

"""simple docstring"""




import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: str					=					""
	a__		: str					=					(
	    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
	)
	a__		: str					=					None  # compression type in fsspec. ex: "gzip"
	a__		: str					=					None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz



	def __init__(  self     :       Optional[int]			,    _lowercase     :       str = ""			,    _lowercase     :       Optional[str] = None			,    _lowercase     :       Optional[dict] = None			,    **_lowercase     :       Dict						):
		super().__init__(self			,    **_lowercase						)
		# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
		__UpperCAmelCase      		= fsspec.open(
		    _lowercase			,    mode='''rb'''			,    protocol=_lowercase			,    compression=self.compression			,    client_kwargs={
		        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
		        '''trust_env''': True,  # Enable reading proxy env variables.
		        **(target_options or {}).pop('''client_kwargs'''			,    {}						),  # To avoid issues if it was already passed.
		    }			,    **(target_options or {})			,    )
		__UpperCAmelCase      		= os.path.basename(self.file.path.split('''::'''						)[0]						)
		__UpperCAmelCase      		= (
		    self.compressed_name[: self.compressed_name.rindex('''.'''						)]
		    if '''.''' in self.compressed_name
		    else self.compressed_name
		)
		__UpperCAmelCase      		= None



	@classmethod
	def        a  (  cls     :       Tuple			,    _lowercase     :       Any						):
		# compressed file paths are always relative to the archive root
		return super()._strip_protocol(_lowercase						).lstrip('''/'''						)



	def        a  (  self     :       Dict						):
		if self.dir_cache is None:
			__UpperCAmelCase      		= {**self.file.fs.info(self.file.path						), '''name''': self.uncompressed_name}
			__UpperCAmelCase      		= {f['''name''']: f}



	def        a  (  self     :       Optional[int]			,    _lowercase     :       str						):
		return self.file.open().read()



	def        a  (  self     :       Optional[Any]			,    _lowercase     :       str			,    _lowercase     :       str = "rb"			,    _lowercase     :       Optional[Any]=None			,    _lowercase     :       Any=True			,    _lowercase     :       Tuple=None			,    **_lowercase     :       Optional[Any]			,    ):
		__UpperCAmelCase      		= self._strip_protocol(_lowercase						)
		if mode != "rb":
			raise ValueError(F'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''						)
		return self.file.open()






class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Dict					=					"bz2"
	a__		: List[str]					=					"bz2"
	a__		: List[str]					=					".bz2"






class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Dict					=					"gzip"
	a__		: Tuple					=					"gzip"
	a__		: Optional[int]					=					".gz"






class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: int					=					"lz4"
	a__		: List[str]					=					"lz4"
	a__		: Union[str, Any]					=					".lz4"






class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Union[str, Any]					=					"xz"
	a__		: Optional[Any]					=					"xz"
	a__		: List[str]					=					".xz"






class      _UpperCAmelCase   (							_lowerCAmelCase       ):
	a__		: Optional[Any]					=					"zstd"
	a__		: Any					=					"zstd"
	a__		: Union[str, Any]					=					".zst"



	def __init__(  self     :       Optional[Any]			,    _lowercase     :       str			,    _lowercase     :       str = "rb"			,    _lowercase     :       Optional[str] = None			,    _lowercase     :       Optional[dict] = None			,    _lowercase     :       int = DEFAULT_BLOCK_SIZE			,    **_lowercase     :       Any			,    ):
		super().__init__(
		    fo=_lowercase			,    mode=_lowercase			,    target_protocol=_lowercase			,    target_options=_lowercase			,    block_size=_lowercase			,    **_lowercase			,    )
		# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
		#
		# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
		#     out.close = close
		# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
		#
		# see https://github.com/intake/filesystem_spec/issues/725
		__UpperCAmelCase      		= self.file.__enter__
		class      _UpperCAmelCase   :



			def __init__(  self     :       int			,    _lowercase     :       Optional[int]						):
				__UpperCAmelCase      		= file_



			def __enter__(  self     :       Tuple						):
				self._file.__enter__()
				return self



			def __exit__(  self     :       Union[str, Any]			,    *_lowercase     :       int			,    **_lowercase     :       int						):
				self._file.__exit__(*_lowercase			,    **_lowercase						)



			def __iter__(  self     :       Optional[Any]						):
				return iter(self._file						)



			def        a  (  self     :       int						):
				return next(self._file						)



			def __getattr__(  self     :       Dict			,    _lowercase     :       Union[str, Any]						):
				return getattr(self._file			,    _lowercase						)

		def fixed_enter(*_lowercase     :       Union[str, Any]			,    **_lowercase     :       Optional[Any]						):
			return WrappedFile(_enter(*_lowercase			,    **_lowercase						)						)

		__UpperCAmelCase      		= fixed_enter







'''simple docstring'''




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(DPMSolverSinglestepScheduler,)
					SCREAMING_SNAKE_CASE__				=	(('''num_inference_steps''', 25),)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			{
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """beta_schedule""": """linear""",
							    """solver_order""": 2,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							    """sample_max_value""": 1.0,
							    """algorithm_type""": """dpmsolver++""",
							    """solver_type""": """midpoint""",
							    """lambda_min_clipped""": -float("""inf"""						),
							    """variance_type""": None,
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			List[str]=0      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[str]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				str		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Any		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)
									# copy over dummy past residuals
									SCREAMING_SNAKE_CASE :				Any		=			dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				int		=			scheduler_class.from_pretrained(lowerCamelCase_						)
											new_scheduler.set_timesteps(lowerCamelCase_						)
											# copy over dummy past residuals
											SCREAMING_SNAKE_CASE :				Optional[int]		=			dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			sample, sample
									for t in range(lowerCamelCase_      ,						time_step + scheduler.config.solver_order + 1						):
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
											SCREAMING_SNAKE_CASE :				Optional[int]		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

											assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							pass


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=0      ,						**lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)

									# copy over dummy past residuals (must be after setting timesteps)
									SCREAMING_SNAKE_CASE :				int		=			dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_						)
											SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class.from_pretrained(lowerCamelCase_						)
											# copy over dummy past residuals
											new_scheduler.set_timesteps(lowerCamelCase_						)

											# copy over dummy past residual (must be after setting timesteps)
											SCREAMING_SNAKE_CASE :				Dict		=			dummy_past_residuals[: new_scheduler.config.solver_order]

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
									SCREAMING_SNAKE_CASE :				Dict		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

									assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			int=None      ,						**lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							if scheduler is None:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_scheduler_config(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			10
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				Dict		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							return sample


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			50
							SCREAMING_SNAKE_CASE :				Dict		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]						):
									SCREAMING_SNAKE_CASE :				str		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_574						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				int		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3

							SCREAMING_SNAKE_CASE :				List[str]		=			DEISMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Any		=			UniPCMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverSinglestepScheduler.from_config(scheduler.config						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.check_over_configs(thresholding=lowerCamelCase_						)
							for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
											for threshold in [0.5, 1.0, 2.0]:
													for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(
															    thresholding=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						sample_max_value=lowerCamelCase_      ,						algorithm_type="""dpmsolver++"""      ,						solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



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


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

							'''simple docstring'''



							for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
											for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
															self.check_over_configs(
															    solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															SCREAMING_SNAKE_CASE :				int		=			self.full_loop(
															    solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															assert not torch.isnan(lowerCamelCase_						).any(), "Samples have nan numbers"


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							self.check_over_configs(lower_order_final=lowerCamelCase_						)
							self.check_over_configs(lower_order_final=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							self.check_over_configs(lambda_min_clipped=-float("""inf"""						)						)
							self.check_over_configs(lambda_min_clipped=-5.1						)


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							self.check_over_configs(variance_type=lowerCamelCase_						)
							self.check_over_configs(variance_type="""learned_range"""						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
									self.check_over_forward(num_inference_steps=lowerCamelCase_      ,						time_step=0						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.full_loop()
							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.full_loop(use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_248						) < 1e-3


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.full_loop(prediction_type="""v_prediction"""						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.1_453						) < 1e-3


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.full_loop(prediction_type="""v_prediction"""      ,						use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.0_649						) < 1e-3



					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_scheduler_config(thresholding=lowerCamelCase_      ,						dynamic_thresholding_ratio=0						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			10
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				int		=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				List[Any]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Any		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							assert sample.dtype == torch.floataa



