





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 :Union[str, Any]     =    collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

# CVDF mirror of http://yann.lecun.com/exdb/mnist/
__lowerCAmelCase :List[Any]     =    'https://storage.googleapis.com/cvdf-datasets/mnist/'







def    A				(							UpperCAmelCase				):
					_snake_case       :					List[str]				       =   numpy.dtype(numpy.uintaa				).newbyteorder(">"				)
					return numpy.frombuffer(bytestream.read(4				)  ,  dtype=UpperCAmelCase				)[0]







@deprecated(UpperCAmelCase  ,  "Please use tf.data to implement this functionality."				)
def    A				(							UpperCAmelCase				):
					print("Extracting"  ,  f.name				)
					with gzip.GzipFile(fileobj=UpperCAmelCase				) as bytestream:
										_snake_case       :					List[str]				       =   _readaa(UpperCAmelCase				)
										if magic != 2_051:
															raise ValueError(
															    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)				)
										_snake_case       :					Optional[Any]				       =   _readaa(UpperCAmelCase				)
										_snake_case       :					int				       =   _readaa(UpperCAmelCase				)
										_snake_case       :					Dict				       =   _readaa(UpperCAmelCase				)
										_snake_case       :					Optional[Any]				       =   bytestream.read(rows * cols * num_images				)
										_snake_case       :					Any				       =   numpy.frombuffer(UpperCAmelCase  ,  dtype=numpy.uinta				)
										_snake_case       :					int				       =   data.reshape(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  1				)
										return data







@deprecated(UpperCAmelCase  ,  "Please use tf.one_hot on tensors."				)
def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					_snake_case       :					int				       =   labels_dense.shape[0]
					_snake_case       :					Tuple				       =   numpy.arange(UpperCAmelCase				) * num_classes
					_snake_case       :					Dict				       =   numpy.zeros((num_labels, num_classes)				)
					_snake_case       :					Dict				       =   1
					return labels_one_hot







@deprecated(UpperCAmelCase  ,  "Please use tf.data to implement this functionality."				)
def    A				(							UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase=10				):
					print("Extracting"  ,  f.name				)
					with gzip.GzipFile(fileobj=UpperCAmelCase				) as bytestream:
										_snake_case       :					List[Any]				       =   _readaa(UpperCAmelCase				)
										if magic != 2_049:
															raise ValueError(
															    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)				)
										_snake_case       :					Union[str, Any]				       =   _readaa(UpperCAmelCase				)
										_snake_case       :					Optional[int]				       =   bytestream.read(UpperCAmelCase				)
										_snake_case       :					int				       =   numpy.frombuffer(UpperCAmelCase  ,  dtype=numpy.uinta				)
										if one_hot:
															return _dense_to_one_hot(UpperCAmelCase  ,  UpperCAmelCase				)
										return labels






class        _a:







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


							'''simple docstring'''

							_snake_case       , _snake_case       :					Tuple				       =   random_seed.get_seed(__snake_case				)
							# 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       :					List[str]				       =   dtypes.as_dtype(__snake_case				).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       :					List[Any]				       =   1_0_0_0_0
												_snake_case       :					Tuple				       =   one_hot
							else:
												assert (
												    images.shape[0] == labels.shape[0]
												), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
												_snake_case       :					Optional[int]				       =   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       :					int				       =   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       :					str				       =   images.astype(numpy.floataa				)
																	_snake_case       :					Any				       =   numpy.multiply(__snake_case ,						1.0 / 2_55.0				)
							_snake_case       :					Any				       =   images
							_snake_case       :					int				       =   labels
							_snake_case       :					Optional[int]				       =   0
							_snake_case       :					List[str]				       =   0







		@property
		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							return self._images







		@property
		def        lowercase       (    self				)      ->      str:


							'''simple docstring'''

							return self._labels







		@property
		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							return self._num_examples







		@property
		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							return self._epochs_completed







		def        lowercase       (    self ,						__snake_case ,						__snake_case=False ,						__snake_case=True				)      ->      Tuple:


							'''simple docstring'''

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







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







@deprecated(
    UpperCAmelCase  ,  "Please use alternatives such as:" " tensorflow_datasets.load('mnist')"				)
def    A				(							UpperCAmelCase  ,  UpperCAmelCase=False  ,  UpperCAmelCase=False  ,  UpperCAmelCase=dtypes.floataa  ,  UpperCAmelCase=True  ,  UpperCAmelCase=5_000  ,  UpperCAmelCase=None  ,  UpperCAmelCase=DEFAULT_SOURCE_URL  ,  ):
					if fake_data:

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

										_snake_case       :					List[Any]				       =   fake()
										_snake_case       :					Optional[Any]				       =   fake()
										_snake_case       :					str				       =   fake()
										return _Datasets(train=UpperCAmelCase  ,  validation=UpperCAmelCase  ,  test=UpperCAmelCase				)

					if not source_url:  # empty string check
										_snake_case       :					List[Any]				       =   DEFAULT_SOURCE_URL

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

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

					_snake_case       :					Any				       =   _maybe_download(
					    UpperCAmelCase  ,  UpperCAmelCase  ,  source_url + train_labels_file				)
					with gfile.Open(UpperCAmelCase  ,  "rb"				) as f:
										_snake_case       :					Optional[int]				       =   _extract_labels(UpperCAmelCase  ,  one_hot=UpperCAmelCase				)

					_snake_case       :					Dict				       =   _maybe_download(
					    UpperCAmelCase  ,  UpperCAmelCase  ,  source_url + test_images_file				)
					with gfile.Open(UpperCAmelCase  ,  "rb"				) as f:
										_snake_case       :					Tuple				       =   _extract_images(UpperCAmelCase				)

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

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

					_snake_case       :					Tuple				       =   train_images[:validation_size]
					_snake_case       :					Tuple				       =   train_labels[:validation_size]
					_snake_case       :					Optional[int]				       =   train_images[validation_size:]
					_snake_case       :					List[Any]				       =   train_labels[validation_size:]

					_snake_case       :					Optional[int]				       =   {"dtype": dtype, "reshape": reshape, "seed": seed}

					_snake_case       :					Optional[int]				       =   _DataSet(UpperCAmelCase  ,  UpperCAmelCase  ,  **UpperCAmelCase				)
					_snake_case       :					Tuple				       =   _DataSet(UpperCAmelCase  ,  UpperCAmelCase  ,  **UpperCAmelCase				)
					_snake_case       :					List[str]				       =   _DataSet(UpperCAmelCase  ,  UpperCAmelCase  ,  **UpperCAmelCase				)

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






import datasets

from .evaluate import evaluate


__lowerCAmelCase :Any     =    '\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n'

__lowerCAmelCase :Union[str, Any]     =    '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'

__lowerCAmelCase :Optional[Any]     =    '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the CUAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'aupr\': Area Under the Precision-Recall curve\n    \'prec_at_80_recall\': Precision at 80% recall\n    \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n    >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n    >>> cuad_metric = datasets.load_metric("cuad")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'






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







		def        lowercase       (    self				)      ->      Optional[int]:


							'''simple docstring'''

							return datasets.MetricInfo(
							    description=_DESCRIPTION ,						citation=_CITATION ,						inputs_description=_KWARGS_DESCRIPTION ,						features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.features.Sequence(datasets.Value("string"				)				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {
							                        "text": datasets.Value("string"				),
							                        "answer_start": datasets.Value("int32"				),
							                    }				),
							            },
							        }				) ,						codebase_urls=["https://www.atticusprojectai.org/cuad"] ,						reference_urls=["https://www.atticusprojectai.org/cuad"] ,						)







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
							_snake_case       :					Union[str, Any]				       =   [
							    {
							        "paragraphs": [
							            {
							                "qas": [
							                    {
							                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
							                        "id": ref["id"],
							                    }
							                    for ref in references
							                ]
							            }
							        ]
							    }
							]
							_snake_case       :					Union[str, Any]				       =   evaluate(dataset=__snake_case ,						predictions=__snake_case				)
							return score