import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     (KDPMaDiscreteScheduler,)
  lowercase_			=     10




  def 	__lowercase(      self     :	str     , **a_     :	str				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		{
           'num_train_timesteps': 1100,
           'beta_start': 0.0001,
           'beta_end': 0.02,
           'beta_schedule': 'linear',
       }

       config.update(**a_				)
       return config




  def 	__lowercase(      self     :	Tuple				)->       str:




       """simple docstring"""
       for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=a_				)




  def 	__lowercase(      self     :	Union[str, Any]				)->       Tuple:




       """simple docstring"""
       for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]     , [0.0002, 0.002, 0.02]				):
            self.check_over_configs(beta_start=a_     , beta_end=a_				)




  def 	__lowercase(      self     :	Tuple				)->       int:




       """simple docstring"""
       for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=a_				)




  def 	__lowercase(      self     :	Optional[int]				)->       Tuple:




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




  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__				:				Any			=		self.get_scheduler_config(prediction_type='v_prediction'				)
       SCREAMING_SNAKE_CASE__				:				int			=		scheduler_class(**a_				)

       scheduler.set_timesteps(self.num_inference_steps				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.dummy_model()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		sample.to(a_				)

       for i, t in enumerate(scheduler.timesteps				):
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		scheduler.scale_model_input(a_     , a_				)

            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_     , a_				)

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		scheduler.step(a_     , a_     , a_				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		output.prev_sample

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				List[str]			=		torch.mean(torch.abs(a_				)				)

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 4.6_9_3_4e-0_7				) < 1e-2
            assert abs(result_mean.item() - 6.1_1_1_2e-1_0				) < 1e-3
       else:
            # CUDA
            assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2e-0_7				) < 1e-2
            assert abs(result_mean.item() - 0.0002				) < 1e-3




  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       if torch_device == "mps":
            return
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		scheduler_class(**a_				)

       scheduler.set_timesteps(self.num_inference_steps				)

       SCREAMING_SNAKE_CASE__				:				str			=		self.dummy_model()
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE__				:				Tuple			=		sample.to(a_				)

       for i, t in enumerate(scheduler.timesteps				):
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		scheduler.scale_model_input(a_     , a_				)

            SCREAMING_SNAKE_CASE__				:				int			=		model(a_     , a_				)

            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scheduler.step(a_     , a_     , a_				)
            SCREAMING_SNAKE_CASE__				:				Tuple			=		output.prev_sample

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				str			=		torch.mean(torch.abs(a_				)				)

       if torch_device in ["cpu", "mps"]:
            assert abs(result_sum.item() - 20.4125				) < 1e-2
            assert abs(result_mean.item() - 0.0266				) < 1e-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125				) < 1e-2
            assert abs(result_mean.item() - 0.0266				) < 1e-3




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       if torch_device == "mps":
            return
       SCREAMING_SNAKE_CASE__				:				Any			=		self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.get_scheduler_config()
       SCREAMING_SNAKE_CASE__				:				int			=		scheduler_class(**a_				)

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

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.dummy_model()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.dummy_sample_deter.to(a_				) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scheduler.scale_model_input(a_     , a_				)

            SCREAMING_SNAKE_CASE__				:				Any			=		model(a_     , a_				)

            SCREAMING_SNAKE_CASE__				:				Dict			=		scheduler.step(a_     , a_     , a_				)
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		output.prev_sample

       SCREAMING_SNAKE_CASE__				:				Any			=		torch.sum(torch.abs(a_				)				)
       SCREAMING_SNAKE_CASE__				:				str			=		torch.mean(torch.abs(a_				)				)

       if str(a_				).startswith('cpu'				):
            # The following sum varies between 148 and 156 on mps. Why?
            assert abs(result_sum.item() - 20.4125				) < 1e-2
            assert abs(result_mean.item() - 0.0266				) < 1e-3
       else:
            # CUDA
            assert abs(result_sum.item() - 20.4125				) < 1e-2
            assert abs(result_mean.item() - 0.0266				) < 1e-3




# 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.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def     _a				(      lowercase__  :			Any ):







     '''simple docstring'''


     return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		gather(lowercase__ )
     assert gathered_tensor.tolist() == list(range(1     ,  state.num_processes**2 + 1 ) )







def     _a				(      lowercase__  :			List[Any] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				str			=		[state.process_index]
     SCREAMING_SNAKE_CASE__				:				Any			=		gather_object(lowercase__ )
     assert len(lowercase__ ) == state.num_processes, f'''{gathered_obj}, {len(lowercase__ )} != {state.num_processes}'''
     assert gathered_obj == list(range(state.num_processes ) ), f'''{gathered_obj} != {list(range(state.num_processes ) )}'''







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		broadcast(lowercase__ )
     assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
     assert broadcasted_tensor.tolist() == list(range(1     ,  state.num_processes + 1 ) )







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.is_main_process:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.arange(state.num_processes + 1 ).to(state.device )
     else:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		torch.arange(state.num_processes ).to(state.device )
     SCREAMING_SNAKE_CASE__				:				Any			=		pad_across_processes(lowercase__ )
     assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
     if not state.is_main_process:
          assert padded_tensor.tolist() == list(range(0     ,  state.num_processes ) ) + [0]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				str			=		reduce(lowercase__     ,  'sum' )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		torch.tensor([4.0, 6] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if state.num_processes != 2:
          return
     SCREAMING_SNAKE_CASE__				:				Any			=		create_tensor(lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		reduce(lowercase__     ,  'mean' )
     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		torch.tensor([2.0, 3] ).to(state.device )
     assert torch.allclose(lowercase__     ,  lowercase__ ), f'''{reduced_tensor} != {truth_tensor}'''







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     main()







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		PartialState()
     state.print(f'''State: {state}''' )
     state.print('testing gather' )
     test_gather(lowercase__ )
     state.print('testing gather_object' )
     test_gather_object(lowercase__ )
     state.print('testing broadcast' )
     test_broadcast(lowercase__ )
     state.print('testing pad_across_processes' )
     test_pad_across_processes(lowercase__ )
     state.print('testing reduce_sum' )
     test_reduce_sum(lowercase__ )
     state.print('testing reduce_mean' )
     test_reduce_mean(lowercase__ )


if __name__ == "__main__":
      main()



