



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

snake_case__					:     Optional[int]    		=   collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])

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



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			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    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	print("Extracting"  ,	f.name   )
	with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE   ) as bytestream:
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		if magic != 2_0_5_1:
			raise ValueError(
			    "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			bytestream.read(rows * cols * num_images   )
		__lowercase		       =			numpy.frombuffer(_SCREAMING_SNAKE_CASE  ,	dtype=numpy.uinta   )
		__lowercase		       =			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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			labels_dense.shape[0]
	__lowercase		       =			numpy.arange(_SCREAMING_SNAKE_CASE   ) * num_classes
	__lowercase		       =			numpy.zeros((num_labels, num_classes)   )
	__lowercase		       =			1
	return labels_one_hot



@deprecated(_SCREAMING_SNAKE_CASE  ,	"Please use tf.data to implement this functionality."   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=1_0   ):
	print("Extracting"  ,	f.name   )
	with gzip.GzipFile(fileobj=_SCREAMING_SNAKE_CASE   ) as bytestream:
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		if magic != 2_0_4_9:
			raise ValueError(
			    "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)   )
		__lowercase		       =			_readaa(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			bytestream.read(_SCREAMING_SNAKE_CASE   )
		__lowercase		       =			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   _A   :


						'''simple docstring'''





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






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			random_seed.get_seed(lowerCamelCase	)
							# If op level seed is not set, use whatever graph level seed is returned
							numpy.random.seed(seeda if seed is None else seeda	)
							__lowercase		       =			dtypes.as_dtype(lowerCamelCase	).base_dtype
							if dtype not in (dtypes.uinta, dtypes.floataa):
								raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype	)
							if fake_data:
								__lowercase		       =			10_000
								__lowercase		       =			one_hot
							else:
								assert (
								    images.shape[0] == labels.shape[0]
								), f"""images.shape: {images.shape} labels.shape: {labels.shape}"""
								__lowercase		       =			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
									__lowercase		       =			images.reshape(
									    images.shape[0]							,      images.shape[1] * images.shape[2]	)
								if dtype == dtypes.floataa:
									# Convert from [0, 255] -> [0.0, 1.0].
									__lowercase		       =			images.astype(numpy.floataa	)
									__lowercase		       =			numpy.multiply(lowerCamelCase							,      1.0 / 255.0	)
							__lowercase		       =			images
							__lowercase		       =			labels
							__lowercase		       =			0
							__lowercase		       =			0



						@property
						def     _snake_case	(	self	:							Union[str, Any]	):






							'''simple docstring'''



							return self._images



						@property
						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							return self._labels



						@property
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							return self._num_examples



						@property
						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							return self._epochs_completed



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Optional[int]							,      lowerCamelCase	:							Dict=False							,      lowerCamelCase	:							List[str]=True	):






							'''simple docstring'''



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



@deprecated(_SCREAMING_SNAKE_CASE  ,	"Please write your own downloading logic."   )
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if not gfile.Exists(_SCREAMING_SNAKE_CASE   ):
		gfile.MakeDirs(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			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:
			__lowercase		       =			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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=False  ,	_SCREAMING_SNAKE_CASE=dtypes.floataa  ,	_SCREAMING_SNAKE_CASE=True  ,	_SCREAMING_SNAKE_CASE=5_0_0_0  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=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   )

		__lowercase		       =			fake()
		__lowercase		       =			fake()
		__lowercase		       =			fake()
		return _Datasets(train=_SCREAMING_SNAKE_CASE  ,	validation=_SCREAMING_SNAKE_CASE  ,	test=_SCREAMING_SNAKE_CASE   )

	if not source_url:  # empty string check
		__lowercase		       =			DEFAULT_SOURCE_URL

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

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

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

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

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

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

	__lowercase		       =			train_images[:validation_size]
	__lowercase		       =			train_labels[:validation_size]
	__lowercase		       =			train_images[validation_size:]
	__lowercase		       =			train_labels[validation_size:]

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

	__lowercase		       =			_DataSet(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_DataSet(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			_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 logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case__					:     Optional[Any]    		=   logging.getLogger()



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"\n".join(_SCREAMING_SNAKE_CASE   )
	Path(_SCREAMING_SNAKE_CASE   ).open("w"   ).writelines(_SCREAMING_SNAKE_CASE   )


snake_case__					:     List[str]    		=   """patrickvonplaten/t5-tiny-random"""
snake_case__					:     int    		=   """sshleifer/bart-tiny-random"""
snake_case__					:     Union[str, Any]    		=   """sshleifer/tiny-mbart"""

snake_case__					:     List[str]    		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class   _A   ( _lowercase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()
							__lowercase		       =			[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
							_dump_articles(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								run_generate()
								assert Path(lowerCamelCase	).exists()
								# os.remove(Path(output_file_name))



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([BART_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([T5_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()

							__lowercase		       =			{
							    "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
							    "de": [
							        "Maschinelles Lernen ist großartig, oder?",
							        "Ich esse gerne Bananen",
							        "Morgen ist wieder ein toller Tag!",
							    ],
							}

							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	)
							__lowercase		       =			str(tmp_dir / "scores.json"	)
							__lowercase		       =			str(tmp_dir / "val.target"	)
							_dump_articles(lowerCamelCase							,      text["en"]	)
							_dump_articles(lowerCamelCase							,      text["de"]	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {str(lowerCamelCase	)}
            {str(lowerCamelCase	)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
							testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]	)

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								with CaptureStdout() as cs:
									run_search()
								__lowercase		       =			[" num_beams | length_penalty", model, "Best score args"]
								__lowercase		       =			["Info"]
								if "translation" in task:
									expected_strings.append("bleu"	)
								else:
									expected_strings.extend(lowerCamelCase	)
								for w in expected_strings:
									assert w in cs.out
								for w in un_expected_strings:
									assert w not in cs.out
								assert Path(lowerCamelCase	).exists()
								os.remove(Path(lowerCamelCase	)	)



