
'''simple docstring'''





import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
__lowerCAmelCase					:			str			      =		logging.getLogger(__name__)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[str]:
    __lowercase		:			Optional[Any]	    =       git.Repo(search_parent_directories=UpperCamelCase__	)
    __lowercase		:			int	    =       {
        '''repo_id''': str(UpperCamelCase__	),
        '''repo_sha''': str(repo.head.object.hexsha	),
        '''repo_branch''': str(repo.active_branch	),
    }

    with open(os.path.join(UpperCamelCase__	,					'''git_log.json'''	)	,					'''w'''	) as f:
        json.dump(UpperCamelCase__	,					UpperCamelCase__	,					indent=4	)


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:






    if params.n_gpu <= 0:
        __lowercase		:			Optional[Any]	    =       0
        __lowercase		:			Optional[Any]	    =       -1
        __lowercase		:			List[Any]	    =       True
        __lowercase		:			Any	    =       False
        return

    assert torch.cuda.is_available()

    logger.info('''Initializing GPUs'''	)
    if params.n_gpu > 1:
        assert params.local_rank != -1

        __lowercase		:			Tuple	    =       int(os.environ['''WORLD_SIZE''']	)
        __lowercase		:			int	    =       int(os.environ['''N_GPU_NODE''']	)
        __lowercase		:			Optional[int]	    =       int(os.environ['''RANK''']	)

        # number of nodes / node ID
        __lowercase		:			Dict	    =       params.world_size // params.n_gpu_per_node
        __lowercase		:			Dict	    =       params.global_rank // params.n_gpu_per_node
        __lowercase		:			List[str]	    =       True

        assert params.n_nodes == int(os.environ['''N_NODES''']	)
        assert params.node_id == int(os.environ['''NODE_RANK''']	)

    # local job (single GPU)
    else:
        assert params.local_rank == -1

        __lowercase		:			Optional[Any]	    =       1
        __lowercase		:			Tuple	    =       0
        __lowercase		:			Union[str, Any]	    =       0
        __lowercase		:			Optional[int]	    =       0
        __lowercase		:			List[str]	    =       1
        __lowercase		:			List[Any]	    =       1
        __lowercase		:			Tuple	    =       False

    # sanity checks
    assert params.n_nodes >= 1
    assert 0 <= params.node_id < params.n_nodes
    assert 0 <= params.local_rank <= params.global_rank < params.world_size
    assert params.world_size == params.n_nodes * params.n_gpu_per_node

    # define whether this is the master process / if we are in multi-node distributed mode
    __lowercase		:			List[Any]	    =       params.node_id == 0 and params.local_rank == 0
    __lowercase		:			Tuple	    =       params.n_nodes > 1

    # summary
    __lowercase		:			Optional[int]	    =       F'--- Global rank: {params.global_rank} - '
    logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes	)
    logger.info(PREFIX + '''Node ID        : %i''' % params.node_id	)
    logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank	)
    logger.info(PREFIX + '''World size     : %i''' % params.world_size	)
    logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node	)
    logger.info(PREFIX + '''Master         : %s''' % str(params.is_master	)	)
    logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node	)	)
    logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu	)	)
    logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()	)

    # set GPU device
    torch.cuda.set_device(params.local_rank	)

    # initialize multi-GPU
    if params.multi_gpu:
        logger.info('''Initializing PyTorch distributed'''	)
        torch.distributed.init_process_group(
            init_method='''env://'''	,					backend='''nccl'''	,					)





def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						List[Any]:
    np.random.seed(args.seed	)
    torch.manual_seed(args.seed	)
    if args.n_gpu > 0:
        torch.cuda.manual_seed_all(args.seed	)




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,       [0.0_02, 0.02, 0.2, 2]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          self.check_over_configs(thresholding=_snake_case				)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=_snake_case				,       prediction_type=_snake_case				,       sample_max_value=_snake_case				,       )




      def 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          assert torch.sum(torch.abs(scheduler._get_variance(0				) - 0.0				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487				) - 0.0_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Optional[int]	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       scheduler.step(_snake_case				,       _snake_case				,       _snake_case				,       generator=_snake_case				).prev_sample

              # if t > 0:
              #     noise = self.dummy_sample_deter
              #     variance = scheduler.get_variance(t) ** (0.5) * noise
              #
              # sample = pred_prev_sample + variance
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

          for i, timestep in enumerate(_snake_case				):
              if i == len(_snake_case				) - 1:
                  __lowercase		:			Optional[Any]	    =       -1
              else:
                  __lowercase		:			List[str]	    =       timesteps[i + 1]

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Any	    =       [100, 87, 50, 51, 0]

          with self.assertRaises(_snake_case				,       msg='''`custom_timesteps` must be in descending order.'''				):
              scheduler.set_timesteps(timesteps=_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Optional[int]	    =       self.scheduler_classes[0]
          __lowercase		:			Union[str, Any]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       len(_snake_case				)

          with self.assertRaises(_snake_case				,       msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''				):
              scheduler.set_timesteps(num_inference_steps=_snake_case				,       timesteps=_snake_case				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [scheduler.config.num_train_timesteps]

          with self.assertRaises(
              _snake_case				,       msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''				,       ):
              scheduler.set_timesteps(timesteps=_snake_case				)


