





"""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  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Union[str, Any]         =BertJapaneseTokenizer
							lowerCamelCase     :      List[Any]         =False
							lowerCamelCase     :      List[Any]         =True






							def 					__a     (     self   )			->				List[Any]:
								super().setUp()

								a   :	Optional[int]			=       [
								    "[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",
								    "、",
								    "##、",
								    "。",
								    "##。",
								]

								a   :	Dict			=       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 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	Union[str, Any]			=       "こんにちは、世界。 \nこんばんは、世界。"
								a   :	Any			=       "こんにちは 、 世界 。 こんばんは 、 世界 。"
								return input_text, output_text






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								a, a   :	Tuple			=       self.get_input_output_texts(lowerCAmelCase__   )
								a   :	List[str]			=       tokenizer.encode(lowerCAmelCase__   ,      add_special_tokens=lowerCAmelCase__   )
								a   :	Optional[Any]			=       tokenizer.decode(lowerCAmelCase__   ,      clean_up_tokenization_spaces=lowerCAmelCase__   )
								return text, ids






							def 					__a     (     self   )			->				str:
								pass  # TODO add if relevant






							def 					__a     (     self   )			->				str:
								pass  # TODO add if relevant






							def 					__a     (     self   )			->				str:
								pass  # TODO add if relevant






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	List[Any]			=       self.tokenizer_class(self.vocab_file   )

								a   :	Optional[int]			=       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 					__a     (     self   )			->				Optional[Any]:
								a   :	List[str]			=       self.tokenizer_class(self.vocab_file   ,      word_tokenizer_type="mecab"   )
								self.assertIsNotNone(lowerCAmelCase__   )

								a   :	int			=       "こんにちは、世界。\nこんばんは、世界。"
								a   :	List[str]			=       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]   )

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

								with open(lowerCAmelCase__   ,      "rb"   ) as handle:
									a   :	List[Any]			=       pickle.load(lowerCAmelCase__   )

								a   :	Union[str, Any]			=       tokenizer_new.tokenize(lowerCAmelCase__   )

								self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Any			=       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 					__a     (     self   )			->				str:
								try:
									a   :	List[str]			=       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 					__a     (     self   )			->				Tuple:
								try:
									a   :	Union[str, Any]			=       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 					__a     (     self   )			->				List[Any]:
								a   :	int			=       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 					__a     (     self   )			->				Union[str, Any]:
								try:
									a   :	List[str]			=       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 					__a     (     self   )			->				Optional[Any]:
								a   :	int			=       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 					__a     (     self   )			->				Union[str, Any]:
								a   :	Union[str, Any]			=       self.tokenizer_class(self.vocab_file   ,      word_tokenizer_type="sudachi"   )
								self.assertIsNotNone(lowerCAmelCase__   )

								a   :	int			=       "こんにちは、世界。\nこんばんは、世界。"
								a   :	Dict			=       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]   )

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

								with open(lowerCAmelCase__   ,      "rb"   ) as handle:
									a   :	Any			=       pickle.load(lowerCAmelCase__   )

								a   :	Optional[Any]			=       tokenizer_new.tokenize(lowerCAmelCase__   )

								self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@require_sudachi
							def 					__a     (     self   )			->				Any:
								a   :	Tuple			=       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 					__a     (     self   )			->				Dict:
								a   :	int			=       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 					__a     (     self   )			->				int:
								a   :	int			=       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 					__a     (     self   )			->				Dict:
								a   :	Optional[Any]			=       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 					__a     (     self   )			->				Tuple:
								a   :	Union[str, Any]			=       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 					__a     (     self   )			->				str:
								a   :	List[Any]			=       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 					__a     (     self   )			->				List[str]:
								a   :	List[Any]			=       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 					__a     (     self   )			->				Tuple:
								a   :	List[str]			=       self.tokenizer_class(self.vocab_file   ,      word_tokenizer_type="jumanpp"   )
								self.assertIsNotNone(lowerCAmelCase__   )

								a   :	str			=       "こんにちは、世界。\nこんばんは、世界。"
								a   :	List[str]			=       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]   )

								a   :	List[Any]			=       os.path.join(self.tmpdirname   ,      "tokenizer.bin"   )
								with open(lowerCAmelCase__   ,      "wb"   ) as handle:
									pickle.dump(lowerCAmelCase__   ,      lowerCAmelCase__   )

								with open(lowerCAmelCase__   ,      "rb"   ) as handle:
									a   :	str			=       pickle.load(lowerCAmelCase__   )

								a   :	List[str]			=       tokenizer_new.tokenize(lowerCAmelCase__   )

								self.assertListEqual(lowerCAmelCase__   ,      lowerCAmelCase__   )






							@require_jumanpp
							def 					__a     (     self   )			->				List[str]:
								a   :	int			=       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 					__a     (     self   )			->				Union[str, Any]:
								a   :	Optional[Any]			=       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 					__a     (     self   )			->				Any:
								a   :	List[Any]			=       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 					__a     (     self   )			->				Any:
								a   :	Tuple			=       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 					__a     (     self   )			->				Tuple:
								a   :	Optional[Any]			=       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 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       ["[UNK]", "[CLS]", "[SEP]", "こんにちは", "こん", "にちは", "ばんは", "##こん", "##にちは", "##ばんは"]

								a   :	Optional[Any]			=       {}
								for i, token in enumerate(lowerCAmelCase__   ):
									a   :	int			=       i
								a   :	Optional[Any]			=       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 					__a     (     self   )			->				int:
								a   :	List[str]			=       BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp"   )
								a   :	Tuple			=       tokenizer.subword_tokenizer

								a   :	List[str]			=       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", "▁。"]   )

								a   :	Dict			=       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 					__a     (     self   )			->				Any:
								a   :	int			=       self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese"   )

								a   :	List[Any]			=       tokenizer.encode("ありがとう。"   ,      add_special_tokens=lowerCAmelCase__   )
								a   :	Union[str, Any]			=       tokenizer.encode("どういたしまして。"   ,      add_special_tokens=lowerCAmelCase__   )

								a   :	List[Any]			=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__   )
								a   :	Any			=       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  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Any         =BertJapaneseTokenizer
							lowerCamelCase     :      Tuple         =False






							def 					__a     (     self   )			->				Tuple:
								super().setUp()

								a   :	Union[str, Any]			=       ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]

								a   :	Tuple			=       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 					__a     (     self   ,      **lowerCAmelCase__   )			->				str:
								return BertJapaneseTokenizer.from_pretrained(self.tmpdirname   ,      subword_tokenizer_type="character"   ,      **lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[Any]:
								a   :	List[str]			=       "こんにちは、世界。 \nこんばんは、世界。"
								a   :	int			=       "こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"
								return input_text, output_text






							def 					__a     (     self   )			->				int:
								pass  # TODO add if relevant






							def 					__a     (     self   )			->				Optional[Any]:
								pass  # TODO add if relevant






							def 					__a     (     self   )			->				Any:
								pass  # TODO add if relevant






							def 					__a     (     self   )			->				Dict:
								a   :	int			=       self.tokenizer_class(self.vocab_file   ,      subword_tokenizer_type="character"   )

								a   :	List[Any]			=       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 					__a     (     self   )			->				str:
								a   :	Union[str, Any]			=       ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]

								a   :	Optional[int]			=       {}
								for i, token in enumerate(lowerCAmelCase__   ):
									a   :	Any			=       i
								a   :	List[Any]			=       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 					__a     (     self   )			->				List[str]:
								a   :	Dict			=       self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char"   )

								a   :	List[str]			=       tokenizer.encode("ありがとう。"   ,      add_special_tokens=lowerCAmelCase__   )
								a   :	Optional[int]			=       tokenizer.encode("どういたしまして。"   ,      add_special_tokens=lowerCAmelCase__   )

								a   :	Dict			=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__   )
								a   :	List[Any]			=       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  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				List[str]:
								a   :	List[Any]			=       "cl-tohoku/bert-base-japanese"
								a   :	Any			=       AutoTokenizer.from_pretrained(lowerCAmelCase__   )
								self.assertIsInstance(lowerCAmelCase__   ,      lowerCAmelCase__   )


class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				List[str]:
								a   :	str			=       "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."   )   )
								a   :	Union[str, Any]			=       "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 itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


a		:				List[Any]  			=    random.Random()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]					, _lowercase		:					int=1.0					, _lowercase		:					Optional[int]=None					, _lowercase		:					Union[str, Any]=None      )				->Optional[Any]:






	'''simple docstring'''






	if rng is None:
		a   :	Tuple			=       global_rng

	a   :	Tuple			=       []
	for batch_idx in range(shape[0]      ):
		values.append([]      )
		for _ in range(shape[1]      ):
			values[-1].append(rng.random() * scale      )

	return values


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=400   ,      lowerCAmelCase__=2000   ,      lowerCAmelCase__=1   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=1_6000   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=64   ,      lowerCAmelCase__="hann_window"   ,      lowerCAmelCase__=80   ,      lowerCAmelCase__=7600   ,      lowerCAmelCase__=1E-10   ,      lowerCAmelCase__=True   ,      )			->				Optional[Any]:
								a   :	int			=       parent
								a   :	Tuple			=       batch_size
								a   :	Dict			=       min_seq_length
								a   :	Any			=       max_seq_length
								a   :	Optional[int]			=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a   :	Union[str, Any]			=       feature_size
								a   :	Tuple			=       padding_value
								a   :	str			=       sampling_rate
								a   :	Dict			=       do_normalize
								a   :	str			=       num_mel_bins
								a   :	List[str]			=       hop_length
								a   :	str			=       win_length
								a   :	Optional[Any]			=       win_function
								a   :	List[str]			=       fmin
								a   :	Any			=       fmax
								a   :	Optional[int]			=       mel_floor
								a   :	Tuple			=       return_attention_mask






							def 					__a     (     self   )			->				Optional[Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "do_normalize": self.do_normalize,
								    "num_mel_bins": self.num_mel_bins,
								    "hop_length": self.hop_length,
								    "win_length": self.win_length,
								    "win_function": self.win_function,
								    "fmin": self.fmin,
								    "fmax": self.fmax,
								    "mel_floor": self.mel_floor,
								    "return_attention_mask": self.return_attention_mask,
								}






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Tuple:
								def _flatten(lowerCAmelCase__   ):
									return list(itertools.chain(*lowerCAmelCase__   )   )

								if equal_length:
									a   :	Union[str, Any]			=       floats_list((self.batch_size, self.max_seq_length)   )
								else:
									# make sure that inputs increase in size
									a   :	str			=       [
									    _flatten(floats_list((x, self.feature_size)   )   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs






							def 					__a     (     self   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=False   )			->				Dict:
								if equal_length:
									a   :	str			=       [floats_list((self.max_seq_length, self.num_mel_bins)   ) for _ in range(self.batch_size   )]
								else:
									# make sure that inputs increase in size
									a   :	Any			=       [
									    floats_list((x, self.num_mel_bins)   )
									    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff   )
									]

								if numpify:
									a   :	Optional[int]			=       [np.asarray(lowerCAmelCase__   ) for x in speech_inputs]

								return speech_inputs


@require_torch
class  __UpperCamelCase       (		a__	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =SpeechTaFeatureExtractor






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       SpeechTaFeatureExtractionTester(self   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								self.assertTrue(np.all(np.mean(lowerCAmelCase__   ,      axis=0   ) < 1E-3   )   )
								self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__   ,      axis=0   ) - 1   ) < 1E-3   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Any			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Any			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test not batched input
								a   :	Optional[int]			=       feat_extract(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	Optional[Any]			=       feat_extract(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	int			=       feat_extract(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				Optional[Any]:
								a   :	List[Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Dict			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]

								a   :	int			=       ["longest", "max_length", "do_not_pad"]
								a   :	Tuple			=       [None, 1600, None]
								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	Dict			=       feat_extract(lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      return_tensors="np"   )
									a   :	List[Any]			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self.assertTrue(input_values[0][800:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self.assertTrue(input_values[0][1000:].sum() < 1E-6   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				str:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	List[str]			=       range(800   ,      1400   ,      200   )
								a   :	List[str]			=       [floats_list((1, x)   )[0] for x in lengths]

								a   :	Any			=       ["longest", "max_length", "do_not_pad"]
								a   :	Any			=       [None, 1600, None]

								for max_length, padding in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									a   :	List[Any]			=       feat_extract(lowerCAmelCase__   ,      max_length=lowerCAmelCase__   ,      padding=lowerCAmelCase__   )
									a   :	Dict			=       processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:800]   )
									self._check_zero_mean_unit_variance(input_values[1][:1000]   )
									self._check_zero_mean_unit_variance(input_values[2][:1200]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	int			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Union[str, Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="max_length"   ,      return_tensors="np"   )
								a   :	List[Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1]   )
								self._check_zero_mean_unit_variance(input_values[2]   )






							def 					__a     (     self   )			->				Dict:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Tuple			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	List[Any]			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=1000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Union[str, Any]			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length < longest -> then pad to max_length
								self.assertTrue(input_values.shape == (3, 1000)   )

								a   :	List[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	int			=       feat_extract(
								    lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      max_length=2000   ,      padding="longest"   ,      return_tensors="np"   )
								a   :	Dict			=       processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :800]   )
								self._check_zero_mean_unit_variance(input_values[1, :1000]   )
								self._check_zero_mean_unit_variance(input_values[2]   )

								# make sure that if max_length > longest -> then pad to longest
								self.assertTrue(input_values.shape == (3, 1200)   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								a   :	Any			=       np.random.rand(100   ).astype(np.floataa   )
								a   :	Optional[Any]			=       np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
									a   :	str			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="np"   )
									self.assertTrue(np_processed.input_values.dtype == np.floataa   )
									a   :	List[str]			=       feature_extractor.pad([{"input_values": inputs}]   ,      return_tensors="pt"   )
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )






							def 					__a     (     self   )			->				Tuple:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								a   :	Union[str, Any]			=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in range(800   ,      1400   ,      200   )]
								a   :	Tuple			=       [np.asarray(lowerCAmelCase__   ) for speech_input in speech_inputs]

								# Test feature size
								a   :	Union[str, Any]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      padding=lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								self.assertTrue(input_values.ndim == 3   )
								self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins   )

								# Test not batched input
								a   :	Dict			=       feature_extractor(speech_inputs[0]   ,      return_tensors="np"   ).input_values
								a   :	List[Any]			=       feature_extractor(np_speech_inputs[0]   ,      return_tensors="np"   ).input_values
								self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test batched
								a   :	Optional[int]			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	Any			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )

								# Test 2-D numpy arrays are batched.
								a   :	Optional[Any]			=       [floats_list((1, x)   )[0] for x in (800, 800, 800)]
								a   :	List[Any]			=       np.asarray(lowerCAmelCase__   )
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="np"   ).input_values
								for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__   ,      lowerCAmelCase__   ):
									self.assertTrue(np.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )






							def 					__a     (     self   )			->				str:
								a   :	Optional[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Any			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Union[str, Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								self.assertTrue(all(len(lowerCAmelCase__   ) == len(lowerCAmelCase__   ) for x, y in zip(lowerCAmelCase__   ,      processed_features[input_name]   )   )   )

								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="np"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	Dict			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Tuple:
								a   :	Tuple			=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__   )
								a   :	Optional[Any]			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   ,      tensor_type="pt"   )

								a   :	Tuple			=       processed_features[input_name]

								if len(batch_features_input.shape   ) < 3:
									a   :	List[str]			=       batch_features_input[:, :, None]

								self.assertTrue(
								    batch_features_input.shape
								    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]   ), self.feat_extract_tester.num_mel_bins)   )






							@require_torch
							def 					__a     (     self   )			->				Optional[Any]:
								a   :	Dict			=       self.feature_extraction_class(**self.feat_extract_dict   )
								a   :	Optional[int]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	List[str]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Tuple			=       feat_extract.num_mel_bins  # hack!

								a   :	List[Any]			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )[input_name]
								a   :	Any			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="pt"   )[input_name]

								self.assertTrue(abs(input_np.astype(np.floataa   ).sum() - input_pt.numpy().astype(np.floataa   ).sum()   ) < 1E-2   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Any			=       self.feat_extract_dict
								a   :	Optional[Any]			=       True
								a   :	Union[str, Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	Any			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	int			=       feat_extract.model_input_names[0]

								a   :	List[Any]			=       BatchFeature({input_name: speech_inputs}   )

								a   :	Union[str, Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Dict			=       feat_extract.pad(lowerCAmelCase__   ,      padding="longest"   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(list(processed.attention_mask.shape   )   ,      list(processed[input_name].shape[:2]   )   )
								self.assertListEqual(processed.attention_mask.sum(-1   ).tolist()   ,      lowerCAmelCase__   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	Tuple			=       self.feat_extract_dict
								a   :	str			=       True
								a   :	Optional[Any]			=       self.feature_extraction_class(**lowerCAmelCase__   )
								a   :	List[Any]			=       self.feat_extract_tester.prepare_inputs_for_target()
								a   :	Dict			=       [len(lowerCAmelCase__   ) for x in speech_inputs]
								a   :	Optional[Any]			=       feat_extract.model_input_names[0]

								a   :	str			=       BatchFeature({input_name: speech_inputs}   )
								a   :	Optional[Any]			=       min(lowerCAmelCase__   )

								a   :	List[Any]			=       feat_extract.num_mel_bins  # hack!

								a   :	Any			=       feat_extract.pad(
								    lowerCAmelCase__   ,      padding="max_length"   ,      max_length=lowerCAmelCase__   ,      truncation=lowerCAmelCase__   ,      return_tensors="np"   )
								self.assertIn("attention_mask"   ,      lowerCAmelCase__   )
								self.assertListEqual(
								    list(processed_pad.attention_mask.shape   )   ,      [processed_pad[input_name].shape[0], max_length]   )
								self.assertListEqual(
								    processed_pad.attention_mask[:, :max_length].sum(-1   ).tolist()   ,      [max_length for x in speech_inputs]   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								from datasets import load_dataset

								a   :	Tuple			=       load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,      "clean"   ,      split="validation"   )
								# automatic decoding with librispeech
								a   :	Optional[Any]			=       ds.sort("id"   ).select(range(lowerCAmelCase__   )   )[:num_samples]["audio"]

								return [x["array"] for x in speech_samples]






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	List[Any]			=       torch.tensor(
								    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
								     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
								     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
								     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
								     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
								     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]   )
								# fmt: on

								a   :	List[str]			=       self._load_datasamples(1   )
								a   :	Union[str, Any]			=       SpeechTaFeatureExtractor()
								a   :	str			=       feature_extractor(lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 9_3680)   )
								self.assertTrue(torch.allclose(input_values[0, :30]   ,      lowerCAmelCase__   ,      atol=1E-6   )   )






							def 					__a     (     self   )			->				Union[str, Any]:
								# fmt: off
								a   :	Tuple			=       torch.tensor(
								    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
								     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
								     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
								     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]   )
								# fmt: on

								a   :	Dict			=       self._load_datasamples(1   )
								a   :	Tuple			=       SpeechTaFeatureExtractor()
								a   :	Optional[int]			=       feature_extractor(audio_target=lowerCAmelCase__   ,      return_tensors="pt"   ).input_values
								self.assertEquals(input_values.shape   ,      (1, 366, 80)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,      lowerCAmelCase__   ,      atol=1E-4   )   )


