






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

a__       =     collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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

def _UpperCAmelCase   (	a						:	Optional[Any] ):
							snake_case__	  =       numpy.dtype(numpy.uintaa ).newbyteorder(""">""" )
							return numpy.frombuffer(bytestream.read(4 )  ,					dtype=a )[0]

@deprecated(a  ,					"""Please use tf.data to implement this functionality.""" )
def _UpperCAmelCase   (	a						:	Dict ):
							print("""Extracting"""  ,					f.name )
							with gzip.GzipFile(fileobj=a ) as bytestream:
														snake_case__	  =       _readaa(a )
														if magic != 2051:
																					raise ValueError(
																					    """Invalid magic number %d in MNIST image file: %s""" % (magic, f.name) )
														snake_case__	  =       _readaa(a )
														snake_case__	  =       _readaa(a )
														snake_case__	  =       _readaa(a )
														snake_case__	  =       bytestream.read(rows * cols * num_images )
														snake_case__	  =       numpy.frombuffer(a  ,					dtype=numpy.uinta )
														snake_case__	  =       data.reshape(a  ,					a  ,					a  ,					1 )
														return data

@deprecated(a  ,					"""Please use tf.one_hot on tensors.""" )
def _UpperCAmelCase   (	a						:	str  ,					a						:	Any ):
							snake_case__	  =       labels_dense.shape[0]
							snake_case__	  =       numpy.arange(a ) * num_classes
							snake_case__	  =       numpy.zeros((num_labels, num_classes) )
							snake_case__	  =       1
							return labels_one_hot

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


class    _lowerCAmelCase			:



				"""simple docstring"""



				@deprecated(
				    UpperCamelCase__			,		"""Please use alternatives such as official/mnist/_DataSet.py"""
				    """ from tensorflow/models."""			,		)
				def __init__(      self    :    List[str]			,		UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Any=False			,		UpperCamelCase__    :    List[Any]=False			,		UpperCamelCase__    :    List[Any]=dtypes.floataa			,		UpperCamelCase__    :    Union[str, Any]=True			,		UpperCamelCase__    :    Any=None			,		):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       random_seed.get_seed(UpperCamelCase__)
											# If op level seed is not set, use whatever graph level seed is returned
											numpy.random.seed(seeda if seed is None else seeda)
											snake_case__	  =       dtypes.as_dtype(UpperCamelCase__).base_dtype
											if dtype not in (dtypes.uinta, dtypes.floataa):
																		raise TypeError("""Invalid image dtype %r, expected uint8 or float32""" % dtype)
											if fake_data:
																		snake_case__	  =       1_0_0_0_0
																		snake_case__	  =       one_hot
											else:
																		assert (
																		    images.shape[0] == labels.shape[0]
																		), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
																		snake_case__	  =       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
																									snake_case__	  =       images.reshape(
																									    images.shape[0]			,		images.shape[1] * images.shape[2])
																		if dtype == dtypes.floataa:
																									# Convert from [0, 255] -> [0.0, 1.0].
																									snake_case__	  =       images.astype(numpy.floataa)
																									snake_case__	  =       numpy.multiply(UpperCamelCase__			,		1.0 / 2_5_5.0)
											snake_case__	  =       images
											snake_case__	  =       labels
											snake_case__	  =       0
											snake_case__	  =       0



				@property
				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											return self._images



				@property
				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											return self._labels



				@property
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											return self._num_examples



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return self._epochs_completed



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Optional[Any]=False			,		UpperCamelCase__    :    Union[str, Any]=True):






											'''simple docstring'''







											if fake_data:
																		snake_case__	  =       [1] * 7_8_4
																		snake_case__	  =       [1] + [0] * 9 if self.one_hot else 0
																		return (
																		    [fake_image for _ in range(UpperCamelCase__)],
																		    [fake_label for _ in range(UpperCamelCase__)],
																		)
											snake_case__	  =       self._index_in_epoch
											# Shuffle for the first epoch
											if self._epochs_completed == 0 and start == 0 and shuffle:
																		snake_case__	  =       numpy.arange(self._num_examples)
																		numpy.random.shuffle(UpperCamelCase__)
																		snake_case__	  =       self.images[perma]
																		snake_case__	  =       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
																		snake_case__	  =       self._num_examples - start
																		snake_case__	  =       self._images[start : self._num_examples]
																		snake_case__	  =       self._labels[start : self._num_examples]
																		# Shuffle the data
																		if shuffle:
																									snake_case__	  =       numpy.arange(self._num_examples)
																									numpy.random.shuffle(UpperCamelCase__)
																									snake_case__	  =       self.images[perm]
																									snake_case__	  =       self.labels[perm]
																		# Start next epoch
																		snake_case__	  =       0
																		snake_case__	  =       batch_size - rest_num_examples
																		snake_case__	  =       self._index_in_epoch
																		snake_case__	  =       self._images[start:end]
																		snake_case__	  =       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
																		snake_case__	  =       self._index_in_epoch
																		return self._images[start:end], self._labels[start:end]


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



@deprecated(
    a  ,					"""Please use alternatives such as:""" """ tensorflow_datasets.load('mnist')""" )
def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Optional[int]=False  ,					a						:	Optional[Any]=False  ,					a						:	List[Any]=dtypes.floataa  ,					a						:	Dict=True  ,					a						:	List[str]=5000  ,					a						:	Optional[Any]=None  ,					a						:	Union[str, Any]=DEFAULT_SOURCE_URL  ,					):
							if fake_data:

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

														snake_case__	  =       fake()
														snake_case__	  =       fake()
														snake_case__	  =       fake()
														return _Datasets(train=a  ,					validation=a  ,					test=a )

							if not source_url:  # empty string check
														snake_case__	  =       DEFAULT_SOURCE_URL

							snake_case__	  =       """train-images-idx3-ubyte.gz"""
							snake_case__	  =       """train-labels-idx1-ubyte.gz"""
							snake_case__	  =       """t10k-images-idx3-ubyte.gz"""
							snake_case__	  =       """t10k-labels-idx1-ubyte.gz"""

							snake_case__	  =       _maybe_download(
							    a  ,					a  ,					source_url + train_images_file )
							with gfile.Open(a  ,					"""rb""" ) as f:
														snake_case__	  =       _extract_images(a )

							snake_case__	  =       _maybe_download(
							    a  ,					a  ,					source_url + train_labels_file )
							with gfile.Open(a  ,					"""rb""" ) as f:
														snake_case__	  =       _extract_labels(a  ,					one_hot=a )

							snake_case__	  =       _maybe_download(
							    a  ,					a  ,					source_url + test_images_file )
							with gfile.Open(a  ,					"""rb""" ) as f:
														snake_case__	  =       _extract_images(a )

							snake_case__	  =       _maybe_download(
							    a  ,					a  ,					source_url + test_labels_file )
							with gfile.Open(a  ,					"""rb""" ) as f:
														snake_case__	  =       _extract_labels(a  ,					one_hot=a )

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

							snake_case__	  =       train_images[:validation_size]
							snake_case__	  =       train_labels[:validation_size]
							snake_case__	  =       train_images[validation_size:]
							snake_case__	  =       train_labels[validation_size:]

							snake_case__	  =       {"""dtype""": dtype, """reshape""": reshape, """seed""": seed}

							snake_case__	  =       _DataSet(a  ,					a  ,					**a )
							snake_case__	  =       _DataSet(a  ,					a  ,					**a )
							snake_case__	  =       _DataSet(a  ,					a  ,					**a )

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








import comet  # From: unbabel-comet
import torch

import datasets


a__       =     datasets.logging.get_logger(__name__)

a__       =     """\
@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\",
}
"""

a__       =     """\
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.
"""

a__       =     """
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    _lowerCAmelCase			(		datasets.Metric      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''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 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Dict):






											'''simple docstring'''







											if self.config_name == "default":
																		snake_case__	  =       comet.load_from_checkpoint(comet.download_model("""wmt20-comet-da"""))
											else:
																		snake_case__	  =       comet.load_from_checkpoint(comet.download_model(self.config_name))



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    str			,		UpperCamelCase__    :    Tuple			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    int=None			,		UpperCamelCase__    :    List[Any]=False):






											'''simple docstring'''







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