



"""simple docstring"""







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

import numpy as np
import torch

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


__snake_case	:				Optional[int]  					=			logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
class       UpperCamelCase  (					a				):





					"""simple docstring"""






					_lowerCamelCase					: torch.FloatTensor


class       UpperCamelCase  (					a      , a				):





					"""simple docstring"""






					_lowerCamelCase					: int				   =1





					@register_to_config
					def __init__(					self :							int	,      _lowerCamelCase :							int = 4_0	,      _lowerCamelCase :							float = 0.002	,      _lowerCamelCase :							float = 80.0	,      _lowerCamelCase :							float = 0.5	,      _lowerCamelCase :							float = 1.0	,      _lowerCamelCase :							float = 7.0	,      _lowerCamelCase :							bool = True	,      ):
										# standard deviation of the initial noise distribution
										A__				=   sigma_max

										A__				=   np.linspace(0	,      1	,      _lowerCamelCase )
										A__				=   self._convert_to_karras(_lowerCamelCase )
										A__				=   self.sigma_to_t(_lowerCamelCase )

										# setable values
										A__				=   None
										A__				=   torch.from_numpy(_lowerCamelCase )
										A__				=   torch.from_numpy(_lowerCamelCase )
										A__				=   False
										A__				=   False





					def 						A__  (					self :							str	,      _lowerCamelCase :							Optional[Any]	,      _lowerCamelCase :							Optional[int]=None ):
										if schedule_timesteps is None:
															A__				=   self.timesteps

										A__				=   (schedule_timesteps == timestep).nonzero()
										return indices.item()





					def 						A__  (					self :							Optional[int]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Union[float, torch.FloatTensor] ):
										# Get sigma corresponding to timestep
										if isinstance(_lowerCamelCase	,      torch.Tensor ):
															A__				=   timestep.to(self.timesteps.device )
										A__				=   self.index_for_timestep(_lowerCamelCase )
										A__				=   self.sigmas[step_idx]

										A__				=   sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

										A__				=   True
										return sample





					def 						A__  (					self :							Dict	,      _lowerCamelCase :							Union[float, np.ndarray] ):
										if not isinstance(_lowerCamelCase	,      np.ndarray ):
															A__				=   np.array(_lowerCamelCase	,      dtype=np.floataa )

										A__				=   1_0_0_0 * 0.25 * np.log(sigmas + 1E-44 )

										return timesteps





					def 						A__  (					self :							Any	,      _lowerCamelCase :							Optional[int] = None	,      _lowerCamelCase :							Union[str, torch.device] = None	,      _lowerCamelCase :							Optional[List[int]] = None	,      ):
										if num_inference_steps is None and timesteps is None:
															raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.''' )

										if num_inference_steps is not None and timesteps is not None:
															raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.''' )

										# Follow DDPMScheduler custom timesteps logic
										if timesteps is not None:
															for i in range(1	,      len(_lowerCamelCase ) ):
																				if timesteps[i] >= timesteps[i - 1]:
																									raise ValueError('''`timesteps` must be in descending order.''' )

															if timesteps[0] >= self.config.num_train_timesteps:
																				raise ValueError(
																				    F'''`timesteps` must start before `self.config.train_timesteps`:'''
																				    F''' {self.config.num_train_timesteps}.''' )

															A__				=   np.array(_lowerCamelCase	,      dtype=np.intaa )
															A__				=   True
										else:
															if num_inference_steps > self.config.num_train_timesteps:
																				raise ValueError(
																				    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
																				    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
																				    F''' maximal {self.config.num_train_timesteps} timesteps.''' )

															A__				=   num_inference_steps

															A__				=   self.config.num_train_timesteps // self.num_inference_steps
															A__				=   (np.arange(0	,      _lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(np.intaa )
															A__				=   False

										# Map timesteps to Karras sigmas directly for multistep sampling
										# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
										A__				=   self.config.num_train_timesteps
										A__				=   timesteps[::-1].copy()
										A__				=   ramp / (num_train_timesteps - 1)
										A__				=   self._convert_to_karras(_lowerCamelCase )
										A__				=   self.sigma_to_t(_lowerCamelCase )

										A__				=   np.concatenate([sigmas, [self.sigma_min]] ).astype(np.floataa )
										A__				=   torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase )

										if str(_lowerCamelCase ).startswith('''mps''' ):
															# mps does not support float64
															A__				=   torch.from_numpy(_lowerCamelCase ).to(_lowerCamelCase	,      dtype=torch.floataa )
										else:
															A__				=   torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase )





					def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							Any ):
										A__				=   self.config.sigma_min
										A__				=   self.config.sigma_max

										A__				=   self.config.rho
										A__				=   sigma_min ** (1 / rho)
										A__				=   sigma_max ** (1 / rho)
										A__				=   (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
										return sigmas





					def 						A__  (					self :							Dict	,      _lowerCamelCase :							Optional[int] ):
										A__				=   self.config.sigma_data

										A__				=   sigma_data**2 / (sigma**2 + sigma_data**2)
										A__				=   sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out





					def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Dict ):
										A__				=   self.config.sigma_min
										A__				=   self.config.sigma_data

										A__				=   sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
										A__				=   (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
										return c_skip, c_out





					def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Union[float, torch.FloatTensor]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							Optional[torch.Generator] = None	,      _lowerCamelCase :							bool = True	,      ):

										if (
										    isinstance(_lowerCamelCase	,      _lowerCamelCase )
										    or isinstance(_lowerCamelCase	,      torch.IntTensor )
										    or isinstance(_lowerCamelCase	,      torch.LongTensor )
										):
															raise ValueError(
															    (
															        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
															        F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
															        ''' one of the `scheduler.timesteps` as a timestep.'''
															    )	,      )

										if not self.is_scale_input_called:
															logger.warning(
															    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
															    '''See `StableDiffusionPipeline` for a usage example.''' )

										if isinstance(_lowerCamelCase	,      torch.Tensor ):
															A__				=   timestep.to(self.timesteps.device )

										A__				=   self.config.sigma_min
										A__				=   self.config.sigma_max

										A__				=   self.index_for_timestep(_lowerCamelCase )

										# sigma_next corresponds to next_t in original implementation
										A__				=   self.sigmas[step_index]
										if step_index + 1 < self.config.num_train_timesteps:
															A__				=   self.sigmas[step_index + 1]
										else:
															# Set sigma_next to sigma_min
															A__				=   self.sigmas[-1]

										# Get scalings for boundary conditions
										A__	,     A__				=   self.get_scalings_for_boundary_condition(_lowerCamelCase )

										# 1. Denoise model output using boundary conditions
										A__				=   c_out * model_output + c_skip * sample
										if self.config.clip_denoised:
															A__				=   denoised.clamp(-1	,      1 )

										# 2. Sample z ~ N(0, s_noise^2 * I)
										# Noise is not used for onestep sampling.
										if len(self.timesteps ) > 1:
															A__				=   randn_tensor(
															    model_output.shape	,      dtype=model_output.dtype	,      device=model_output.device	,      generator=_lowerCamelCase )
										else:
															A__				=   torch.zeros_like(_lowerCamelCase )
										A__				=   noise * self.config.s_noise

										A__				=   sigma_next.clamp(min=_lowerCamelCase	,      max=_lowerCamelCase )

										# 3. Return noisy sample
										# tau = sigma_hat, eps = sigma_min
										A__				=   denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

										if not return_dict:
															return (prev_sample,)

										return CMStochasticIterativeSchedulerOutput(prev_sample=_lowerCamelCase )





					def 						A__  (					self :							Optional[Any]	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      _lowerCamelCase :							torch.FloatTensor	,      ):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										A__				=   self.sigmas.to(device=original_samples.device	,      dtype=original_samples.dtype )
										if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCamelCase ):
															# mps does not support float64
															A__				=   self.timesteps.to(original_samples.device	,      dtype=torch.floataa )
															A__				=   timesteps.to(original_samples.device	,      dtype=torch.floataa )
										else:
															A__				=   self.timesteps.to(original_samples.device )
															A__				=   timesteps.to(original_samples.device )

										A__				=   [(schedule_timesteps == t).nonzero().item() for t in timesteps]

										A__				=   sigmas[step_indices].flatten()
										while len(sigma.shape ) < len(original_samples.shape ):
															A__				=   sigma.unsqueeze(-1 )

										A__				=   original_samples + noise * sigma
										return noisy_samples





					def __len__(					self :							List[Any] ):
										return self.config.num_train_timesteps








"""simple docstring"""







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case	:				List[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          with patch.object(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



