
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					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    List[Any]          =			42
			__UpperCamelCase   :    Dict          =			42




class 							_UpperCamelCase					(       _A  ,							_A	):






			'''simple docstring'''

			__UpperCamelCase   :    int          =			1

			@register_to_config
			def __init__(							self			:							str    ,						snake_case_			:							int = 2000    ,						snake_case_			:							float = 0.15    ,						snake_case_			:							float = 0.01    ,						snake_case_			:							float = 1348.0    ,						snake_case_			:							float = 1e-5    ,						snake_case_			:							int = 1    ,						):
							# standard deviation of the initial noise distribution
							UpperCamelCase_:				Optional[Any]						=    sigma_max

							# setable values
							UpperCamelCase_:				List[Any]						=    None

							self.set_sigmas(_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase   )

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							Optional[int] = None   ):
							return sample

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							int    ,						snake_case_			:							float = None    ,						snake_case_			:							Union[str, torch.device] = None   ):
							UpperCamelCase_:				List[Any]						=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

							UpperCamelCase_:				Union[str, Any]						=    torch.linspace(1    ,						_lowercase    ,						_lowercase    ,						device=_lowercase   )

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							int    ,						snake_case_			:							float = None    ,						snake_case_			:							float = None    ,						snake_case_			:							float = None   ):
							UpperCamelCase_:				Optional[int]						=    sigma_min if sigma_min is not None else self.config.sigma_min
							UpperCamelCase_:				Tuple						=    sigma_max if sigma_max is not None else self.config.sigma_max
							UpperCamelCase_:				Dict						=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
							if self.timesteps is None:
											self.set_timesteps(_lowercase    ,						_lowercase   )

							UpperCamelCase_:				Union[str, Any]						=    sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
							UpperCamelCase_:				Any						=    torch.exp(torch.linspace(math.log(_lowercase   )    ,						math.log(_lowercase   )    ,						_lowercase   )   )
							UpperCamelCase_:				Dict						=    torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]   )

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							str    ,						snake_case_			:							List[Any]   ):
							return torch.where(
							    timesteps == 0    ,						torch.zeros_like(t.to(timesteps.device   )   )    ,						self.discrete_sigmas[timesteps - 1].to(timesteps.device   )    ,						)

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							int    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							Optional[torch.Generator] = None    ,						snake_case_			:							bool = True    ,						):
							if self.timesteps is None:
											raise ValueError(
											    """`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"""   )

							UpperCamelCase_:				Dict						=    timestep * torch.ones(
							    sample.shape[0]    ,						device=sample.device   )  # torch.repeat_interleave(timestep, sample.shape[0])
							UpperCamelCase_:				Tuple						=    (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
							UpperCamelCase_:				List[str]						=    timesteps.to(self.discrete_sigmas.device   )

							UpperCamelCase_:				List[Any]						=    self.discrete_sigmas[timesteps].to(sample.device   )
							UpperCamelCase_:				List[Any]						=    self.get_adjacent_sigma(_lowercase    ,						_lowercase   ).to(sample.device   )
							UpperCamelCase_:				Union[str, Any]						=    torch.zeros_like(_lowercase   )
							UpperCamelCase_:				str						=    (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
							UpperCamelCase_:				int						=    diffusion.flatten()
							while len(diffusion.shape   ) < len(sample.shape   ):
											UpperCamelCase_:				Optional[int]						=    diffusion.unsqueeze(-1   )
							UpperCamelCase_:				str						=    drift - diffusion**2 * model_output

							#  equation 6: sample noise for the diffusion term of
							UpperCamelCase_:				str						=    randn_tensor(
							    sample.shape    ,						layout=sample.layout    ,						generator=_lowercase    ,						device=sample.device    ,						dtype=sample.dtype   )
							UpperCamelCase_:				int						=    sample - drift  # subtract because `dt` is a small negative timestep
							# TODO is the variable diffusion the correct scaling term for the noise?
							UpperCamelCase_:				List[Any]						=    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=_lowercase    ,						prev_sample_mean=_lowercase   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							Optional[torch.Generator] = None    ,						snake_case_			:							bool = True    ,						):
							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
							UpperCamelCase_:				List[Any]						=    randn_tensor(sample.shape    ,						layout=sample.layout    ,						generator=_lowercase   ).to(sample.device   )

							# compute step size from the model_output, the noise, and the snr
							UpperCamelCase_:				Dict						=    torch.norm(model_output.reshape(model_output.shape[0]    ,						-1   )    ,						dim=-1   ).mean()
							UpperCamelCase_:				Tuple						=    torch.norm(noise.reshape(noise.shape[0]    ,						-1   )    ,						dim=-1   ).mean()
							UpperCamelCase_:				Optional[int]						=    (self.config.snr * noise_norm / grad_norm) ** 2 * 2
							UpperCamelCase_:				Any						=    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
							UpperCamelCase_:				Optional[int]						=    step_size.flatten()
							while len(step_size.shape   ) < len(sample.shape   ):
											UpperCamelCase_:				Union[str, Any]						=    step_size.unsqueeze(-1   )
							UpperCamelCase_:				Union[str, Any]						=    sample + step_size * model_output
							UpperCamelCase_:				Dict						=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

							if not return_dict:
											return (prev_sample,)

							return SchedulerOutput(prev_sample=_lowercase   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							torch.FloatTensor    ,						snake_case_			:							torch.FloatTensor    ,						):
							# Make sure sigmas and timesteps have the same device and dtype as original_samples
							UpperCamelCase_:				Optional[Any]						=    timesteps.to(original_samples.device   )
							UpperCamelCase_:				Optional[Any]						=    self.discrete_sigmas.to(original_samples.device   )[timesteps]
							UpperCamelCase_:				str						=    (
							    noise * sigmas[:, None, None, None]
							    if noise is not None
							    else torch.randn_like(_lowercase   ) * sigmas[:, None, None, None]
							)
							UpperCamelCase_:				Optional[int]						=    noise + original_samples
							return noisy_samples




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












'''simple docstring'''
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		CTRLTokenizer
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								A_									= ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
								A_									= dict(zip(_lowercase      ,       range(len(_lowercase))))
								A_									= ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
								A_									= {'unk_token': '<unk>'}

								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['vocab_file'])
								A_									= os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES['merges_file'])
								with open(self.vocab_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write(json.dumps(_lowercase) + '\n')
								with open(self.merges_file      ,       'w'      ,       encoding='utf-8') as fp:
											fp.write('\n'.join(_lowercase))



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any])    ->							Dict:
								A_									= 'adapt react readapt apt'
								A_									= 'adapt react readapt apt'
								return input_text, output_text



					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= CTRLTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map)
								A_									= 'adapt react readapt apt'
								A_									= 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
								A_									= tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokens + [tokenizer.unk_token]

								A_									= [0, 1, 2, 4, 5, 1, 0, 3, 6]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase)      ,       _lowercase)




