


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     (IPNDMScheduler,)
						lowerCamelCase_         =     (('num_inference_steps', 50),)




						def  _snake_case					(	self						:   Tuple   ,     **__A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									a__ :List[str]						  =    {"num_train_timesteps": 1000}
									config.update(**__A						)
									return config




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Any=0   ,     **__A						:   List[str]						)      ->Any:






									"""simple docstring"""






									a__ :Optional[int]						  =    dict(self.forward_default_kwargs						)
									a__ :Union[str, Any]						  =    kwargs.pop("num_inference_steps"   ,     __A						)
									a__ :Optional[int]						  =    self.dummy_sample
									a__ :List[Any]						  =    0.1 * sample
									a__ :Tuple						  =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												a__ :str						  =    self.get_scheduler_config(**__A						)
												a__ :int						  =    scheduler_class(**__A						)
												scheduler.set_timesteps(__A						)
												# copy over dummy past residuals
												a__ :int						  =    dummy_past_residuals[:]

												if time_step is None:
															a__ :Any						  =    scheduler.timesteps[len(scheduler.timesteps						) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(__A						)
															a__ :str						  =    scheduler_class.from_pretrained(__A						)
															new_scheduler.set_timesteps(__A						)
															# copy over dummy past residuals
															a__ :Optional[Any]						  =    dummy_past_residuals[:]

												a__ :str						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :Dict						  =    new_scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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

												a__ :Optional[int]						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :Dict						  =    new_scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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




						def  _snake_case					(	self						:   List[Any]						)      ->Optional[Any]:






									"""simple docstring"""






									pass




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   Tuple=0   ,     **__A						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    dict(self.forward_default_kwargs						)
									a__ :Tuple						  =    kwargs.pop("num_inference_steps"   ,     __A						)
									a__ :str						  =    self.dummy_sample
									a__ :int						  =    0.1 * sample
									a__ :int						  =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

												# copy over dummy past residuals (must be after setting timesteps)
												a__ :Tuple						  =    dummy_past_residuals[:]

												if time_step is None:
															a__ :Tuple						  =    scheduler.timesteps[len(scheduler.timesteps						) // 2]

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

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

												a__ :Dict						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :List[str]						  =    new_scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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

												a__ :Any						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :Union[str, Any]						  =    new_scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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




						def  _snake_case					(	self						:   List[Any]   ,     **__A						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    self.scheduler_classes[0]
									a__ :str						  =    self.get_scheduler_config(**__A						)
									a__ :int						  =    scheduler_class(**__A						)

									a__ :List[str]						  =    10
									a__ :str						  =    self.dummy_model()
									a__ :int						  =    self.dummy_sample_deter
									scheduler.set_timesteps(__A						)

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

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

									return sample




						def  _snake_case					(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									a__ :List[Any]						  =    dict(self.forward_default_kwargs						)

									a__ :str						  =    kwargs.pop("num_inference_steps"   ,     __A						)

									for scheduler_class in self.scheduler_classes:
												a__ :List[str]						  =    self.get_scheduler_config()
												a__ :Optional[int]						  =    scheduler_class(**__A						)

												a__ :str						  =    self.dummy_sample
												a__ :Optional[int]						  =    0.1 * sample

												if num_inference_steps is not None and hasattr(__A   ,     "set_timesteps"						):
															scheduler.set_timesteps(__A						)
												elif num_inference_steps is not None and not hasattr(__A   ,     "set_timesteps"						):
															a__ :Dict						  =    num_inference_steps

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

												a__ :int						  =    scheduler.timesteps[5]
												a__ :Union[str, Any]						  =    scheduler.timesteps[6]

												a__ :str						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :int						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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

												a__ :Optional[int]						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample
												a__ :List[Any]						  =    scheduler.step(__A   ,     __A   ,     __A   ,     **__A						).prev_sample

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




						def  _snake_case					(	self						:   Optional[Any]						)      ->str:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Optional[int]						)      ->str:






									"""simple docstring"""






									for t, num_inference_steps in zip([1, 5, 10]   ,     [10, 50, 100]						):
												self.check_over_forward(num_inference_steps=__A   ,     time_step=__A						)






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






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.full_loop()
									a__ :Optional[Any]						  =    torch.mean(torch.abs(__A						)						)

									assert abs(result_mean.item() - 2540529						) < 10






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


snake_case__			 =						[
    '''EAGER''',
    '''AOT_EAGER''',
    '''INDUCTOR''',
    '''NVFUSER''',
    '''AOT_NVFUSER''',
    '''AOT_CUDAGRAPHS''',
    '''OFI''',
    '''FX2TRT''',
    '''ONNXRT''',
    '''IPEX''',
]


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   Tuple=None	,  a	:   int=None	,  a	:   List[str]=None				)    ->	int:





			"""simple docstring"""
			a__ :Tuple						  =    True
			while ask_again:
						a__ :str						  =    input(a				)
						try:
									if default is not None and len(a				) == 0:
												return default
									return convert_value(a				) if convert_value is not None else result
						except Exception:
									if error_message is not None:
												print(a				)


def 				lowerCamelCase__	(      a	:   int	,  a	:   Union[str, Any]=[]	,  a	:   str=None	,  a	:   str=0				)    ->	List[str]:





			"""simple docstring"""
			a__ :str						  =    BulletMenu(a	,  a				)
			a__ :int						  =    menu.run(default_choice=a				)
			return convert_value(a				) if convert_value is not None else result


def 				lowerCamelCase__	(      a	:   Optional[Any]				)    ->	Dict:





			"""simple docstring"""
			a__ :str						  =    int(a				)
			return ComputeEnvironment(["LOCAL_MACHINE", "AMAZON_SAGEMAKER"][value]				)


def 				lowerCamelCase__	(      a	:   Tuple				)    ->	List[str]:





			"""simple docstring"""
			a__ :Union[str, Any]						  =    int(a				)
			return DistributedType(["NO", "MULTI_CPU", "MULTI_XPU", "MULTI_GPU", "MULTI_NPU", "TPU"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	str:





			"""simple docstring"""
			a__ :int						  =    int(a				)
			return DynamoBackend(DYNAMO_BACKENDS[value]				).value


def 				lowerCamelCase__	(      a	:   Any				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return PrecisionType(["no", "fp16", "bf16", "fp8"][value]				)


def 				lowerCamelCase__	(      a	:   List[Any]				)    ->	Union[str, Any]:





			"""simple docstring"""
			a__ :Optional[Any]						  =    int(a				)
			return SageMakerDistributedType(["NO", "DATA_PARALLEL", "MODEL_PARALLEL"][value]				)


def 				lowerCamelCase__	(      a	:   Union[str, Any]				)    ->	List[str]:





			"""simple docstring"""
			return {"yes": True, "no": False}[value.lower()]







class 					lowerCAmelCase_ (       argparse.RawDescriptionHelpFormatter):




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Tuple   ,     __A						:   int   ,     __A						:   Tuple   ,     __A						:   Any						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    super()._format_usage(__A   ,     __A   ,     __A   ,     __A						)
									a__ :Any						  =    usage.replace("<command> [<args>] "   ,     ""						)
									return usage


