





'''simple docstring'''



import os
import pickle
import unittest

from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
    VOCAB_FILES_NAMES,
    BertJapaneseTokenizer,
    CharacterTokenizer,
    JumanppTokenizer,
    MecabTokenizer,
    SudachiTokenizer,
    WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi

from ...test_tokenization_common import TokenizerTesterMixin


@custom_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__lowercase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   BertJapaneseTokenizer
     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						Optional[int]      =   True

     def 	UpperCamelCase(					self	):
       super().setUp()

       lowercase     		=				[
           "[UNK]",
           "[CLS]",
           "[SEP]",
           "こんにちは",
           "こん",
           "にちは",
           "ばんは",
           "##こん",
           "##にちは",
           "##ばんは",
           "世界",
           "##世界",
           "、",
           "##、",
           "。",
           "##。",
       ]

       lowercase     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["vocab_file"]	)
       with open(self.vocab_file						,   "w"						,   encoding="utf-8"	) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       lowercase     		=				"こんにちは、世界。 \nこんばんは、世界。"
       lowercase     		=				"こんにちは 、 世界 。 こんばんは 、 世界 。"
       return input_text, output_text

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       lowercase     		=				self.get_input_output_texts(lowerCamelCase	)
       lowercase     		=				tokenizer.encode(lowerCamelCase						,   add_special_tokens=lowerCamelCase	)
       lowercase     		=				tokenizer.decode(lowerCamelCase						,   clean_up_tokenization_spaces=lowerCamelCase	)
       return text, ids

     def 	UpperCamelCase(					self	):
       pass  # TODO add if relevant

     def 	UpperCamelCase(					self	):
       pass  # TODO add if relevant

     def 	UpperCamelCase(					self	):
       pass  # TODO add if relevant

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class(self.vocab_file	)

       lowercase     		=				tokenizer.tokenize("こんにちは、世界。\nこんばんは、世界。"	)
       self.assertListEqual(lowerCamelCase						,   ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"]	)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [3, 12, 10, 14, 4, 9, 12, 10, 14]	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class(self.vocab_file						,   word_tokenizer_type="mecab"	)
       self.assertIsNotNone(lowerCamelCase	)

       lowercase     		=				"こんにちは、世界。\nこんばんは、世界。"
       lowercase     		=				tokenizer.tokenize(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"]	)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [3, 12, 10, 14, 4, 9, 12, 10, 14]	)

       lowercase     		=				os.path.join(self.tmpdirname						,   "tokenizer.bin"	)
       with open(lowerCamelCase						,   "wb"	) as handle:
         pickle.dump(lowerCamelCase						,   lowerCamelCase	)

       with open(lowerCamelCase						,   "rb"	) as handle:
         lowercase     		=				pickle.load(lowerCamelCase	)

       lowercase     		=				tokenizer_new.tokenize(lowerCamelCase	)

       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				MecabTokenizer(mecab_dic="ipadic"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップルストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"]						,   )

     def 	UpperCamelCase(					self	):
       try:
         lowercase     		=				MecabTokenizer(mecab_dic="unidic_lite"	)
       except ModuleNotFoundError:
         return

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"]						,   )

     def 	UpperCamelCase(					self	):
       try:
         lowercase     		=				MecabTokenizer(mecab_dic="unidic"	)
       except ModuleNotFoundError:
         return

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"]						,   )

     def 	UpperCamelCase(					self	):
       lowercase     		=				MecabTokenizer(do_lower_case=lowerCamelCase						,   mecab_dic="ipadic"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップルストア", "で", "iphone", "8", "が", "発売", "さ", "れ", "た", "。"]						,   )

     def 	UpperCamelCase(					self	):
       try:
         lowercase     		=				MecabTokenizer(
             do_lower_case=lowerCamelCase						,   normalize_text=lowerCamelCase						,   mecab_option="-d /usr/local/lib/mecab/dic/jumandic"	)
       except RuntimeError:
         # if dict doesn't exist in the system, previous code raises this error.
         return

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れた", "\u3000", "。"]						,   )

     def 	UpperCamelCase(					self	):
       lowercase     		=				MecabTokenizer(normalize_text=lowerCamelCase						,   mecab_dic="ipadic"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["ｱｯﾌﾟﾙストア", "で", "iPhone", "８", "が", "発売", "さ", "れ", "た", "　", "。"]						,   )

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class(self.vocab_file						,   word_tokenizer_type="sudachi"	)
       self.assertIsNotNone(lowerCamelCase	)

       lowercase     		=				"こんにちは、世界。\nこんばんは、世界。"
       lowercase     		=				tokenizer.tokenize(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"]	)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [3, 12, 10, 14, 4, 9, 12, 10, 14]	)

       lowercase     		=				os.path.join(self.tmpdirname						,   "tokenizer.bin"	)
       with open(lowerCamelCase						,   "wb"	) as handle:
         pickle.dump(lowerCamelCase						,   lowerCamelCase	)

       with open(lowerCamelCase						,   "rb"	) as handle:
         lowercase     		=				pickle.load(lowerCamelCase	)

       lowercase     		=				tokenizer_new.tokenize(lowerCamelCase	)

       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(sudachi_dict_type="core"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   [" ", "\t", "アップル", "ストア", "で", "iPhone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "]						,   )

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(sudachi_dict_type="core"						,   sudachi_split_mode="A"	)

       self.assertListEqual(tokenizer.tokenize("外国人参政権"	)						,   ["外国", "人", "参政", "権"]	)

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(sudachi_dict_type="core"						,   sudachi_split_mode="B"	)

       self.assertListEqual(tokenizer.tokenize("外国人参政権"	)						,   ["外国人", "参政権"]	)

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(sudachi_dict_type="core"						,   sudachi_split_mode="C"	)

       self.assertListEqual(tokenizer.tokenize("外国人参政権"	)						,   ["外国人参政権"]	)

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(do_lower_case=lowerCamelCase						,   sudachi_dict_type="core"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   [" ", "\t", "アップル", "ストア", "で", "iphone", "8", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", " ", "。", " ", " "]						,   )

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(normalize_text=lowerCamelCase						,   sudachi_dict_type="core"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   [" ", "\t", "ｱｯﾌﾟﾙ", "ストア", "で", "iPhone", "８", " ", "が", " ", " ", "\n ", "発売", "さ", "れ", "た", "\u3000", "。", " ", " "]						,   )

     @require_sudachi
     def 	UpperCamelCase(					self	):
       lowercase     		=				SudachiTokenizer(trim_whitespace=lowerCamelCase						,   sudachi_dict_type="core"	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れ", "た", "。"]						,   )

     @require_jumanpp
     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class(self.vocab_file						,   word_tokenizer_type="jumanpp"	)
       self.assertIsNotNone(lowerCamelCase	)

       lowercase     		=				"こんにちは、世界。\nこんばんは、世界。"
       lowercase     		=				tokenizer.tokenize(lowerCamelCase	)
       self.assertListEqual(lowerCamelCase						,   ["こんにちは", "、", "世界", "。", "こん", "##ばんは", "、", "世界", "。"]	)
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [3, 12, 10, 14, 4, 9, 12, 10, 14]	)

       lowercase     		=				os.path.join(self.tmpdirname						,   "tokenizer.bin"	)
       with open(lowerCamelCase						,   "wb"	) as handle:
         pickle.dump(lowerCamelCase						,   lowerCamelCase	)

       with open(lowerCamelCase						,   "rb"	) as handle:
         lowercase     		=				pickle.load(lowerCamelCase	)

       lowercase     		=				tokenizer_new.tokenize(lowerCamelCase	)

       self.assertListEqual(lowerCamelCase						,   lowerCamelCase	)

     @require_jumanpp
     def 	UpperCamelCase(					self	):
       lowercase     		=				JumanppTokenizer()

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップル", "ストア", "で", "iPhone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"]						,   )

     @require_jumanpp
     def 	UpperCamelCase(					self	):
       lowercase     		=				JumanppTokenizer(do_lower_case=lowerCamelCase	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップル", "ストア", "で", "iphone", "8", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"]						,   )

     @require_jumanpp
     def 	UpperCamelCase(					self	):
       lowercase     		=				JumanppTokenizer(normalize_text=lowerCamelCase	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["ｱ", "ｯ", "ﾌ", "ﾟ", "ﾙ", "ストア", "で", "iPhone", "８", "\u3000", "が", "\u3000", "\u3000", "\u3000", "発売", "さ", "れた", "\u3000", "。"]						,   )

     @require_jumanpp
     def 	UpperCamelCase(					self	):
       lowercase     		=				JumanppTokenizer(trim_whitespace=lowerCamelCase	)

       self.assertListEqual(
           tokenizer.tokenize(" \tｱｯﾌﾟﾙストアでiPhone８ が  \n 発売された　。  "	)						,   ["アップル", "ストア", "で", "iPhone", "8", "が", "発売", "さ", "れた", "。"]						,   )

     @require_jumanpp
     def 	UpperCamelCase(					self	):
       lowercase     		=				JumanppTokenizer()

       self.assertListEqual(
           tokenizer.tokenize("ありがとうございますm(_ _)ｍ見つけるのが大変です。"	)						,   ["ありがとう", "ございます", "m(_ _)m", "見つける", "の", "が", "大変です", "。"]						,   )

     def 	UpperCamelCase(					self	):
       lowercase     		=				["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]

       lowercase     		=				{}
       for i, token in enumerate(lowerCamelCase	):
         lowercase     		=				i
       lowercase     		=				WordpieceTokenizer(vocab=lowerCamelCase						,   unk_token="[UNK]"	)

       self.assertListEqual(tokenizer.tokenize(""	)						,   []	)

       self.assertListEqual(tokenizer.tokenize("こんにちは"	)						,   ["こんにちは"]	)

       self.assertListEqual(tokenizer.tokenize("こんばんは"	)						,   ["こん", "##ばんは"]	)

       self.assertListEqual(tokenizer.tokenize("こんばんは こんばんにちは こんにちは"	)						,   ["こん", "##ばんは", "[UNK]", "こんにちは"]	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp"	)
       lowercase     		=				tokenizer.subword_tokenizer

       lowercase     		=				subword_tokenizer.tokenize("国境 の 長い トンネル を 抜ける と 雪国 であった 。"	)
       self.assertListEqual(lowerCamelCase						,   ["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"]	)

       lowercase     		=				subword_tokenizer.tokenize("こんばんは こんばん にち は こんにちは"	)
       self.assertListEqual(lowerCamelCase						,   ["▁こん", "ばん", "は", "▁こん", "ばん", "▁に", "ち", "▁は", "▁こんにちは"]	)






     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese"	)

       lowercase     		=				tokenizer.encode("ありがとう。"						,   add_special_tokens=lowerCamelCase	)
       lowercase     		=				tokenizer.encode("どういたしまして。"						,   add_special_tokens=lowerCamelCase	)

       lowercase     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase	)
       lowercase     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase						,   lowerCamelCase	)

       # 2 is for "[CLS]", 3 is for "[SEP]"
       assert encoded_sentence == [2] + text + [3]
       assert encoded_pair == [2] + text + [3] + text_a + [3]







@custom_tokenizers
class       __SCREAMING_SNAKE_CASE	(						__lowercase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   BertJapaneseTokenizer
     UpperCAmelCase__       :						Optional[Any]      =   False

     def 	UpperCamelCase(					self	):
       super().setUp()

       lowercase     		=				["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]

       lowercase     		=				os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES["vocab_file"]	)
       with open(self.vocab_file						,   "w"						,   encoding="utf-8"	) as vocab_writer:
         vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)

     def 	UpperCamelCase(					self						,   **lowerCamelCase	):
       return BertJapaneseTokenizer.from_pretrained(self.tmpdirname						,   subword_tokenizer_type="character"						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       lowercase     		=				"こんにちは、世界。 \nこんばんは、世界。"
       lowercase     		=				"こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
       return input_text, output_text

     def 	UpperCamelCase(					self	):
       pass  # TODO add if relevant

     def 	UpperCamelCase(					self	):
       pass  # TODO add if relevant

     def 	UpperCamelCase(					self	):
       pass  # TODO add if relevant

     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class(self.vocab_file						,   subword_tokenizer_type="character"	)

       lowercase     		=				tokenizer.tokenize("こんにちは、世界。 \nこんばんは、世界。"	)
       self.assertListEqual(
           lowerCamelCase						,   ["こ", "ん", "に", "ち", "は", "、", "世", "界", "。", "こ", "ん", "ば", "ん", "は", "、", "世", "界", "。"]	)
       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(lowerCamelCase	)						,   [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12]	)

     def 	UpperCamelCase(					self	):
       lowercase     		=				["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]

       lowercase     		=				{}
       for i, token in enumerate(lowerCamelCase	):
         lowercase     		=				i
       lowercase     		=				CharacterTokenizer(vocab=lowerCamelCase						,   unk_token="[UNK]"	)

       self.assertListEqual(tokenizer.tokenize(""	)						,   []	)

       self.assertListEqual(tokenizer.tokenize("こんにちは"	)						,   ["こ", "ん", "に", "ち", "は"]	)

       self.assertListEqual(tokenizer.tokenize("こんにちほ"	)						,   ["こ", "ん", "に", "ち", "[UNK]"]	)






     def 	UpperCamelCase(					self	):
       lowercase     		=				self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char"	)

       lowercase     		=				tokenizer.encode("ありがとう。"						,   add_special_tokens=lowerCamelCase	)
       lowercase     		=				tokenizer.encode("どういたしまして。"						,   add_special_tokens=lowerCamelCase	)

       lowercase     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase	)
       lowercase     		=				tokenizer.build_inputs_with_special_tokens(lowerCamelCase						,   lowerCamelCase	)

       # 2 is for "[CLS]", 3 is for "[SEP]"
       assert encoded_sentence == [2] + text + [3]
       assert encoded_pair == [2] + text + [3] + text_a + [3]







@custom_tokenizers
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       lowercase     		=				"cl-tohoku/bert-base-japanese"
       lowercase     		=				AutoTokenizer.from_pretrained(lowerCamelCase	)
       self.assertIsInstance(lowerCamelCase						,   lowerCamelCase	)







class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self	):
       lowercase     		=				"cl-tohoku/bert-base-japanese"
       with self.assertLogs("transformers"						,   level="WARNING"	) as cm:
         BertTokenizer.from_pretrained(lowerCamelCase	)
         self.assertTrue(
             cm.records[0].message.startswith(
                 "The tokenizer class you load from this checkpoint is not the same type as the class this function"
                 " is called from."	)	)
       lowercase     		=				"bert-base-cased"
       with self.assertLogs("transformers"						,   level="WARNING"	) as cm:
         BertJapaneseTokenizer.from_pretrained(lowerCamelCase	)
         self.assertTrue(
             cm.records[0].message.startswith(
                 "The tokenizer class you load from this checkpoint is not the same type as the class this function"
                 " is called from."	)	)









'''simple docstring'''



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

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

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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				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__       ):







  '''simple docstring'''



  print("Extracting"					,	f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    if magic != 20_51:
      raise ValueError(
          "Invalid magic number %d in MNIST image file: %s" % (magic, f.name)       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				bytestream.read(rows * cols * num_images       )
    _snake_case     		=				numpy.frombuffer(SCREAMING_SNAKE_CASE__					,	dtype=numpy.uinta       )
    _snake_case     		=				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__       ):







  '''simple docstring'''



  _snake_case     		=				labels_dense.shape[0]
  _snake_case     		=				numpy.arange(SCREAMING_SNAKE_CASE__       ) * num_classes
  _snake_case     		=				numpy.zeros((num_labels, num_classes)       )
  _snake_case     		=				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__=10       ):







  '''simple docstring'''



  print("Extracting"					,	f.name       )
  with gzip.GzipFile(fileobj=SCREAMING_SNAKE_CASE__       ) as bytestream:
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    if magic != 20_49:
      raise ValueError(
          "Invalid magic number %d in MNIST label file: %s" % (magic, f.name)       )
    _snake_case     		=				_readaa(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				bytestream.read(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				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       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     @deprecated(
         lowerCamelCase						,   "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						,   ):
       _snake_case		,		_snake_case     		=				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	)
       _snake_case     		=				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:
         _snake_case     		=				10_000
         _snake_case     		=				one_hot
       else:
         assert (
             images.shape[0] == labels.shape[0]
         ), F'''images.shape: {images.shape} labels.shape: {labels.shape}'''
         _snake_case     		=				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     		=				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     		=				images.astype(numpy.floataa	)
           _snake_case     		=				numpy.multiply(lowerCamelCase						,   1.0 / 255.0	)
       _snake_case     		=				images
       _snake_case     		=				labels
       _snake_case     		=				0
       _snake_case     		=				0

     @property
     def 	UpperCamelCase(					self	):
       return self._images

     @property
     def 	UpperCamelCase(					self	):
       return self._labels

     @property
     def 	UpperCamelCase(					self	):
       return self._num_examples

     @property
     def 	UpperCamelCase(					self	):
       return self._epochs_completed






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







  '''simple docstring'''



  if not gfile.Exists(SCREAMING_SNAKE_CASE__       ):
    gfile.MakeDirs(SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				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:
      _snake_case     		=				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__=50_00					,	SCREAMING_SNAKE_CASE__=None					,	SCREAMING_SNAKE_CASE__=DEFAULT_SOURCE_URL					,	):







  '''simple docstring'''



  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__       )

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

  if not source_url:  # empty string check
    _snake_case     		=				DEFAULT_SOURCE_URL

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

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

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

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

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

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

  _snake_case     		=				train_images[:validation_size]
  _snake_case     		=				train_labels[:validation_size]
  _snake_case     		=				train_images[validation_size:]
  _snake_case     		=				train_labels[validation_size:]

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

  _snake_case     		=				_DataSet(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				_DataSet(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				_DataSet(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__       )

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


