


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				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=numpy.dtype(numpy.uintaa				).newbyteorder('>'				)
     return numpy.frombuffer(bytestream.read(4				)			,     dtype=SCREAMING_SNAKE_CASE__				)[0]

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please use tf.data to implement this functionality.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_51:
               raise ValueError(
                   'Invalid magic number %d in MNIST image file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(rows * cols * num_images				)
          __UpperCamelCase    							=numpy.frombuffer(SCREAMING_SNAKE_CASE__			,     dtype=numpy.uinta				)
          __UpperCamelCase    							=data.reshape(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     1				)
          return data

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

@deprecated(SCREAMING_SNAKE_CASE__			,     'Please use tf.data to implement this functionality.'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					Dict=False			,     SCREAMING_SNAKE_CASE__			:					str=10				):
     print('Extracting'			,     f.name				)
     with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__				) as bytestream:
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          if magic != 20_49:
               raise ValueError(
                   'Invalid magic number %d in MNIST label file: %s' % (magic, f.name)				)
          __UpperCamelCase    							=_readaa(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=bytestream.read(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=numpy.frombuffer(SCREAMING_SNAKE_CASE__			,     dtype=numpy.uinta				)
          if one_hot:
               return _dense_to_one_hot(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
          return labels






class     UpperCAmelCase__       :



    """simple docstring"""






    @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					,   )  ->       Optional[int]:
         __UpperCamelCase							,      __UpperCamelCase    							=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    							=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    							=10000
              __UpperCamelCase    							=one_hot
         else:
              assert (
                  images.shape[0] == labels.shape[0]
              ), f'images.shape: {images.shape} labels.shape: {labels.shape}'
              __UpperCamelCase    							=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    							=images.reshape(
                       images.shape[0]					,   images.shape[1] * images.shape[2]    )
              if dtype == dtypes.floataa:
                   # Convert from [0, 255] -> [0.0, 1.0].
                   __UpperCamelCase    							=images.astype(numpy.floataa    )
                   __UpperCamelCase    							=numpy.multiply(A_					,   1.0 / 255.0    )
         __UpperCamelCase    							=images
         __UpperCamelCase    							=labels
         __UpperCamelCase    							=0
         __UpperCamelCase    							=0






    @property
    def _a					(			self    )  ->       Tuple:
         return self._images






    @property
    def _a					(			self    )  ->       Union[str, Any]:
         return self._labels






    @property
    def _a					(			self    )  ->       Optional[Any]:
         return self._num_examples






    @property
    def _a					(			self    )  ->       List[str]:
         return self._epochs_completed






    def _a					(			self					,   A_					,   A_=False					,   A_=True    )  ->       Optional[Any]:
         if fake_data:
              __UpperCamelCase    							=[1] * 784
              __UpperCamelCase    							=[1] + [0] * 9 if self.one_hot else 0
              return (
                  [fake_image for _ in range(A_    )],
                  [fake_label for _ in range(A_    )],
              )
         __UpperCamelCase    							=self._index_in_epoch
         # Shuffle for the first epoch
         if self._epochs_completed == 0 and start == 0 and shuffle:
              __UpperCamelCase    							=numpy.arange(self._num_examples    )
              numpy.random.shuffle(A_    )
              __UpperCamelCase    							=self.images[perma]
              __UpperCamelCase    							=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    							=self._num_examples - start
              __UpperCamelCase    							=self._images[start : self._num_examples]
              __UpperCamelCase    							=self._labels[start : self._num_examples]
              # Shuffle the data
              if shuffle:
                   __UpperCamelCase    							=numpy.arange(self._num_examples    )
                   numpy.random.shuffle(A_    )
                   __UpperCamelCase    							=self.images[perm]
                   __UpperCamelCase    							=self.labels[perm]
              # Start next epoch
              __UpperCamelCase    							=0
              __UpperCamelCase    							=batch_size - rest_num_examples
              __UpperCamelCase    							=self._index_in_epoch
              __UpperCamelCase    							=self._images[start:end]
              __UpperCamelCase    							=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    							=self._index_in_epoch
              return self._images[start:end], self._labels[start:end]

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





@deprecated(
    SCREAMING_SNAKE_CASE__			,     'Please use alternatives such as:' ' tensorflow_datasets.load(\'mnist\')'				)
def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[int]			,     SCREAMING_SNAKE_CASE__			:					int=False			,     SCREAMING_SNAKE_CASE__			:					str=False			,     SCREAMING_SNAKE_CASE__			:					Union[str, Any]=dtypes.floataa			,     SCREAMING_SNAKE_CASE__			:					Optional[int]=True			,     SCREAMING_SNAKE_CASE__			:					str=50_00			,     SCREAMING_SNAKE_CASE__			:					List[Any]=None			,     SCREAMING_SNAKE_CASE__			:					str=DEFAULT_SOURCE_URL			,     ):
     if fake_data:

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

          __UpperCamelCase    							=fake()
          __UpperCamelCase    							=fake()
          __UpperCamelCase    							=fake()
          return _Datasets(train=SCREAMING_SNAKE_CASE__			,     validation=SCREAMING_SNAKE_CASE__			,     test=SCREAMING_SNAKE_CASE__				)

     if not source_url:  # empty string check
          __UpperCamelCase    							=DEFAULT_SOURCE_URL

     __UpperCamelCase    							='train-images-idx3-ubyte.gz'
     __UpperCamelCase    							='train-labels-idx1-ubyte.gz'
     __UpperCamelCase    							='t10k-images-idx3-ubyte.gz'
     __UpperCamelCase    							='t10k-labels-idx1-ubyte.gz'

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + train_images_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_images(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + train_labels_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_labels(SCREAMING_SNAKE_CASE__			,     one_hot=SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + test_images_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_images(SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=_maybe_download(
         SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     source_url + test_labels_file				)
     with gfile.Open(SCREAMING_SNAKE_CASE__			,     'rb'				) as f:
          __UpperCamelCase    							=_extract_labels(SCREAMING_SNAKE_CASE__			,     one_hot=SCREAMING_SNAKE_CASE__				)

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

     __UpperCamelCase    							=train_images[:validation_size]
     __UpperCamelCase    							=train_labels[:validation_size]
     __UpperCamelCase    							=train_images[validation_size:]
     __UpperCamelCase    							=train_labels[validation_size:]

     __UpperCamelCase    							={'dtype': dtype, 'reshape': reshape, 'seed': seed}

     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=_DataSet(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     **SCREAMING_SNAKE_CASE__				)

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







import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

SCREAMING_SNAKE_CASE__   :			int												=    "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

SCREAMING_SNAKE_CASE__   :			Optional[Any]												=    "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"

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






		def  __A       (		self		:							Dict  )     ->							Tuple:
						if version.parse(scb.__version__  ) < version.parse('''1.4.12'''  ):
										raise ImportWarning(
										    '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
										    '''You can install it with `pip install "sacrebleu>=1.4.12"`.'''  )
						return datasets.MetricInfo(
						    description=_DESCRIPTION							,			citation=_CITATION							,			homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf'''							,			inputs_description=_KWARGS_DESCRIPTION							,			features=datasets.Features(
						        {
						            '''predictions''': datasets.Value('''string'''							,			id='''sequence'''  ),
						            '''references''': datasets.Sequence(datasets.Value('''string'''							,			id='''sequence'''  )							,			id='''references'''  ),
						        }  )							,			codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf''']							,			reference_urls=[
						        '''https://github.com/m-popovic/chrF''',
						    ]							,			)







		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.CHAR_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.WORD_ORDER							,			SCREAMING_SNAKE_CASE__		:							int = CHRF.BETA							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			SCREAMING_SNAKE_CASE__		:							bool = False							,			)     ->							Optional[Any]:
						__lowerCamelCase    					=	len(references[0]  )
						if any(len(SCREAMING_SNAKE_CASE__  ) != references_per_prediction for refs in references  ):
										raise ValueError('''Sacrebleu requires the same number of references for each prediction'''  )
						__lowerCamelCase    					=	[[refs[i] for refs in references] for i in range(SCREAMING_SNAKE_CASE__  )]

						__lowerCamelCase    					=	CHRF(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	sb_chrf.corpus_score(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						return {
						    "score": output.score,
						    "char_order": output.char_order,
						    "word_order": output.word_order,
						    "beta": output.beta,
						}



