




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      int				= (DDPMParallelScheduler,)
     def        A__      (						self	:		Optional[int]       ,						**__lowerCamelCase	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      {
           '''num_train_timesteps''': 10_00,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''variance_type''': '''fixed_small''',
           '''clip_sample''': True,
       }

       config.update(**__lowerCamelCase						)
       return config
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       for timesteps in [1, 5, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=__lowerCamelCase						)
     def        A__      (						self	:		List[Any]						):



       """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=__lowerCamelCase       ,						beta_end=__lowerCamelCase						)
     def        A__      (						self	:		str						):



       """simple docstring"""
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=__lowerCamelCase						)
     def        A__      (						self	:		str						):



       """simple docstring"""
       for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=__lowerCamelCase						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=__lowerCamelCase						)
     def        A__      (						self	:		Tuple						):



       """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        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=__lowerCamelCase						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       for t in [0, 5_00, 9_99]:
         self.check_over_forward(time_step=__lowerCamelCase						)
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      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_0979						)						) < 1e-5
       assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.02						)						) < 1e-5
     def        A__      (						self	:		str						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

       lowerCAmelCase__										=      len(__lowerCamelCase						)

       lowerCAmelCase__										=      self.dummy_model()
       lowerCAmelCase__										=      self.dummy_sample_deter
       lowerCAmelCase__										=      self.dummy_sample_deter + 0.1
       lowerCAmelCase__										=      self.dummy_sample_deter - 0.1

       lowerCAmelCase__										=      samplea.shape[0]
       lowerCAmelCase__										=      torch.stack([samplea, samplea, samplea]       ,						dim=0						)
       lowerCAmelCase__										=      torch.arange(__lowerCamelCase						)[0:3, None].repeat(1       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      model(samples.flatten(0       ,						1						)       ,						timesteps.flatten(0       ,						1						)						)
       lowerCAmelCase__										=      scheduler.batch_step_no_noise(__lowerCamelCase       ,						timesteps.flatten(0       ,						1						)       ,						samples.flatten(0       ,						1						)						)

       lowerCAmelCase__										=      torch.sum(torch.abs(__lowerCamelCase						)						)
       lowerCAmelCase__										=      torch.mean(torch.abs(__lowerCamelCase						)						)

       assert abs(result_sum.item() - 1153.1833						) < 1e-2
       assert abs(result_mean.item() - 0.5005						) < 1e-3
     def        A__      (						self	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

       lowerCAmelCase__										=      len(__lowerCamelCase						)

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

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

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

         lowerCAmelCase__										=      pred_prev_sample

       lowerCAmelCase__										=      torch.sum(torch.abs(__lowerCamelCase						)						)
       lowerCAmelCase__										=      torch.mean(torch.abs(__lowerCamelCase						)						)

       assert abs(result_sum.item() - 258.9606						) < 1e-2
       assert abs(result_mean.item() - 0.3372						) < 1e-3
     def        A__      (						self	:		Tuple						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config(prediction_type='''v_prediction'''						)
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

       lowerCAmelCase__										=      len(__lowerCamelCase						)

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

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

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

         lowerCAmelCase__										=      pred_prev_sample

       lowerCAmelCase__										=      torch.sum(torch.abs(__lowerCamelCase						)						)
       lowerCAmelCase__										=      torch.mean(torch.abs(__lowerCamelCase						)						)

       assert abs(result_sum.item() - 202.0296						) < 1e-2
       assert abs(result_mean.item() - 0.2631						) < 1e-3
     def        A__      (						self	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

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

       scheduler.set_timesteps(timesteps=__lowerCamelCase						)

       lowerCAmelCase__										=      scheduler.timesteps

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

         lowerCAmelCase__										=      scheduler.previous_timestep(__lowerCamelCase						)
         lowerCAmelCase__										=      prev_t.item()

         self.assertEqual(__lowerCamelCase       ,						__lowerCamelCase						)
     def        A__      (						self	:		Optional[Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

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

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



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

       lowerCAmelCase__										=      [1_00, 87, 50, 1, 0]
       lowerCAmelCase__										=      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        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      self.scheduler_classes[0]
       lowerCAmelCase__										=      self.get_scheduler_config()
       lowerCAmelCase__										=      scheduler_class(**__lowerCamelCase						)

       lowerCAmelCase__										=      [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						)







import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__  :  List[str]					=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       lowerCAmelCase__										=      nn.LayerNorm(config.hidden_size						)
       lowerCAmelCase__										=      nn.Linear(config.hidden_size       ,						self.proj_size						)

       # uncondition for scaling
       lowerCAmelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)



     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

