





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

import torch

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





@dataclass
class UpperCAmelCase_				( SCREAMING_SNAKE_CASE_ ):
  __lowerCAmelCase   :						str							=   42
  __lowerCAmelCase   :						Any							=   42





class UpperCAmelCase_				( SCREAMING_SNAKE_CASE_     ,		SCREAMING_SNAKE_CASE_ ):
  __lowerCAmelCase   :						Tuple							=   1


  @register_to_config
  def __init__(							self ,__snake_case = 2_0_0_0 ,__snake_case = 0.15 ,__snake_case = 0.01 ,__snake_case = 1348.0 ,__snake_case = 1E-5 ,__snake_case = 1 ,):



       """simple docstring"""





       A_          =       sigma_max

       # setable values
       A_          =       None

       self.set_sigmas(snake_case__ ,snake_case__ ,snake_case__ ,snake_case__ )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ):



       """simple docstring"""





       return sample


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ):



       """simple docstring"""





       A_          =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

       A_          =       torch.linspace(1 ,snake_case__ ,snake_case__ ,device=snake_case__ )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = None ):



       """simple docstring"""





       A_          =       sigma_min if sigma_min is not None else self.config.sigma_min
       A_          =       sigma_max if sigma_max is not None else self.config.sigma_max
       A_          =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
            self.set_timesteps(snake_case__ ,snake_case__ )

       A_          =       sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       A_          =       torch.exp(torch.linspace(math.log(snake_case__ ) ,math.log(snake_case__ ) ,snake_case__ ) )
       A_          =       torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       return torch.where(
           timesteps == 0 ,torch.zeros_like(t.to(timesteps.device ) ) ,self.discrete_sigmas[timesteps - 1].to(timesteps.device ) ,)


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,__snake_case = None ,__snake_case = True ,):



       """simple docstring"""





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

       A_          =       timestep * torch.ones(
           sample.shape[0] ,device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
       A_          =       (timestep * (len(self.timesteps ) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       A_          =       timesteps.to(self.discrete_sigmas.device )

       A_          =       self.discrete_sigmas[timesteps].to(sample.device )
       A_          =       self.get_adjacent_sigma(snake_case__ ,snake_case__ ).to(sample.device )
       A_          =       torch.zeros_like(snake_case__ )
       A_          =       (sigma**2 - adjacent_sigma**2) ** 0.5

       # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
       # also equation 47 shows the analog from SDE models to ancestral sampling methods
       A_          =       diffusion.flatten()
       while len(diffusion.shape ) < len(sample.shape ):
            A_          =       diffusion.unsqueeze(-1 )
       A_          =       drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       A_          =       randn_tensor(
           sample.shape ,layout=sample.layout ,generator=snake_case__ ,device=sample.device ,dtype=sample.dtype )
       A_          =       sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       A_          =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
            return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=snake_case__ ,prev_sample_mean=snake_case__ )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case = None ,__snake_case = True ,):



       """simple docstring"""





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

       # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
       # sample noise for correction
       A_          =       randn_tensor(sample.shape ,layout=sample.layout ,generator=snake_case__ ).to(sample.device )

       # compute step size from the model_output, the noise, and the snr
       A_          =       torch.norm(model_output.reshape(model_output.shape[0] ,-1 ) ,dim=-1 ).mean()
       A_          =       torch.norm(noise.reshape(noise.shape[0] ,-1 ) ,dim=-1 ).mean()
       A_          =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       A_          =       step_size * torch.ones(sample.shape[0] ).to(sample.device )
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       A_          =       step_size.flatten()
       while len(step_size.shape ) < len(sample.shape ):
            A_          =       step_size.unsqueeze(-1 )
       A_          =       sample + step_size * model_output
       A_          =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(prev_sample=snake_case__ )


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ,__snake_case ,):



       """simple docstring"""





       A_          =       timesteps.to(original_samples.device )
       A_          =       self.discrete_sigmas.to(original_samples.device )[timesteps]
       A_          =       (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(snake_case__ ) * sigmas[:, None, None, None]
       )
       A_          =       noise + original_samples
       return noisy_samples




  def __len__(							self ):



       """simple docstring"""





       return self.config.num_train_timesteps







'''simple docstring'''







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     RoFormerTokenizer
     __magic_name__           =     RoFormerTokenizerFast
     __magic_name__           =     True
     __magic_name__           =     True







     def a						(			self ):

            '''simple docstring'''

            super().setUp()







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self   ,						**snake_case__ ):

            '''simple docstring'''

            return self.rust_tokenizer_class.from_pretrained('junnyu/roformer_chinese_base'   ,						**snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							'永和服装饰品有限公司,今天天气非常好'
            _lowerCAmelCase     :				str       =							'永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'
            return input_text, output_text







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							self.get_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				int       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				List[Any]       =							tokenizer.tokenize(snake_case__ )

            self.assertListEqual(snake_case__   ,						output_text.split() )

            _lowerCAmelCase     :				Optional[Any]       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[str]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.get_rust_tokenizer()
            _lowerCAmelCase      ,  _lowerCAmelCase     :				Any       =							self.get_chinese_input_output_texts()
            _lowerCAmelCase     :				Optional[int]       =							tokenizer.tokenize(snake_case__ )
            self.assertListEqual(snake_case__   ,						output_text.split() )
            _lowerCAmelCase     :				Tuple       =							tokens + [tokenizer.unk_token]
            _lowerCAmelCase     :				List[Any]       =							[2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )   ,						snake_case__ )







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass







     def a						(			self ):

            '''simple docstring'''

            pass

