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

						# Make a hash from all this code
						__lowerCAmelCase     =  full_str.encode("""utf-8"""						)
						return shaaaa(_lowerCAmelCase						).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''')


"""simple docstring"""
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		(DDIMParallelScheduler,)
							_snake_case						      =		(('''eta''', 0.0), ('''num_inference_steps''', 5_0))




							def    A__						( self    ,				**snake_case_				)       ->  Optional[Any]:
													__lowerCAmelCase     =  {
													    """num_train_timesteps""": 1_000,
													    """beta_start""": 0.0_001,
													    """beta_end""": 0.02,
													    """beta_schedule""": """linear""",
													    """clip_sample""": True,
													}

													config.update(**snake_case_				)
													return config




							def    A__						( self    ,				**snake_case_				)       ->  Tuple:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(**snake_case_				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter

													scheduler.set_timesteps(snake_case_				)

													for t in scheduler.timesteps:
																			__lowerCAmelCase     =  model(snake_case_    ,				snake_case_				)
																			__lowerCAmelCase     =  scheduler.step(snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				).prev_sample

													return sample




							def    A__						( self				)       ->  List[str]:
													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=snake_case_				)

													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config(steps_offset=1				)
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)
													scheduler.set_timesteps(5				)
													assert torch.equal(scheduler.timesteps    ,				torch.LongTensor([801, 601, 401, 201, 1]				)				)




							def    A__						( self				)       ->  int:
													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=snake_case_    ,				beta_end=snake_case_				)




							def    A__						( self				)       ->  str:
													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=snake_case_				)




							def    A__						( self				)       ->  Tuple:
													for prediction_type in ["epsilon", "v_prediction"]:
																			self.check_over_configs(prediction_type=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=snake_case_				)




							def    A__						( self				)       ->  Dict:
													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=snake_case_				)




							def    A__						( self				)       ->  str:
													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													self.check_over_configs(thresholding=snake_case_				)
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=snake_case_    ,				prediction_type=snake_case_    ,				sample_max_value=snake_case_    ,				)




							def    A__						( self				)       ->  Union[str, Any]:
													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, num_inference_steps in zip([1, 10, 50]    ,				[10, 50, 500]				):
																			self.check_over_forward(time_step=snake_case_    ,				num_inference_steps=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													for t, eta in zip([1, 10, 49]    ,				[0.0, 0.5, 1.0]				):
																			self.check_over_forward(time_step=snake_case_    ,				eta=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420    ,				400				) - 0.14_771				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980    ,				960				) - 0.32_460				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0    ,				0				) - 0.0				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487    ,				486				) - 0.00_979				)				) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999    ,				998				) - 0.02				)				) < 1e-5




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.scheduler_classes[0]
													__lowerCAmelCase     =  self.get_scheduler_config()
													__lowerCAmelCase     =  scheduler_class(**snake_case_				)

													__lowerCAmelCase   ,     __lowerCAmelCase     =  10, 0.0
													scheduler.set_timesteps(snake_case_				)

													__lowerCAmelCase     =  self.dummy_model()
													__lowerCAmelCase     =  self.dummy_sample_deter
													__lowerCAmelCase     =  self.dummy_sample_deter + 0.1
													__lowerCAmelCase     =  self.dummy_sample_deter - 0.1

													__lowerCAmelCase     =  samplea.shape[0]
													__lowerCAmelCase     =  torch.stack([samplea, samplea, samplea]    ,				dim=0				)
													__lowerCAmelCase     =  torch.arange(snake_case_				)[0:3, None].repeat(1    ,				snake_case_				)

													__lowerCAmelCase     =  model(samples.flatten(0    ,				1				)    ,				timesteps.flatten(0    ,				1				)				)
													__lowerCAmelCase     =  scheduler.batch_step_no_noise(snake_case_    ,				timesteps.flatten(0    ,				1				)    ,				samples.flatten(0    ,				1				)    ,				snake_case_				)

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

													assert abs(result_sum.item() - 1_147.7_904				) < 1e-2
													assert abs(result_mean.item() - 0.4_982				) < 1e-3




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  self.full_loop()

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

													assert abs(result_sum.item() - 172.0_067				) < 1e-2
													assert abs(result_mean.item() - 0.223_967				) < 1e-3




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  self.full_loop(prediction_type="""v_prediction"""				)

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

													assert abs(result_sum.item() - 52.5_302				) < 1e-2
													assert abs(result_mean.item() - 0.0_684				) < 1e-3




							def    A__						( self				)       ->  Tuple:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.8_295				) < 1e-2
													assert abs(result_mean.item() - 0.1_951				) < 1e-3




							def    A__						( self				)       ->  Union[str, Any]:
													# We specify different beta, so that the first alpha is 0.99
													__lowerCAmelCase     =  self.full_loop(set_alpha_to_one=snake_case_    ,				beta_start=0.01				)
													__lowerCAmelCase     =  torch.sum(torch.abs(snake_case_				)				)
													__lowerCAmelCase     =  torch.mean(torch.abs(snake_case_				)				)

													assert abs(result_sum.item() - 149.0_784				) < 1e-2
													assert abs(result_mean.item() - 0.1_941				) < 1e-3
