





'''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 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  BertJapaneseTokenizer
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  True





	def   _snake_case   (		self		)      ->		int:
		super().setUp()

		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',
		    '、',
		    '##、',
		    '。',
		    '##。',
		]

		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   _snake_case   (		self      ,       UpperCamelCase_		)      ->		str:
		snake_case__	       =   'こんにちは、世界。 \nこんばんは、世界。'
		snake_case__	       =   'こんにちは 、 世界 。 こんばんは 、 世界 。'
		return input_text, output_text





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		int:
		snake_case__       ,  snake_case__	       =   self.get_input_output_texts(UpperCamelCase_		)
		snake_case__	       =   tokenizer.encode(UpperCamelCase_      ,       add_special_tokens=UpperCamelCase_		)
		snake_case__	       =   tokenizer.decode(UpperCamelCase_      ,       clean_up_tokenization_spaces=UpperCamelCase_		)
		return text, ids





	def   _snake_case   (		self		)      ->		Tuple:
		pass  # TODO add if relevant





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





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





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.tokenizer_class(self.vocab_file		)

		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   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   self.tokenizer_class(self.vocab_file      ,       word_tokenizer_type='mecab'		)
		self.assertIsNotNone(UpperCamelCase_		)

		snake_case__	       =   'こんにちは、世界。\nこんばんは、世界。'
		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]		)

		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:
			snake_case__	       =   pickle.load(UpperCamelCase_		)

		snake_case__	       =   tokenizer_new.tokenize(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[str]:
		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   _snake_case   (		self		)      ->		Dict:
		try:
			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   _snake_case   (		self		)      ->		int:
		try:
			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   _snake_case   (		self		)      ->		Optional[Any]:
		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   _snake_case   (		self		)      ->		List[Any]:
		try:
			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   _snake_case   (		self		)      ->		Any:
		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   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   self.tokenizer_class(self.vocab_file      ,       word_tokenizer_type='sudachi'		)
		self.assertIsNotNone(UpperCamelCase_		)

		snake_case__	       =   'こんにちは、世界。\nこんばんは、世界。'
		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]		)

		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:
			snake_case__	       =   pickle.load(UpperCamelCase_		)

		snake_case__	       =   tokenizer_new.tokenize(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_      ,       UpperCamelCase_		)





	@require_sudachi
	def   _snake_case   (		self		)      ->		Any:
		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   _snake_case   (		self		)      ->		Optional[int]:
		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   _snake_case   (		self		)      ->		Dict:
		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   _snake_case   (		self		)      ->		int:
		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   _snake_case   (		self		)      ->		List[str]:
		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   _snake_case   (		self		)      ->		List[Any]:
		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   _snake_case   (		self		)      ->		List[Any]:
		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   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   self.tokenizer_class(self.vocab_file      ,       word_tokenizer_type='jumanpp'		)
		self.assertIsNotNone(UpperCamelCase_		)

		snake_case__	       =   'こんにちは、世界。\nこんばんは、世界。'
		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]		)

		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:
			snake_case__	       =   pickle.load(UpperCamelCase_		)

		snake_case__	       =   tokenizer_new.tokenize(UpperCamelCase_		)

		self.assertListEqual(UpperCamelCase_      ,       UpperCamelCase_		)





	@require_jumanpp
	def   _snake_case   (		self		)      ->		List[str]:
		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   _snake_case   (		self		)      ->		Optional[Any]:
		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   _snake_case   (		self		)      ->		Tuple:
		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   _snake_case   (		self		)      ->		List[Any]:
		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   _snake_case   (		self		)      ->		Tuple:
		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   _snake_case   (		self		)      ->		Tuple:
		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']

		snake_case__	       =   {}
		for i, token in enumerate(UpperCamelCase_		):
			snake_case__	       =   i
		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   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp'		)
		snake_case__	       =   tokenizer.subword_tokenizer

		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', '▁。']		)

		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   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese'		)

		snake_case__	       =   tokenizer.encode('ありがとう。'      ,       add_special_tokens=UpperCamelCase_		)
		snake_case__	       =   tokenizer.encode('どういたしまして。'      ,       add_special_tokens=UpperCamelCase_		)

		snake_case__	       =   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_		)
		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 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  BertJapaneseTokenizer
	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		Any:
		super().setUp()

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

		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   _snake_case   (		self      ,       **UpperCamelCase_		)      ->		List[Any]:
		return BertJapaneseTokenizer.from_pretrained(self.tmpdirname      ,       subword_tokenizer_type='character'      ,       **UpperCamelCase_		)





	def   _snake_case   (		self      ,       UpperCamelCase_		)      ->		Union[str, Any]:
		snake_case__	       =   'こんにちは、世界。 \nこんばんは、世界。'
		snake_case__	       =   'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
		return input_text, output_text





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





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





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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   self.tokenizer_class(self.vocab_file      ,       subword_tokenizer_type='character'		)

		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   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']

		snake_case__	       =   {}
		for i, token in enumerate(UpperCamelCase_		):
			snake_case__	       =   i
		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   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char'		)

		snake_case__	       =   tokenizer.encode('ありがとう。'      ,       add_special_tokens=UpperCamelCase_		)
		snake_case__	       =   tokenizer.encode('どういたしまして。'      ,       add_special_tokens=UpperCamelCase_		)

		snake_case__	       =   tokenizer.build_inputs_with_special_tokens(UpperCamelCase_		)
		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 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   'cl-tohoku/bert-base-japanese'
		snake_case__	       =   AutoTokenizer.from_pretrained(UpperCamelCase_		)
		self.assertIsInstance(UpperCamelCase_      ,       UpperCamelCase_		)






class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		List[Any]:
		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.'		)		)
		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 argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=None							)   ->List[str]:
	# set parameter of one layer
	assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
	snake_case__	       =   nn.Parameter(UpperCAmelCase_							)
	if bias is not None:
		assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
		snake_case__	       =   nn.Parameter(UpperCAmelCase_							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)

	set_param(
	    torch_layer.self_attention.query_key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# set torch weights for 1-to-1 comparison
	snake_case__	       =   np.asarray(weights[0]							)
	snake_case__	       =   np.asarray(weights[1]							)
	snake_case__	       =   np.asarray(weights[2]							)
	snake_case__	       =   np.asarray(weights[3]							)

	set_param(
	    torch_layer.self_attention.query					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.key					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.self_attention.value					,				torch.tensor(UpperCAmelCase_							).transpose(1					,				2							).contiguous().view(-1					,				UpperCAmelCase_							)					,				)
	set_param(
	    torch_layer.output.dense					,				torch.tensor(UpperCAmelCase_							).view(-1					,				UpperCAmelCase_							).contiguous().transpose(0					,				1							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:
	# layernorm 1
	snake_case__	       =   weights[0][0][0]
	snake_case__	       =   np.asarray(layer_norm_a[0]							)
	snake_case__	       =   np.asarray(layer_norm_a[1]							)
	set_param(
	    torch_block.attention.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# lsh weights + output
	snake_case__	       =   weights[0][1]
	if len(UpperCAmelCase_							) < 4:
		set_layer_weights_in_torch_lsh(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)
	else:
		set_layer_weights_in_torch_local(UpperCAmelCase_					,				torch_block.attention					,				UpperCAmelCase_							)

	# intermediate weighs
	snake_case__	       =   weights[2][0][1][2]

	# Chunked Feed Forward
	if len(UpperCAmelCase_							) == 4:
		snake_case__	       =   intermediate_weights[2]

	# layernorm 2
	snake_case__	       =   np.asarray(intermediate_weights[0][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[0][1]							)
	set_param(
	    torch_block.feed_forward.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate dense
	snake_case__	       =   np.asarray(intermediate_weights[1][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[1][1]							)
	set_param(
	    torch_block.feed_forward.dense.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)

	# intermediate out
	snake_case__	       =   np.asarray(intermediate_weights[4][0]							)
	snake_case__	       =   np.asarray(intermediate_weights[4][1]							)
	set_param(
	    torch_block.feed_forward.output.dense					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Union[str, Any]:
	# reformer model
	snake_case__	       =   torch_model.reformer

	# word embeds
	snake_case__	       =   np.asarray(weights[1]							)
	set_param(
	    torch_model_reformer.embeddings.word_embeddings					,				torch.tensor(UpperCAmelCase_							)					,				)

	if isinstance(weights[3]					,				UpperCAmelCase_							):
		snake_case__	       =   torch_model_reformer.embeddings.position_embeddings
		for emb_idx in range(len(position_embeddings.weights							)							):
			snake_case__	       =   np.asarray(weights[3][emb_idx][0]							)
			assert (
			    position_embeddings.weights[emb_idx].shape == emb_weights.shape
			), f'''{position_embeddings[emb_idx]} emb does not match'''
			snake_case__	       =   nn.Parameter(torch.tensor(UpperCAmelCase_							)							)

	snake_case__	       =   weights[5]
	assert len(torch_model_reformer.encoder.layers							) * 4 == len(
	    UpperCAmelCase_							), "HF and trax model do not have the same number of layers"
	for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers							):
		snake_case__	       =   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
		set_block_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# output layer norm
	snake_case__	       =   np.asarray(weights[7][0]							)
	snake_case__	       =   np.asarray(weights[7][1]							)
	set_param(
	    torch_model_reformer.encoder.layer_norm					,				torch.tensor(UpperCAmelCase_							)					,				torch.tensor(UpperCAmelCase_							)					,				)

	# output embeddings
	snake_case__	       =   np.asarray(weights[9][0]							)
	snake_case__	       =   np.asarray(weights[9][1]							)
	set_param(
	    torch_model.lm_head.decoder					,				torch.tensor(UpperCAmelCase_							).transpose(0					,				1							).contiguous()					,				torch.tensor(UpperCAmelCase_							)					,				)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->List[Any]:
	# Initialise PyTorch model
	snake_case__	       =   ReformerConfig.from_json_file(UpperCAmelCase_							)
	print(f'''Building PyTorch model from configuration: {config}'''							)
	snake_case__	       =   ReformerModelWithLMHead(UpperCAmelCase_							)

	with open(UpperCAmelCase_					,				'rb'							) as f:
		snake_case__	       =   pickle.load(UpperCAmelCase_							)['weights']

	set_model_weights_in_torch(UpperCAmelCase_					,				UpperCAmelCase_					,				config.hidden_size							)

	# Save pytorch-model
	print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
	torch.save(model.state_dict()					,				UpperCAmelCase_							)


if __name__ == "__main__":
			a__					:			Optional[int]          =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained Reformer model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			a__					:			Dict          =     parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



