numpy.frombuffer(_lowerCAmelCase							,       dtype=numpy.uinta					)
  UpperCamelCase     : Optional[Any]	     =       data.reshape(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       1					)
  return data



@deprecated(_lowerCAmelCase							,       "Please use tf.one_hot on tensors."					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : List[str]	     =       labels_dense.shape[0]
 UpperCamelCase     : str	     =       numpy.arange(_lowerCAmelCase					) * num_classes
 UpperCamelCase     : Optional[Any]	     =       numpy.zeros((num_labels, num_classes)					)
 UpperCamelCase     : Dict	     =       1
 return labels_one_hot



@deprecated(_lowerCAmelCase							,       "Please use tf.data to implement this functionality."					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=10					)						->			str:
 print("Extracting"							,       f.name					)
 with gzip.GzipFile(fileobj=_lowerCAmelCase					) as bytestream:
  UpperCamelCase     : int	     =       _readaa(_lowerCAmelCase					)
  if magic != 2049:
   raise ValueError(
       "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)					)
  UpperCamelCase     : List[str]	     =       _readaa(_lowerCAmelCase					)
  UpperCamelCase     : List[Any]	     =       bytestream.read(_lowerCAmelCase					)
  UpperCamelCase     : List[str]	     =       numpy.frombuffer(_lowerCAmelCase							,       dtype=numpy.uinta					)
  if one_hot:
   return _dense_to_one_hot(_lowerCAmelCase							,       _lowerCAmelCase					)
  return labels



class  A__       :





      @deprecated(
          A_	,							"Please use alternatives such as official/mnist/_DataSet.py"
          " from tensorflow/models."	,							)
      def __init__(			self	,							A_	,							A_	,							A_=False	,							A_=False	,							A_=dtypes.floataa	,							A_=True	,							A_=None	,							):
       '''simple docstring'''




       UpperCamelCase     ,   UpperCamelCase     : int	     =       random_seed.get_seed(A_       )
       # If op level seed is not set, use whatever graph level seed is returned
       numpy.random.seed(seeda if seed is None else seeda       )
       UpperCamelCase     : Optional[Any]	     =       dtypes.as_dtype(A_       ).base_dtype
       if dtype not in (dtypes.uinta, dtypes.floataa):
        raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype       )
       if fake_data:
        UpperCamelCase     : List[str]	     =       1_0000
        UpperCamelCase     : int	     =       one_hot
       else:
        assert (
            images.shape[0] == labels.shape[0]
        ), F"""images.shape: {images.shape} labels.shape: {labels.shape}"""
        UpperCamelCase     : Optional[Any]	     =       images.shape[0]

        # Convert shape from [num examples, rows, columns, depth]
        # to [num examples, rows*columns] (assuming depth == 1)
        if reshape:
         assert images.shape[3] == 1
         UpperCamelCase     : int	     =       images.reshape(
             images.shape[0]	,							images.shape[1] * images.shape[2]       )
        if dtype == dtypes.floataa:
         # Convert from [0, 255] -> [0.0, 1.0].
         UpperCamelCase     : str	     =       images.astype(numpy.floataa       )
         UpperCamelCase     : str	     =       numpy.multiply(A_	,							1.0 / 2_55.0       )
       UpperCamelCase     : Optional[int]	     =       images
       UpperCamelCase     : str	     =       labels
       UpperCamelCase     : Optional[Any]	     =       0
       UpperCamelCase     : Optional[int]	     =       0





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._images





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._labels





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._num_examples





      @property
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return self._epochs_completed





      def 	__UpperCamelCase(			self	,							A_	,							A_=False	,							A_=True       ):
       '''simple docstring'''




       if fake_data:
        UpperCamelCase     : Optional[int]	     =       [1] * 784
        UpperCamelCase     : Optional[Any]	     =       [1] + [0] * 9 if self.one_hot else 0
        return (
            [fake_image for _ in range(A_       )],
            [fake_label for _ in range(A_       )],
        )
       UpperCamelCase     : Optional[Any]	     =       self._index_in_epoch
       # Shuffle for the first epoch
       if self._epochs_completed == 0 and start == 0 and shuffle:
        UpperCamelCase     : Optional[Any]	     =       numpy.arange(self._num_examples       )
        numpy.random.shuffle(A_       )
        UpperCamelCase     : int	     =       self.images[perma]
        UpperCamelCase     : Any	     =       self.labels[perma]
       # Go to the next epoch
       if start + batch_size > self._num_examples:
        # Finished epoch
        self._epochs_completed += 1
        # Get the rest examples in this epoch
        UpperCamelCase     : List[Any]	     =       self._num_examples - start
        UpperCamelCase     : Union[str, Any]	     =       self._images[start : self._num_examples]
        UpperCamelCase     : str	     =       self._labels[start : self._num_examples]
        # Shuffle the data
        if shuffle:
         UpperCamelCase     : Union[str, Any]	     =       numpy.arange(self._num_examples       )
         numpy.random.shuffle(A_       )
         UpperCamelCase     : Union[str, Any]	     =       self.images[perm]
         UpperCamelCase     : Union[str, Any]	     =       self.labels[perm]
        # Start next epoch
        UpperCamelCase     : Tuple	     =       0
        UpperCamelCase     : Tuple	     =       batch_size - rest_num_examples
        UpperCamelCase     : List[str]	     =       self._index_in_epoch
        UpperCamelCase     : Dict	     =       self._images[start:end]
        UpperCamelCase     : int	     =       self._labels[start:end]
        return (
            numpy.concatenate((images_rest_part, images_new_part)	,							axis=0       ),
            numpy.concatenate((labels_rest_part, labels_new_part)	,							axis=0       ),
        )
       else:
        self._index_in_epoch += batch_size
        UpperCamelCase     : Union[str, Any]	     =       self._index_in_epoch
        return self._images[start:end], self._labels[start:end]



@deprecated(_lowerCAmelCase							,       "Please write your own downloading logic."					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Union[str, Any]:
 if not gfile.Exists(_lowerCAmelCase					):
  gfile.MakeDirs(_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       os.path.join(_lowerCAmelCase							,       _lowerCAmelCase					)
 if not gfile.Exists(_lowerCAmelCase					):
  urllib.request.urlretrieve(_lowerCAmelCase							,       _lowerCAmelCase					)  # noqa: S310
  with gfile.GFile(_lowerCAmelCase					) as f:
   UpperCamelCase     : Optional[int]	     =       f.size()
  print("Successfully downloaded"							,       _lowerCAmelCase							,       _lowerCAmelCase							,       "bytes."					)
 return filepath



@deprecated(
    _lowerCAmelCase							,       "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"					)
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase=False							,       _lowerCAmelCase=False							,       _lowerCAmelCase=dtypes.floataa							,       _lowerCAmelCase=True							,       _lowerCAmelCase=5000							,       _lowerCAmelCase=None							,       _lowerCAmelCase=DEFAULT_SOURCE_URL							,       )						->			List[str]:
 if fake_data:

  def fake():
   return _DataSet(
       []							,       []							,       fake_data=_lowerCAmelCase							,       one_hot=_lowerCAmelCase							,       dtype=_lowerCAmelCase							,       seed=_lowerCAmelCase					)

  UpperCamelCase     : Any	     =       fake()
  UpperCamelCase     : List[str]	     =       fake()
  UpperCamelCase     : Union[str, Any]	     =       fake()
  return _Datasets(train=_lowerCAmelCase							,       validation=_lowerCAmelCase							,       test=_lowerCAmelCase					)

 if not source_url:  # empty string check
  UpperCamelCase     : str	     =       DEFAULT_SOURCE_URL

 UpperCamelCase     : List[str]	     =       "train-images-idx3-ubyte.gz"
 UpperCamelCase     : Optional[int]	     =       "train-labels-idx1-ubyte.gz"
 UpperCamelCase     : List[str]	     =       "t10k-images-idx3-ubyte.gz"
 UpperCamelCase     : Union[str, Any]	     =       "t10k-labels-idx1-ubyte.gz"

 UpperCamelCase     : Optional[int]	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + train_images_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : List[str]	     =       _extract_images(_lowerCAmelCase					)

 UpperCamelCase     : Dict	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + train_labels_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : List[Any]	     =       _extract_labels(_lowerCAmelCase							,       one_hot=_lowerCAmelCase					)

 UpperCamelCase     : Any	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + test_images_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : Any	     =       _extract_images(_lowerCAmelCase					)

 UpperCamelCase     : List[str]	     =       _maybe_download(
     _lowerCAmelCase							,       _lowerCAmelCase							,       source_url + test_labels_file					)
 with gfile.Open(_lowerCAmelCase							,       "rb"					) as f:
  UpperCamelCase     : str	     =       _extract_labels(_lowerCAmelCase							,       one_hot=_lowerCAmelCase					)

 if not 0 <= validation_size <= len(_lowerCAmelCase					):
  UpperCamelCase     : Any	     =       (
      "Validation size should be between 0 and "
      F"""{len(_lowerCAmelCase					)}. Received: {validation_size}."""
  )
  raise ValueError(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       train_images[:validation_size]
 UpperCamelCase     : int	     =       train_labels[:validation_size]
 UpperCamelCase     : List[str]	     =       train_images[validation_size:]
 UpperCamelCase     : Union[str, Any]	     =       train_labels[validation_size:]

 UpperCamelCase     : List[str]	     =       {"dtype": dtype, "reshape": reshape, "seed": seed}

 UpperCamelCase     : List[str]	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)
 UpperCamelCase     : Any	     =       _DataSet(_lowerCAmelCase							,       _lowerCAmelCase							,       **_lowerCAmelCase					)

 return _Datasets(train=_lowerCAmelCase							,       validation=_lowerCAmelCase							,       test=_lowerCAmelCase					)
