


'''simple docstring'''
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


__snake_case											=					get_logger(__name__)
class        lowercase     :

		"""simple docstring"""

		_a   			=    'dummy_data'
		_a   			=    'datasets'
		_a   			=    False




		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = False   ,       UpperCamelCase_ = True   ,       UpperCamelCase_ = None   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			0
							UpperCamelCase__       :int							 =			dataset_name
							UpperCamelCase__       :Optional[int]							 =			cache_dir
							UpperCamelCase__       :int							 =			use_local_dummy_data
							UpperCamelCase__       :Tuple							 =			config
							# download_callbacks take a single url as input
							UpperCamelCase__       :List[Callable]							 =			download_callbacks or []
							# if False, it doesn't load existing files and it returns the paths of the dummy files relative
							# to the dummy_data zip file root
							UpperCamelCase__       :List[Any]							 =			load_existing_dummy_data

							# TODO(PVP, QL) might need to make this more general
							UpperCamelCase__       :Union[str, Any]							 =			str(UpperCamelCase_							)
							# to be downloaded
							UpperCamelCase__       :int							 =			None
							UpperCamelCase__       :Union[str, Any]							 =			None




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if self._dummy_file is None:
												UpperCamelCase__       :str							 =			self.download_dummy_data()
							return self._dummy_file




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if self.config is not None:
												# structure is dummy / config_name / version_name
												return os.path.join('''dummy'''   ,       self.config.name   ,       self.version_name							)
							# structure is dummy / version_name
							return os.path.join('''dummy'''   ,       self.version_name							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return os.path.join(self.dummy_data_folder   ,       '''dummy_data.zip'''							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			(
							    self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
							)

							UpperCamelCase__       :Optional[Any]							 =			cached_path(
							    UpperCamelCase_   ,       cache_dir=self.cache_dir   ,       extract_compressed_file=UpperCamelCase_   ,       force_extract=UpperCamelCase_							)

							return os.path.join(UpperCamelCase_   ,       self.dummy_file_name							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return os.path.join(self.datasets_scripts_dir   ,       self.dataset_name   ,       self.dummy_zip_file							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if self._bucket_url is None:
												UpperCamelCase__       :str							 =			hf_github_url(self.dataset_name   ,       self.dummy_zip_file.replace(os.sep   ,       '''/'''							)							)
							return self._bucket_url




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if os.path.isdir(self.dummy_file							):
												return self.dummy_file
							# else cut off path to file -> example `xsum`.
							return "/".join(self.dummy_file.replace(os.sep   ,       '''/'''							).split('''/'''							)[:-1]							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       *UpperCamelCase_							):

							'''simple docstring'''


							if self.load_existing_dummy_data:
												# dummy data is downloaded and tested
												UpperCamelCase__       :int							 =			self.dummy_file
							else:
												# dummy data cannot be downloaded and only the path to dummy file is returned
												UpperCamelCase__       :str							 =			self.dummy_file_name

							# special case when data_url is a dict
							if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												return self.create_dummy_data_dict(UpperCamelCase_   ,       UpperCamelCase_							)
							elif isinstance(UpperCamelCase_   ,       (list, tuple)							):
												return self.create_dummy_data_list(UpperCamelCase_   ,       UpperCamelCase_							)
							else:
												return self.create_dummy_data_single(UpperCamelCase_   ,       UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       *UpperCamelCase_							):

							'''simple docstring'''


							return self.download_and_extract(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self.download_and_extract(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							return path




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return {}




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			{}
							for key, single_urls in data_url.items():
												for download_callback in self.download_callbacks:
																	if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
																						for single_url in single_urls:
																											download_callback(UpperCamelCase_							)
																	else:
																						UpperCamelCase__       :Optional[int]							 =			single_urls
																						download_callback(UpperCamelCase_							)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
												if isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
																	UpperCamelCase__       :Tuple							 =			[os.path.join(UpperCamelCase_   ,       urllib.parse.quote_plus(Path(UpperCamelCase_							).name							)							) for x in single_urls]
												else:
																	UpperCamelCase__       :List[Any]							 =			single_urls
																	UpperCamelCase__       :Optional[Any]							 =			os.path.join(UpperCamelCase_   ,       urllib.parse.quote_plus(Path(UpperCamelCase_							).name							)							)
												UpperCamelCase__       :List[str]							 =			value

							# make sure that values are unique
							if all(isinstance(UpperCamelCase_   ,       UpperCamelCase_							) for i in dummy_data_dict.values()							) and len(set(dummy_data_dict.values()							)							) < len(
							    dummy_data_dict.values()							):
												# append key to value to make its name unique
												UpperCamelCase__       :str							 =			{key: value + key for key, value in dummy_data_dict.items()}

							return dummy_data_dict




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			[]
							# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
							UpperCamelCase__       :str							 =			all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}'''   ,       UpperCamelCase_							)							) for url in data_url							)
							UpperCamelCase__       :str							 =			all(
							    url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed'''							) for url in data_url							)
							if data_url and (is_tf_records or is_pubmed_records):
												UpperCamelCase__       :Tuple							 =			[data_url[0]] * len(UpperCamelCase_							)
							for single_url in data_url:
												for download_callback in self.download_callbacks:
																	download_callback(UpperCamelCase_							)
												# we force the name of each key to be the last file / folder name of the url path
												# if the url has arguments, we need to encode them with urllib.parse.quote_plus
												UpperCamelCase__       :Optional[int]							 =			os.path.join(UpperCamelCase_   ,       urllib.parse.quote_plus(single_url.split('''/'''							)[-1]							)							)
												dummy_data_list.append(UpperCamelCase_							)
							return dummy_data_list




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_							):

							'''simple docstring'''


							for download_callback in self.download_callbacks:
												download_callback(UpperCamelCase_							)
							# we force the name of each key to be the last file / folder name of the url path
							# if the url has arguments, we need to encode them with urllib.parse.quote_plus
							UpperCamelCase__       :Dict							 =			os.path.join(UpperCamelCase_   ,       urllib.parse.quote_plus(data_url.split('''/'''							)[-1]							)							)
							if os.path.exists(UpperCamelCase_							) or not self.load_existing_dummy_data:
												return value
							else:
												# Backward compatibility, maybe deprecate at one point.
												# For many datasets with single url calls to dl_manager.download_and_extract,
												# the dummy_data.zip file is actually the zipped downloaded file
												# while now we expected the dummy_data.zip file to be a directory containing
												# the downloaded file.
												return path_to_dummy_data




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							pass




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							def _iter_archive_members(UpperCamelCase_							):
												# this preserves the order of the members inside the ZIP archive
												UpperCamelCase__       :str							 =			Path(self.dummy_file							).parent
												UpperCamelCase__       :Union[str, Any]							 =			path.relative_to(UpperCamelCase_							)
												with ZipFile(self.local_path_to_dummy_data							) as zip_file:
																	UpperCamelCase__       :int							 =			zip_file.namelist()
												for member in members:
																	if member.startswith(relative_path.as_posix()							):
																						yield dummy_parent_path.joinpath(UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			Path(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			_iter_archive_members(UpperCamelCase_							) if self.use_local_dummy_data else path.rglob('''*'''							)
							for file_path in file_paths:
												if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''')							):
																	yield file_path.relative_to(UpperCamelCase_							).as_posix(), file_path.open('''rb'''							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							if not isinstance(UpperCamelCase_   ,       UpperCamelCase_							):
												UpperCamelCase__       :Optional[int]							 =			[paths]
							for path in paths:
												if os.path.isfile(UpperCamelCase_							):
																	if os.path.basename(UpperCamelCase_							).startswith(('''.''', '''__''')							):
																						return
																	yield path
												else:
																	for dirpath, dirnames, filenames in os.walk(UpperCamelCase_							):
																						if os.path.basename(UpperCamelCase_							).startswith(('''.''', '''__''')							):
																											continue
																						dirnames.sort()
																						for filename in sorted(UpperCamelCase_							):
																											if filename.startswith(('''.''', '''__''')							):
																																continue
																											yield os.path.join(UpperCamelCase_   ,       UpperCamelCase_							)



'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config(steps_offset=1							)
							UpperCamelCase__       :str							 =			scheduler_class(**UpperCamelCase_							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t in [1, 10, 49]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       num_inference_steps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, eta in zip([1, 10, 49]   ,       [0.0, 0.5, 1.0]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       eta=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 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							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Dict							 =			self.get_scheduler_config()
							UpperCamelCase__       :Any							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

							UpperCamelCase__       :List[str]							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3