
"""simple docstring"""




# 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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__				=					{
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__				=					[
						    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'MgpstrModel',
						    'MgpstrPreTrainedModel',
						    'MgpstrForSceneTextRecognition',
						]

if TYPE_CHECKING:
						from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
						from .processing_mgp_str import MgpstrProcessor
						from .tokenization_mgp_str import MgpstrTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mgp_str import (
												    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MgpstrForSceneTextRecognition,
												    MgpstrModel,
												    MgpstrPreTrainedModel,
												)
else:
						import sys

						lowerCAmelCase__				=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







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

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""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=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

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

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







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

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

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

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

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

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

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass