
'''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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					torch.FloatTensor
		a							:					torch.FloatTensor
		a							:					Optional[torch.FloatTensor]       =					None


class 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase			):
		a							:					List[Any]       =					2




		@register_to_config
		def __init__(				self					,						UpperCamelCase = 0.02					,						UpperCamelCase = 100					,						UpperCamelCase = 1.0_07					,						UpperCamelCase = 80					,						UpperCamelCase = 0.05					,						UpperCamelCase = 50					,						):
								# standard deviation of the initial noise distribution
								_SCREAMING_SNAKE_CASE				= sigma_max

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								return sample




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase = None      ):
								_SCREAMING_SNAKE_CASE				= num_inference_steps
								_SCREAMING_SNAKE_CASE				= np.arange(0					,						self.num_inference_steps      )[::-1].copy()
								_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      ).to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= [
								    (
								        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
								]
								_SCREAMING_SNAKE_CASE				= torch.tensor(UpperCamelCase					,						dtype=torch.floataa					,						device=UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = None      ):
								if self.config.s_min <= sigma <= self.config.s_max:
														_SCREAMING_SNAKE_CASE				= min(self.config.s_churn / self.num_inference_steps					,						2**0.5 - 1      )
								else:
														_SCREAMING_SNAKE_CASE				= 0

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

								return sample_hat, sigma_hat




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = True					,						):
								_SCREAMING_SNAKE_CASE				= sample_hat + sigma_hat * model_output
								_SCREAMING_SNAKE_CASE				= (sample_hat - pred_original_sample) / sigma_hat
								_SCREAMING_SNAKE_CASE				= sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
														return (sample_prev, derivative)

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




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = True					,						):
								_SCREAMING_SNAKE_CASE				= sample_prev + sigma_prev * model_output
								_SCREAMING_SNAKE_CASE				= (sample_prev - pred_original_sample) / sigma_prev
								_SCREAMING_SNAKE_CASE				= 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=UpperCamelCase					,						derivative=UpperCamelCase					,						pred_original_sample=UpperCamelCase      )




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								raise NotImplementedError()

'''simple docstring'''







from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Dict       =					DistilBertTokenizer
		a							:					Tuple       =					DistilBertTokenizerFast
		a							:					List[str]       =					True




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= DistilBertTokenizer.from_pretrained("distilbert-base-uncased"      )

								_SCREAMING_SNAKE_CASE				= tokenizer.encode("sequence builders"					,						add_special_tokens=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= tokenizer.encode("multi-sequence build"					,						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      )

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]