



import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest


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






					A__				:		Union[str, Any]			       =    (UnCLIPScheduler,)



					def     _a							(     self					:       List[str]				,					**_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    {
								    'num_train_timesteps': 10_00,
								    'variance_type': 'fixed_small_log',
								    'clip_sample': True,
								    'clip_sample_range': 1.0,
								    'prediction_type': 'epsilon',
								}

								config.update(**_snake_case					)
								return config



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=_snake_case					)



					def     _a							(     self					:       str					):






								"""simple docstring"""


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



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






								"""simple docstring"""


								for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=_snake_case					)



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       str					):






								"""simple docstring"""


								for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
														if prev_timestep is not None and prev_timestep >= time_step:
																	continue

														self.check_over_forward(time_step=_snake_case				,					prev_timestep=_snake_case					)



					def     _a							(     self					:       Any					):






								"""simple docstring"""


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

								assert torch.sum(torch.abs(scheduler._get_variance(0					) - 1.00_00E-10					)					) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87					) - 0.054_9625					)					) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99					) - 0.999_4987					)					) < 1E-5



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






								"""simple docstring"""


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

								A__       =    0.5

								assert scheduler._get_variance(1				,					predicted_variance=_snake_case					) - -10.171_2790 < 1E-5
								assert scheduler._get_variance(4_87				,					predicted_variance=_snake_case					) - -5.799_8052 < 1E-5
								assert scheduler._get_variance(9_99				,					predicted_variance=_snake_case					) - -0.001_0011 < 1E-5



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






								"""simple docstring"""


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

								A__       =    scheduler.timesteps

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

								for i, t in enumerate(_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

											A__       =    pred_prev_sample

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

								assert abs(result_sum.item() - 252.268_2495					) < 1E-2
								assert abs(result_mean.item() - 0.328_4743					) < 1E-3



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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

								scheduler.set_timesteps(25					)

								A__       =    scheduler.timesteps

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

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

											if i + 1 == timesteps.shape[0]:
														A__       =    None
											else:
														A__       =    timesteps[i + 1]

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

											A__       =    pred_prev_sample

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

								assert abs(result_sum.item() - 258.204_4983					) < 1E-2
								assert abs(result_mean.item() - 0.336_2038					) < 1E-3



					def     _a							(     self					:       str					):






								"""simple docstring"""


								pass



					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								pass









# 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)




