


'''simple docstring'''

import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger
class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  Optional[Any]						      =							"""config.json"""



      @register_to_config
      def __init__(self					:					Tuple     ,		UpperCamelCase					:					Dict=2     ,		UpperCamelCase					:					List[str]=5     ,		UpperCamelCase					:					List[Any]=(2, 5)     ,		UpperCamelCase					:					List[Any]="for diffusion"     ,		UpperCamelCase					:					Dict=[1, 3]     ,		):

             '''simple docstring'''







             pass






class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  Optional[int]						      =							"""config.json"""



      @register_to_config
      def __init__(self					:					Any     ,		UpperCamelCase					:					Union[str, Any]=2     ,		UpperCamelCase					:					Tuple=5     ,		UpperCamelCase					:					Optional[int]=(2, 5)     ,		UpperCamelCase					:					Any="for diffusion"     ,		UpperCamelCase					:					List[str]=[1, 3]     ,		):

             '''simple docstring'''







             pass






class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  Optional[int]						      =							"""config.json"""



      @register_to_config
      def __init__(self					:					str     ,		UpperCamelCase					:					Dict=2     ,		UpperCamelCase					:					str=5     ,		UpperCamelCase					:					Optional[Any]=(2, 5)     ,		UpperCamelCase					:					Tuple="for diffusion"     ,		UpperCamelCase					:					List[Any]=[1, 3]     ,		UpperCamelCase					:					Optional[Any]=[1, 3]     ,		):

             '''simple docstring'''







             pass






class 						__lowerCAmelCase		(lowercase_  ):





      '''simple docstring'''






      lowerCAmelCase__      :  Optional[Any]						      =							"""config.json"""



      @register_to_config
      def __init__(self					:					Optional[int]     ,		UpperCamelCase					:					Any=2     ,		UpperCamelCase					:					Any=5     ,		UpperCamelCase					:					int=(2, 5)     ,		UpperCamelCase					:					Optional[Any]="for diffusion"     ,		UpperCamelCase					:					List[Any]=[1, 5]     ,		UpperCamelCase					:					Union[str, Any]=[5, 4]     ,		):

             '''simple docstring'''







             pass






class 						__lowerCAmelCase		(unittest.TestCase  ):





      '''simple docstring'''






      def    UpperCamelCase__	(self					:					int				):

             '''simple docstring'''







             with self.assertRaises(UpperCamelCase				):
                    ConfigMixin.load_config('''dummy_path'''				)



      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             lowercase__       =				SampleObject()
             lowercase__       =				obj.config
             assert config["a"] == 2
             assert config["b"] == 5
             assert config["c"] == (2, 5)
             assert config["d"] == "for diffusion"
             assert config["e"] == [1, 3]

             # init ignore private arguments
             lowercase__       =				SampleObject(_name_or_path='''lalala'''				)
             lowercase__       =				obj.config
             assert config["a"] == 2
             assert config["b"] == 5
             assert config["c"] == (2, 5)
             assert config["d"] == "for diffusion"
             assert config["e"] == [1, 3]

             # can override default
             lowercase__       =				SampleObject(c=6				)
             lowercase__       =				obj.config
             assert config["a"] == 2
             assert config["b"] == 5
             assert config["c"] == 6
             assert config["d"] == "for diffusion"
             assert config["e"] == [1, 3]

             # can use positional arguments.
             lowercase__       =				SampleObject(1     ,		c=6				)
             lowercase__       =				obj.config
             assert config["a"] == 1
             assert config["b"] == 5
             assert config["c"] == 6
             assert config["d"] == "for diffusion"
             assert config["e"] == [1, 3]



      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				SampleObject()
             lowercase__       =				obj.config

             assert config["a"] == 2
             assert config["b"] == 5
             assert config["c"] == (2, 5)
             assert config["d"] == "for diffusion"
             assert config["e"] == [1, 3]

             with tempfile.TemporaryDirectory() as tmpdirname:
                    obj.save_config(UpperCamelCase				)
                    lowercase__       =				SampleObject.from_config(SampleObject.load_config(UpperCamelCase				)				)
                    lowercase__       =				new_obj.config

             # unfreeze configs
             lowercase__       =				dict(UpperCamelCase				)
             lowercase__       =				dict(UpperCamelCase				)

             assert config.pop('''c'''				) == (2, 5)  # instantiated as tuple
             assert new_config.pop('''c'''				) == [2, 5]  # saved & loaded as list because of json
             config.pop('''_use_default_values'''				)
             assert config == new_config



      def    UpperCamelCase__	(self					:					List[str]				):

             '''simple docstring'''







             lowercase__       =				logging.get_logger('''diffusers.configuration_utils'''				)
             # 30 for warning
             logger.setLevel(30				)

             with CaptureLogger(UpperCamelCase				) as cap_logger:
                    lowercase__       =				DDIMScheduler.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-torch'''     ,		subfolder='''scheduler'''				)

             assert ddim.__class__ == DDIMScheduler
             # no warning should be thrown
             assert cap_logger.out == ""



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				logging.get_logger('''diffusers.configuration_utils'''				)
             # 30 for warning
             logger.setLevel(30				)

             with CaptureLogger(UpperCamelCase				) as cap_logger:
                    lowercase__       =				EulerDiscreteScheduler.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-torch'''     ,		subfolder='''scheduler'''				)

             assert euler.__class__ == EulerDiscreteScheduler
             # no warning should be thrown
             assert cap_logger.out == ""



      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				logging.get_logger('''diffusers.configuration_utils'''				)
             # 30 for warning
             logger.setLevel(30				)

             with CaptureLogger(UpperCamelCase				) as cap_logger:
                    lowercase__       =				EulerAncestralDiscreteScheduler.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-torch'''     ,		subfolder='''scheduler'''				)

             assert euler.__class__ == EulerAncestralDiscreteScheduler
             # no warning should be thrown
             assert cap_logger.out == ""



      def    UpperCamelCase__	(self					:					Tuple				):

             '''simple docstring'''







             lowercase__       =				logging.get_logger('''diffusers.configuration_utils'''				)
             # 30 for warning
             logger.setLevel(30				)

             with CaptureLogger(UpperCamelCase				) as cap_logger:
                    lowercase__       =				PNDMScheduler.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-torch'''     ,		subfolder='''scheduler'''				)

             assert pndm.__class__ == PNDMScheduler
             # no warning should be thrown
             assert cap_logger.out == ""



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				logging.get_logger('''diffusers.configuration_utils'''				)
             # 30 for warning
             logger.setLevel(30				)

             with CaptureLogger(UpperCamelCase				) as cap_logger:
                    lowercase__       =				DDPMScheduler.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-torch'''     ,		subfolder='''scheduler'''     ,		prediction_type='''sample'''     ,		beta_end=8     ,		)

             with CaptureLogger(UpperCamelCase				) as cap_logger_a:
                    lowercase__       =				DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''     ,		beta_start=88				)

             assert ddpm.__class__ == DDPMScheduler
             assert ddpm.config.prediction_type == "sample"
             assert ddpm.config.beta_end == 8
             assert ddpm_a.config.beta_start == 88

             # no warning should be thrown
             assert cap_logger.out == ""
             assert cap_logger_a.out == ""



      def    UpperCamelCase__	(self					:					Optional[Any]				):

             '''simple docstring'''







             lowercase__       =				logging.get_logger('''diffusers.configuration_utils'''				)
             # 30 for warning
             logger.setLevel(30				)

             with CaptureLogger(UpperCamelCase				) as cap_logger:
                    lowercase__       =				DPMSolverMultistepScheduler.from_pretrained(
                        '''hf-internal-testing/tiny-stable-diffusion-torch'''     ,		subfolder='''scheduler'''				)

             assert dpm.__class__ == DPMSolverMultistepScheduler
             # no warning should be thrown
             assert cap_logger.out == ""






      def    UpperCamelCase__	(self					:					List[Any]				):

             '''simple docstring'''







             lowercase__       =				SampleObject()

             lowercase__       =				{k: v for k, v in config.config.items() if not k.startswith('''_'''				)}

             # make sure that default config has all keys in `_use_default_values`
             assert set(config_dict.keys()				) == set(config.config._use_default_values				)

             with tempfile.TemporaryDirectory() as tmpdirname:
                    config.save_config(UpperCamelCase				)

                    # now loading it with SampleObject2 should put f into `_use_default_values`
                    lowercase__       =				SampleObjecta.from_config(UpperCamelCase				)

                    assert "f" in config._use_default_values
                    assert config.f == [1, 3]

             # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
             # **BECAUSE** it is part of `config._use_default_values`
             lowercase__       =				SampleObjecta.from_config(config.config				)
             assert new_config.f == [5, 4]

             config.config._use_default_values.pop()
             lowercase__       =				SampleObjecta.from_config(config.config				)
             assert new_config_a.f == [1, 3]

             # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
             assert new_config_a.e == [1, 3]








'''simple docstring'''

import torch

from accelerate import Accelerator, DistributedType
class 						__lowerCAmelCase		:





      '''simple docstring'''






      def __enter__(self					:					List[Any]				):

             '''simple docstring'''







             if self.enabled:
                    lowercase__       =				self.model.no_sync()
                    self.model_sync_obj.__enter__()

             return self



      def __exit__(self					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]     ,		UpperCamelCase					:					Optional[int]				):

             '''simple docstring'''







             if self.enabled:
                    # Average all models on exit
                    self._sync_and_avg_model_params()
                    self.model_sync_obj.__exit__(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				)



      def __init__(self					:					Dict     ,		UpperCamelCase					:					Accelerator     ,		UpperCamelCase					:					torch.nn.Module     ,		UpperCamelCase					:					int     ,		UpperCamelCase					:					bool = True				):

             '''simple docstring'''







             if accelerator.distributed_type not in [
                 DistributedType.NO,
                 DistributedType.MULTI_CPU,
                 DistributedType.MULTI_GPU,
             ]:
                    raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''				)
             lowercase__       =				enabled and accelerator.distributed_type != DistributedType.NO
             lowercase__       =				0
             if self.enabled:
                    lowercase__       =				accelerator
                    lowercase__       =				model
                    lowercase__       =				local_sgd_steps



      def    UpperCamelCase__	(self					:					List[str]				):

             '''simple docstring'''







             self.num_steps += 1
             if not self.enabled:
                    return

             if self.num_steps % self.local_sgd_steps == 0:
                    self._sync_and_avg_model_params()






      def    UpperCamelCase__	(self					:					Any				):

             '''simple docstring'''







             self.accelerator.wait_for_everyone()
             with self.accelerator.autocast():
                    for param in self.model.parameters():
                           lowercase__       =				self.accelerator.reduce(param.data     ,		reduction='''mean'''				)




