
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       42
							__lowercase      		=       42
							__lowercase      		=       None


class 						__UpperCAmelCase      (					_lowerCamelCase						,   _lowerCamelCase						):
							__lowercase      		=       2


							@register_to_config
							def __init__(       self    ,   lowerCAmelCase_ = 0.02    ,   lowerCAmelCase_ = 1_00    ,   lowerCAmelCase_ = 1.007    ,   lowerCAmelCase_ = 80    ,   lowerCAmelCase_ = 0.05    ,   lowerCAmelCase_ = 50    ,   ):





								"""simple docstring"""






								_snake_case   						=		sigma_max

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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								return sample


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								_snake_case   						=		num_inference_steps
								_snake_case   						=		np.arange(0    ,   self.num_inference_steps  )[::-1].copy()
								_snake_case   						=		torch.from_numpy(lowerCAmelCase_  ).to(lowerCAmelCase_  )
								_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
								]
								_snake_case   						=		torch.tensor(lowerCAmelCase_    ,   dtype=torch.floataa    ,   device=lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								if self.config.s_min <= sigma <= self.config.s_max:
									_snake_case   						=		min(self.config.s_churn / self.num_inference_steps    ,   2**0.5 - 1  )
								else:
									_snake_case   						=		0

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

								return sample_hat, sigma_hat


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = True    ,   ):





								"""simple docstring"""






								_snake_case   						=		sample_hat + sigma_hat * model_output
								_snake_case   						=		(sample_hat - pred_original_sample) / sigma_hat
								_snake_case   						=		sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = True    ,   ):





								"""simple docstring"""






								_snake_case   						=		sample_prev + sigma_prev * model_output
								_snake_case   						=		(sample_prev - pred_original_sample) / sigma_prev
								_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=lowerCAmelCase_    ,   derivative=lowerCAmelCase_    ,   pred_original_sample=lowerCAmelCase_  )




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								raise NotImplementedError()



'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
					from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'    ,   return_dict=lowerCAmelCase_  ).to(lowerCAmelCase_  )
								_snake_case   						=		AutoTokenizer.from_pretrained('google/mt5-small'  )

								_snake_case   						=		tokenizer('Hello there'    ,   return_tensors='pt'  ).input_ids
								_snake_case   						=		tokenizer('Hi I am'    ,   return_tensors='pt'  ).input_ids

								_snake_case   						=		model(input_ids.to(lowerCAmelCase_  )    ,   labels=labels.to(lowerCAmelCase_  )  ).loss
								_snake_case   						=		-(labels.shape[-1] * loss.item())

								_snake_case   						=		-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE  ) < 1E-4  )

