





'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 				UpperCAmelCase  (    unittest.TestCase     ):




      @property
      def        lowercase__					(    self    :				Optional[int]						)       ->   Optional[int]:
            torch.manual_seed(0						)
            _lowerCAmelCase							  =							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""")    ,    )
            return model




      def        lowercase__					(    self    :				Dict						)       ->   Union[str, Any]:
            _lowerCAmelCase							  =							"""cpu"""
            _lowerCAmelCase							  =							self.dummy_uncond_unet
            _lowerCAmelCase							  =							DDPMScheduler()

            _lowerCAmelCase							  =							DDPMPipeline(unet=__snake_case    ,    scheduler=__snake_case						)
            ddpm.to(__snake_case						)
            ddpm.set_progress_bar_config(disable=__snake_case						)

            _lowerCAmelCase							  =							torch.Generator(device=__snake_case						).manual_seed(0						)
            _lowerCAmelCase							  =							ddpm(generator=__snake_case    ,    num_inference_steps=2    ,    output_type="""numpy"""						).images

            _lowerCAmelCase							  =							torch.Generator(device=__snake_case						).manual_seed(0						)
            _lowerCAmelCase							  =							ddpm(generator=__snake_case    ,    num_inference_steps=2    ,    output_type="""numpy"""    ,    return_dict=__snake_case						)[0]

            _lowerCAmelCase							  =							image[0, -3:, -3:, -1]
            _lowerCAmelCase							  =							image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            _lowerCAmelCase							  =							np.array(
                [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.0_00, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]						)

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




      def        lowercase__					(    self    :				Tuple						)       ->   Dict:
            _lowerCAmelCase							  =							self.dummy_uncond_unet
            _lowerCAmelCase							  =							DDPMScheduler(prediction_type="""sample"""						)

            _lowerCAmelCase							  =							DDPMPipeline(unet=__snake_case    ,    scheduler=__snake_case						)
            ddpm.to(__snake_case						)
            ddpm.set_progress_bar_config(disable=__snake_case						)

            _lowerCAmelCase							  =							torch.manual_seed(0						)
            _lowerCAmelCase							  =							ddpm(generator=__snake_case    ,    num_inference_steps=2    ,    output_type="""numpy"""						).images

            _lowerCAmelCase							  =							torch.manual_seed(0						)
            _lowerCAmelCase							  =							ddpm(generator=__snake_case    ,    num_inference_steps=2    ,    output_type="""numpy"""						)[0]

            _lowerCAmelCase							  =							image[0, -3:, -3:, -1]
            _lowerCAmelCase							  =							image_eps[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            _lowerCAmelCase							  =							1E-2 if torch_device != """mps""" else 3E-2
            assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance




@slow
@require_torch_gpu
class 				UpperCAmelCase  (    unittest.TestCase     ):




      def        lowercase__					(    self    :				Tuple						)       ->   Tuple:
            _lowerCAmelCase							  =							"""google/ddpm-cifar10-32"""

            _lowerCAmelCase							  =							UNetaDModel.from_pretrained(__snake_case						)
            _lowerCAmelCase							  =							DDPMScheduler.from_pretrained(__snake_case						)

            _lowerCAmelCase							  =							DDPMPipeline(unet=__snake_case    ,    scheduler=__snake_case						)
            ddpm.to(__snake_case						)
            ddpm.set_progress_bar_config(disable=__snake_case						)

            _lowerCAmelCase							  =							torch.manual_seed(0						)
            _lowerCAmelCase							  =							ddpm(generator=__snake_case    ,    output_type="""numpy"""						).images

            _lowerCAmelCase							  =							image[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            _lowerCAmelCase							  =							np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85]						)
            assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2













'''simple docstring'''






import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


A__       :     Optional[Any]          =get_logger(__name__)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = None							,			):





      """simple docstring"""


      for i, state in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Model weights saved in {output_model_file}"						)
      # Optimizer states
      for i, opt in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							opt.state_dict()
            _lowerCAmelCase							  =							f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Optimizer state saved in {output_optimizer_file}"						)
      # Scheduler states
      for i, scheduler in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							scheduler.state_dict()
            _lowerCAmelCase							  =							f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Scheduler state saved in {output_scheduler_file}"						)
      # GradScaler state
      if scaler is not None:
            _lowerCAmelCase							  =							scaler.state_dict()
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            torch.save(lowerCAmelCase							,			lowerCAmelCase						)
            logger.info(f"Gradient scaler state saved in {output_scaler_file}"						)
      # Random number generator states
      _lowerCAmelCase							  =							{}
      _lowerCAmelCase							  =							f"{RNG_STATE_NAME}_{process_index}.pkl"
      _lowerCAmelCase							  =							random.getstate()
      _lowerCAmelCase							  =							np.random.get_state()
      _lowerCAmelCase							  =							torch.get_rng_state()
      if is_xpu_available():
            _lowerCAmelCase							  =							torch.xpu.get_rng_state_all()
      else:
            _lowerCAmelCase							  =							torch.cuda.get_rng_state_all()
      if is_tpu_available():
            _lowerCAmelCase							  =							xm.get_rng_state()
      _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
      torch.save(lowerCAmelCase							,			lowerCAmelCase						)
      logger.info(f"Random states saved in {output_states_file}"						)
      return output_dir
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase=None							,			lowerCAmelCase=None							,			**lowerCAmelCase							,			):





      """simple docstring"""


      if map_location not in [None, "cpu", "on_device"]:
            raise TypeError(
                """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""						)
      if map_location is None:
            _lowerCAmelCase							  =							"""cpu"""
      elif map_location == "on_device":
            _lowerCAmelCase							  =							PartialState().device
      # Model states
      for i, model in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{MODEL_NAME}.bin" if i == 0 else f"{MODEL_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            models[i].load_state_dict(torch.load(lowerCAmelCase							,			map_location=lowerCAmelCase						)							,			**lowerCAmelCase						)
      logger.info("""All model weights loaded successfully"""						)

      # Optimizer states
      for i, opt in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{OPTIMIZER_NAME}.bin" if i == 0 else f"{OPTIMIZER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            _lowerCAmelCase							  =							torch.load(lowerCAmelCase							,			map_location=lowerCAmelCase						)
            optimizers[i].load_state_dict(lowerCAmelCase						)
      logger.info("""All optimizer states loaded successfully"""						)

      # Scheduler states
      for i, scheduler in enumerate(lowerCAmelCase						):
            _lowerCAmelCase							  =							f"{SCHEDULER_NAME}.bin" if i == 0 else f"{SCHEDULER_NAME}_{i}.bin"
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            scheduler.load_state_dict(torch.load(lowerCAmelCase						)						)
      logger.info("""All scheduler states loaded successfully"""						)

      # GradScaler state
      if scaler is not None:
            _lowerCAmelCase							  =							os.path.join(lowerCAmelCase							,			lowerCAmelCase						)
            scaler.load_state_dict(torch.load(lowerCAmelCase						)						)
            logger.info("""GradScaler state loaded successfully"""						)

      # Random states
      try:
            _lowerCAmelCase							  =							torch.load(os.path.join(lowerCAmelCase							,			f"{RNG_STATE_NAME}_{process_index}.pkl"						)						)
            random.setstate(states["""random_state"""]						)
            np.random.set_state(states["""numpy_random_seed"""]						)
            torch.set_rng_state(states["""torch_manual_seed"""]						)
            if is_xpu_available():
                  torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]						)
            else:
                  torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]						)
            if is_tpu_available():
                  xm.set_rng_state(states["""xm_seed"""]						)
            logger.info("""All random states loaded successfully"""						)
      except Exception:
            logger.info("""Could not load random states"""						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 0						):





      """simple docstring"""


      _lowerCAmelCase							  =							Path(lowerCAmelCase						) / f"custom_checkpoint_{index}.pkl"
      logger.info(f"Saving the state of {get_pretty_name(lowerCAmelCase						)} to {save_location}"						)
      torch.save(obj.state_dict()							,			lowerCAmelCase						)





def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase = 0						):





      """simple docstring"""


      _lowerCAmelCase							  =							f"{path}/custom_checkpoint_{index}.pkl"
      logger.info(f"Loading the state of {get_pretty_name(lowerCAmelCase						)} from {load_location}"						)
      obj.load_state_dict(torch.load(lowerCAmelCase							,			map_location="""cpu"""						)						)






