


"""simple docstring"""
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





class        __A       (UpperCamelCase__):





							'''simple docstring'''

							__lowercase:						Optional[int]							 =      (DEISMultistepScheduler,)
							__lowercase:						Any							 =      (("""num_inference_steps""", 25),)



							def 						lowerCAmelCase    (			self							: Dict						,    **UpperCAmelCase_							: Union[str, Any]    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						{
														    """num_train_timesteps""": 1_000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														    """solver_order""": 2,
														}

														config.update(**__a    )
														return config



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: List[Any]=0						,    **UpperCAmelCase_							: Any    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						dict(self.forward_default_kwargs    )
														snake_case_          =						kwargs.pop("""num_inference_steps"""						,    __a    )
														snake_case_          =						self.dummy_sample
														snake_case_          =						0.1 * sample
														snake_case_          =						[residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					snake_case_          =						self.get_scheduler_config(**__a    )
																					snake_case_          =						scheduler_class(**__a    )
																					scheduler.set_timesteps(__a    )
																					# copy over dummy past residuals
																					snake_case_          =						dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(__a    )
																												snake_case_          =						scheduler_class.from_pretrained(__a    )
																												new_scheduler.set_timesteps(__a    )
																												# copy over dummy past residuals
																												snake_case_          =						dummy_past_residuals[: new_scheduler.config.solver_order]

																					snake_case_    ,  snake_case_          =						sample, sample
																					for t in range(__a						,    time_step + scheduler.config.solver_order + 1    ):
																												snake_case_          =						scheduler.step(__a						,    __a						,    __a						,    **__a    ).prev_sample
																												snake_case_          =						new_scheduler.step(__a						,    __a						,    __a						,    **__a    ).prev_sample

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



							def 						lowerCAmelCase    (			self							: Any    )   ->int:



														"""simple docstring"""



														pass



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Any=0						,    **UpperCAmelCase_							: Optional[int]    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						dict(self.forward_default_kwargs    )
														snake_case_          =						kwargs.pop("""num_inference_steps"""						,    __a    )
														snake_case_          =						self.dummy_sample
														snake_case_          =						0.1 * sample
														snake_case_          =						[residual + 0.2, residual + 0.15, residual + 0.10]

														for scheduler_class in self.scheduler_classes:
																					snake_case_          =						self.get_scheduler_config()
																					snake_case_          =						scheduler_class(**__a    )
																					scheduler.set_timesteps(__a    )

																					# copy over dummy past residuals (must be after setting timesteps)
																					snake_case_          =						dummy_past_residuals[: scheduler.config.solver_order]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(__a    )
																												snake_case_          =						scheduler_class.from_pretrained(__a    )
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(__a    )

																												# copy over dummy past residual (must be after setting timesteps)
																												snake_case_          =						dummy_past_residuals[: new_scheduler.config.solver_order]

																					snake_case_          =						scheduler.step(__a						,    __a						,    __a						,    **__a    ).prev_sample
																					snake_case_          =						new_scheduler.step(__a						,    __a						,    __a						,    **__a    ).prev_sample

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



							def 						lowerCAmelCase    (			self							: List[Any]						,    UpperCAmelCase_							: Optional[Any]=None						,    **UpperCAmelCase_							: Optional[int]    )   ->Any:



														"""simple docstring"""



														if scheduler is None:
																					snake_case_          =						self.scheduler_classes[0]
																					snake_case_          =						self.get_scheduler_config(**__a    )
																					snake_case_          =						scheduler_class(**__a    )

														snake_case_          =						self.scheduler_classes[0]
														snake_case_          =						self.get_scheduler_config(**__a    )
														snake_case_          =						scheduler_class(**__a    )

														snake_case_          =						10
														snake_case_          =						self.dummy_model()
														snake_case_          =						self.dummy_sample_deter
														scheduler.set_timesteps(__a    )

														for i, t in enumerate(scheduler.timesteps    ):
																					snake_case_          =						model(__a						,    __a    )
																					snake_case_          =						scheduler.step(__a						,    __a						,    __a    ).prev_sample

														return sample



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						dict(self.forward_default_kwargs    )

														snake_case_          =						kwargs.pop("""num_inference_steps"""						,    __a    )

														for scheduler_class in self.scheduler_classes:
																					snake_case_          =						self.get_scheduler_config()
																					snake_case_          =						scheduler_class(**__a    )

																					snake_case_          =						self.dummy_sample
																					snake_case_          =						0.1 * sample

																					if num_inference_steps is not None and hasattr(__a						,    """set_timesteps"""    ):
																												scheduler.set_timesteps(__a    )
																					elif num_inference_steps is not None and not hasattr(__a						,    """set_timesteps"""    ):
																												snake_case_          =						num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					snake_case_          =						[residual + 0.2, residual + 0.15, residual + 0.10]
																					snake_case_          =						dummy_past_residuals[: scheduler.config.solver_order]

																					snake_case_          =						scheduler.timesteps[5]
																					snake_case_          =						scheduler.timesteps[6]

																					snake_case_          =						scheduler.step(__a						,    __a						,    __a						,    **__a    ).prev_sample
																					snake_case_          =						scheduler.step(__a						,    __a						,    __a						,    **__a    ).prev_sample

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



							def 						lowerCAmelCase    (			self							: int    )   ->str:



														"""simple docstring"""



														snake_case_          =						DEISMultistepScheduler(**self.get_scheduler_config()    )
														snake_case_          =						self.full_loop(scheduler=__a    )
														snake_case_          =						torch.mean(torch.abs(__a    )    )

														assert abs(result_mean.item() - 0.23_916    ) < 1E-3

														snake_case_          =						DPMSolverSinglestepScheduler.from_config(scheduler.config    )
														snake_case_          =						DPMSolverMultistepScheduler.from_config(scheduler.config    )
														snake_case_          =						UniPCMultistepScheduler.from_config(scheduler.config    )
														snake_case_          =						DEISMultistepScheduler.from_config(scheduler.config    )

														snake_case_          =						self.full_loop(scheduler=__a    )
														snake_case_          =						torch.mean(torch.abs(__a    )    )

														assert abs(result_mean.item() - 0.23_916    ) < 1E-3



							def 						lowerCAmelCase    (			self							: Any    )   ->int:



														"""simple docstring"""



														for timesteps in [25, 50, 100, 999, 1_000]:
																					self.check_over_configs(num_train_timesteps=__a    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														self.check_over_configs(thresholding=__a    )
														for order in [1, 2, 3]:
																					for solver_type in ["logrho"]:
																												for threshold in [0.5, 1.0, 2.0]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    thresholding=__a						,    prediction_type=__a						,    sample_max_value=__a						,    algorithm_type="""deis"""						,    solver_order=__a						,    solver_type=__a						,    )



							def 						lowerCAmelCase    (			self							: str    )   ->List[Any]:



														"""simple docstring"""



														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=__a    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Union[str, Any]:



														"""simple docstring"""



														for algorithm_type in ["deis"]:
																					for solver_type in ["logrho"]:
																												for order in [1, 2, 3]:
																																			for prediction_type in ["epsilon", "sample"]:
																																										self.check_over_configs(
																																										    solver_order=__a						,    solver_type=__a						,    prediction_type=__a						,    algorithm_type=__a						,    )
																																										snake_case_          =						self.full_loop(
																																										    solver_order=__a						,    solver_type=__a						,    prediction_type=__a						,    algorithm_type=__a						,    )
																																										assert not torch.isnan(__a    ).any(), "Samples have nan numbers"



							def 						lowerCAmelCase    (			self							: Dict    )   ->List[Any]:



														"""simple docstring"""



														self.check_over_configs(lower_order_final=__a    )
														self.check_over_configs(lower_order_final=__a    )



							def 						lowerCAmelCase    (			self							: Any    )   ->int:



														"""simple docstring"""



														for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																					self.check_over_forward(num_inference_steps=__a						,    time_step=0    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self.full_loop()
														snake_case_          =						torch.mean(torch.abs(__a    )    )

														assert abs(result_mean.item() - 0.23_916    ) < 1E-3



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						self.full_loop(prediction_type="""v_prediction"""    )
														snake_case_          =						torch.mean(torch.abs(__a    )    )

														assert abs(result_mean.item() - 0.091    ) < 1E-3



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.scheduler_classes[0]
														snake_case_          =						self.get_scheduler_config(thresholding=__a						,    dynamic_thresholding_ratio=0    )
														snake_case_          =						scheduler_class(**__a    )

														snake_case_          =						10
														snake_case_          =						self.dummy_model()
														snake_case_          =						self.dummy_sample_deter.half()
														scheduler.set_timesteps(__a    )

														for i, t in enumerate(scheduler.timesteps    ):
																					snake_case_          =						model(__a						,    __a    )
																					snake_case_          =						scheduler.step(__a						,    __a						,    __a    ).prev_sample

														assert sample.dtype == torch.floataa







"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

__SCREAMING_SNAKE_CASE							:      Tuple						=      [
    'cross_validation.py',
    'gradient_accumulation.py',
    'local_sgd.py',
    'multi_process_metrics.py',
    'memory.py',
    'automatic_gradient_accumulation.py',
    'fsdp_with_peak_mem_tracking.py',
    'deepspeed_with_config_support.py',
    'megatron_lm_gpt_pretraining.py',
]





class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: str						,    UpperCAmelCase_							: str						,    UpperCAmelCase_							: bool						,    UpperCAmelCase_							: str = None						,    UpperCAmelCase_							: list = None    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						None
														snake_case_          =						os.path.abspath(os.path.join("""examples"""						,    """by_feature"""    )    )
														snake_case_          =						os.path.abspath("""examples"""    )
														for item in os.listdir(UpperCAmelCase_    ):
																					if item not in EXCLUDE_EXAMPLES:
																												snake_case_          =						os.path.join(UpperCAmelCase_						,    UpperCAmelCase_    )
																												if os.path.isfile(UpperCAmelCase_    ) and ".py" in item_path:
																																			with self.subTest(
																																			    tested_script=UpperCAmelCase_						,    feature_script=UpperCAmelCase_						,    tested_section="""main()""" if parser_only else """training_function()"""						,    ):
																																										snake_case_          =						compare_against_test(
																																										    os.path.join(UpperCAmelCase_						,    UpperCAmelCase_    )						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )
																																										snake_case_          =						"""\n""".join(UpperCAmelCase_    )
																																										if special_strings is not None:
																																																	for string in special_strings:
																																																								snake_case_          =						diff.replace(UpperCAmelCase_						,    """"""    )
																																										self.assertEqual(UpperCAmelCase_						,    """"""    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->List[Any]:



														"""simple docstring"""



														self.one_complete_example("""complete_nlp_example.py"""						,    UpperCAmelCase_    )
														self.one_complete_example("""complete_nlp_example.py"""						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						os.path.abspath(os.path.join("""examples"""						,    """cv_example.py"""    )    )
														snake_case_          =						[
														    """ """ * 16 + """{\n\n""",
														    """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
														    """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
														    """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
														    """ """ * 20 + """\"epoch\": epoch,\n\n""",
														    """ """ * 16 + """},\n\n""",
														    """ """ * 16 + """step=epoch,\n""",
														    """ """ * 12,
														    """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
														]
														self.one_complete_example("""complete_cv_example.py"""						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )
														self.one_complete_example("""complete_cv_example.py"""						,    UpperCAmelCase_						,    UpperCAmelCase_						,    UpperCAmelCase_    )





@mock.patch.dict(os.environ  ,						{"""TESTING_MOCKED_DATALOADERS""": """1"""})
class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						str							 =      False



							@classmethod
							def 						lowerCAmelCase    (			cls							: Any    )   ->List[str]:



														"""simple docstring"""



														super().setUpClass()
														snake_case_          =						tempfile.mkdtemp()
														snake_case_          =						os.path.join(cls._tmpdir						,    """default_config.yml"""    )

														write_basic_config(save_location=cls.configPath    )
														snake_case_          =						["""accelerate""", """launch""", """--config_file""", cls.configPath]



							@classmethod
							def 						lowerCAmelCase    (			cls							: List[str]    )   ->int:



														"""simple docstring"""



														super().tearDownClass()
														shutil.rmtree(cls._tmpdir    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps epoch
        --output_dir {self.tmpdir}
        """.split()
														run_command(self._launch_args + testargs    )
														self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,    """epoch_0"""    )    )    )



							def 						lowerCAmelCase    (			self							: Any    )   ->Optional[Any]:



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --checkpointing_steps 1
        --output_dir {self.tmpdir}
        """.split()
														snake_case_          =						run_command(self._launch_args + testargs    )
														self.assertTrue(os.path.exists(os.path.join(self.tmpdir						,    """step_2"""    )    )    )



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->str:



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir						,    "epoch_0"    )}
        """.split()
														snake_case_          =						run_command(self._launch_args + testargs						,    return_stdout=UpperCAmelCase_    )
														self.assertNotIn("""epoch 0:"""						,    UpperCAmelCase_    )
														self.assertIn("""epoch 1:"""						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						F"""
        examples/by_feature/checkpointing.py
        --resume_from_checkpoint {os.path.join(self.tmpdir						,    "step_2"    )}
        """.split()
														snake_case_          =						run_command(self._launch_args + testargs						,    return_stdout=UpperCAmelCase_    )
														if torch.cuda.is_available():
																					snake_case_          =						torch.cuda.device_count()
														else:
																					snake_case_          =						1
														if num_processes > 1:
																					self.assertNotIn("""epoch 0:"""						,    UpperCAmelCase_    )
																					self.assertIn("""epoch 1:"""						,    UpperCAmelCase_    )
														else:
																					self.assertIn("""epoch 0:"""						,    UpperCAmelCase_    )
																					self.assertIn("""epoch 1:"""						,    UpperCAmelCase_    )



							@slow
							def 						lowerCAmelCase    (			self							: Dict    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
														with mock.patch.dict(os.environ						,    {"""TESTING_MOCKED_DATALOADERS""": """0"""}    ):
																					snake_case_          =						run_command(self._launch_args + testargs						,    return_stdout=UpperCAmelCase_    )
																					snake_case_          =						re.findall("""({.+})"""						,    UpperCAmelCase_    )
																					snake_case_          =						[r for r in results if """accuracy""" in r][-1]
																					snake_case_          =						ast.literal_eval(UpperCAmelCase_    )
																					self.assertGreaterEqual(results["""accuracy"""]						,    0.75    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->int:



														"""simple docstring"""



														snake_case_          =						["""examples/by_feature/multi_process_metrics.py"""]
														run_command(self._launch_args + testargs    )



							@require_trackers
							@mock.patch.dict(os.environ						,    {"""WANDB_MODE""": """offline"""}    )
							def 						lowerCAmelCase    (			self							: Dict    )   ->Optional[Any]:



														"""simple docstring"""



														with tempfile.TemporaryDirectory() as tmpdir:
																					snake_case_          =						F"""
            examples/by_feature/tracking.py
            --with_tracking
            --project_dir {tmpdir}
            """.split()
																					run_command(self._launch_args + testargs    )
																					self.assertTrue(os.path.exists(os.path.join(UpperCAmelCase_						,    """tracking"""    )    )    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						["""examples/by_feature/gradient_accumulation.py"""]
														run_command(self._launch_args + testargs    )



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						["""examples/by_feature/local_sgd.py"""]
														run_command(self._launch_args + testargs    )



