






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__	:			Tuple			   = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])

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






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			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   a			(						lowerCamelCase_				):






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






@deprecated(__A		,   '''Please use tf.one_hot on tensors.'''				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''
				lowercase__    =			labels_dense.shape[0]
				lowercase__    =			numpy.arange(__A				) * num_classes
				lowercase__    =			numpy.zeros((num_labels, num_classes)				)
				lowercase__    =			1
				return labels_one_hot






@deprecated(__A		,   '''Please use tf.data to implement this functionality.'''				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_=False		,   lowerCamelCase_=10				):






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

				"""simple docstring"""




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

								'''simple docstring'''
								lowercase__    =			random_seed.get_seed(SCREAMING_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  )
								lowercase__    =			dtypes.as_dtype(SCREAMING_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:
												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(SCREAMING_SNAKE_CASE__,  1.0 / 255.0  )
								lowercase__    =			images
								lowercase__    =			labels
								lowercase__    =			0
								lowercase__    =			0




				@property
				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								return self._images




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

								'''simple docstring'''
								return self._labels




				@property
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								return self._num_examples




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

								'''simple docstring'''
								return self._epochs_completed




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any]=False,  lowerCamelCase						:    Tuple=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(SCREAMING_SNAKE_CASE__  )],
												    [fake_label for _ in range(SCREAMING_SNAKE_CASE__  )],
												)
								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(SCREAMING_SNAKE_CASE__  )
												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(SCREAMING_SNAKE_CASE__  )
																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(__A		,   '''Please write your own downloading logic.'''				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_				):






				'''simple docstring'''

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






@deprecated(
    __A		,   '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')'''				)
def   a			(						lowerCamelCase_		,   lowerCamelCase_=False		,   lowerCamelCase_=False		,   lowerCamelCase_=dtypes.floataa		,   lowerCamelCase_=True		,   lowerCamelCase_=5000		,   lowerCamelCase_=None		,   lowerCamelCase_=DEFAULT_SOURCE_URL		,   ):






				'''simple docstring'''

				if fake_data:

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

								lowercase__    =			fake()
								lowercase__    =			fake()
								lowercase__    =			fake()
								return _Datasets(train=__A		,   validation=__A		,   test=__A				)

				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(
				    __A		,   __A		,   source_url + train_images_file				)
				with gfile.Open(__A		,   '''rb'''				) as f:
								lowercase__    =			_extract_images(__A				)

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

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

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

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

				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(__A		,   __A		,   **__A				)
				lowercase__    =			_DataSet(__A		,   __A		,   **__A				)
				lowercase__    =			_DataSet(__A		,   __A		,   **__A				)

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








import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    unittest.TestCase			):
      snake_case__				:     Any        =						GPTSanJapaneseTokenizer
      snake_case__				:     Tuple        =						False
      snake_case__				:     str        =						{'''do_clean_text''': False, '''add_prefix_space''': False}
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			str:
             super().setUp()

             # fmt: off
             a_					:		Union[str, Any]							=  ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
             # fmt: on
             a_					:		int							=  {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
             a_					:		List[Any]							=  {'unk_token': '<unk>'}

             a_					:		Any							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['vocab_file']     )
             a_					:		List[str]							=  os.path.join(self.tmpdirname     ,							VOCAB_FILES_NAMES['emoji_file']     )
             with open(self.vocab_file     ,							'w'     ,							encoding='utf-8'     ) as vocab_writer:
                    vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )
             with open(self.emoji_file     ,							'w'     ) as emoji_writer:
                    emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE__     )     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     ,							**SCREAMING_SNAKE_CASE__				:       Tuple     )		->			int:
             kwargs.update(self.special_tokens_map     )
             return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname     ,							**SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			int:
             a_					:		Optional[int]							=  'こんにちは、世界。 \nこんばんは、㔺界。😀'
             a_					:		List[str]							=  'こんにちは、世界。 \nこんばんは、世界。😀'
             return input_text, output_text
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Dict:
             a_							,		a_					:		Union[str, Any]							=  self.get_input_output_texts(SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							add_special_tokens=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     ,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     )
             return text, ids
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             pass  # TODO add if relevant
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Any:
             pass  # TODO add if relevant
      def        SCREAMING_SNAKE_CASE							(       self				:       Any     )		->			Tuple:
             pass  # TODO add if relevant
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			List[str]:
             a_					:		List[str]							=  self.get_tokenizer()

             # Testing tokenization
             a_					:		List[Any]							=  'こんにちは、世界。　こんばんは、㔺界。'
             a_					:		Optional[int]							=  ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
             a_					:		Dict							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             # Testing conversion to ids without special tokens
             a_					:		Tuple							=  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
             a_					:		List[Any]							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             # Testing conversion to ids with special tokens
             a_					:		int							=  tokens + [tokenizer.unk_token]
             a_					:		int							=  [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
             a_					:		Tuple							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Dict:
             a_					:		Union[str, Any]							=  self.get_tokenizer()

             # Testing tokenization
             a_					:		Dict							=  'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
             a_					:		List[Any]							=  'こんにちは、、、、世界。こんばんは、、、、世界。'
             a_					:		Any							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[Any]							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Dict:
             a_					:		Tuple							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             # Testing tokenization
             a_					:		List[Any]							=  'こんにちは、世界。'
             a_					:		int							=  'こんばんは、㔺界。😀'
             a_					:		Dict							=  'こんにちは、世界。こんばんは、世界。😀'
             a_					:		Optional[int]							=  tokenizer.encode(prefix_text + input_text     )
             a_					:		Any							=  tokenizer.encode(''     ,							prefix_text=prefix_text + input_text     )
             a_					:		Union[str, Any]							=  tokenizer.encode(SCREAMING_SNAKE_CASE__     ,							prefix_text=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  tokenizer.decode(SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Optional[int]:
             a_					:		Tuple							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             # Testing tokenization
             a_					:		str							=  'こんにちは、世界。'
             a_					:		List[str]							=  'こんばんは、㔺界。😀'

             a_					:		str							=  len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) - 2
             a_					:		Tuple							=  len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) - 2

             a_					:		Optional[Any]							=  [1] + [0] * (len_prefix + len_text + 1)
             a_					:		Optional[Any]							=  [1] * (len_prefix + len_text + 1) + [0]
             a_					:		Tuple							=  [1] + [1] * (len_prefix) + [0] * (len_text + 1)

             a_					:		List[str]							=  tokenizer(prefix_text + input_text     ).token_type_ids
             a_					:		Union[str, Any]							=  tokenizer(''     ,							prefix_text=prefix_text + input_text     ).token_type_ids
             a_					:		Any							=  tokenizer(SCREAMING_SNAKE_CASE__     ,							prefix_text=SCREAMING_SNAKE_CASE__     ).token_type_ids
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			int:
             a_					:		str							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             a_					:		Optional[int]							=  tokenizer.encode('あンいワ'     )
             a_					:		Dict							=  tokenizer.encode(''     ,							prefix_text='あンいワ'     )
             a_					:		Dict							=  tokenizer.encode('いワ'     ,							prefix_text='あン'     )

             self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE__     )     ,							tokenizer.decode(SCREAMING_SNAKE_CASE__     )     )
             self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE__     )     ,							tokenizer.decode(SCREAMING_SNAKE_CASE__     )     )
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertNotEqual(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             self.assertEqual(x_token_a[1]     ,							x_token_a[-1]     )  # SEG token
             self.assertEqual(x_token_a[1]     ,							x_token_a[3]     )  # SEG token
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[Any]:
             a_					:		List[str]							=  self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese'     )

             a_					:		Optional[Any]							=  [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
             a_					:		List[str]							=  tokenizer(SCREAMING_SNAKE_CASE__     ,							padding=SCREAMING_SNAKE_CASE__     )
             a_					:		Dict							=  tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE__     ,							padding=SCREAMING_SNAKE_CASE__     )

             # fmt: off
             a_					:		List[Any]							=  [[3_5_9_9_3, 8_6_4_0, 2_5_9_4_8, 3_5_9_9_8, 3_0_6_4_7, 3_5_6_7_5, 3_5_9_9_9, 3_5_9_9_9], [3_5_9_9_3, 1_0_3_8_2, 9_8_6_8, 3_5_9_9_8, 3_0_6_4_6, 9_4_5_9, 3_0_6_4_6, 3_5_6_7_5]]
             a_					:		Any							=  [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
             a_					:		List[Any]							=  [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
             # fmt: on
             self.assertListEqual(x_token.input_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token.token_type_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token.attention_mask     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token_a.input_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token_a.token_type_ids     ,							SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(x_token_a.attention_mask     ,							SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Dict:
             # Intentionally convert some words to accommodate character fluctuations unique to Japanese
             pass

      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			int:
             # tokenizer has no padding token
             pass


