

import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput

class 				UpperCAmelCase_		(			__UpperCAmelCase				,       __UpperCAmelCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[int]	    =    1


							@register_to_config
							def __init__(			self       ,							_a = 1_0_0_0       ,							_a = None       )    ->    Optional[Any]:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(lowerCamelCase__       )

												# standard deviation of the initial noise distribution
												_a	:    int         =		1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												_a	:    Optional[int]         =		4

												# running values
												_a	:    str         =		[]


							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Tuple:
												_a	:    Optional[int]         =		num_inference_steps
												_a	:    Tuple         =		torch.linspace(1       ,							0       ,							num_inference_steps + 1       )[:-1]
												_a	:    Any         =		torch.cat([steps, torch.tensor([0.0]       )]       )

												if self.config.trained_betas is not None:
																	_a	:    List[Any]         =		torch.tensor(self.config.trained_betas       ,							dtype=torch.floataa       )
												else:
																	_a	:    Optional[Any]         =		torch.sin(steps * math.pi / 2       ) ** 2

												_a	:    str         =		(1.0 - self.betas**2) ** 0.5

												_a	:    Any         =		(torch.atana(self.betas       ,							self.alphas       ) / math.pi * 2)[:-1]
												_a	:    Tuple         =		timesteps.to(lowerCamelCase__       )

												_a	:    Optional[int]         =		[]


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a = True       ,							)    ->    Optional[int]:
												if self.num_inference_steps is None:
																	raise ValueError(
																	    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''       )

												_a	:    int         =		(self.timesteps == timestep).nonzero().item()
												_a	:    List[Any]         =		timestep_index + 1

												_a	:    str         =		sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(lowerCamelCase__       )

												if len(self.ets       ) == 1:
																	_a	:    str         =		self.ets[-1]
												elif len(self.ets       ) == 2:
																	_a	:    Union[str, Any]         =		(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets       ) == 3:
																	_a	:    Union[str, Any]         =		(2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
												else:
																	_a	:    Optional[Any]         =		(1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

												_a	:    Optional[int]         =		self._get_prev_sample(lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       ,							lowerCamelCase__       )

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=lowerCamelCase__       )


							def 				__lowercase				(			self       ,							_a       ,							*_a       ,							**_a       )    ->    Dict:
												return sample


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    int:
												_a	:    Any         =		self.alphas[timestep_index]
												_a	:    Union[str, Any]         =		self.betas[timestep_index]

												_a	:    Tuple         =		self.alphas[prev_timestep_index]
												_a	:    Dict         =		self.betas[prev_timestep_index]

												_a	:    Dict         =		(sample - sigma * ets) / max(lowerCamelCase__       ,							1e-8       )
												_a	:    Any         =		next_alpha * pred + ets * next_sigma

												return prev_sample




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




"""simple docstring"""






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

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__  :  Optional[Any]						     =	logging.getLogger()



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







class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							logging.StreamHandler(sys.stdout			)
									logger.addHandler(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]			):
									UpperCAmelCase__             =							get_gpu_count()

									if n_gpu > 1:
													pass
													# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
													# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
													# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
													# cmd = [sys.executable] + distributed_args + args
													# execute_subprocess_async(cmd, env=self.get_env())
													# XXX: test the results - need to save them first into .json file
									else:
													args.insert(0    ,'run_glue_deebert.py'			)
													with patch.object(lowerCamelCase__    ,'argv'    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							run_glue_deebert.main()
																	for value in result.values():
																					self.assertGreaterEqual(lowerCamelCase__    ,0.6_6_6			)







					@slow
					@require_torch_non_multi_gpu
					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)

									UpperCAmelCase__             =							'\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            '.split()
									self.run_and_check(lowerCamelCase__			)



