





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 lowerCAmelCase__(__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	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 lowerCAmelCase__(__snake_case							)       ->   Any:







	'''simple docstring'''







	print('''Extracting''' ,f.name							)
	with gzip.GzipFile(fileobj=UpperCamelCase__							) as bytestream:
		lowerCamelCase__              =	_readaa(UpperCamelCase__							)
		if magic != 2051:
			raise ValueError(
			    '''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name)							)
		lowerCamelCase__              =	_readaa(UpperCamelCase__							)
		lowerCamelCase__              =	_readaa(UpperCamelCase__							)
		lowerCamelCase__              =	_readaa(UpperCamelCase__							)
		lowerCamelCase__              =	bytestream.read(rows * cols * num_images							)
		lowerCamelCase__              =	numpy.frombuffer(UpperCamelCase__ ,dtype=numpy.uinta							)
		lowerCamelCase__              =	data.reshape(UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ ,1							)
		return data





@deprecated(UpperCamelCase__ ,'''Please use tf.one_hot on tensors.'''							)
def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   Union[str, Any]:







	'''simple docstring'''







	lowerCamelCase__              =	labels_dense.shape[0]
	lowerCamelCase__              =	numpy.arange(UpperCamelCase__							) * num_classes
	lowerCamelCase__              =	numpy.zeros((num_labels, num_classes)							)
	lowerCamelCase__              =	1
	return labels_one_hot





@deprecated(UpperCamelCase__ ,'''Please use tf.data to implement this functionality.'''							)
def lowerCAmelCase__(__snake_case ,__snake_case=False ,__snake_case=10							)       ->   str:







	'''simple docstring'''







	print('''Extracting''' ,f.name							)
	with gzip.GzipFile(fileobj=UpperCamelCase__							) as bytestream:
		lowerCamelCase__              =	_readaa(UpperCamelCase__							)
		if magic != 2049:
			raise ValueError(
			    '''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name)							)
		lowerCamelCase__              =	_readaa(UpperCamelCase__							)
		lowerCamelCase__              =	bytestream.read(UpperCamelCase__							)
		lowerCamelCase__              =	numpy.frombuffer(UpperCamelCase__ ,dtype=numpy.uinta							)
		if one_hot:
			return _dense_to_one_hot(UpperCamelCase__ ,UpperCamelCase__							)
		return labels






class       __A					:

			'''simple docstring'''







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



				'''simple docstring'''







				lowerCamelCase__              =	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							)
				lowerCamelCase__              =	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:
					lowerCamelCase__              =	1_0_0_0_0
					lowerCamelCase__              =	one_hot
				else:
					assert (
					    images.shape[0] == labels.shape[0]
					), F'images.shape: {images.shape} labels.shape: {labels.shape}'
					lowerCamelCase__              =	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
						lowerCamelCase__              =	images.reshape(
						    images.shape[0] ,						images.shape[1] * images.shape[2]							)
					if dtype == dtypes.floataa:
						# Convert from [0, 255] -> [0.0, 1.0].
						lowerCamelCase__              =	images.astype(numpy.floataa							)
						lowerCamelCase__              =	numpy.multiply(__a ,						1.0 / 2_5_5.0							)
				lowerCamelCase__              =	images
				lowerCamelCase__              =	labels
				lowerCamelCase__              =	0
				lowerCamelCase__              =	0





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self._images





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self._labels





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self._num_examples





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self._epochs_completed





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=False ,						__lowerCAmelCase=True							):



				'''simple docstring'''







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





@deprecated(UpperCamelCase__ ,'''Please write your own downloading logic.'''							)
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	if not gfile.Exists(UpperCamelCase__							):
		gfile.MakeDirs(UpperCamelCase__							)
	lowerCamelCase__              =	os.path.join(UpperCamelCase__ ,UpperCamelCase__							)
	if not gfile.Exists(UpperCamelCase__							):
		urllib.request.urlretrieve(UpperCamelCase__ ,UpperCamelCase__							)  # noqa: S310
		with gfile.GFile(UpperCamelCase__							) as f:
			lowerCamelCase__              =	f.size()
		print('''Successfully downloaded''' ,UpperCamelCase__ ,UpperCamelCase__ ,'''bytes.'''							)
	return filepath





@deprecated(
    UpperCamelCase__ ,'''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''							)
def lowerCAmelCase__(__snake_case ,__snake_case=False ,__snake_case=False ,__snake_case=dtypes.floataa ,__snake_case=True ,__snake_case=5000 ,__snake_case=None ,__snake_case=DEFAULT_SOURCE_URL ,)       ->   Tuple:







	'''simple docstring'''







	if fake_data:

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

		lowerCamelCase__              =	fake()
		lowerCamelCase__              =	fake()
		lowerCamelCase__              =	fake()
		return _Datasets(train=UpperCamelCase__ ,validation=UpperCamelCase__ ,test=UpperCamelCase__							)

	if not source_url:  # empty string check
		lowerCamelCase__              =	DEFAULT_SOURCE_URL

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

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

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

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

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

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

	lowerCamelCase__              =	train_images[:validation_size]
	lowerCamelCase__              =	train_labels[:validation_size]
	lowerCamelCase__              =	train_images[validation_size:]
	lowerCamelCase__              =	train_labels[validation_size:]

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

	lowerCamelCase__              =	_DataSet(UpperCamelCase__ ,UpperCamelCase__ ,**UpperCamelCase__							)
	lowerCamelCase__              =	_DataSet(UpperCamelCase__ ,UpperCamelCase__ ,**UpperCamelCase__							)
	lowerCamelCase__              =	_DataSet(UpperCamelCase__ ,UpperCamelCase__ ,**UpperCamelCase__							)

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








# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	{
	    '''en''': '''Machine learning is great, isn\'t it?''',
	    '''ru''': '''Машинное обучение - это здорово, не так ли?''',
	    '''de''': '''Maschinelles Lernen ist großartig, nicht wahr?''',
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	lowerCamelCase__              =	{
	    '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
	    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
	    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
	}
	lowerCamelCase__              =	F'{src_lang}-{tgt_lang}'

	lowerCamelCase__              =	F'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "allenai/{model_name}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n'
	model_card_dir.mkdir(parents=__snake_case ,exist_ok=__snake_case							)
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	print(F'Generating {path}'							)
	with open(__snake_case ,'''w''' ,encoding='''utf-8'''							) as f:
		f.write(__snake_case							)


# make sure we are under the root of the project
_a						=   Path(__file__).resolve().parent.parent.parent
_a						=   repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			_a						=   model_cards_dir / "allenai" / model_name
			write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



