import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class       lowercase__		(     _snake_case):
						UpperCamelCase_  =	(PNDMScheduler,)
						UpperCamelCase_  =	(("""num_inference_steps""", 50),)


						def  __A ( self			:       Optional[int]					,	**UpperCamelCase__			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							{
												    "num_train_timesteps": 1000,
												    "beta_start": 0.0001,
												    "beta_end": 0.02,
												    "beta_schedule": "linear",
												}

												config.update(**snake_case_		)
												return config


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Tuple=0					,	**UpperCamelCase__			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							dict(self.forward_default_kwargs		)
												SCREAMING_SNAKE_CASE							:					Dict           =							kwargs.pop('''num_inference_steps'''					,	snake_case_		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.dummy_sample
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							0.1 * sample
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

																		SCREAMING_SNAKE_CASE							:					Tuple           =							scheduler.step_plms(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample
																		SCREAMING_SNAKE_CASE							:					str           =							new_scheduler.step_plms(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

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


						def  __A ( self			:       str		):





												'''simple docstring'''





												pass


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							dict(self.forward_default_kwargs		)
												SCREAMING_SNAKE_CASE							:					str           =							kwargs.pop('''num_inference_steps'''					,	snake_case_		)
												SCREAMING_SNAKE_CASE							:					str           =							self.dummy_sample
												SCREAMING_SNAKE_CASE							:					List[Any]           =							0.1 * sample
												SCREAMING_SNAKE_CASE							:					Tuple           =							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

																		with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(snake_case_		)
																								SCREAMING_SNAKE_CASE							:					Tuple           =							scheduler_class.from_pretrained(snake_case_		)
																								# copy over dummy past residuals
																								new_scheduler.set_timesteps(snake_case_		)

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

																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							scheduler.step_prk(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample
																		SCREAMING_SNAKE_CASE							:					str           =							new_scheduler.step_prk(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

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

																		SCREAMING_SNAKE_CASE							:					List[str]           =							scheduler.step_plms(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							new_scheduler.step_plms(snake_case_					,	snake_case_					,	snake_case_					,	**snake_case_		).prev_sample

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


						def  __A ( self			:       str					,	**UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.get_scheduler_config(**snake_case_		)
												SCREAMING_SNAKE_CASE							:					int           =							scheduler_class(**snake_case_		)

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

												for i, t in enumerate(scheduler.prk_timesteps		):
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							model(snake_case_					,	snake_case_		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							scheduler.step_prk(snake_case_					,	snake_case_					,	snake_case_		).prev_sample

												for i, t in enumerate(scheduler.plms_timesteps		):
																		SCREAMING_SNAKE_CASE							:					str           =							model(snake_case_					,	snake_case_		)
																		SCREAMING_SNAKE_CASE							:					Tuple           =							scheduler.step_plms(snake_case_					,	snake_case_					,	snake_case_		).prev_sample

												return sample


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							dict(self.forward_default_kwargs		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							kwargs.pop('''num_inference_steps'''					,	snake_case_		)

												for scheduler_class in self.scheduler_classes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							scheduler_class(**snake_case_		)

																		SCREAMING_SNAKE_CASE							:					Tuple           =							self.dummy_sample
																		SCREAMING_SNAKE_CASE							:					Tuple           =							0.1 * sample

																		if num_inference_steps is not None and hasattr(snake_case_					,	'''set_timesteps'''		):
																								scheduler.set_timesteps(snake_case_		)
																		elif num_inference_steps is not None and not hasattr(snake_case_					,	'''set_timesteps'''		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_inference_steps

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

																		SCREAMING_SNAKE_CASE							:					Dict           =							scheduler.step_prk(snake_case_					,	0					,	snake_case_					,	**snake_case_		).prev_sample
																		SCREAMING_SNAKE_CASE							:					Any           =							scheduler.step_prk(snake_case_					,	1					,	snake_case_					,	**snake_case_		).prev_sample

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

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler.step_plms(snake_case_					,	0					,	snake_case_					,	**snake_case_		).prev_sample
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							scheduler.step_plms(snake_case_					,	1					,	snake_case_					,	**snake_case_		).prev_sample

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


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												for steps_offset in [0, 1]:
																		self.check_over_configs(steps_offset=snake_case_		)

												SCREAMING_SNAKE_CASE							:					str           =							self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE							:					int           =							self.get_scheduler_config(steps_offset=1		)
												SCREAMING_SNAKE_CASE							:					str           =							scheduler_class(**snake_case_		)
												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  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												for beta_start, beta_end in zip([0.0001, 0.001]					,	[0.002, 0.02]		):
																		self.check_over_configs(beta_start=snake_case_					,	beta_end=snake_case_		)


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





												'''simple docstring'''





												for schedule in ["linear", "squaredcos_cap_v2"]:
																		self.check_over_configs(beta_schedule=snake_case_		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





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


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												for t in [1, 5, 10]:
																		self.check_over_forward(time_step=snake_case_		)


						def  __A ( self			:       Any		):





												'''simple docstring'''





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


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							27

												for scheduler_class in self.scheduler_classes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.dummy_sample
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							0.1 * sample

																		SCREAMING_SNAKE_CASE							:					int           =							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							scheduler_class(**snake_case_		)

																		scheduler.set_timesteps(snake_case_		)

																		# 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]		):
																								SCREAMING_SNAKE_CASE							:					List[str]           =							scheduler.step_prk(snake_case_					,	snake_case_					,	snake_case_		).prev_sample


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												with self.assertRaises(snake_case_		):
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.scheduler_classes[0]
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.get_scheduler_config()
																		SCREAMING_SNAKE_CASE							:					Any           =							scheduler_class(**snake_case_		)

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


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





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.full_loop()
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.sum(torch.abs(snake_case_		)		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.mean(torch.abs(snake_case_		)		)

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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.full_loop(prediction_type='''v_prediction'''		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sum(torch.abs(snake_case_		)		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.mean(torch.abs(snake_case_		)		)

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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.full_loop(set_alpha_to_one=snake_case_					,	beta_start=0.01		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sum(torch.abs(snake_case_		)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							torch.mean(torch.abs(snake_case_		)		)

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



						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.full_loop(set_alpha_to_one=snake_case_					,	beta_start=0.01		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.sum(torch.abs(snake_case_		)		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.mean(torch.abs(snake_case_		)		)

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






# 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


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





