

"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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


@dataclass
class 		_A       (			__a					):
      __a								=	42
      __a								=	42
      __a								=	None






class 		_A       (			__a				,       __a					):
      __a								=	2





      @register_to_config
      def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = 0.02    ,						SCREAMING_SNAKE_CASE__ = 100    ,						SCREAMING_SNAKE_CASE__ = 1.0_07    ,						SCREAMING_SNAKE_CASE__ = 80    ,						SCREAMING_SNAKE_CASE__ = 0.05    ,						SCREAMING_SNAKE_CASE__ = 50    ,						)   ->					List[str]:
         # standard deviation of the initial noise distribution
         lowerCamelCase__						=			sigma_max

         # setable values
         lowerCamelCase__						=			None
         lowerCamelCase__						=			None
         lowerCamelCase__						=			None  # sigma(t_i)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					torch.FloatTensor:
         return sample





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					str:
         lowerCamelCase__						=			num_inference_steps
         lowerCamelCase__						=			np.arange(0    ,						self.num_inference_steps		)[::-1].copy()
         lowerCamelCase__						=			torch.from_numpy(SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			[
             (
                 self.config.sigma_max**2
                 * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
             )
             for i in self.timesteps
         ]
         lowerCamelCase__						=			torch.tensor(SCREAMING_SNAKE_CASE__    ,						dtype=torch.floataa    ,						device=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = None		)   ->					Tuple[torch.FloatTensor, float]:
         if self.config.s_min <= sigma <= self.config.s_max:
            lowerCamelCase__						=			min(self.config.s_churn / self.num_inference_steps    ,						2**0.5 - 1		)
         else:
            lowerCamelCase__						=			0

         # sample eps ~ N(0, S_noise^2 * I)
         lowerCamelCase__						=			self.config.s_noise * randn_tensor(sample.shape    ,						generator=SCREAMING_SNAKE_CASE__		).to(sample.device		)
         lowerCamelCase__						=			sigma + gamma * sigma
         lowerCamelCase__						=			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

         return sample_hat, sigma_hat





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = True    ,						)   ->					Union[KarrasVeOutput, Tuple]:
         lowerCamelCase__						=			sample_hat + sigma_hat * model_output
         lowerCamelCase__						=			(sample_hat - pred_original_sample) / sigma_hat
         lowerCamelCase__						=			sample_hat + (sigma_prev - sigma_hat) * derivative

         if not return_dict:
            return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=SCREAMING_SNAKE_CASE__    ,						derivative=SCREAMING_SNAKE_CASE__    ,						pred_original_sample=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = True    ,						)   ->					Union[KarrasVeOutput, Tuple]:
         lowerCamelCase__						=			sample_prev + sigma_prev * model_output
         lowerCamelCase__						=			(sample_prev - pred_original_sample) / sigma_prev
         lowerCamelCase__						=			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

         if not return_dict:
            return (sample_prev, derivative)

         return KarrasVeOutput(
             prev_sample=SCREAMING_SNAKE_CASE__    ,						derivative=SCREAMING_SNAKE_CASE__    ,						pred_original_sample=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[str]:
         raise NotImplementedError()







"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class 		_A       (			__a				,       unittest.TestCase					):
      __a								=	PhobertTokenizer
      __a								=	False





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

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         lowerCamelCase__						=			["T@@", "i", "I", "R@@", "r", "e@@"]
         lowerCamelCase__						=			dict(zip(SCREAMING_SNAKE_CASE__    ,						range(len(SCREAMING_SNAKE_CASE__		)		)		)		)
         lowerCamelCase__						=			["#version: 0.2", "l à</w>"]
         lowerCamelCase__						=			{"unk_token": "<unk>"}

         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["vocab_file"]		)
         lowerCamelCase__						=			os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["merges_file"]		)

         with open(self.vocab_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            for token in vocab_tokens:
               fp.write(f'{token} {vocab_tokens[token]}\n'		)
         with open(self.merges_file    ,						"w"    ,						encoding="utf-8"		) as fp:
            fp.write("\n".join(SCREAMING_SNAKE_CASE__		)		)





      def 					_lowerCamelCase  (  self    ,						**SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         kwargs.update(self.special_tokens_map		)
         return PhobertTokenizer.from_pretrained(self.tmpdirname    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
         return input_text, output_text





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			PhobertTokenizer(self.vocab_file    ,						self.merges_file    ,						**self.special_tokens_map		)
         lowerCamelCase__						=			"Tôi là VinAI Research"
         lowerCamelCase__						=			"T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
         lowerCamelCase__						=			tokenizer.tokenize(SCREAMING_SNAKE_CASE__		)
         print(SCREAMING_SNAKE_CASE__		)
         self.assertListEqual(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			tokens + [tokenizer.unk_token]

         lowerCamelCase__						=			[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__		)




