



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		int			       =    (DDPMScheduler,)



					def     _a							(     self					:       Optional[Any]				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    {
								    'num_train_timesteps': 10_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'variance_type': 'fixed_small',
								    'clip_sample': True,
								}

								config.update(**_snake_case					)
								return config



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=_snake_case					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]				,					[0.002, 0.02, 0.2, 2]					):
											self.check_over_configs(beta_start=_snake_case				,					beta_end=_snake_case					)



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								for variance in ["fixed_small", "fixed_large", "other"]:
											self.check_over_configs(variance_type=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								self.check_over_configs(thresholding=_snake_case					)
								for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample", "v_prediction"]:
														self.check_over_configs(
														    thresholding=_snake_case				,					prediction_type=_snake_case				,					sample_max_value=_snake_case				,					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								for t in [0, 5_00, 9_99]:
											self.check_over_forward(time_step=_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								assert torch.sum(torch.abs(scheduler._get_variance(0					) - 0.0					)					) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.0_0979					)					) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.02					)					) < 1E-5



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    len(_snake_case					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter
								A__       =    torch.manual_seed(0					)

								for t in reversed(range(_snake_case					)					):
											# 1. predict noise residual
											A__       =    model(_snake_case				,					_snake_case					)

											# 2. predict previous mean of sample x_t-1
											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case				,					generator=_snake_case					).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											A__       =    pred_prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								assert abs(result_sum.item() - 258.9606					) < 1E-2
								assert abs(result_mean.item() - 0.3372					) < 1E-3



					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config(prediction_type='v_prediction'					)
								A__       =    scheduler_class(**_snake_case					)

								A__       =    len(_snake_case					)

								A__       =    self.dummy_model()
								A__       =    self.dummy_sample_deter
								A__       =    torch.manual_seed(0					)

								for t in reversed(range(_snake_case					)					):
											# 1. predict noise residual
											A__       =    model(_snake_case				,					_snake_case					)

											# 2. predict previous mean of sample x_t-1
											A__       =    scheduler.step(_snake_case				,					_snake_case				,					_snake_case				,					generator=_snake_case					).prev_sample

											# if t > 0:
											#     noise = self.dummy_sample_deter
											#     variance = scheduler.get_variance(t) ** (0.5) * noise
											#
											# sample = pred_prev_sample + variance
											A__       =    pred_prev_sample

								A__       =    torch.sum(torch.abs(_snake_case					)					)
								A__       =    torch.mean(torch.abs(_snake_case					)					)

								assert abs(result_sum.item() - 202.0296					) < 1E-2
								assert abs(result_mean.item() - 0.2631					) < 1E-3



					def     _a							(     self					:       Any					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [1_00, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=_snake_case					)

								A__       =    scheduler.timesteps

								for i, timestep in enumerate(_snake_case					):
											if i == len(_snake_case					) - 1:
														A__       =    -1
											else:
														A__       =    timesteps[i + 1]

											A__       =    scheduler.previous_timestep(_snake_case					)
											A__       =    prev_t.item()

											self.assertEqual(_snake_case				,					_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [1_00, 87, 50, 51, 0]

								with self.assertRaises(_snake_case				,					msg='`custom_timesteps` must be in descending order.'					):
											scheduler.set_timesteps(timesteps=_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [1_00, 87, 50, 1, 0]
								A__       =    len(_snake_case					)

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



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.scheduler_classes[0]
								A__       =    self.get_scheduler_config()
								A__       =    scheduler_class(**_snake_case					)

								A__       =    [scheduler.config.num_train_timesteps]

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









# Copyright 2023 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.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       =      {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




