


# 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,
    is_vision_available,
)


__UpperCAmelCase      			=  {
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TvltModel""",
         """TvltForPreTraining""",
         """TvltForAudioVisualClassification""",
         """TvltPreTrainedModel""",
     ]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  ["""TvltImageProcessor"""]


if TYPE_CHECKING:
     from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
     from .processing_tvlt import TvltProcessor
     from .feature_extraction_tvlt import TvltFeatureExtractor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tvlt import (
              TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TvltForAudioVisualClassification,
              TvltForPreTraining,
              TvltModel,
              TvltPreTrainedModel,
          )

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .image_processing_tvlt import TvltImageProcessor


else:
     import sys

     __UpperCAmelCase      			=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







      def lowercase__		( self):
            for schedule in ["linear", "scaled_linear"]:
                  self.check_over_configs(beta_schedule=_UpperCAmelCase)







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







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps					,					device=_UpperCAmelCase)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



