





import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class 			__A       (      a__  ):




      """simple docstring"""







      UpperCAmelCase__						=      (DDPMParallelScheduler,)


      def 				__snake_case			(		self      ,							**a__):



             """simple docstring"""





             _lowerCamelCase							:     int	=		{
                 '''num_train_timesteps''': 1000,
                 '''beta_start''': 0.0001,
                 '''beta_end''': 0.02,
                 '''beta_schedule''': '''linear''',
                 '''variance_type''': '''fixed_small''',
                 '''clip_sample''': True,
             }

             config.update(**lowercase__)
             return config


      def 				__snake_case			(		self):



             """simple docstring"""





             for timesteps in [1, 5, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,							[0.002, 0.02, 0.2, 2]):
                    self.check_over_configs(beta_start=lowercase__      ,							beta_end=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             for variance in ["fixed_small", "fixed_large", "other"]:
                    self.check_over_configs(variance_type=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





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


      def 				__snake_case			(		self):



             """simple docstring"""





             for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             for t in [0, 500, 999]:
                    self.check_over_forward(time_step=lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		self.scheduler_classes[0]
             _lowerCamelCase							:     Any	=		self.get_scheduler_config()
             _lowerCamelCase							:     Any	=		scheduler_class(**lowercase__)

             assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1e-5
             assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.scheduler_classes[0]
             _lowerCamelCase							:     Tuple	=		self.get_scheduler_config()
             _lowerCamelCase							:     Optional[Any]	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     Optional[int]	=		len(lowercase__)

             _lowerCamelCase							:     Dict	=		self.dummy_model()
             _lowerCamelCase							:     Dict	=		self.dummy_sample_deter
             _lowerCamelCase							:     List[Any]	=		self.dummy_sample_deter + 0.1
             _lowerCamelCase							:     Dict	=		self.dummy_sample_deter - 0.1

             _lowerCamelCase							:     str	=		samplea.shape[0]
             _lowerCamelCase							:     Optional[int]	=		torch.stack([samplea, samplea, samplea]      ,							dim=0)
             _lowerCamelCase							:     List[str]	=		torch.arange(lowercase__)[0:3, None].repeat(1      ,							lowercase__)

             _lowerCamelCase							:     int	=		model(samples.flatten(0      ,							1)      ,							timesteps.flatten(0      ,							1))
             _lowerCamelCase							:     str	=		scheduler.batch_step_no_noise(lowercase__      ,							timesteps.flatten(0      ,							1)      ,							samples.flatten(0      ,							1))

             _lowerCamelCase							:     int	=		torch.sum(torch.abs(lowercase__))
             _lowerCamelCase							:     Union[str, Any]	=		torch.mean(torch.abs(lowercase__))

             assert abs(result_sum.item() - 1153.1833) < 1e-2
             assert abs(result_mean.item() - 0.5005) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.scheduler_classes[0]
             _lowerCamelCase							:     List[str]	=		self.get_scheduler_config()
             _lowerCamelCase							:     Optional[int]	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     List[str]	=		len(lowercase__)

             _lowerCamelCase							:     Optional[Any]	=		self.dummy_model()
             _lowerCamelCase							:     Dict	=		self.dummy_sample_deter
             _lowerCamelCase							:     Optional[int]	=		torch.manual_seed(0)

             for t in reversed(range(lowercase__)):
                    # 1. predict noise residual
                    _lowerCamelCase							:     Union[str, Any]	=		model(lowercase__      ,							lowercase__)

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

                    _lowerCamelCase							:     List[str]	=		pred_prev_sample

             _lowerCamelCase							:     int	=		torch.sum(torch.abs(lowercase__))
             _lowerCamelCase							:     Tuple	=		torch.mean(torch.abs(lowercase__))

             assert abs(result_sum.item() - 258.9606) < 1e-2
             assert abs(result_mean.item() - 0.3372) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.scheduler_classes[0]
             _lowerCamelCase							:     str	=		self.get_scheduler_config(prediction_type='''v_prediction''')
             _lowerCamelCase							:     Dict	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     Dict	=		len(lowercase__)

             _lowerCamelCase							:     Union[str, Any]	=		self.dummy_model()
             _lowerCamelCase							:     Optional[int]	=		self.dummy_sample_deter
             _lowerCamelCase							:     List[Any]	=		torch.manual_seed(0)

             for t in reversed(range(lowercase__)):
                    # 1. predict noise residual
                    _lowerCamelCase							:     List[str]	=		model(lowercase__      ,							lowercase__)

                    # 2. predict previous mean of sample x_t-1
                    _lowerCamelCase							:     Union[str, Any]	=		scheduler.step(lowercase__      ,							lowercase__      ,							lowercase__      ,							generator=lowercase__).prev_sample

                    _lowerCamelCase							:     Optional[Any]	=		pred_prev_sample

             _lowerCamelCase							:     List[Any]	=		torch.sum(torch.abs(lowercase__))
             _lowerCamelCase							:     str	=		torch.mean(torch.abs(lowercase__))

             assert abs(result_sum.item() - 202.0296) < 1e-2
             assert abs(result_mean.item() - 0.2631) < 1e-3


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		self.scheduler_classes[0]
             _lowerCamelCase							:     Optional[Any]	=		self.get_scheduler_config()
             _lowerCamelCase							:     Union[str, Any]	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     str	=		[100, 87, 50, 1, 0]

             scheduler.set_timesteps(timesteps=lowercase__)

             _lowerCamelCase							:     List[str]	=		scheduler.timesteps

             for i, timestep in enumerate(lowercase__):
                    if i == len(lowercase__) - 1:
                           _lowerCamelCase							:     int	=		-1
                    else:
                           _lowerCamelCase							:     Optional[Any]	=		timesteps[i + 1]

                    _lowerCamelCase							:     str	=		scheduler.previous_timestep(lowercase__)
                    _lowerCamelCase							:     Tuple	=		prev_t.item()

                    self.assertEqual(lowercase__      ,							lowercase__)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     str	=		self.scheduler_classes[0]
             _lowerCamelCase							:     int	=		self.get_scheduler_config()
             _lowerCamelCase							:     int	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     Dict	=		[100, 87, 50, 51, 0]

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


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		self.scheduler_classes[0]
             _lowerCamelCase							:     Any	=		self.get_scheduler_config()
             _lowerCamelCase							:     Any	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     Tuple	=		[100, 87, 50, 1, 0]
             _lowerCamelCase							:     List[Any]	=		len(lowercase__)

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



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     int	=		self.scheduler_classes[0]
             _lowerCamelCase							:     int	=		self.get_scheduler_config()
             _lowerCamelCase							:     Union[str, Any]	=		scheduler_class(**lowercase__)

             _lowerCamelCase							:     Union[str, Any]	=		[scheduler.config.num_train_timesteps]

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase			       =      {'configuration_opt': ['OPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OPTConfig']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'OPT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'OPTForCausalLM',
           'OPTModel',
           'OPTPreTrainedModel',
           'OPTForSequenceClassification',
           'OPTForQuestionAnswering',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      ['TFOPTForCausalLM', 'TFOPTModel', 'TFOPTPreTrainedModel']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase			       =      [
           'FlaxOPTForCausalLM',
           'FlaxOPTModel',
           'FlaxOPTPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_opt import (
                  OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  OPTForCausalLM,
                  OPTForQuestionAnswering,
                  OPTForSequenceClassification,
                  OPTModel,
                  OPTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
       import sys

       _lowerCamelCase			       =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

