



import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							import PIL.Image

							from .features import FeatureType


__SCREAMING_SNAKE_CASE           =  None
__SCREAMING_SNAKE_CASE           =  """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__SCREAMING_SNAKE_CASE           =  [
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]

@dataclass
class 	lowerCamelCase_    :






						'''simple docstring'''
						a__   =  True
						a__   =  None
						# Automatically constructed
						a__   =  "PIL.Image.Image"
						a__   =  pa.struct({"bytes": pa.binary(), "path": pa.string()}						)
						a__   =  field(default="Image"						,init=_A						,repr=_A						)







						def __call__(	self      :			str )      ->				Union[str, Any]:
									return self.pa_type







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	__lowerCamelCase      :			Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] )      ->				dict:
									if config.PIL_AVAILABLE:
												import PIL.Image
									else:
												raise ImportError("To support encoding images, please install 'Pillow'." )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												A							:					Tuple              =						np.array(__lowerCamelCase )

									if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												return {"path": value, "bytes": None}
									elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
												return {"path": None, "bytes": value}
									elif isinstance(__lowerCamelCase	,	np.ndarray ):
												# convert the image array to PNG/TIFF bytes
												return encode_np_array(__lowerCamelCase )
									elif isinstance(__lowerCamelCase	,	PIL.Image.Image ):
												# convert the PIL image to bytes (default format is PNG/TIFF)
												return encode_pil_image(__lowerCamelCase )
									elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
												# we set "bytes": None to not duplicate the data if they're already available locally
												return {"bytes": None, "path": value.get("path" )}
									elif value.get("bytes" ) is not None or value.get("path" ) is not None:
												# store the image bytes, and path is used to infer the image format using the file extension
												return {"bytes": value.get("bytes" ), "path": value.get("path" )}
									else:
												raise ValueError(
												    F"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			dict	,	__lowerCamelCase      :			int=None )      ->				"PIL.Image.Image":
									if not self.decode:
												raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )

									if config.PIL_AVAILABLE:
												import PIL.Image
									else:
												raise ImportError("To support decoding images, please install 'Pillow'." )

									if token_per_repo_id is None:
												A							:					List[Any]              =						{}

									A       , A							:					Any              =						value["path"], value["bytes"]
									if bytes_ is None:
												if path is None:
															raise ValueError(F"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
												else:
															if is_local_path(__lowerCamelCase ):
																		A							:					List[Any]              =						PIL.Image.open(__lowerCamelCase )
															else:
																		A							:					Any              =						path.split("::" )[-1]
																		try:
																					A							:					int              =						string_to_dict(__lowerCamelCase	,	config.HUB_DATASETS_URL )["repo_id"]
																					A							:					Tuple              =						token_per_repo_id.get(__lowerCamelCase )
																		except ValueError:
																					A							:					Any              =						None
																		with xopen(__lowerCamelCase	,	"rb"	,	use_auth_token=__lowerCamelCase ) as f:
																					A							:					Optional[Any]              =						BytesIO(f.read() )
																		A							:					List[Any]              =						PIL.Image.open(bytes_ )
									else:
												A							:					Union[str, Any]              =						PIL.Image.open(BytesIO(bytes_ ) )
									image.load()  # to avoid "Too many open files" errors
									return image







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Union["FeatureType", Dict[str, "FeatureType"]]:
									from .features import Value

									return (
									    self
									    if self.decode
									    else {
									        "bytes": Value("binary" ),
									        "path": Value("string" ),
									    }
									)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Union[pa.StringArray, pa.StructArray, pa.ListArray] )      ->				pa.StructArray:
									if pa.types.is_string(storage.type ):
												A							:					Dict              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.binary() )
												A							:					List[Any]              =						pa.StructArray.from_arrays([bytes_array, storage]	,	["bytes", "path"]	,	mask=storage.is_null() )
									elif pa.types.is_binary(storage.type ):
												A							:					Optional[int]              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.string() )
												A							:					int              =						pa.StructArray.from_arrays([storage, path_array]	,	["bytes", "path"]	,	mask=storage.is_null() )
									elif pa.types.is_struct(storage.type ):
												if storage.type.get_field_index("bytes" ) >= 0:
															A							:					Optional[int]              =						storage.field("bytes" )
												else:
															A							:					Optional[Any]              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.binary() )
												if storage.type.get_field_index("path" ) >= 0:
															A							:					Optional[Any]              =						storage.field("path" )
												else:
															A							:					Dict              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.string() )
												A							:					Optional[Any]              =						pa.StructArray.from_arrays([bytes_array, path_array]	,	["bytes", "path"]	,	mask=storage.is_null() )
									elif pa.types.is_list(storage.type ):
												A							:					int              =						pa.array(
												    [encode_np_array(np.array(__lowerCamelCase ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()]	,	type=pa.binary()	,	)
												A							:					Dict              =						pa.array([None] * len(__lowerCamelCase )	,	type=pa.string() )
												A							:					List[str]              =						pa.StructArray.from_arrays(
												    [bytes_array, path_array]	,	["bytes", "path"]	,	mask=bytes_array.is_null() )
									return array_cast(__lowerCamelCase	,	self.pa_type )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			pa.StructArray )      ->				pa.StructArray:

									@no_op_if_value_is_null
									def path_to_bytes(__lowerCamelCase      :			Optional[int] ):
												with xopen(__lowerCamelCase	,	"rb" ) as f:
															A							:					Union[str, Any]              =						f.read()
												return bytes_

									A							:					int              =						pa.array(
									    [
									        (path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
									        for x in storage.to_pylist()
									    ]	,	type=pa.binary()	,	)
									A							:					List[str]              =						pa.array(
									    [os.path.basename(__lowerCamelCase ) if path is not None else None for path in storage.field("path" ).to_pylist()]	,	type=pa.string()	,	)
									A							:					Dict              =						pa.StructArray.from_arrays([bytes_array, path_array]	,	["bytes", "path"]	,	mask=bytes_array.is_null() )
									return array_cast(__lowerCamelCase	,	self.pa_type )







def 	UpperCAmelCase   (						):
			if config.PIL_AVAILABLE:
						import PIL.Image
			else:
						raise ImportError("To support encoding images, please install 'Pillow'."    )

			global _IMAGE_COMPRESSION_FORMATS
			if _IMAGE_COMPRESSION_FORMATS is None:
						PIL.Image.init()
						A							:					Any              =						list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
			return _IMAGE_COMPRESSION_FORMATS




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			A							:					int              =						BytesIO()
			if image.format in list_image_compression_formats():
						A							:					str              =						image.format
			else:
						A							:					List[Any]              =						"PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
			image.save(_lowerCamelCase ,    format=_lowerCamelCase    )
			return buffer.getvalue()




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			if hasattr(_lowerCamelCase ,    "filename"    ) and image.filename != "":
						return {"path": image.filename, "bytes": None}
			else:
						return {"path": None, "bytes": image_to_bytes(_lowerCamelCase    )}




def 	UpperCAmelCase   (						_lowerCamelCase    ):
			if config.PIL_AVAILABLE:
						import PIL.Image
			else:
						raise ImportError("To support encoding images, please install 'Pillow'."    )

			A							:					Tuple              =						array.dtype
			A							:					Union[str, Any]              =						dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
			A							:					Union[str, Any]              =						dtype.kind
			A							:					List[Any]              =						dtype.itemsize

			A							:					int              =						None

			# Multi-channel array case (only np.dtype("|u1") is allowed)
			if array.shape[2:]:
						A							:					Dict              =						np.dtype("|u1"    )
						if dtype_kind not in ["u", "i"]:
									raise TypeError(
									    f"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."""    )
						if dtype is not dest_dtype:
									warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""    )
    # Exact match
			elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
						A							:					Union[str, Any]              =						dtype
			else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
						while dtype_itemsize >= 1:
									A							:					int              =						dtype_byteorder + dtype_kind + str(_lowerCamelCase    )
									A							:					int              =						np.dtype(_lowerCamelCase    )
									if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
												warnings.warn(f"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"""    )
												break
									else:
												dtype_itemsize //= 2

			if dest_dtype is None:
						raise TypeError(
						    f"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"""    )
			A							:					str              =						PIL.Image.fromarray(array.astype(_lowerCamelCase    )    )
			return {"path": None, "bytes": image_to_bytes(_lowerCamelCase    )}






def 	UpperCAmelCase   (						_lowerCamelCase    ):
			if config.PIL_AVAILABLE:
						import PIL.Image
			else:
						raise ImportError("To support encoding images, please install 'Pillow'."    )

			if objs:
						A       , A							:					Dict              =						first_non_null_value(_lowerCamelCase    )
						if isinstance(_lowerCamelCase ,    _lowerCamelCase    ):
									return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
						if isinstance(_lowerCamelCase ,    np.ndarray    ):
									A							:					int              =						no_op_if_value_is_null(_lowerCamelCase    )
									return [obj_to_image_dict_func(_lowerCamelCase    ) for obj in objs]
						elif isinstance(_lowerCamelCase ,    PIL.Image.Image    ):
									A							:					Optional[Any]              =						no_op_if_value_is_null(_lowerCamelCase    )
									return [obj_to_image_dict_func(_lowerCamelCase    ) for obj in objs]
						else:
									return objs
			else:
						return objs




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (PNDMScheduler,)
						a__   =  (("num_inference_steps", 50),)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			str )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]=0	,	**__lowerCamelCase      :			Any )      ->				Tuple:
									A							:					Dict              =						dict(self.forward_default_kwargs )
									A							:					Dict              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.dummy_sample
									A							:					List[Any]              =						0.1 * sample
									A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Any              =						self.get_scheduler_config(**__lowerCamelCase )
												A							:					int              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )
												# copy over dummy past residuals
												A							:					Tuple              =						dummy_past_residuals[:]

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

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					int              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[str]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]=0	,	**__lowerCamelCase      :			Tuple )      ->				str:
									A							:					List[str]              =						dict(self.forward_default_kwargs )
									A							:					Optional[int]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					List[str]              =						self.dummy_sample
									A							:					Any              =						0.1 * sample
									A							:					Any              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.get_scheduler_config()
												A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )

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

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__lowerCamelCase )
															A							:					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)
															A							:					Optional[Any]              =						dummy_past_residuals[:]

												A							:					Union[str, Any]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					Dict              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Union[str, Any]              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					List[Any]              =						self.get_scheduler_config(**__lowerCamelCase )
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					List[str]              =						10
									A							:					Union[str, Any]              =						self.dummy_model()
									A							:					int              =						self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCamelCase )

									for i, t in enumerate(scheduler.prk_timesteps ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Optional[int]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps ):
												A							:					Tuple              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Any:
									A							:					Union[str, Any]              =						dict(self.forward_default_kwargs )

									A							:					Union[str, Any]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )

									for scheduler_class in self.scheduler_classes:
												A							:					Dict              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												A							:					List[Any]              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

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

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

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						scheduler.step_prk(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Any              =						scheduler.step_plms(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						scheduler.step_plms(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									for timesteps in [1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__lowerCamelCase )

									A							:					Dict              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config(steps_offset=1 )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
									scheduler.set_timesteps(10 )
									assert torch.equal(
									    scheduler.timesteps	,	torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									for beta_start, beta_end in zip([0.0001, 0.001]	,	[0.002, 0.02] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									for t in [1, 5, 10]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									for t, num_inference_steps in zip([1, 5, 10]	,	[10, 50, 1_00] ):
												self.check_over_forward(num_inference_steps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									A							:					str              =						27

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.dummy_sample
												A							:					List[Any]              =						0.1 * sample

												A							:					List[Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.set_timesteps(__lowerCamelCase )

												# 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] ):
															A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									with self.assertRaises(__lowerCamelCase ):
												A							:					Union[str, Any]              =						self.scheduler_classes[0]
												A							:					Union[str, Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									A							:					Optional[Any]              =						self.full_loop()
									A							:					Tuple              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 198.1318 ) < 1e-2
									assert abs(result_mean.item() - 0.2580 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Any              =						self.full_loop(prediction_type="v_prediction" )
									A							:					Union[str, Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					List[str]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 67.3986 ) < 1e-2
									assert abs(result_mean.item() - 0.0878 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				List[str]:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					Dict              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Any              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 230.0399 ) < 1e-2
									assert abs(result_mean.item() - 0.2995 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					List[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 186.9482 ) < 1e-2
									assert abs(result_mean.item() - 0.2434 ) < 1e-3