


"""simple docstring"""






import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 			lowerCamelCase_		(			lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					GPTSanJapaneseTokenizer
				_lowerCAmelCase      :      List[str]      							=					False
				_lowerCAmelCase      :      List[str]      							=					{"do_clean_text": False, "add_prefix_space": False}



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

								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				["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>", "<|bagoftoken|>", "<|endoftext|>"]
								# fmt: on
								SCREAMING_SNAKE_CASE__     							=				{"emoji": {"\ud83d\ude00": "<|emoji1|>"}, "emoji_inv": {"<|emoji1|>": "\ud83d\ude00"}}  # 😀
								SCREAMING_SNAKE_CASE__     							=				{"unk_token": "<unk>"}

								SCREAMING_SNAKE_CASE__     							=				os.path.join(self.tmpdirname       ,		VOCAB_FILES_NAMES["vocab_file"]     )
								SCREAMING_SNAKE_CASE__     							=				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       ,		**UpperCAmelCase__     ):
								kwargs.update(self.special_tokens_map     )
								return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、世界。 \nこんばんは、㔺界。😀"
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、世界。 \nこんばんは、世界。😀"
								return input_text, output_text



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



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



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



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()

								# Testing tokenization
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、世界。　こんばんは、㔺界。"
								SCREAMING_SNAKE_CASE__     							=				["こん", "にちは", "、", "世界", "。", "<SP>", "こん", "ばんは", "、", "㔺界", "。"]
								SCREAMING_SNAKE_CASE__     							=				tokenizer.tokenize(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								# Testing conversion to ids without special tokens
								SCREAMING_SNAKE_CASE__     							=				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
								SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )

								# Testing conversion to ids with special tokens
								SCREAMING_SNAKE_CASE__     							=				tokens + [tokenizer.unk_token]
								SCREAMING_SNAKE_CASE__     							=				[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
								SCREAMING_SNAKE_CASE__     							=				tokenizer.convert_tokens_to_ids(UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.get_tokenizer()

								# Testing tokenization
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、、、、世界。こんばんは、、、、世界。"
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.decode(UpperCAmelCase__     )
								self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"     )

								# Testing tokenization
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、世界。"
								SCREAMING_SNAKE_CASE__     							=				"こんばんは、㔺界。😀"
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、世界。こんばんは、世界。😀"
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(prefix_text + input_text     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(""       ,		prefix_text=prefix_text + input_text     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(UpperCAmelCase__       ,		prefix_text=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.decode(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.decode(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.decode(UpperCAmelCase__     )
								self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"     )

								# Testing tokenization
								SCREAMING_SNAKE_CASE__     							=				"こんにちは、世界。"
								SCREAMING_SNAKE_CASE__     							=				"こんばんは、㔺界。😀"

								SCREAMING_SNAKE_CASE__     							=				len(tokenizer.encode(UpperCAmelCase__     )     ) - 2
								SCREAMING_SNAKE_CASE__     							=				len(tokenizer.encode(UpperCAmelCase__     )     ) - 2

								SCREAMING_SNAKE_CASE__     							=				[1] + [0] * (len_prefix + len_text + 1)
								SCREAMING_SNAKE_CASE__     							=				[1] * (len_prefix + len_text + 1) + [0]
								SCREAMING_SNAKE_CASE__     							=				[1] + [1] * (len_prefix) + [0] * (len_text + 1)

								SCREAMING_SNAKE_CASE__     							=				tokenizer(prefix_text + input_text     ).token_type_ids
								SCREAMING_SNAKE_CASE__     							=				tokenizer(""       ,		prefix_text=prefix_text + input_text     ).token_type_ids
								SCREAMING_SNAKE_CASE__     							=				tokenizer(UpperCAmelCase__       ,		prefix_text=UpperCAmelCase__     ).token_type_ids
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertListEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"     )

								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode("あンいワ"     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode(""       ,		prefix_text="あンいワ"     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.encode("いワ"       ,		prefix_text="あン"     )

								self.assertEqual(tokenizer.decode(UpperCAmelCase__     )       ,		tokenizer.decode(UpperCAmelCase__     )     )
								self.assertEqual(tokenizer.decode(UpperCAmelCase__     )       ,		tokenizer.decode(UpperCAmelCase__     )     )
								self.assertNotEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertNotEqual(UpperCAmelCase__       ,		UpperCAmelCase__     )
								self.assertEqual(x_token_a[1]       ,		x_token_a[-1]     )  # SEG token
								self.assertEqual(x_token_a[1]       ,		x_token_a[3]     )  # SEG token



				@slow
				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer_class.from_pretrained("Tanrei/GPTSAN-japanese"     )

								SCREAMING_SNAKE_CASE__     							=				[["武田信玄", "は、"], ["織田信長", "の配下の、"]]
								SCREAMING_SNAKE_CASE__     							=				tokenizer(UpperCAmelCase__       ,		padding=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				tokenizer.batch_encode_plus(UpperCAmelCase__       ,		padding=UpperCAmelCase__     )

								# fmt: off
								SCREAMING_SNAKE_CASE__     							=				[[3_5993, 8640, 2_5948, 3_5998, 3_0647, 3_5675, 3_5999, 3_5999], [3_5993, 1_0382, 9868, 3_5998, 3_0646, 9459, 3_0646, 3_5675]]
								SCREAMING_SNAKE_CASE__     							=				[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
								SCREAMING_SNAKE_CASE__     							=				[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
								# fmt: on
								self.assertListEqual(x_token.input_ids       ,		UpperCAmelCase__     )
								self.assertListEqual(x_token.token_type_ids       ,		UpperCAmelCase__     )
								self.assertListEqual(x_token.attention_mask       ,		UpperCAmelCase__     )
								self.assertListEqual(x_token_a.input_ids       ,		UpperCAmelCase__     )
								self.assertListEqual(x_token_a.token_type_ids       ,		UpperCAmelCase__     )
								self.assertListEqual(x_token_a.attention_mask       ,		UpperCAmelCase__     )



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






				def   lowerCAmelCase__		(						self     ):
								# tokenizer has no padding token
								pass




"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"     )

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

								# compute
								SCREAMING_SNAKE_CASE__     							=				-1.0 / len(self.timesteps     )

								SCREAMING_SNAKE_CASE__     							=				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								SCREAMING_SNAKE_CASE__     							=				beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												SCREAMING_SNAKE_CASE__     							=				beta_t.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-0.5 * beta_t * x

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

								# add noise
								SCREAMING_SNAKE_CASE__     							=				randn_tensor(x.shape       ,		layout=x.layout       ,		generator=UpperCAmelCase__       ,		device=x.device       ,		dtype=x.dtype     )
								SCREAMING_SNAKE_CASE__     							=				x_mean + diffusion * math.sqrt(-dt     ) * noise

								return x, x_mean






				def __len__(						self     ):
								return self.config.num_train_timesteps
