

'''simple docstring'''



import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	""
			__snake_case        =	(
			    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
			)
			__snake_case        =	None  # compression type in fsspec. ex: "gzip"
			__snake_case        =	None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						List[Any] = ""		,      lowerCAmelCase_	:						List[Any] = None		,      lowerCAmelCase_	:						str = None		,      **lowerCAmelCase_	:						Any			)      ->		Union[str, Any]:





									'''simple docstring'''
									super().__init__(self		,      **a__			)
									# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
									A__					:       Any     =fsspec.open(
									    a__		,      mode="""rb"""		,      protocol=a__		,      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 {})		,      )
									A__					:       Any     =os.path.basename(self.file.path.split("""::"""			)[0]			)
									A__					:       List[str]     =(
									    self.compressed_name[: self.compressed_name.rindex("""."""			)]
									    if """.""" in self.compressed_name
									    else self.compressed_name
									)
									A__					:       Tuple     =None


			@classmethod
			def lowercase__						(				cls	:						Union[str, Any]		,      lowerCAmelCase_	:						Optional[int]			)      ->		str:





									'''simple docstring'''
									return super()._strip_protocol(a__			).lstrip("""/"""			)


			def lowercase__						(				self	:						Dict			)      ->		int:





									'''simple docstring'''




									if self.dir_cache is None:
															A__					:       Optional[Any]     ={**self.file.fs.info(self.file.path			), """name""": self.uncompressed_name}
															A__					:       str     ={f["""name"""]: f}


			def lowercase__						(				self	:						Optional[Any]		,      lowerCAmelCase_	:						List[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									return self.file.open().read()



			def lowercase__						(				self	:						str		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Optional[int] = "rb"		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						List[Any]=True		,      lowerCAmelCase_	:						int=None		,      **lowerCAmelCase_	:						Any		,      )      ->		int:





									'''simple docstring'''
									A__					:       Dict     =self._strip_protocol(a__			)
									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 lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	"bz2"
			__snake_case        =	"bz2"
			__snake_case        =	".bz2"



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	"gzip"
			__snake_case        =	"gzip"
			__snake_case        =	".gz"



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	"lz4"
			__snake_case        =	"lz4"
			__snake_case        =	".lz4"



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	"xz"
			__snake_case        =	"xz"
			__snake_case        =	".xz"



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	"zstd"
			__snake_case        =	"zstd"
			__snake_case        =	".zst"


			def __init__(				self	:						Optional[Any]		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						int = "rb"		,      lowerCAmelCase_	:						List[str] = None		,      lowerCAmelCase_	:						Optional[int] = None		,      lowerCAmelCase_	:						Optional[Any] = DEFAULT_BLOCK_SIZE		,      **lowerCAmelCase_	:						Optional[int]		,      )      ->		Optional[int]:





									'''simple docstring'''
									super().__init__(
									    fo=a__		,      mode=a__		,      target_protocol=a__		,      target_options=a__		,      block_size=a__		,      **a__		,      )
									# 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
									A__					:       int     =self.file.__enter__



									class lowerCamelCase			:



												'''simple docstring'''






												def __init__(				self	:						Tuple		,      lowerCAmelCase_	:						Any			)      ->		Optional[int]:





																		'''simple docstring'''
																		A__					:       str     =file_


												def __enter__(				self	:						List[str]			)      ->		Union[str, Any]:





																		'''simple docstring'''
																		self._file.__enter__()
																		return self


												def __exit__(				self	:						Optional[int]		,      *lowerCAmelCase_	:						List[Any]		,      **lowerCAmelCase_	:						List[str]			)      ->		List[Any]:





																		'''simple docstring'''
																		self._file.__exit__(*a__		,      **a__			)


												def __iter__(				self	:						Tuple			)      ->		str:





																		'''simple docstring'''
																		return iter(self._file			)


												def lowercase__						(				self	:						int			)      ->		Tuple:





																		'''simple docstring'''
																		return next(self._file			)



												def __getattr__(				self	:						int		,      lowerCAmelCase_	:						Optional[Any]			)      ->		List[str]:





																		'''simple docstring'''
																		return getattr(self._file		,      a__			)

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

									A__					:       Any     =fixed_enter




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

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


			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[int]:





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[int]:





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

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


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

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


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

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


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

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



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

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

