

"""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)

a					:str    =       [
    "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 					_a			(						self	,    _a	,    _a	,    _a = None	,    _a = None  )  ->      List[Any]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


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





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	os.path.abspath(os.path.join("""examples"""	,    """cv_example.py"""  )  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	[
               """ """ * 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"""	,    lowerCAmelCase__	,    lowerCAmelCase__	,    lowerCAmelCase__  )
           self.one_complete_example("""complete_cv_example.py"""	,    lowerCAmelCase__	,    lowerCAmelCase__	,    lowerCAmelCase__  )





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






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = False

     @classmethod
     def 					_a			(						cls  )  ->      Optional[Any]:


           """simple docstring"""


           super().setUpClass()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tempfile.mkdtemp()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(cls._tmpdir	,    """default_config.yml"""  )

           write_basic_config(save_location=cls.configPath  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	["""accelerate""", """launch""", """--config_file""", cls.configPath]

     @classmethod
     def 					_a			(						cls  )  ->      str:


           """simple docstring"""


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

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	f'''\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '''.split()
           run_command(self._launch_args + testargs  )
           self.assertTrue(os.path.exists(os.path.join(self.tmpdir	,    """epoch_0"""  )  )  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	f'''\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '''.split()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	run_command(self._launch_args + testargs  )
           self.assertTrue(os.path.exists(os.path.join(self.tmpdir	,    """step_2"""  )  )  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	f'''\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir	,    'epoch_0'  )}\n        '''.split()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	run_command(self._launch_args + testargs	,    return_stdout=lowerCAmelCase__  )
           self.assertNotIn("""epoch 0:"""	,    lowerCAmelCase__  )
           self.assertIn("""epoch 1:"""	,    lowerCAmelCase__  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	f'''\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir	,    'step_2'  )}\n        '''.split()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	run_command(self._launch_args + testargs	,    return_stdout=lowerCAmelCase__  )
           if torch.cuda.is_available():
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.cuda.device_count()
           else:
                 SCREAMING_SNAKE_CASE__   :      int      		=	1
           if num_processes > 1:
                 self.assertNotIn("""epoch 0:"""	,    lowerCAmelCase__  )
                 self.assertIn("""epoch 1:"""	,    lowerCAmelCase__  )
           else:
                 self.assertIn("""epoch 0:"""	,    lowerCAmelCase__  )
                 self.assertIn("""epoch 1:"""	,    lowerCAmelCase__  )

     @slow
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

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


           """simple docstring"""


           with tempfile.TemporaryDirectory() as tmpdir:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	f'''\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '''.split()
                 run_command(self._launch_args + testargs  )
                 self.assertTrue(os.path.exists(os.path.join(lowerCAmelCase__	,    """tracking"""  )  )  )

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


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







"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DDPMScheduler,)

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]	,    [0.002, 0.02, 0.2, 2]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

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


           """simple docstring"""


           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a  )

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


           """simple docstring"""


           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a  )

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           self.check_over_configs(thresholding=_a  )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=_a	,    prediction_type=_a	,    sample_max_value=_a	,    )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00_979  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      str      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      str      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      str      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 258.9_606  ) < 1E-2
           assert abs(result_mean.item() - 0.3_372  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	len(_a  )

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_model()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.dummy_sample_deter
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.manual_seed(0  )

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      int      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

                 # if t > 0:
                 #     noise = self.dummy_sample_deter
                 #     variance = scheduler.get_variance(t) ** (0.5) * noise
                 #
                 # sample = pred_prev_sample + variance
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	pred_prev_sample

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 202.0_296  ) < 1E-2
           assert abs(result_mean.item() - 0.2_631  ) < 1E-3

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler.timesteps

           for i, timestep in enumerate(_a  ):
                 if i == len(_a  ) - 1:
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	-1
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	timesteps[i + 1]

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.previous_timestep(_a  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prev_t.item()

                 self.assertEqual(_a	,    _a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[100, 87, 50, 51, 0]

           with self.assertRaises(_a	,    msg="""`custom_timesteps` must be in descending order."""  ):
                 scheduler.set_timesteps(timesteps=_a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[100, 87, 50, 1, 0]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(_a  )

           with self.assertRaises(_a	,    msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""  ):
                 scheduler.set_timesteps(num_inference_steps=_a	,    timesteps=_a  )





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[scheduler.config.num_train_timesteps]

           with self.assertRaises(
               _a	,    msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""	,    ):
                 scheduler.set_timesteps(timesteps=_a  )




