
"""simple docstring"""







import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest







@require_torchsde
class 							__magic_name__     (		UpperCamelCase_   ):
					_SCREAMING_SNAKE_CASE       :					List[str]     							=   (DPMSolverSDEScheduler,)
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   10





					def 							lowerCAmelCase      (				self   :							List[str]							,						**snake_case_   :							Tuple    ):
												__snake_case     			=       {
												    'num_train_timesteps': 1100,
												    'beta_start': 0.0001,
												    'beta_end': 0.02,
												    'beta_schedule': 'linear',
												    'noise_sampler_seed': 0,
												}

												config.update(**__a    )
												return config





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






												for timesteps in [10, 50, 100, 1000]:
																			self.check_over_configs(num_train_timesteps=__a    )





					def 							lowerCAmelCase      (				self   :							Any    ):






												for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]							,						[0.0002, 0.002, 0.02]    ):
																			self.check_over_configs(beta_start=__a							,						beta_end=__a    )





					def 							lowerCAmelCase      (				self   :							List[Any]    ):






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





					def 							lowerCAmelCase      (				self   :							Optional[int]    ):






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





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

												scheduler.set_timesteps(self.num_inference_steps    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												__snake_case     			=       sample.to(__a    )

												for i, t in enumerate(scheduler.timesteps    ):
																			__snake_case     			=       scheduler.scale_model_input(__a							,						__a    )

																			__snake_case     			=       model(__a							,						__a    )

																			__snake_case     			=       scheduler.step(__a							,						__a							,						__a    )
																			__snake_case     			=       output.prev_sample

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

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 167.47821044921875    ) < 1e-2
																			assert abs(result_mean.item() - 0.2178705964565277    ) < 1e-3
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 171.59352111816406    ) < 1e-2
																			assert abs(result_mean.item() - 0.22342906892299652    ) < 1e-3
												else:
																			assert abs(result_sum.item() - 162.52383422851562    ) < 1e-2
																			assert abs(result_mean.item() - 0.211619570851326    ) < 1e-3





					def 							lowerCAmelCase      (				self   :							str    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config(prediction_type="v_prediction"    )
												__snake_case     			=       scheduler_class(**__a    )

												scheduler.set_timesteps(self.num_inference_steps    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												__snake_case     			=       sample.to(__a    )

												for i, t in enumerate(scheduler.timesteps    ):
																			__snake_case     			=       scheduler.scale_model_input(__a							,						__a    )

																			__snake_case     			=       model(__a							,						__a    )

																			__snake_case     			=       scheduler.step(__a							,						__a							,						__a    )
																			__snake_case     			=       output.prev_sample

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

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 124.77149200439453    ) < 1e-2
																			assert abs(result_mean.item() - 0.16226289014816284    ) < 1e-3
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 128.1663360595703    ) < 1e-2
																			assert abs(result_mean.item() - 0.16688326001167297    ) < 1e-3
												else:
																			assert abs(result_sum.item() - 119.8487548828125    ) < 1e-2
																			assert abs(result_mean.item() - 0.1560530662536621    ) < 1e-3





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

												scheduler.set_timesteps(self.num_inference_steps							,						device=__a    )

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

												for t in scheduler.timesteps:
																			__snake_case     			=       scheduler.scale_model_input(__a							,						__a    )

																			__snake_case     			=       model(__a							,						__a    )

																			__snake_case     			=       scheduler.step(__a							,						__a							,						__a    )
																			__snake_case     			=       output.prev_sample

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

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 167.46957397460938    ) < 1e-2
																			assert abs(result_mean.item() - 0.21805934607982635    ) < 1e-3
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 171.59353637695312    ) < 1e-2
																			assert abs(result_mean.item() - 0.22342908382415771    ) < 1e-3
												else:
																			assert abs(result_sum.item() - 162.52383422851562    ) < 1e-2
																			assert abs(result_mean.item() - 0.211619570851326    ) < 1e-3





					def 							lowerCAmelCase      (				self   :							List[str]    ):
												__snake_case     			=       self.scheduler_classes[0]
												__snake_case     			=       self.get_scheduler_config()
												__snake_case     			=       scheduler_class(**__a							,						use_karras_sigmas=__a    )

												scheduler.set_timesteps(self.num_inference_steps							,						device=__a    )

												__snake_case     			=       self.dummy_model()
												__snake_case     			=       self.dummy_sample_deter.to(__a    ) * scheduler.init_noise_sigma
												__snake_case     			=       sample.to(__a    )

												for t in scheduler.timesteps:
																			__snake_case     			=       scheduler.scale_model_input(__a							,						__a    )

																			__snake_case     			=       model(__a							,						__a    )

																			__snake_case     			=       scheduler.step(__a							,						__a							,						__a    )
																			__snake_case     			=       output.prev_sample

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

												if torch_device in ["mps"]:
																			assert abs(result_sum.item() - 176.66974135742188    ) < 1e-2
																			assert abs(result_mean.item() - 0.23003872730981811    ) < 1e-2
												elif torch_device in ["cuda"]:
																			assert abs(result_sum.item() - 177.63653564453125    ) < 1e-2
																			assert abs(result_mean.item() - 0.23003872730981811    ) < 1e-2
												else:
																			assert abs(result_sum.item() - 170.3135223388672    ) < 1e-2
																			assert abs(result_mean.item() - 0.23003872730981811    ) < 1e-2


"""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""")
