






'''simple docstring'''







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class _lowercase			(       _lowercase	):
							a          = (PNDMScheduler,)
							a          = (("""num_inference_steps""", 50),)




							def 	lowerCamelCase_       (							self:       List[str]    ,							**UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= {
														    """num_train_timesteps""": 1_000,
														    """beta_start""": 0.0_001,
														    """beta_end""": 0.02,
														    """beta_schedule""": """linear""",
														}

														config.update(**UpperCamelCase__  )
														return config




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							UpperCamelCase__:       Tuple=0    ,							**UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Optional[Any] 						= dict(self.forward_default_kwargs  )
														lowerCamelCase__      :			Union[str, Any] 						= kwargs.pop("""num_inference_steps"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= self.dummy_sample
														lowerCamelCase__      :			Any 						= 0.1 * sample
														lowerCamelCase__      :			Any 						= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__      :			List[str] 						= self.get_scheduler_config(**UpperCamelCase__  )
																					lowerCamelCase__      :			Tuple 						= scheduler_class(**UpperCamelCase__  )
																					scheduler.set_timesteps(UpperCamelCase__  )
																					# copy over dummy past residuals
																					lowerCamelCase__      :			Union[str, Any] 						= dummy_past_residuals[:]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__  )
																												lowerCamelCase__      :			Tuple 						= scheduler_class.from_pretrained(UpperCamelCase__  )
																												new_scheduler.set_timesteps(UpperCamelCase__  )
																												# copy over dummy past residuals
																												lowerCamelCase__      :			str 						= dummy_past_residuals[:]

																					lowerCamelCase__      :			Union[str, Any] 						= scheduler.step_prk(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			int 						= new_scheduler.step_prk(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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

																					lowerCamelCase__      :			int 						= scheduler.step_plms(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			int 						= new_scheduler.step_plms(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														pass




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Any=0    ,							**UpperCamelCase__:       Optional[Any]  ):
														lowerCamelCase__      :			List[str] 						= dict(self.forward_default_kwargs  )
														lowerCamelCase__      :			List[Any] 						= kwargs.pop("""num_inference_steps"""    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Any 						= self.dummy_sample
														lowerCamelCase__      :			int 						= 0.1 * sample
														lowerCamelCase__      :			Optional[int] 						= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__      :			int 						= self.get_scheduler_config()
																					lowerCamelCase__      :			int 						= scheduler_class(**UpperCamelCase__  )
																					scheduler.set_timesteps(UpperCamelCase__  )

																					# copy over dummy past residuals (must be after setting timesteps)
																					lowerCamelCase__      :			Union[str, Any] 						= dummy_past_residuals[:]

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												scheduler.save_config(UpperCamelCase__  )
																												lowerCamelCase__      :			int 						= scheduler_class.from_pretrained(UpperCamelCase__  )
																												# copy over dummy past residuals
																												new_scheduler.set_timesteps(UpperCamelCase__  )

																												# copy over dummy past residual (must be after setting timesteps)
																												lowerCamelCase__      :			Optional[int] 						= dummy_past_residuals[:]

																					lowerCamelCase__      :			List[Any] 						= scheduler.step_prk(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			List[Any] 						= new_scheduler.step_prk(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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

																					lowerCamelCase__      :			Tuple 						= scheduler.step_plms(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			Optional[Any] 						= new_scheduler.step_plms(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       Any    ,							**UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			List[Any] 						= self.scheduler_classes[0]
														lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config(**UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= scheduler_class(**UpperCamelCase__  )

														lowerCamelCase__      :			str 						= 10
														lowerCamelCase__      :			Union[str, Any] 						= self.dummy_model()
														lowerCamelCase__      :			Optional[int] 						= self.dummy_sample_deter
														scheduler.set_timesteps(UpperCamelCase__  )

														for i, t in enumerate(scheduler.prk_timesteps  ):
																					lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			Tuple 						= scheduler.step_prk(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample

														for i, t in enumerate(scheduler.plms_timesteps  ):
																					lowerCamelCase__      :			List[str] 						= model(UpperCamelCase__    ,							UpperCamelCase__  )
																					lowerCamelCase__      :			List[Any] 						= scheduler.step_plms(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample

														return sample




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Any 						= dict(self.forward_default_kwargs  )

														lowerCamelCase__      :			Tuple 						= kwargs.pop("""num_inference_steps"""    ,							UpperCamelCase__  )

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__      :			int 						= self.get_scheduler_config()
																					lowerCamelCase__      :			List[Any] 						= scheduler_class(**UpperCamelCase__  )

																					lowerCamelCase__      :			Tuple 						= self.dummy_sample
																					lowerCamelCase__      :			Optional[Any] 						= 0.1 * sample

																					if num_inference_steps is not None and hasattr(UpperCamelCase__    ,							"""set_timesteps"""  ):
																												scheduler.set_timesteps(UpperCamelCase__  )
																					elif num_inference_steps is not None and not hasattr(UpperCamelCase__    ,							"""set_timesteps"""  ):
																												lowerCamelCase__      :			List[str] 						= num_inference_steps

																					# copy over dummy past residuals (must be done after set_timesteps)
																					lowerCamelCase__      :			int 						= [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
																					lowerCamelCase__      :			List[str] 						= dummy_past_residuals[:]

																					lowerCamelCase__      :			Any 						= scheduler.step_prk(UpperCamelCase__    ,							0    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			int 						= scheduler.step_prk(UpperCamelCase__    ,							1    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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

																					lowerCamelCase__      :			Tuple 						= scheduler.step_plms(UpperCamelCase__    ,							0    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample
																					lowerCamelCase__      :			Union[str, Any] 						= scheduler.step_plms(UpperCamelCase__    ,							1    ,							UpperCamelCase__    ,							**UpperCamelCase__  ).prev_sample

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




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														for timesteps in [100, 1_000]:
																					self.check_over_configs(num_train_timesteps=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= self.scheduler_classes[0]
														lowerCamelCase__      :			int 						= self.get_scheduler_config(steps_offset=1  )
														lowerCamelCase__      :			Tuple 						= scheduler_class(**UpperCamelCase__  )
														scheduler.set_timesteps(10  )
														assert torch.equal(
														    scheduler.timesteps    ,							torch.LongTensor(
														        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]  )    ,							)




							def 	lowerCamelCase_       (							self:       List[str]  ):
														for beta_start, beta_end in zip([0.0_001, 0.001]    ,							[0.002, 0.02]  ):
																					self.check_over_configs(beta_start=UpperCamelCase__    ,							beta_end=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=UpperCamelCase__  )




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




							def 	lowerCamelCase_       (							self:       str  ):
														for t in [1, 5, 10]:
																					self.check_over_forward(time_step=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       int  ):
														for t, num_inference_steps in zip([1, 5, 10]    ,							[10, 50, 100]  ):
																					self.check_over_forward(num_inference_steps=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       int  ):
														# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
														lowerCamelCase__      :			int 						= 27

														for scheduler_class in self.scheduler_classes:
																					lowerCamelCase__      :			List[str] 						= self.dummy_sample
																					lowerCamelCase__      :			Dict 						= 0.1 * sample

																					lowerCamelCase__      :			Optional[Any] 						= self.get_scheduler_config()
																					lowerCamelCase__      :			Optional[int] 						= scheduler_class(**UpperCamelCase__  )

																					scheduler.set_timesteps(UpperCamelCase__  )

																					# before power of 3 fix, would error on first step, so we only need to do two
																					for i, t in enumerate(scheduler.prk_timesteps[:2]  ):
																												lowerCamelCase__      :			Any 						= scheduler.step_prk(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  ).prev_sample




							def 	lowerCamelCase_       (							self:       List[str]  ):
														with self.assertRaises(UpperCamelCase__  ):
																					lowerCamelCase__      :			List[str] 						= self.scheduler_classes[0]
																					lowerCamelCase__      :			str 						= self.get_scheduler_config()
																					lowerCamelCase__      :			str 						= scheduler_class(**UpperCamelCase__  )

																					scheduler.step_plms(self.dummy_sample    ,							1    ,							self.dummy_sample  ).prev_sample




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.full_loop()
														lowerCamelCase__      :			Optional[Any] 						= torch.sum(torch.abs(UpperCamelCase__  )  )
														lowerCamelCase__      :			Any 						= torch.mean(torch.abs(UpperCamelCase__  )  )

														assert abs(result_sum.item() - 198.1_318  ) < 1e-2
														assert abs(result_mean.item() - 0.2_580  ) < 1e-3




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.full_loop(prediction_type="""v_prediction"""  )
														lowerCamelCase__      :			Optional[Any] 						= torch.sum(torch.abs(UpperCamelCase__  )  )
														lowerCamelCase__      :			str 						= torch.mean(torch.abs(UpperCamelCase__  )  )

														assert abs(result_sum.item() - 67.3_986  ) < 1e-2
														assert abs(result_mean.item() - 0.0_878  ) < 1e-3




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														# We specify different beta, so that the first alpha is 0.99
														lowerCamelCase__      :			Optional[int] 						= self.full_loop(set_alpha_to_one=UpperCamelCase__    ,							beta_start=0.01  )
														lowerCamelCase__      :			int 						= torch.sum(torch.abs(UpperCamelCase__  )  )
														lowerCamelCase__      :			Dict 						= torch.mean(torch.abs(UpperCamelCase__  )  )

														assert abs(result_sum.item() - 230.0_399  ) < 1e-2
														assert abs(result_mean.item() - 0.2_995  ) < 1e-3




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														# We specify different beta, so that the first alpha is 0.99
														lowerCamelCase__      :			List[str] 						= self.full_loop(set_alpha_to_one=UpperCamelCase__    ,							beta_start=0.01  )
														lowerCamelCase__      :			Union[str, Any] 						= torch.sum(torch.abs(UpperCamelCase__  )  )
														lowerCamelCase__      :			Union[str, Any] 						= torch.mean(torch.abs(UpperCamelCase__  )  )

														assert abs(result_sum.item() - 186.9_482  ) < 1e-2
														assert abs(result_mean.item() - 0.2_434  ) < 1e-3









'''simple docstring'''







import os
from typing import Optional

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

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




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




							@classmethod
							def 	lowerCamelCase_       (							cls:       Optional[int]    ,							UpperCamelCase__:       Optional[int]  ):
														# compressed file paths are always relative to the archive root
														return super()._strip_protocol(UpperCamelCase__  ).lstrip("""/"""  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.dir_cache is None:
																					lowerCamelCase__      :			Dict 						= {**self.file.fs.info(self.file.path  ), """name""": self.uncompressed_name}
																					lowerCamelCase__      :			int 						= {f["""name"""]: f}




							def 	lowerCamelCase_       (							self:       List[str]    ,							UpperCamelCase__:       str  ):
														return self.file.open().read()




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[int]=None    ,							UpperCamelCase__:       Tuple=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Optional[Any]    ,							):
														lowerCamelCase__      :			Union[str, Any] 						= self._strip_protocol(UpperCamelCase__  )
														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 _lowercase			(       _lowercase	):
							a          = """bz2"""
							a          = """bz2"""
							a          = """.bz2"""



class _lowercase			(       _lowercase	):
							a          = """gzip"""
							a          = """gzip"""
							a          = """.gz"""



class _lowercase			(       _lowercase	):
							a          = """lz4"""
							a          = """lz4"""
							a          = """.lz4"""



class _lowercase			(       _lowercase	):
							a          = """xz"""
							a          = """xz"""
							a          = """.xz"""



class _lowercase			(       _lowercase	):
							a          = """zstd"""
							a          = """zstd"""
							a          = """.zst"""




							def __init__(							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str = "rb"    ,							UpperCamelCase__:       Optional[str] = None    ,							UpperCamelCase__:       Optional[dict] = None    ,							UpperCamelCase__:       int = DEFAULT_BLOCK_SIZE    ,							**UpperCamelCase__:       Dict    ,							):
														super().__init__(
														    fo=UpperCamelCase__    ,							mode=UpperCamelCase__    ,							target_protocol=UpperCamelCase__    ,							target_options=UpperCamelCase__    ,							block_size=UpperCamelCase__    ,							**UpperCamelCase__    ,							)
														# 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
														lowerCamelCase__      :			Tuple 						= self.file.__enter__

														class _lowercase			:




																					def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Any  ):
																												lowerCamelCase__      :			Optional[int] 						= file_




																					def __enter__(							self:       List[Any]  ):
																												self._file.__enter__()
																												return self




																					def __exit__(							self:       Any    ,							*UpperCamelCase__:       str    ,							**UpperCamelCase__:       Any  ):
																												self._file.__exit__(*UpperCamelCase__    ,							**UpperCamelCase__  )




																					def __iter__(							self:       Any  ):
																												return iter(self._file  )




																					def 	lowerCamelCase_       (							self:       List[Any]  ):
																												return next(self._file  )




																					def __getattr__(							self:       List[str]    ,							UpperCamelCase__:       Dict  ):
																												return getattr(self._file    ,							UpperCamelCase__  )

														def fixed_enter(*UpperCamelCase__:       Union[str, Any]    ,							**UpperCamelCase__:       List[str]  ):
																					return WrappedFile(_enter(*UpperCamelCase__    ,							**UpperCamelCase__  )  )

														lowerCamelCase__      :			Optional[Any] 						= fixed_enter

