"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest


class 				a (						_SCREAMING_SNAKE_CASE      ):
    _snake_case   :     Optional[Any]   							=							(IPNDMScheduler,)
    _snake_case   :     Any   							=							(('num_inference_steps', 50),)


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , **__lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					{"""num_train_timesteps""": 1000}
      config.update(**__lowerCAmelCase    )
      return config


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     Tuple=0      , **__lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					dict(self.forward_default_kwargs    )
      _UpperCAmelCase         =					kwargs.pop("""num_inference_steps"""      , __lowerCAmelCase    )
      _UpperCAmelCase         =					self.dummy_sample
      _UpperCAmelCase         =					0.1 * sample
      _UpperCAmelCase         =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        _UpperCAmelCase         =					self.get_scheduler_config(**__lowerCAmelCase    )
        _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )
        scheduler.set_timesteps(__lowerCAmelCase    )
        # copy over dummy past residuals
        _UpperCAmelCase         =					dummy_past_residuals[:]

        if time_step is None:
          _UpperCAmelCase         =					scheduler.timesteps[len(scheduler.timesteps    ) // 2]

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

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					new_scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

        assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					new_scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

        assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"


    def    lowerCAmelCase_     (    self		:     str    ):
      pass


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     int=0      , **__lowerCAmelCase		:     Any    ):
      _UpperCAmelCase         =					dict(self.forward_default_kwargs    )
      _UpperCAmelCase         =					kwargs.pop("""num_inference_steps"""      , __lowerCAmelCase    )
      _UpperCAmelCase         =					self.dummy_sample
      _UpperCAmelCase         =					0.1 * sample
      _UpperCAmelCase         =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        _UpperCAmelCase         =					self.get_scheduler_config()
        _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )
        scheduler.set_timesteps(__lowerCAmelCase    )

        # copy over dummy past residuals (must be after setting timesteps)
        _UpperCAmelCase         =					dummy_past_residuals[:]

        if time_step is None:
          _UpperCAmelCase         =					scheduler.timesteps[len(scheduler.timesteps    ) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(__lowerCAmelCase    )
          _UpperCAmelCase         =					scheduler_class.from_pretrained(__lowerCAmelCase    )
          # copy over dummy past residuals
          new_scheduler.set_timesteps(__lowerCAmelCase    )

          # copy over dummy past residual (must be after setting timesteps)
          _UpperCAmelCase         =					dummy_past_residuals[:]

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					new_scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

        assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					new_scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

        assert torch.sum(torch.abs(output - new_output    )    ) < 1e-5, "Scheduler outputs are not identical"


    def    lowerCAmelCase_     (    self		:     Optional[int]      , **__lowerCAmelCase		:     Optional[Any]    ):
      _UpperCAmelCase         =					self.scheduler_classes[0]
      _UpperCAmelCase         =					self.get_scheduler_config(**__lowerCAmelCase    )
      _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

      _UpperCAmelCase         =					10
      _UpperCAmelCase         =					self.dummy_model()
      _UpperCAmelCase         =					self.dummy_sample_deter
      scheduler.set_timesteps(__lowerCAmelCase    )

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

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

      return sample


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					dict(self.forward_default_kwargs    )

      _UpperCAmelCase         =					kwargs.pop("""num_inference_steps"""      , __lowerCAmelCase    )

      for scheduler_class in self.scheduler_classes:
        _UpperCAmelCase         =					self.get_scheduler_config()
        _UpperCAmelCase         =					scheduler_class(**__lowerCAmelCase    )

        _UpperCAmelCase         =					self.dummy_sample
        _UpperCAmelCase         =					0.1 * sample

        if num_inference_steps is not None and hasattr(__lowerCAmelCase      , """set_timesteps"""    ):
          scheduler.set_timesteps(__lowerCAmelCase    )
        elif num_inference_steps is not None and not hasattr(__lowerCAmelCase      , """set_timesteps"""    ):
          _UpperCAmelCase         =					num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        _UpperCAmelCase         =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
        _UpperCAmelCase         =					dummy_past_residuals[:]

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

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

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

        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample
        _UpperCAmelCase         =					scheduler.step(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , **__lowerCAmelCase    ).prev_sample

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


    def    lowerCAmelCase_     (    self		:     Any    ):
      for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=__lowerCAmelCase      , time_step=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      for t, num_inference_steps in zip([1, 5, 10]      , [10, 50, 100]    ):
        self.check_over_forward(num_inference_steps=__lowerCAmelCase      , time_step=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     int    ):
      _UpperCAmelCase         =					self.full_loop()
      _UpperCAmelCase         =					torch.mean(torch.abs(__lowerCAmelCase    )    )

      assert abs(result_mean.item() - 254_0529    ) < 10




'''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


__A					:  Dict											=      {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A					:  str											=      ["TimmBackbone"]


if TYPE_CHECKING:
  from .configuration_timm_backbone import TimmBackboneConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timm_backbone import TimmBackbone

else:
  import sys

  __A					:  Dict											=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



