


'''simple docstring'''







import numpy as np



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__="utf-8"     ,   UpperCamelCase__="surrogatepass"		):






				"""simple docstring"""



				__magic_name__				:					Dict						       =       bin(int.from_bytes(text.encode(UpperCamelCase__     ,   UpperCamelCase__		)     ,   "big"		)		)[2:]
				return bits.zfill(8 * ((len(UpperCamelCase__		) + 7) // 8)		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__="utf-8"     ,   UpperCamelCase__="surrogatepass"		):






				"""simple docstring"""



				__magic_name__				:					int						       =       int(UpperCamelCase__     ,   2		)
				return n.to_bytes((n.bit_length() + 7) // 8     ,   "big"		).decode(UpperCamelCase__     ,   UpperCamelCase__		) or "\0"



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				if size_par + len(UpperCamelCase__		) <= 2**size_par - (len(UpperCamelCase__		) - 1):
								raise ValueError("size of parity don't match with size of data"		)

				__magic_name__				:					int						       =       []
				__magic_name__				:					str						       =       []
				__magic_name__				:					int						       =       [bin(UpperCamelCase__		)[2:] for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		)]

				# sorted information data for the size of the output data
				__magic_name__				:					Tuple						       =       []
				# data position template + parity
				__magic_name__				:					Any						       =       []
				# parity bit counter
				__magic_name__				:					Optional[Any]						       =       0
				# counter position of data bits
				__magic_name__				:					Dict						       =       0

				for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		):
								# Performs a template of bit positions - who should be given,
								# and who should be parity
								if qtd_bp < size_par:
												if (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
																data_out_gab.append("P"		)
																__magic_name__				:					int						       =       qtd_bp + 1
												else:
																data_out_gab.append("D"		)
								else:
												data_out_gab.append("D"		)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data[cont_data]		)
												cont_data += 1
								else:
												data_ord.append(UpperCamelCase__		)

    # Calculates parity
				__magic_name__				:					str						       =       0  # parity bit counter
				for bp in range(1     ,   size_par + 1		):
								# Bit counter one for a given parity
								__magic_name__				:					Any						       =       0
								# counter to control the loop reading
								__magic_name__				:					Optional[Any]						       =       0
								for x in data_ord:
												if x is not None:
																try:
																				__magic_name__				:					Optional[int]						       =       (bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				__magic_name__				:					Optional[Any]						       =       "0"
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(cont_bo % 2		)

								qtd_bp += 1

				# Mount the message
				__magic_name__				:					Any						       =       0  # parity bit counter
				for x in range(0     ,   size_par + len(UpperCamelCase__		)		):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]		)		)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]		)

				return data_out




def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       []
				# Parity bit counter
				__magic_name__				:					List[Any]						       =       0
				# Counter p data bit reading
				__magic_name__				:					int						       =       0
				# list of parity received
				__magic_name__				:					List[str]						       =       []
				__magic_name__				:					Optional[Any]						       =       []

				for x in range(1     ,   len(UpperCamelCase__		) + 1		):
								# Performs a template of bit positions - who should be given,
								#  and who should be parity
								if qtd_bp < size_par and (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
												data_out_gab.append("P"		)
												__magic_name__				:					Optional[Any]						       =       qtd_bp + 1
								else:
												data_out_gab.append("D"		)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_output.append(data[cont_data]		)
								else:
												parity_received.append(data[cont_data]		)
								cont_data += 1

				# -----------calculates the parity with the data
				__magic_name__				:					Union[str, Any]						       =       []
				__magic_name__				:					List[Any]						       =       []
				__magic_name__				:					Dict						       =       [bin(UpperCamelCase__		)[2:] for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		)]

				#  sorted information data for the size of the output data
				__magic_name__				:					Union[str, Any]						       =       []
				# Data position feedback + parity
				__magic_name__				:					Union[str, Any]						       =       []
				# Parity bit counter
				__magic_name__				:					Dict						       =       0
				# Counter p data bit reading
				__magic_name__				:					Union[str, Any]						       =       0

				for x in range(1     ,   size_par + len(UpperCamelCase__		) + 1		):
								# Performs a template position of bits - who should be given,
								# and who should be parity
								if qtd_bp < size_par and (np.log(UpperCamelCase__		) / np.log(2		)).is_integer():
												data_out_gab.append("P"		)
												__magic_name__				:					Union[str, Any]						       =       qtd_bp + 1
								else:
												data_out_gab.append("D"		)

								# Sorts the data to the new output size
								if data_out_gab[-1] == "D":
												data_ord.append(data_output[cont_data]		)
												cont_data += 1
								else:
												data_ord.append(UpperCamelCase__		)

    # Calculates parity
				__magic_name__				:					int						       =       0  # parity bit counter
				for bp in range(1     ,   size_par + 1		):
								# Bit counter one for a certain parity
								__magic_name__				:					Dict						       =       0
								# Counter to control loop reading
								__magic_name__				:					Dict						       =       0
								for x in data_ord:
												if x is not None:
																try:
																				__magic_name__				:					Optional[int]						       =       (bin_pos[cont_loop])[-1 * (bp)]
																except IndexError:
																				__magic_name__				:					Union[str, Any]						       =       "0"
																if aux == "1" and x == "1":
																				cont_bo += 1
												cont_loop += 1
								parity.append(str(cont_bo % 2		)		)

								qtd_bp += 1

				# Mount the message
				__magic_name__				:					List[str]						       =       0  # Parity bit counter
				for x in range(0     ,   size_par + len(UpperCamelCase__		)		):
								if data_ord[x] is None:
												data_out.append(str(parity[cont_bp]		)		)
												cont_bp += 1
								else:
												data_out.append(data_ord[x]		)

				__magic_name__				:					Tuple						       =       parity_received == parity
				return data_output, ack



'''simple docstring'''







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 		_snake_case    (				snake_case_  ,  unittest.TestCase		):






						'''simple docstring'''







						__snake_case   =      GPTNeoXJapaneseTokenizer
						__snake_case   =      False
						__snake_case   =      {"do_clean_text": False, "add_prefix_space": False}







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

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

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







						def lowerCAmelCase__ (  self:	Tuple      ,							**__UpperCamelCase:	str     )							->	Union[str, Any]:
										kwargs.update(self.special_tokens_map     )
										return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,							**__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	List[str]      ,							__UpperCamelCase:	str     )							->	List[str]:
										__magic_name__				:					str						       =       "こんにちは、世界。 \nこんばんは、㔺界。😀"
										__magic_name__				:					str						       =       "こんにちは、世界。 \nこんばんは、世界。😀"
										return input_text, output_text







						def lowerCAmelCase__ (  self:	Union[str, Any]      ,							__UpperCamelCase:	List[Any]     )							->	Dict:
										__magic_name__       ,     __magic_name__				:					Union[str, Any]						       =       self.get_input_output_texts(__UpperCamelCase     )
										__magic_name__				:					List[Any]						       =       tokenizer.encode(__UpperCamelCase      ,							add_special_tokens=__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       tokenizer.decode(__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )
										return text, ids







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Union[str, Any]:
										pass  # TODO add if relevant







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







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







						def lowerCAmelCase__ (  self:	Union[str, Any]     )							->	Dict:
										__magic_name__				:					List[str]						       =       self.get_tokenizer()

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

										# Testing conversion to ids without special tokens
										__magic_name__				:					int						       =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
										__magic_name__				:					str						       =       tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
										self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase     )

										# Testing conversion to ids with special tokens
										__magic_name__				:					str						       =       tokens + [tokenizer.unk_token]
										__magic_name__				:					Tuple						       =       [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
										__magic_name__				:					Dict						       =       tokenizer.convert_tokens_to_ids(__UpperCamelCase     )
										self.assertListEqual(__UpperCamelCase      ,							__UpperCamelCase     )







						@slow
						def lowerCAmelCase__ (  self:	List[Any]     )							->	Union[str, Any]:
										__magic_name__				:					List[str]						       =       self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"     )

										__magic_name__				:					List[Any]						       =       tokenizer.encode("ありがとう。"      ,							add_special_tokens=__UpperCamelCase     )
										__magic_name__				:					Optional[Any]						       =       tokenizer.encode("どういたしまして。"      ,							add_special_tokens=__UpperCamelCase     )

										__magic_name__				:					List[Any]						       =       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase     )
										__magic_name__				:					str						       =       tokenizer.build_inputs_with_special_tokens(__UpperCamelCase      ,							__UpperCamelCase     )

										assert encoded_sentence == ids_a
										assert encoded_pair == ids_a + ids_a







						def lowerCAmelCase__ (  self:	Tuple     )							->	Dict:
										# Intentionally convert some words to accommodate character fluctuations unique to Japanese
										pass







						def lowerCAmelCase__ (  self:	Dict     )							->	Optional[int]:
										# tokenizer has no padding token
										pass