


'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   (DDPMScheduler,)


    def        A   (  self      :							Union[str, Any]				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     List[Any]     = {
               'num_train_timesteps': 1000,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'variance_type': 'fixed_small',
               'clip_sample': True,
           }

           config.update(**SCREAMING_SNAKE_CASE)
           return config


    def        A   (  self      :							List[str]):
           for timesteps in [1, 5, 100, 1000]:
                  self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[str]):
           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=SCREAMING_SNAKE_CASE				,							beta_end=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           for schedule in ["linear", "squaredcos_cap_v2"]:
                  self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							str):
           for variance in ["fixed_small", "fixed_large", "other"]:
                  self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           for clip_sample in [True, False]:
                  self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE)


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


    def        A   (  self      :							Optional[int]):
           for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]):
           for t in [0, 500, 999]:
                  self.check_over_forward(time_step=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           _A					:     Tuple     = self.scheduler_classes[0]
           _A					:     Union[str, Any]     = self.get_scheduler_config()
           _A					:     Dict     = scheduler_class(**SCREAMING_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_0979)) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5


    def        A   (  self      :							Optional[Any]):
           _A					:     Optional[int]     = self.scheduler_classes[0]
           _A					:     Optional[int]     = self.get_scheduler_config()
           _A					:     List[Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = len(SCREAMING_SNAKE_CASE)

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

           for t in reversed(range(SCREAMING_SNAKE_CASE)):
                  # 1. predict noise residual
                  _A					:     List[Any]     = model(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)

                  # 2. predict previous mean of sample x_t-1
                  _A					:     int     = scheduler.step(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							generator=SCREAMING_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
                  _A					:     Union[str, Any]     = pred_prev_sample

           _A					:     Union[str, Any]     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     Tuple     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

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


    def        A   (  self      :							Optional[int]):
           _A					:     Optional[Any]     = self.scheduler_classes[0]
           _A					:     Dict     = self.get_scheduler_config(prediction_type='v_prediction')
           _A					:     Optional[Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)

           _A					:     str     = len(SCREAMING_SNAKE_CASE)

           _A					:     str     = self.dummy_model()
           _A					:     Optional[Any]     = self.dummy_sample_deter
           _A					:     Any     = torch.manual_seed(0)

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

                  # 2. predict previous mean of sample x_t-1
                  _A					:     Any     = scheduler.step(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							generator=SCREAMING_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
                  _A					:     Union[str, Any]     = pred_prev_sample

           _A					:     List[str]     = torch.sum(torch.abs(SCREAMING_SNAKE_CASE))
           _A					:     Optional[int]     = torch.mean(torch.abs(SCREAMING_SNAKE_CASE))

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


    def        A   (  self      :							Tuple):
           _A					:     str     = self.scheduler_classes[0]
           _A					:     Tuple     = self.get_scheduler_config()
           _A					:     Union[str, Any]     = scheduler_class(**SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = [100, 87, 50, 1, 0]

           scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = scheduler.timesteps

           for i, timestep in enumerate(SCREAMING_SNAKE_CASE):
                  if i == len(SCREAMING_SNAKE_CASE) - 1:
                         _A					:     str     = -1
                  else:
                         _A					:     Any     = timesteps[i + 1]

                  _A					:     str     = scheduler.previous_timestep(SCREAMING_SNAKE_CASE)
                  _A					:     Any     = prev_t.item()

                  self.assertEqual(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     int     = self.scheduler_classes[0]
           _A					:     Optional[int]     = self.get_scheduler_config()
           _A					:     str     = scheduler_class(**SCREAMING_SNAKE_CASE)

           _A					:     int     = [100, 87, 50, 51, 0]

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


    def        A   (  self      :							Any):
           _A					:     Union[str, Any]     = self.scheduler_classes[0]
           _A					:     List[Any]     = self.get_scheduler_config()
           _A					:     str     = scheduler_class(**SCREAMING_SNAKE_CASE)

           _A					:     str     = [100, 87, 50, 1, 0]
           _A					:     List[str]     = len(SCREAMING_SNAKE_CASE)

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


    def        A   (  self      :							Union[str, Any]):
           _A					:     str     = self.scheduler_classes[0]
           _A					:     Dict     = self.get_scheduler_config()
           _A					:     Dict     = scheduler_class(**SCREAMING_SNAKE_CASE)

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

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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A				:					str    						=			{
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Optional[Any]    						=			['''BlipImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					List[Any]    						=			[
       '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BlipModel''',
       '''BlipPreTrainedModel''',
       '''BlipForConditionalGeneration''',
       '''BlipForQuestionAnswering''',
       '''BlipVisionModel''',
       '''BlipTextModel''',
       '''BlipForImageTextRetrieval''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A				:					Union[str, Any]    						=			[
       '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFBlipModel''',
       '''TFBlipPreTrainedModel''',
       '''TFBlipForConditionalGeneration''',
       '''TFBlipForQuestionAnswering''',
       '''TFBlipVisionModel''',
       '''TFBlipTextModel''',
       '''TFBlipForImageTextRetrieval''',
   ]

if TYPE_CHECKING:
   from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
   from .processing_blip import BlipProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_blip import BlipImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blip import (
          BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlipForConditionalGeneration,
          BlipForImageTextRetrieval,
          BlipForQuestionAnswering,
          BlipModel,
          BlipPreTrainedModel,
          BlipTextModel,
          BlipVisionModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blip import (
          TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFBlipForConditionalGeneration,
          TFBlipForImageTextRetrieval,
          TFBlipForQuestionAnswering,
          TFBlipModel,
          TFBlipPreTrainedModel,
          TFBlipTextModel,
          TFBlipVisionModel,
      )

else:
   import sys

   A				:					Optional[int]    						=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






