





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    42
					A__				 =    42

class SCREAMING_SNAKE_CASE_ (					lowercase_							,       lowercase_   ):





					"""simple docstring"""


					A__				 =    1





					@register_to_config
					def __init__( self	,  _lowerCAmelCase = 2000	,  _lowerCAmelCase = 0.15	,  _lowerCAmelCase = 0.01	,  _lowerCAmelCase = 1_348.0	,  _lowerCAmelCase = 1E-5	,  _lowerCAmelCase = 1	,  ):
										# standard deviation of the initial noise distribution
										lowerCamelCase__       =       sigma_max

										# setable values
										lowerCamelCase__       =       None

										self.set_sigmas(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	):
										return sample





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	):
										lowerCamelCase__       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

										lowerCamelCase__       =       torch.linspace(1	,  _lowerCAmelCase	,  _lowerCAmelCase	,  device=_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	,  _lowerCAmelCase = None	):
										lowerCamelCase__       =       sigma_min if sigma_min is not None else self.config.sigma_min
										lowerCamelCase__       =       sigma_max if sigma_max is not None else self.config.sigma_max
										lowerCamelCase__       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
										if self.timesteps is None:
															self.set_timesteps(_lowerCAmelCase	,  _lowerCAmelCase	)

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





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





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = True	,  ):
										if self.timesteps is None:
															raise ValueError(
															    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"	)

										lowerCamelCase__       =       timestep * torch.ones(
										    sample.shape[0]	,  device=sample.device	)  # torch.repeat_interleave(timestep, sample.shape[0])
										lowerCamelCase__       =       (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
										lowerCamelCase__       =       timesteps.to(self.discrete_sigmas.device	)

										lowerCamelCase__       =       self.discrete_sigmas[timesteps].to(sample.device	)
										lowerCamelCase__       =       self.get_adjacent_sigma(_lowerCAmelCase	,  _lowerCAmelCase	).to(sample.device	)
										lowerCamelCase__       =       torch.zeros_like(_lowerCAmelCase	)
										lowerCamelCase__       =       (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
										lowerCamelCase__       =       diffusion.flatten()
										while len(diffusion.shape	) < len(sample.shape	):
															lowerCamelCase__       =       diffusion.unsqueeze(-1	)
										lowerCamelCase__       =       drift - diffusion**2 * model_output

										#  equation 6: sample noise for the diffusion term of
										lowerCamelCase__       =       randn_tensor(
										    sample.shape	,  layout=sample.layout	,  generator=_lowerCAmelCase	,  device=sample.device	,  dtype=sample.dtype	)
										lowerCamelCase__       =       sample - drift  # subtract because `dt` is a small negative timestep
										# TODO is the variable diffusion the correct scaling term for the noise?
										lowerCamelCase__       =       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=_lowerCAmelCase	,  prev_sample_mean=_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase = None	,  _lowerCAmelCase = 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
										lowerCamelCase__       =       randn_tensor(sample.shape	,  layout=sample.layout	,  generator=_lowerCAmelCase	).to(sample.device	)

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

										if not return_dict:
															return (prev_sample,)

										return SchedulerOutput(prev_sample=_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  ):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCamelCase__       =       timesteps.to(original_samples.device	)
										lowerCamelCase__       =       self.discrete_sigmas.to(original_samples.device	)[timesteps]
										lowerCamelCase__       =       (
										    noise * sigmas[:, None, None, None]
										    if noise is not None
										    else torch.randn_like(_lowerCAmelCase	) * sigmas[:, None, None, None]
										)
										lowerCamelCase__       =       noise + original_samples
										return noisy_samples






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








import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class SCREAMING_SNAKE_CASE_ (					unittest.TestCase							,       lowercase_   ):





					"""simple docstring"""


					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       load_tool("text-classification"	)
										self.tool.setup()
										lowerCamelCase__       =       load_tool("text-classification"	,  remote=_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tool("That's quite cool"	,  ["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.remote_tool("That's quite cool"	,  ["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.tool(text="That's quite cool"	,  labels=["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.remote_tool(text="That's quite cool"	,  labels=["positive", "negative"]	)
										self.assertEqual(_lowerCAmelCase	,  "positive"	)

