






import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
													# modified it, it will not be accepted here again, since `auto` values would have been overridden
													_UpperCAmelCase									=			deepcopy(UpperCAmelCase    )
							elif os.path.exists(UpperCAmelCase    ):
													with io.open(UpperCAmelCase       ,    'r'       ,    encoding='utf-8'    ) as f:
																			_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							else:
													try:
																			_UpperCAmelCase									=			baseaa.urlsafe_baadecode(UpperCAmelCase    ).decode('utf-8'    )
																			_UpperCAmelCase									=			json.loads(UpperCAmelCase    )
													except (UnicodeDecodeError, AttributeError, ValueError):
																			raise ValueError(
																			    F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""    )

							_UpperCAmelCase									=			config

							self.set_stage_and_offload()

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_value('zero_optimization.stage'       ,    -1    )

							# offload
							_UpperCAmelCase									=			False
							if self.is_zeroa() or self.is_zeroa():
													_UpperCAmelCase									=			set(['cpu', 'nvme']    )
													_UpperCAmelCase									=			set(
													    [
													        self.get_value('zero_optimization.offload_optimizer.device'    ),
													        self.get_value('zero_optimization.offload_param.device'    ),
													    ]    )
													if len(offload_devices & offload_devices_valid    ) > 0:
																			_UpperCAmelCase									=			True

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.config

							# find the config node of interest if it exists
							_UpperCAmelCase									=			ds_key_long.split('.'    )
							_UpperCAmelCase									=			nodes.pop()
							for node in nodes:
													_UpperCAmelCase									=			config.get(UpperCAmelCase    )
													if config is None:
																			return None, ds_key

							return config, ds_key

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			self.find_config_node(UpperCAmelCase    )
							if config is None:
													return default
							return config.get(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=False    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.config

							# find the config node of interest if it exists
							_UpperCAmelCase									=			ds_key_long.split('.'    )
							for node in nodes:
													_UpperCAmelCase									=			config
													_UpperCAmelCase									=			config.get(UpperCAmelCase    )
													if config is None:
																			if must_exist:
																									raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""    )
																			else:
																									return

        # if found remove it
							if parent_config is not None:
													parent_config.pop(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_value(UpperCAmelCase    )
							return False if value is None else bool(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_value(UpperCAmelCase    )
							return False if value is None else not bool(UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self._stage == 2

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self._stage == 3

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return self._offload





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			engine

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							self.engine.backward(UpperCAmelCase       ,    **UpperCAmelCase    )

							# Deepspeed's `engine.step` performs the following operations:
							# - gradient accumulation check
							# - gradient clipping
							# - optimizer step
							# - zero grad
							# - checking overflow
							# - lr_scheduler step (only if engine.lr_scheduler is not None)
							self.engine.step()
							# and this plugin overrides the above calls with no-ops when Accelerate runs under
							# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
							# training loop that works transparently under many training regimes.





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase       ,    device_placement=UpperCAmelCase       ,    scaler=UpperCAmelCase    )
							_UpperCAmelCase									=			hasattr(self.optimizer       ,    'overflow'    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							if self.__has_overflow__:
													return self.optimizer.overflow
							return False





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0.0_01       ,    UpperCAmelCase=0       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			params
							_UpperCAmelCase									=			lr
							_UpperCAmelCase									=			weight_decay
							_UpperCAmelCase									=			kwargs





class __lowerCamelCase :





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None       ,    UpperCAmelCase=0       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			optimizer
							_UpperCAmelCase									=			total_num_steps
							_UpperCAmelCase									=			warmup_num_steps
							_UpperCAmelCase									=			kwargs


'''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 UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				UNetaDModel
  __UpperCAmelCase				:				KarrasVeScheduler


  def __init__(		self      :     Union[str, Any]							,_a      :     UNetaDModel							,_a      :     KarrasVeScheduler       ):


     '''simple docstring'''






     super().__init__()
     self.register_modules(unet=_a							,scheduler=_a       )


  @torch.no_grad()
  def __call__(		self      :     List[Any]							,_a      :     int = 1							,_a      :     int = 50							,_a      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,_a      :     Optional[str] = "pil"							,_a      :     bool = True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Any			=   self.unet.config.sample_size
     _a				:	Optional[int]			=   (batch_size, 3, img_size, img_size)

     _a				:	Dict			=   self.unet

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

     self.scheduler.set_timesteps(_a       )

     for t in self.progress_bar(self.scheduler.timesteps       ):
        # here sigma_t == t_i from the paper
        _a				:	Optional[int]			=   self.scheduler.schedule[t]
        _a				:	List[str]			=   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				:	List[Any]			=   self.scheduler.add_noise_to_input(_a							,_a							,generator=_a       )

        # 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				:	Optional[int]			=   (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				:	Tuple			=   self.scheduler.step(_a							,_a							,_a							,_a       )

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

     _a				:	Tuple			=   (sample / 2 + 0.5).clamp(0							,1       )
     _a				:	Optional[Any]			=   sample.cpu().permute(0							,2							,3							,1       ).numpy()
     if output_type == "pil":
        _a				:	List[str]			=   self.numpy_to_pil(_a       )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_a       )



