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

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    torch.FloatTensor
						__a     :    Optional[torch.FloatTensor]												=			None




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0.9_99     ,						SCREAMING_SNAKE_CASE="cosine"     ,						):




			"""simple docstring"""


			if alpha_transform_type == "cosine":

						def alpha_bar_fn(SCREAMING_SNAKE_CASE							):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(SCREAMING_SNAKE_CASE							):
									return math.exp(t * -12.0							)

			else:
						raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

			UpperCAmelCase__		:List[Any] 		=    []
			for i in range(SCREAMING_SNAKE_CASE							):
						UpperCAmelCase__		:int 		=    i / num_diffusion_timesteps
						UpperCAmelCase__		:Dict 		=    (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(SCREAMING_SNAKE_CASE							) / alpha_bar_fn(SCREAMING_SNAKE_CASE							)     ,						SCREAMING_SNAKE_CASE							)							)
			return torch.tensor(SCREAMING_SNAKE_CASE     ,						dtype=torch.floataa							)




class 	UpperCamelCase__       (  UpperCAmelCase__						,			UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			[e.name for e in KarrasDiffusionSchedulers]
						__a     :    List[Any]												=			1


						@register_to_config
						def __init__(  self  , A = 10_00  , A = 0.0001  , A = 0.02  , A = "linear"  , A = None  , A = False  , A = "epsilon"  , A = "linspace"  , A = 0  , )  ->Dict:
									if trained_betas is not None:
												UpperCAmelCase__		:str 		=    torch.tensor(A  , dtype=torch.floataa	)
									elif beta_schedule == "linear":
												UpperCAmelCase__		:Optional[Any] 		=    torch.linspace(A  , A  , A  , dtype=torch.floataa	)
									elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												UpperCAmelCase__		:List[str] 		=    (
												    torch.linspace(beta_start**0.5  , beta_end**0.5  , A  , dtype=torch.floataa	) ** 2
												)
									elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												UpperCAmelCase__		:List[str] 		=    betas_for_alpha_bar(A	)
									else:
												raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""	)

									UpperCAmelCase__		:str 		=    1.0 - self.betas
									UpperCAmelCase__		:Tuple 		=    torch.cumprod(self.alphas  , dim=0	)

									UpperCAmelCase__		:Tuple 		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									UpperCAmelCase__		:List[Any] 		=    np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
									UpperCAmelCase__		:Optional[Any] 		=    torch.from_numpy(A	)

									# setable values
									UpperCAmelCase__		:List[Any] 		=    None
									UpperCAmelCase__		:List[str] 		=    use_karras_sigmas
									self.set_timesteps(A  , A	)
									UpperCAmelCase__		:Union[str, Any] 		=    []
									UpperCAmelCase__		:int 		=    False


						@property
						def    A__       (  self	)  ->Optional[int]:
									# standard deviation of the initial noise distribution
									if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5


						def    A__       (  self  , A  , A	)  ->torch.FloatTensor:
									if isinstance(A  , torch.Tensor	):
												UpperCAmelCase__		:List[Any] 		=    timestep.to(self.timesteps.device	)
									UpperCAmelCase__		:Dict 		=    (self.timesteps == timestep).nonzero().item()
									UpperCAmelCase__		:str 		=    self.sigmas[step_index]
									UpperCAmelCase__		:Union[str, Any] 		=    sample / ((sigma**2 + 1) ** 0.5)
									UpperCAmelCase__		:List[Any] 		=    True
									return sample


						def    A__       (  self  , A  , A  , A	)  ->Dict:

									def lms_derivative(A	):
												UpperCAmelCase__		:Optional[Any] 		=    1.0
												for k in range(A	):
															if current_order == k:
																		continue
															prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
												return prod

									UpperCAmelCase__		:Optional[int] 		=    integrate.quad(A  , self.sigmas[t]  , self.sigmas[t + 1]  , epsrel=1e-4	)[0]

									return integrated_coeff


						def    A__       (  self  , A  , A = None	)  ->Any:
									UpperCAmelCase__		:Dict 		=    num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
												UpperCAmelCase__		:Optional[Any] 		=    np.linspace(0  , self.config.num_train_timesteps - 1  , A  , dtype=A	)[
												    ::-1
												].copy()
									elif self.config.timestep_spacing == "leading":
												UpperCAmelCase__		:List[Any] 		=    self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												UpperCAmelCase__		:int 		=    (np.arange(0  , A	) * step_ratio).round()[::-1].copy().astype(A	)
												timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
												UpperCAmelCase__		:str 		=    self.config.num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												UpperCAmelCase__		:Any 		=    (np.arange(self.config.num_train_timesteps  , 0  , -step_ratio	)).round().copy().astype(A	)
												timesteps -= 1
									else:
												raise ValueError(
												    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

									UpperCAmelCase__		:Tuple 		=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
									UpperCAmelCase__		:Optional[Any] 		=    np.log(A	)
									UpperCAmelCase__		:Union[str, Any] 		=    np.interp(A  , np.arange(0  , len(A	)	)  , A	)

									if self.use_karras_sigmas:
												UpperCAmelCase__		:Tuple 		=    self._convert_to_karras(in_sigmas=A	)
												UpperCAmelCase__		:Dict 		=    np.array([self._sigma_to_t(A  , A	) for sigma in sigmas]	)

									UpperCAmelCase__		:Optional[int] 		=    np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

									UpperCAmelCase__		:Dict 		=    torch.from_numpy(A	).to(device=A	)
									if str(A	).startswith('mps'	):
												# mps does not support float64
												UpperCAmelCase__		:Any 		=    torch.from_numpy(A	).to(A  , dtype=torch.floataa	)
									else:
												UpperCAmelCase__		:Dict 		=    torch.from_numpy(A	).to(device=A	)

									UpperCAmelCase__		:Optional[int] 		=    []


						def    A__       (  self  , A  , A	)  ->Any:
									# get log sigma
									UpperCAmelCase__		:int 		=    np.log(A	)

									# get distribution
									UpperCAmelCase__		:List[str] 		=    log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									UpperCAmelCase__		:List[str] 		=    np.cumsum((dists >= 0)  , axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
									UpperCAmelCase__		:Optional[int] 		=    low_idx + 1

									UpperCAmelCase__		:Any 		=    log_sigmas[low_idx]
									UpperCAmelCase__		:Dict 		=    log_sigmas[high_idx]

									# interpolate sigmas
									UpperCAmelCase__		:Optional[Any] 		=    (low - log_sigma) / (low - high)
									UpperCAmelCase__		:Tuple 		=    np.clip(A  , 0  , 1	)

									# transform interpolation to time range
									UpperCAmelCase__		:Dict 		=    (1 - w) * low_idx + w * high_idx
									UpperCAmelCase__		:Any 		=    t.reshape(sigma.shape	)
									return t


						def    A__       (  self  , A	)  ->torch.FloatTensor:
									UpperCAmelCase__		:float 		=    in_sigmas[-1].item()
									UpperCAmelCase__		:float 		=    in_sigmas[0].item()

									UpperCAmelCase__		:str 		=    7.0  # 7.0 is the value used in the paper
									UpperCAmelCase__		:int 		=    np.linspace(0  , 1  , self.num_inference_steps	)
									UpperCAmelCase__		:Union[str, Any] 		=    sigma_min ** (1 / rho)
									UpperCAmelCase__		:int 		=    sigma_max ** (1 / rho)
									UpperCAmelCase__		:List[str] 		=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas


						def    A__       (  self  , A  , A  , A  , A = 4  , A = True  , )  ->Union[LMSDiscreteSchedulerOutput, Tuple]:
									if not self.is_scale_input_called:
												warnings.warn(
												    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
												    'See `StableDiffusionPipeline` for a usage example.'	)

									if isinstance(A  , torch.Tensor	):
												UpperCAmelCase__		:Dict 		=    timestep.to(self.timesteps.device	)
									UpperCAmelCase__		:Dict 		=    (self.timesteps == timestep).nonzero().item()
									UpperCAmelCase__		:Tuple 		=    self.sigmas[step_index]

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
												UpperCAmelCase__		:Optional[int] 		=    sample - sigma * model_output
									elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												UpperCAmelCase__		:Optional[Any] 		=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									elif self.config.prediction_type == "sample":
												UpperCAmelCase__		:Optional[Any] 		=    model_output
									else:
												raise ValueError(
												    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

									# 2. Convert to an ODE derivative
									UpperCAmelCase__		:Optional[int] 		=    (sample - pred_original_sample) / sigma
									self.derivatives.append(A	)
									if len(self.derivatives	) > order:
												self.derivatives.pop(0	)

									# 3. Compute linear multistep coefficients
									UpperCAmelCase__		:Tuple 		=    min(step_index + 1  , A	)
									UpperCAmelCase__		:int 		=    [self.get_lms_coefficient(A  , A  , A	) for curr_order in range(A	)]

									# 4. Compute previous sample based on the derivatives path
									UpperCAmelCase__		:Tuple 		=    sample + sum(
									    coeff * derivative for coeff, derivative in zip(A  , reversed(self.derivatives	)	)	)

									if not return_dict:
												return (prev_sample,)

									return LMSDiscreteSchedulerOutput(prev_sample=A  , pred_original_sample=A	)


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

									UpperCAmelCase__		:Tuple 		=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

									UpperCAmelCase__		:Dict 		=    original_samples + noise * sigma
									return noisy_samples


						def __len__(  self	)  ->int:
									return self.config.num_train_timesteps







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[Any]           =							logging.getLogger()




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    argparse.ArgumentParser()
			parser.add_argument('-f'							)
			UpperCAmelCase__		:List[Any] 		=    parser.parse_args()
			return args.f




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    logging.StreamHandler(sys.stdout	)
									logger.addHandler(A	)

									UpperCAmelCase__		:List[str] 		=    self.get_auto_remove_tmp_dir()
									UpperCAmelCase__		:List[str] 		=    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(A  , 'argv'  , A	):
												UpperCAmelCase__		:Any 		=    run_glue_with_pabee.main()
												for value in result.values():
															self.assertGreaterEqual(A  , 0.75	)





