"""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.

import torch

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



def 					_lowerCamelCase(					a							):
       return (torch.arange(state.num_processes							) + 1.0 + (state.num_processes * state.process_index)).to(state.device							)



def 					_lowerCamelCase(					a							):
       __a     				=  create_tensor(a							)
       __a     				=  gather(a							)
       assert gathered_tensor.tolist() == list(range(1 ,  state.num_processes**2 + 1							)							)



def 					_lowerCamelCase(					a							):
       __a     				=  [state.process_index]
       __a     				=  gather_object(a							)
       assert len(a							) == state.num_processes, F"{gathered_obj}, {len(a							)} != {state.num_processes}"
       assert gathered_obj == list(range(state.num_processes							)							), F"{gathered_obj} != {list(range(state.num_processes							)							)}"



def 					_lowerCamelCase(					a							):
       __a     				=  create_tensor(a							)
       __a     				=  broadcast(a							)
       assert broadcasted_tensor.shape == torch.Size([state.num_processes]							)
       assert broadcasted_tensor.tolist() == list(range(1 ,  state.num_processes + 1							)							)



def 					_lowerCamelCase(					a							):
       # We need to pad the tensor with one more element if we are the main process
       # to ensure that we can pad
       if state.is_main_process:
              __a     				=  torch.arange(state.num_processes + 1							).to(state.device							)
       else:
              __a     				=  torch.arange(state.num_processes							).to(state.device							)
       __a     				=  pad_across_processes(a							)
       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 					_lowerCamelCase(					a							):
       # For now runs on only two processes
       if state.num_processes != 2:
              return
       __a     				=  create_tensor(a							)
       __a     				=  reduce(a ,  "sum"							)
       __a     				=  torch.tensor([4.0, 6]							).to(state.device							)
       assert torch.allclose(a ,  a							), F"{reduced_tensor} != {truth_tensor}"



def 					_lowerCamelCase(					a							):
       # For now runs on only two processes
       if state.num_processes != 2:
              return
       __a     				=  create_tensor(a							)
       __a     				=  reduce(a ,  "mean"							)
       __a     				=  torch.tensor([2.0, 3]							).to(state.device							)
       assert torch.allclose(a ,  a							), F"{reduced_tensor} != {truth_tensor}"



def 					_lowerCamelCase(					a							):
       # For xla_spawn (TPUs)
       main()



def 					_lowerCamelCase(					):
       __a     				=  PartialState()
       state.print(F"State: {state}"							)
       state.print("testing gather"							)
       test_gather(a							)
       state.print("testing gather_object"							)
       test_gather_object(a							)
       state.print("testing broadcast"							)
       test_broadcast(a							)
       state.print("testing pad_across_processes"							)
       test_pad_across_processes(a							)
       state.print("testing reduce_sum"							)
       test_reduce_sum(a							)
       state.print("testing reduce_mean"							)
       test_reduce_mean(a							)


if __name__ == "__main__":
   main()

"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			UNetaDModel
 _snake_case   :			KarrasVeScheduler


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__()
        self.register_modules(unet=lowerCamelCase  ,						scheduler=lowerCamelCase		)






 @torch.no_grad()
 def __call__(				self  ,						lowerCamelCase = 1  ,						lowerCamelCase = 50  ,						lowerCamelCase = None  ,						lowerCamelCase = "pil"  ,						lowerCamelCase = True  ,						**lowerCamelCase  ,						):
        __a     				=  self.unet.config.sample_size
        __a     				=  (batch_size, 3, img_size, img_size)

        __a     				=  self.unet

        # sample x_0 ~ N(0, sigma_0^2 * I)
        __a     				=  randn_tensor(lowerCamelCase  ,						generator=lowerCamelCase  ,						device=self.device		) * self.scheduler.init_noise_sigma

        self.scheduler.set_timesteps(lowerCamelCase		)

        for t in self.progress_bar(self.scheduler.timesteps		):
               # here sigma_t == t_i from the paper
               __a     				=  self.scheduler.schedule[t]
               __a     				=  self.scheduler.schedule[t - 1] if t > 0 else 0

               # 1. Select temporarily increased noise level sigma_hat
               # 2. Add new noise to move from sample_i to sample_hat
               __a				, __a     				=  self.scheduler.add_noise_to_input(lowerCamelCase  ,						lowerCamelCase  ,						generator=lowerCamelCase		)

               # 3. Predict the noise residual given the noise magnitude `sigma_hat`
               # The model inputs and output are adjusted by following eq. (213) in [1].
               __a     				=  (sigma_hat / 2) * model((sample_hat + 1) / 2  ,						sigma_hat / 2		).sample

               # 4. Evaluate dx/dt at sigma_hat
               # 5. Take Euler step from sigma to sigma_prev
               __a     				=  self.scheduler.step(lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		)

               if sigma_prev != 0:
                      # 6. Apply 2nd order correction
                      # The model inputs and output are adjusted by following eq. (213) in [1].
                      __a     				=  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2  ,						sigma_prev / 2		).sample
                      __a     				=  self.scheduler.step_correct(
                          lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						step_output.prev_sample  ,						step_output["derivative"]  ,						)
               __a     				=  step_output.prev_sample

        __a     				=  (sample / 2 + 0.5).clamp(0  ,						1		)
        __a     				=  sample.cpu().permute(0  ,						2  ,						3  ,						1		).numpy()
        if output_type == "pil":
               __a     				=  self.numpy_to_pil(lowerCamelCase		)

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCamelCase		)
