
'''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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					Union[str, Any]       =					BertJapaneseTokenizer
		a							:					Any       =					False
		a							:					Tuple       =					True




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

								_SCREAMING_SNAKE_CASE				= [
								    "[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",
								    "、",
								    "##、",
								    "。",
								    "##。",
								]

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




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




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




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




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




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

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




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

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

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

								with open(UpperCamelCase					,						"rb"      ) as handle:
														_SCREAMING_SNAKE_CASE				= pickle.load(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer_new.tokenize(UpperCamelCase      )

								self.assertListEqual(UpperCamelCase					,						UpperCamelCase      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								try:
														_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								try:
														_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MecabTokenizer(do_lower_case=UpperCamelCase					,						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     lowercase       (				self      ):
								try:
														_SCREAMING_SNAKE_CASE				= MecabTokenizer(
														    do_lower_case=UpperCamelCase					,						normalize_text=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= MecabTokenizer(normalize_text=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class(self.vocab_file					,						word_tokenizer_type="sudachi"      )
								self.assertIsNotNone(UpperCamelCase      )

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

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

								with open(UpperCamelCase					,						"rb"      ) as handle:
														_SCREAMING_SNAKE_CASE				= pickle.load(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer_new.tokenize(UpperCamelCase      )

								self.assertListEqual(UpperCamelCase					,						UpperCamelCase      )




		@require_sudachi
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SudachiTokenizer(do_lower_case=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SudachiTokenizer(normalize_text=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= SudachiTokenizer(trim_whitespace=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class(self.vocab_file					,						word_tokenizer_type="jumanpp"      )
								self.assertIsNotNone(UpperCamelCase      )

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

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

								with open(UpperCamelCase					,						"rb"      ) as handle:
														_SCREAMING_SNAKE_CASE				= pickle.load(UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= tokenizer_new.tokenize(UpperCamelCase      )

								self.assertListEqual(UpperCamelCase					,						UpperCamelCase      )




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

								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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= JumanppTokenizer(normalize_text=UpperCamelCase      )

								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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= JumanppTokenizer(trim_whitespace=UpperCamelCase      )

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

								_SCREAMING_SNAKE_CASE				= {}
								for i, token in enumerate(UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= i
								_SCREAMING_SNAKE_CASE				= WordpieceTokenizer(vocab=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BertJapaneseTokenizer.from_pretrained("nlp-waseda/roberta-base-japanese-with-auto-jumanpp"      )
								_SCREAMING_SNAKE_CASE				= tokenizer.subword_tokenizer

								_SCREAMING_SNAKE_CASE				= 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(UpperCamelCase					,						["▁国境", "▁の", "▁長い", "▁トンネル", "▁を", "▁抜ける", "▁と", "▁雪", "国", "▁であった", "▁。"]      )

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




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

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

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								# 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 			lowerCAmelCase (				__UpperCAmelCase		,     unittest.TestCase			):
		a							:					int       =					BertJapaneseTokenizer
		a							:					int       =					False




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

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

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




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




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




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




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




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

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




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

								_SCREAMING_SNAKE_CASE				= {}
								for i, token in enumerate(UpperCamelCase      ):
														_SCREAMING_SNAKE_CASE				= i
								_SCREAMING_SNAKE_CASE				= CharacterTokenizer(vocab=UpperCamelCase					,						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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.tokenizer_class.from_pretrained("cl-tohoku/bert-base-japanese-char"      )

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

								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.build_inputs_with_special_tokens(UpperCamelCase					,						UpperCamelCase      )

								# 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 			lowerCAmelCase (				unittest.TestCase			):




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


class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "cl-tohoku/bert-base-japanese"
								with self.assertLogs("transformers"					,						level="WARNING"      ) as cm:
														BertTokenizer.from_pretrained(UpperCamelCase      )
														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."      )      )
								_SCREAMING_SNAKE_CASE				= "bert-base-cased"
								with self.assertLogs("transformers"					,						level="WARNING"      ) as cm:
														BertJapaneseTokenizer.from_pretrained(UpperCamelCase      )
														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 numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= process
								_SCREAMING_SNAKE_CASE				= params




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.dataset[i]
								_SCREAMING_SNAKE_CASE				= self.process(UpperCamelCase					,						**self.params      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								_SCREAMING_SNAKE_CASE				= loader
								_SCREAMING_SNAKE_CASE				= infer
								_SCREAMING_SNAKE_CASE				= params
								if loader_batch_size == 1:
														# Let's spare some time by deactivating altogether
														_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= loader_batch_size

								# Internal bookkeeping
								_SCREAMING_SNAKE_CASE				= None
								_SCREAMING_SNAKE_CASE				= None




		def __len__(				self      ):
								return len(self.loader      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								if isinstance(self._loader_batch_data					,						torch.Tensor      ):
														# Batch data is simple tensor, just fetch the slice
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data[self._loader_batch_index]
								else:
														# Batch data is assumed to be BaseModelOutput (or dict)
														_SCREAMING_SNAKE_CASE				= {}
														for k, element in self._loader_batch_data.items():
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Convert ModelOutput to tuple first
																										_SCREAMING_SNAKE_CASE				= element.to_tuple()
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= tuple(np.expand_dims(el[self._loader_batch_index]					,						0      ) for el in element      )
																										continue
																				if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										# Those are stored as lists of tensors so need specific unbatching.
																										if isinstance(element[0]					,						torch.Tensor      ):
																																_SCREAMING_SNAKE_CASE				= tuple(el[self._loader_batch_index].unsqueeze(0      ) for el in element      )
																										elif isinstance(element[0]					,						np.ndarray      ):
																																_SCREAMING_SNAKE_CASE				= tuple(np.expand_dims(el[self._loader_batch_index]					,						0      ) for el in element      )
																										continue
																				if element is None:
																										# This can happen for optional data that get passed around
																										_SCREAMING_SNAKE_CASE				= None
																				elif isinstance(element[self._loader_batch_index]					,						torch.Tensor      ):
																										# Take correct batch data, but make it looked like batch_size=1
																										# For compatibility with other methods within transformers

																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index].unsqueeze(0      )
																				elif isinstance(element[self._loader_batch_index]					,						np.ndarray      ):
																										# Take correct batch data, but make it looked like batch_size=1
																										# For compatibility with other methods within transformers
																										_SCREAMING_SNAKE_CASE				= np.expand_dims(element[self._loader_batch_index]					,						0      )
																				else:
																										# This is typically a list, so no need to `unsqueeze`.
																										_SCREAMING_SNAKE_CASE				= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
														_SCREAMING_SNAKE_CASE				= self._loader_batch_data.__class__(UpperCamelCase      )
								self._loader_batch_index += 1
								return result




		def     lowercase       (				self      ):
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
														# We are currently unrolling a batch so we just need to return
														# the current item within a batch
														return self.loader_batch_item()

								# We're out of items within a batch
								_SCREAMING_SNAKE_CASE				= next(self.iterator      )
								_SCREAMING_SNAKE_CASE				= self.infer(UpperCamelCase					,						**self.params      )
								# We now have a batch of "inferred things".
								if self.loader_batch_size is not None:
														# Try to infer the size of the batch
														if isinstance(UpperCamelCase					,						torch.Tensor      ):
																				_SCREAMING_SNAKE_CASE				= processed
														else:
																				_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																				_SCREAMING_SNAKE_CASE				= processed[key]
														if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
														else:
																				_SCREAMING_SNAKE_CASE				= first_tensor.shape[0]
														if 0 < observed_batch_size < self.loader_batch_size:
																				# could be last batch so we can't unroll as many
																				# elements.
																				_SCREAMING_SNAKE_CASE				= observed_batch_size
														# Setting internal index to unwrap the batch
														_SCREAMING_SNAKE_CASE				= processed
														_SCREAMING_SNAKE_CASE				= 0
														return self.loader_batch_item()
								else:
														# We're not unrolling batches
														return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None      ):
								super().__init__(UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      )




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								_SCREAMING_SNAKE_CASE				= None
								return self




		def     lowercase       (				self      ):
								if self.subiterator is None:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
								try:
														# Try to return next item
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								except StopIteration:
														# When a preprocess iterator ends, we can start lookig at the next item
														# ChunkIterator will keep feeding until ALL elements of iterator
														# all have created their subiterator and have been iterating against.
														#
														# Another way to look at it, is we're basically flattening lists of lists
														# into a single list, but with generators
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														_SCREAMING_SNAKE_CASE				= next(self.subiterator      )
								return processed


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __iter__(				self      ):
								_SCREAMING_SNAKE_CASE				= iter(self.loader      )
								return self




		def     lowercase       (				self      ):
								# Extremely similar to PipelineIterator in its unpacking mechanism
								# BUT, we have an extra required item which is the presence of `is_last`
								# That is because everything is flattened by `PipelineChunkIterator` we
								# need to keep track of how to regroup here in the original `process`
								# boundaries so that `process` and `postprocess` see the same data.

								# This iterator accumulates items (possibly while unbatching) until it
								# its a `is_last` and then just passes it on to the caller.
								_SCREAMING_SNAKE_CASE				= False
								_SCREAMING_SNAKE_CASE				= []
								if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
														while self._loader_batch_index < self.loader_batch_size:
																				_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
																				if is_last:
																										return accumulator

								while not is_last:
														_SCREAMING_SNAKE_CASE				= self.infer(next(self.iterator      )					,						**self.params      )
														if self.loader_batch_size is not None:
																				if isinstance(UpperCamelCase					,						torch.Tensor      ):
																										_SCREAMING_SNAKE_CASE				= processed
																				else:
																										_SCREAMING_SNAKE_CASE				= list(processed.keys()      )[0]
																										_SCREAMING_SNAKE_CASE				= processed[key]
																				if isinstance(UpperCamelCase					,						UpperCamelCase      ):
																										_SCREAMING_SNAKE_CASE				= len(UpperCamelCase      )
																				else:
																										_SCREAMING_SNAKE_CASE				= first_tensor.shape[0]
																				if 0 < observed_batch_size < self.loader_batch_size:
																										# could be last batch so we can't unroll as many
																										# elements.
																										_SCREAMING_SNAKE_CASE				= observed_batch_size
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= 0
																				while self._loader_batch_index < self.loader_batch_size:
																										_SCREAMING_SNAKE_CASE				= self.loader_batch_item()
																										_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																										accumulator.append(UpperCamelCase      )
																										if is_last:
																																return accumulator
														else:
																				_SCREAMING_SNAKE_CASE				= processed
																				_SCREAMING_SNAKE_CASE				= item.pop("is_last"      )
																				accumulator.append(UpperCamelCase      )
								return accumulator


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= key




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								return self.dataset[i][self.key]


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= dataset
								_SCREAMING_SNAKE_CASE				= keya
								_SCREAMING_SNAKE_CASE				= keya




		def __len__(				self      ):
								return len(self.dataset      )




		def __getitem__(				self					,						UpperCamelCase      ):
								return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}