


import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(ScoreSdeVeScheduler,)
							_UpperCAmelCase     =		()






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 4
										SCREAMING_SNAKE_CASE_						:			Dict       						= 3
										SCREAMING_SNAKE_CASE_						:			str       						= 8
										SCREAMING_SNAKE_CASE_						:			int       						= 8

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= torch.rand((batch_size, num_channels, height, width)    )

										return sample






							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= 4
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= 3
										SCREAMING_SNAKE_CASE_						:			Tuple       						= 8
										SCREAMING_SNAKE_CASE_						:			Any       						= 8

										SCREAMING_SNAKE_CASE_						:			Dict       						= batch_size * num_channels * height * width
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.arange(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= sample.reshape(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= sample / num_elems
										SCREAMING_SNAKE_CASE_						:			str       						= sample.permute(3       ,		0       ,		1       ,		2    )

										return sample






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										def model(lowerCAmelCase__       ,		lowerCAmelCase__       ,		*lowerCAmelCase__    ):
													return sample * t / (t + 1)

										return model






							def  UpperCamelCase__			(	self       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= {
										    'num_train_timesteps': 2_0_0_0,
										    'snr': 0.15,
										    'sigma_min': 0.01,
										    'sigma_max': 1_3_4_8,
										    'sampling_eps': 1E-5,
										}

										config.update(**lowerCAmelCase__    )
										return config






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__=0       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= dict(self.forward_default_kwargs    )

										for scheduler_class in self.scheduler_classes:
													SCREAMING_SNAKE_CASE_						:			Any       						= self.dummy_sample
													SCREAMING_SNAKE_CASE_						:			Tuple       						= 0.1 * sample

													SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_scheduler_config(**lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						:			Tuple       						= scheduler_class(**lowerCAmelCase__    )

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCAmelCase__    )
																SCREAMING_SNAKE_CASE_						:			Dict       						= scheduler_class.from_pretrained(lowerCAmelCase__    )

													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scheduler.step_pred(
													    lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= new_scheduler.step_pred(
													    lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= scheduler.step_correct(lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= new_scheduler.step_correct(
													    lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler correction are not identical"






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__=0       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= dict(self.forward_default_kwargs    )
										kwargs.update(lowerCAmelCase__    )

										for scheduler_class in self.scheduler_classes:
													SCREAMING_SNAKE_CASE_						:			Tuple       						= self.dummy_sample
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 0.1 * sample

													SCREAMING_SNAKE_CASE_						:			str       						= self.get_scheduler_config()
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scheduler_class(**lowerCAmelCase__    )

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(lowerCAmelCase__    )
																SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scheduler_class.from_pretrained(lowerCAmelCase__    )

													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= scheduler.step_pred(
													    lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample
													SCREAMING_SNAKE_CASE_						:			Dict       						= new_scheduler.step_pred(
													    lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler outputs are not identical"

													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= scheduler.step_correct(lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample
													SCREAMING_SNAKE_CASE_						:			int       						= new_scheduler.step_correct(
													    lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample

													assert torch.sum(torch.abs(output - new_output    )    ) < 1E-5, "Scheduler correction are not identical"






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for timesteps in [1_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]       ,		[1, 1_0_0, 1_0_0_0]    ):
													self.check_over_configs(sigma_min=lowerCAmelCase__       ,		sigma_max=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for t in [0.1, 0.5, 0.75]:
													self.check_over_forward(time_step=lowerCAmelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= dict(self.forward_default_kwargs    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.scheduler_classes[0]
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self.get_scheduler_config()
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= scheduler_class(**lowerCAmelCase__    )

										SCREAMING_SNAKE_CASE_						:			str       						= 3

										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.dummy_model()
										SCREAMING_SNAKE_CASE_						:			Dict       						= self.dummy_sample_deter

										scheduler.set_sigmas(lowerCAmelCase__    )
										scheduler.set_timesteps(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= torch.manual_seed(0    )

										for i, t in enumerate(scheduler.timesteps    ):
													SCREAMING_SNAKE_CASE_						:			int       						= scheduler.sigmas[i]

													for _ in range(scheduler.config.correct_steps    ):
																with torch.no_grad():
																			SCREAMING_SNAKE_CASE_						:			List[str]       						= model(lowerCAmelCase__       ,		lowerCAmelCase__    )
																SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scheduler.step_correct(lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=lowerCAmelCase__       ,		**lowerCAmelCase__    ).prev_sample

													with torch.no_grad():
																SCREAMING_SNAKE_CASE_						:			Tuple       						= model(lowerCAmelCase__       ,		lowerCAmelCase__    )

													SCREAMING_SNAKE_CASE_						:			str       						= scheduler.step_pred(lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		generator=lowerCAmelCase__       ,		**lowerCAmelCase__    )
													SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Optional[int]       						= output.prev_sample, output.prev_sample_mean

										SCREAMING_SNAKE_CASE_						:			Tuple       						= torch.sum(torch.abs(lowerCAmelCase__    )    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= torch.mean(torch.abs(lowerCAmelCase__    )    )

										assert np.isclose(result_sum.item()       ,		14_372_758_528.0    )
										assert np.isclose(result_mean.item()       ,		18_714_530.0    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= dict(self.forward_default_kwargs    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= kwargs.pop('num_inference_steps'       ,		lowerCAmelCase__    )

										for scheduler_class in self.scheduler_classes:
													SCREAMING_SNAKE_CASE_						:			int       						= self.get_scheduler_config()
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= scheduler_class(**lowerCAmelCase__    )

													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= self.dummy_sample
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= 0.1 * sample

													if num_inference_steps is not None and hasattr(lowerCAmelCase__       ,		'set_timesteps'    ):
																scheduler.set_timesteps(lowerCAmelCase__    )
													elif num_inference_steps is not None and not hasattr(lowerCAmelCase__       ,		'set_timesteps'    ):
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= num_inference_steps

													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= scheduler.step_pred(lowerCAmelCase__       ,		0       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample
													SCREAMING_SNAKE_CASE_						:			Any       						= scheduler.step_pred(lowerCAmelCase__       ,		1       ,		lowerCAmelCase__       ,		generator=torch.manual_seed(0    )       ,		**lowerCAmelCase__    ).prev_sample

													self.assertEqual(output_a.shape       ,		sample.shape    )
													self.assertEqual(output_a.shape       ,		output_a.shape    )









# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





