



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest





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



     _A       =			(UniPCMultistepScheduler,)
     _A       =			(('num_inference_steps', 25),)







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		{
               "num_train_timesteps": 10_00,
               "beta_start": 0.0001,
               "beta_end": 0.02,
               "beta_schedule": "linear",
               "solver_order": 2,
               "solver_type": "bh2",
           }

           config.update(**__a    )
           return config







     def 						_a      (self				,			__a=0				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )
           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )
           lowerCamelCase								=		self.dummy_sample
           lowerCamelCase								=		0.1 * sample
           lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )
                 scheduler.set_timesteps(__a    )
                 # copy over dummy past residuals
                 lowerCamelCase								=		dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a    )
                       lowerCamelCase								=		scheduler_class.from_pretrained(__a    )
                       new_scheduler.set_timesteps(__a    )
                       # copy over dummy past residuals
                       lowerCamelCase								=		dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase			,							lowerCamelCase								=		sample, sample
                 for t in range(__a				,			time_step + scheduler.config.solver_order + 1    ):
                       lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                       lowerCamelCase								=		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 						_a      (self				,			__a=0				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )
           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )
           lowerCamelCase								=		self.dummy_sample
           lowerCamelCase								=		0.1 * sample
           lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config()
                 lowerCamelCase								=		scheduler_class(**__a    )
                 scheduler.set_timesteps(__a    )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase								=		dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(__a    )
                       lowerCamelCase								=		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)
                       lowerCamelCase								=		dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                 lowerCamelCase								=		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 						_a      (self				,			__a=None				,			**__a    ):






           '''simple docstring'''



           if scheduler is None:
                 lowerCamelCase								=		self.scheduler_classes[0]
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )

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

           lowerCamelCase								=		10
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter
           scheduler.set_timesteps(__a    )

           for i, t in enumerate(scheduler.timesteps    ):
                 lowerCamelCase								=		model(__a				,			__a    )
                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a    ).prev_sample

           return sample







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		dict(self.forward_default_kwargs    )

           lowerCamelCase								=		kwargs.pop("num_inference_steps"				,			__a    )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config()
                 lowerCamelCase								=		scheduler_class(**__a    )

                 lowerCamelCase								=		self.dummy_sample
                 lowerCamelCase								=		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"    ):
                       lowerCamelCase								=		num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase								=		[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowerCamelCase								=		dummy_past_residuals[: scheduler.config.solver_order]

                 lowerCamelCase								=		scheduler.timesteps[5]
                 lowerCamelCase								=		scheduler.timesteps[6]

                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample
                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a				,			**__a    ).prev_sample

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







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		UniPCMultistepScheduler(**self.get_scheduler_config()    )
           lowerCamelCase								=		self.full_loop(scheduler=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2464    ) < 1E-3

           lowerCamelCase								=		DPMSolverSinglestepScheduler.from_config(scheduler.config    )
           lowerCamelCase								=		DEISMultistepScheduler.from_config(scheduler.config    )
           lowerCamelCase								=		DPMSolverMultistepScheduler.from_config(scheduler.config    )
           lowerCamelCase								=		UniPCMultistepScheduler.from_config(scheduler.config    )

           lowerCamelCase								=		self.full_loop(scheduler=__a    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2464    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                 self.check_over_configs(num_train_timesteps=__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(thresholding=__a    )
           for order in [1, 2, 3]:
                 for solver_type in ["bh1", "bh2"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=__a				,			prediction_type=__a				,			sample_max_value=__a				,			solver_order=__a				,			solver_type=__a				,			)







     def 						_a      (self    ):






           '''simple docstring'''



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







     def 						_a      (self    ):






           '''simple docstring'''



           for solver_type in ["bh1", "bh2"]:
                 for order in [1, 2, 3]:
                       for prediction_type in ["epsilon", "sample"]:
                             self.check_over_configs(
                                 solver_order=__a				,			solver_type=__a				,			prediction_type=__a				,			)
                             lowerCamelCase								=		self.full_loop(
                                 solver_order=__a				,			solver_type=__a				,			prediction_type=__a				,			)
                             assert not torch.isnan(__a    ).any(), "Samples have nan numbers"







     def 						_a      (self    ):






           '''simple docstring'''



           self.check_over_configs(lower_order_final=__a    )
           self.check_over_configs(lower_order_final=__a    )







     def 						_a      (self    ):






           '''simple docstring'''



           for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                 self.check_over_forward(num_inference_steps=__a				,			time_step=0    )







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop()
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.2464    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.full_loop(prediction_type="v_prediction"    )
           lowerCamelCase								=		torch.mean(torch.abs(__a    )    )

           assert abs(result_mean.item() - 0.1014    ) < 1E-3







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		self.scheduler_classes[0]
           lowerCamelCase								=		self.get_scheduler_config(thresholding=__a				,			dynamic_thresholding_ratio=0    )
           lowerCamelCase								=		scheduler_class(**__a    )

           lowerCamelCase								=		10
           lowerCamelCase								=		self.dummy_model()
           lowerCamelCase								=		self.dummy_sample_deter.half()
           scheduler.set_timesteps(__a    )

           for i, t in enumerate(scheduler.timesteps    ):
                 lowerCamelCase								=		model(__a				,			__a    )
                 lowerCamelCase								=		scheduler.step(__a				,			__a				,			__a    ).prev_sample

           assert sample.dtype == torch.floataa







     def 						_a      (self				,			**__a    ):






           '''simple docstring'''



           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase								=		self.get_scheduler_config(**__a    )
                 lowerCamelCase								=		scheduler_class(**__a    )

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps    )
                 assert len(scheduler.timesteps.unique()    ) == scheduler.num_inference_steps




# 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


a_						:     Tuple	     =		{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     List[Any]	     =		[
         'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XmodForCausalLM',
         'XmodForMaskedLM',
         'XmodForMultipleChoice',
         'XmodForQuestionAnswering',
         'XmodForSequenceClassification',
         'XmodForTokenClassification',
         'XmodModel',
         'XmodPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xmod import (
              XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
              XmodForCausalLM,
              XmodForMaskedLM,
              XmodForMultipleChoice,
              XmodForQuestionAnswering,
              XmodForSequenceClassification,
              XmodForTokenClassification,
              XmodModel,
              XmodPreTrainedModel,
          )

else:
     import sys

     a_						:     str	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)