

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

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



class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Tuple:






         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
                A						:     List[Any]         =deepcopy(__UpperCamelCase     )
         elif os.path.exists(__UpperCamelCase     ):
                with io.open(__UpperCamelCase      ,	'r'      ,	encoding='utf-8'     ) as f:
                       A						:     str         =json.load(__UpperCamelCase     )
         else:
                try:
                       A						:     str         =baseaa.urlsafe_baadecode(__UpperCamelCase     ).decode('utf-8'     )
                       A						:     Union[str, Any]         =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}'     )

         A						:     Any         =config

         self.set_stage_and_offload()







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Optional[int]:
         A						:     Optional[Any]         =self.get_value('zero_optimization.stage'      ,	-1     )

         # offload
         A						:     Union[str, Any]         =False
         if self.is_zeroa() or self.is_zeroa():
                A						:     Optional[int]         =set(['cpu', 'nvme']     )
                A						:     Tuple         =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:
                       A						:     Optional[Any]         =True







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				str:
         A						:     Any         =self.config

         # find the config node of interest if it exists
         A						:     int         =ds_key_long.split('.'     )
         A						:     Union[str, Any]         =nodes.pop()
         for node in nodes:
                A						:     Any         =config.get(__UpperCamelCase     )
                if config is None:
                       return None, ds_key

         return config, ds_key







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					List[Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict=None     )    ->				Optional[Any]:
         A  ,  A						:     List[str]         =self.find_config_node(__UpperCamelCase     )
         if config is None:
                return default
         return config.get(__UpperCamelCase      ,	__UpperCamelCase     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=False     )    ->				List[Any]:
         A						:     List[str]         =self.config

         # find the config node of interest if it exists
         A						:     Union[str, Any]         =ds_key_long.split('.'     )
         for node in nodes:
                A						:     Optional[int]         =config
                A						:     Tuple         =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 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					str     )    ->				Union[str, Any]:
         A						:     List[Any]         =self.get_value(__UpperCamelCase     )
         return False if value is None else bool(__UpperCamelCase     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Dict     )    ->				str:
         A						:     str         =self.get_value(__UpperCamelCase     )
         return False if value is None else not bool(__UpperCamelCase     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				List[Any]:
         return self._stage == 2







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Dict:
         return self._stage == 3







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				List[str]:
         return self._offload







class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Any     )    ->				Optional[int]:
         A						:     Dict         =engine







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	**SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				str:
         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  SCREAMING_SNAKE_CASE_ (					_UpperCAmelCase			):
  '''simple docstring'''


  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					Tuple     )    ->				Union[str, Any]:
         super().__init__(__UpperCamelCase      ,	device_placement=__UpperCamelCase      ,	scaler=__UpperCamelCase     )
         A						:     int         =hasattr(self.optimizer      ,	'overflow'     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict      ,	SCREAMING_SNAKE_CASE__    :					str=None     )    ->				Optional[int]:
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				List[str]:
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Union[str, Any]:






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







class  SCREAMING_SNAKE_CASE_ (					_UpperCAmelCase			):
  '''simple docstring'''


  def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				List[Any]:
         super().__init__(__UpperCamelCase      ,	__UpperCamelCase     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				Any:
         pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed







class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					int=0.0_0_1      ,	SCREAMING_SNAKE_CASE__    :					Dict=0      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				Any:
         A						:     Union[str, Any]         =params
         A						:     Dict         =lr
         A						:     Any         =weight_decay
         A						:     Optional[Any]         =kwargs







class  SCREAMING_SNAKE_CASE_ :
  '''simple docstring'''


  def __init__(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					Tuple=None      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0      ,	**SCREAMING_SNAKE_CASE__    :					List[str]     )    ->				int:
         A						:     List[str]         =optimizer
         A						:     Optional[Any]         =total_num_steps
         A						:     Union[str, Any]         =warmup_num_steps
         A						:     Union[str, Any]         =kwargs







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_   ,						unittest.TestCase			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				DDIMPipeline
  lowercase		:     int            =				UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  lowercase		:     Optional[Any]            =				PipelineTesterMixin.required_optional_params - {
      "num_images_per_prompt",
      "latents",
      "callback",
      "callback_steps",
  }
  lowercase		:     Optional[Any]            =				UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
  lowercase		:     Union[str, Any]            =				False







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				Optional[int]:
         torch.manual_seed(0     )
         A						:     str         =UNetaDModel(
             block_out_channels=(32, 64)      ,	layers_per_block=2      ,	sample_size=32      ,	in_channels=3      ,	out_channels=3      ,	down_block_types=('DownBlock2D', 'AttnDownBlock2D')      ,	up_block_types=('AttnUpBlock2D', 'UpBlock2D')      ,	)
         A						:     Optional[int]         =DDIMScheduler()
         A						:     Optional[Any]         ={'unet': unet, 'scheduler': scheduler}
         return components







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Any      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0     )    ->				Any:
         if str(SCREAMING_SNAKE_CASE__     ).startswith('mps'     ):
                A						:     List[Any]         =torch.manual_seed(SCREAMING_SNAKE_CASE__     )
         else:
                A						:     Union[str, Any]         =torch.Generator(device=SCREAMING_SNAKE_CASE__     ).manual_seed(SCREAMING_SNAKE_CASE__     )
         A						:     Optional[int]         ={
             'batch_size': 1,
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }
         return inputs







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				List[Any]:
         A						:     Union[str, Any]         ='cpu'

         A						:     Tuple         =self.get_dummy_components()
         A						:     Union[str, Any]         =self.pipeline_class(**SCREAMING_SNAKE_CASE__     )
         pipe.to(SCREAMING_SNAKE_CASE__     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     str         =self.get_dummy_inputs(SCREAMING_SNAKE_CASE__     )
         A						:     str         =pipe(**SCREAMING_SNAKE_CASE__     ).images
         A						:     Optional[Any]         =image[0, -3:, -3:, -1]

         self.assertEqual(image.shape      ,	(1, 32, 32, 3)     )
         A						:     Optional[Any]         =np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]     )
         A						:     str         =np.abs(image_slice.flatten() - expected_slice     ).max()
         self.assertLessEqual(SCREAMING_SNAKE_CASE__      ,	1e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				Dict:
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				List[Any]:
         super().test_save_load_local(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[Any]     )    ->				Tuple:
         super().test_save_load_optional_components(expected_max_difference=3e-3     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Dict     )    ->				Tuple:
         super().test_inference_batch_single_identical(expected_max_diff=3e-3     )







@slow
@require_torch_gpu
class  SCREAMING_SNAKE_CASE_ (					unittest.TestCase			):
  '''simple docstring'''


  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				Dict:
         A						:     Any         ='google/ddpm-cifar10-32'

         A						:     Optional[int]         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     Tuple         =DDIMScheduler()

         A						:     int         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddim.to(SCREAMING_SNAKE_CASE__     )
         ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =torch.manual_seed(0     )
         A						:     Optional[Any]         =ddim(generator=SCREAMING_SNAKE_CASE__      ,	eta=0.0      ,	output_type='numpy'     ).images

         A						:     str         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A						:     Tuple         =np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







  def 		SCREAMING_SNAKE_CASE_		(				self    :					List[str]     )    ->				int:
         A						:     Optional[int]         ='google/ddpm-ema-bedroom-256'

         A						:     str         =UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
         A						:     str         =DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE__     )

         A						:     Tuple         =DDIMPipeline(unet=SCREAMING_SNAKE_CASE__      ,	scheduler=SCREAMING_SNAKE_CASE__     )
         ddpm.to(SCREAMING_SNAKE_CASE__     )
         ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__     )

         A						:     Any         =torch.manual_seed(0     )
         A						:     Optional[int]         =ddpm(generator=SCREAMING_SNAKE_CASE__      ,	output_type='numpy'     ).images

         A						:     List[Any]         =image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_56, 2_56, 3)
         A						:     Optional[int]         =np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]     )

         assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2






