



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  int       				=    (DDPMScheduler,)


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				**lowerCAmelCase  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**__lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int)	->				Any:




         """simple docstring"""

         for timesteps in [1, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=__lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Dict:




         """simple docstring"""

         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=__lowerCAmelCase			,				beta_end=__lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				Any:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Union[str, Any]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Tuple)	->				List[Any]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

         for t in [0, 5_00, 9_99]:
               self.check_over_forward(time_step=__lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Dict)	->				str:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         lowercase__     =  len(__lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  torch.manual_seed(0)

         for t in reversed(range(__lowerCAmelCase)):
               # 1. predict noise residual
               lowercase__     =  model(__lowerCAmelCase			,				__lowerCAmelCase)

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

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

         lowercase__     =  torch.sum(torch.abs(__lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(__lowerCAmelCase))

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


   def 			UpperCAmelCase		(						self  :			int)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(prediction_type='v_prediction')
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         lowercase__     =  len(__lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  torch.manual_seed(0)

         for t in reversed(range(__lowerCAmelCase)):
               # 1. predict noise residual
               lowercase__     =  model(__lowerCAmelCase			,				__lowerCAmelCase)

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

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

         lowercase__     =  torch.sum(torch.abs(__lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(__lowerCAmelCase))

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


   def 			UpperCAmelCase		(						self  :			int)	->				List[str]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         lowercase__     =  [1_00, 87, 50, 1, 0]

         scheduler.set_timesteps(timesteps=__lowerCAmelCase)

         lowercase__     =  scheduler.timesteps

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

               lowercase__     =  scheduler.previous_timestep(__lowerCAmelCase)
               lowercase__     =  prev_t.item()

               self.assertEqual(__lowerCAmelCase			,				__lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[Any])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         lowercase__     =  [1_00, 87, 50, 51, 0]

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


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         lowercase__     =  [1_00, 87, 50, 1, 0]
         lowercase__     =  len(__lowerCAmelCase)

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



   def 			UpperCAmelCase		(						self  :			int)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**__lowerCAmelCase)

         lowercase__     =  [scheduler.config.num_train_timesteps]

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











from typing import TYPE_CHECKING

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


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       a__      :				Optional[Any]   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
