
"""simple docstring"""







import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Union[str, Any]     							=   (CMStochasticIterativeScheduler,)
					_SCREAMING_SNAKE_CASE       :					List[Any]     							=   10





					def 							lowerCAmelCase      (				self   :							Union[str, Any]							,						**snake_case_   :							Union[str, Any]    ):
												__snake_case     			=       {
												    "num_train_timesteps": 201,
												    "sigma_min": 0.002,
												    "sigma_max": 80.0,
												}

												config.update(**snake_case_    )
												return config





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       10

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

												scheduler.set_timesteps(snake_case_    )

												__snake_case     			=       scheduler.timesteps[0]
												__snake_case     			=       scheduler.timesteps[1]

												__snake_case     			=       self.dummy_sample
												__snake_case     			=       0.1 * sample

												__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_    ).prev_sample
												__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_    ).prev_sample

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





					def 							lowerCAmelCase      (				self   :							int    ):
												for timesteps in [10, 50, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												for clip_denoised in [True, False]:
																			self.check_over_configs(clip_denoised=snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       1
												scheduler.set_timesteps(snake_case_    )
												__snake_case     			=       scheduler.timesteps

												__snake_case     			=       torch.manual_seed(0    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(snake_case_    ):
																			# 1. scale model input
																			__snake_case     			=       scheduler.scale_model_input(snake_case_							,						snake_case_    )

																			# 2. predict noise residual
																			__snake_case     			=       model(snake_case_							,						snake_case_    )

																			# 3. predict previous sample x_t-1
																			__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						generator=snake_case_    ).prev_sample

																			__snake_case     			=       pred_prev_sample

												__snake_case     			=       torch.sum(torch.abs(snake_case_    )    )
												__snake_case     			=       torch.mean(torch.abs(snake_case_    )    )

												assert abs(result_sum.item() - 192.7614    ) < 1e-2
												assert abs(result_mean.item() - 0.2510    ) < 1e-3





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [106, 0]
												scheduler.set_timesteps(timesteps=snake_case_    )
												__snake_case     			=       scheduler.timesteps

												__snake_case     			=       torch.manual_seed(0    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter * scheduler.init_noise_sigma

												for t in timesteps:
																			# 1. scale model input
																			__snake_case     			=       scheduler.scale_model_input(snake_case_							,						snake_case_    )

																			# 2. predict noise residual
																			__snake_case     			=       model(snake_case_							,						snake_case_    )

																			# 3. predict previous sample x_t-1
																			__snake_case     			=       scheduler.step(snake_case_							,						snake_case_							,						snake_case_							,						generator=snake_case_    ).prev_sample

																			__snake_case     			=       pred_prev_sample

												__snake_case     			=       torch.sum(torch.abs(snake_case_    )    )
												__snake_case     			=       torch.mean(torch.abs(snake_case_    )    )

												assert abs(result_sum.item() - 347.6357    ) < 1e-2
												assert abs(result_mean.item() - 0.4527    ) < 1e-3





					def 							lowerCAmelCase      (				self   :							Dict    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [39, 30, 12, 15, 0]

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





					def 							lowerCAmelCase      (				self   :							Union[str, Any]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [39, 30, 12, 1, 0]
												__snake_case     			=       len(snake_case_    )

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





					def 							lowerCAmelCase      (				self   :							List[Any]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**snake_case_    )

												__snake_case     			=       [scheduler.config.num_train_timesteps]

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


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
