




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				(DDIMParallelScheduler,)
    lowerCAmelCase        =				(('''eta''', 0.0), ('''num_inference_steps''', 50))





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							str       =      {
             '''num_train_timesteps''': 1_000,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
             '''clip_sample''': True,
         }

         config.update(**_SCREAMING_SNAKE_CASE  )
         return config





    def 							a__				(				self       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							str       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							Optional[Any]       =      self.get_scheduler_config(**_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Dict       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      10, 0.0

         UpperCAmelCase_      :							Dict       =      self.dummy_model()
         UpperCAmelCase_      :							int       =      self.dummy_sample_deter

         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         for t in scheduler.timesteps:
              UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Union[str, Any]       =      scheduler.step(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ).prev_sample

         return sample





    def 							a__				(				self  )			->		List[str]:
         for timesteps in [100, 500, 1_000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Dict:
         for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[Any]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							str       =      self.get_scheduler_config(steps_offset=1  )
         UpperCAmelCase_      :							str       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )
         scheduler.set_timesteps(5  )
         assert torch.equal(scheduler.timesteps       ,torch.LongTensor([801, 601, 401, 201, 1]  )  )





    def 							a__				(				self  )			->		Any:
         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=_SCREAMING_SNAKE_CASE       ,beta_end=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[Any]:
         for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[int]:
         for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Dict:
         for timestep_spacing in ["trailing", "leading"]:
              self.check_over_configs(timestep_spacing=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Optional[Any]:
         for rescale_betas_zero_snr in [True, False]:
              self.check_over_configs(rescale_betas_zero_snr=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Union[str, Any]:
         self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE  )
         for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=_SCREAMING_SNAKE_CASE       ,prediction_type=_SCREAMING_SNAKE_CASE       ,sample_max_value=_SCREAMING_SNAKE_CASE       ,)





    def 							a__				(				self  )			->		Union[str, Any]:
         for t in [1, 10, 49]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         for t, num_inference_steps in zip([1, 10, 50]       ,[10, 50, 500]  ):
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE       ,num_inference_steps=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Dict:
         for t, eta in zip([1, 10, 49]       ,[0.0, 0.5, 1.0]  ):
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE       ,eta=_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		int:
         UpperCAmelCase_      :							int       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							Optional[int]       =      self.get_scheduler_config()
         UpperCAmelCase_      :							str       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         assert torch.sum(torch.abs(scheduler._get_variance(0       ,0  ) - 0.0  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420       ,400  ) - 0.1_47_71  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980       ,960  ) - 0.3_24_60  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0       ,0  ) - 0.0  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487       ,486  ) - 0.0_09_79  )  ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999       ,998  ) - 0.02  )  ) < 1e-5





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							List[Any]       =      self.scheduler_classes[0]
         UpperCAmelCase_      :							Dict       =      self.get_scheduler_config()
         UpperCAmelCase_      :							Tuple       =      scheduler_class(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      10, 0.0
         scheduler.set_timesteps(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      self.dummy_model()
         UpperCAmelCase_      :							List[Any]       =      self.dummy_sample_deter
         UpperCAmelCase_      :							Optional[Any]       =      self.dummy_sample_deter + 0.1
         UpperCAmelCase_      :							List[Any]       =      self.dummy_sample_deter - 0.1

         UpperCAmelCase_      :							Optional[Any]       =      samplea.shape[0]
         UpperCAmelCase_      :							Union[str, Any]       =      torch.stack([samplea, samplea, samplea]       ,dim=0  )
         UpperCAmelCase_      :							Dict       =      torch.arange(_SCREAMING_SNAKE_CASE  )[0:3, None].repeat(1       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[int]       =      model(samples.flatten(0       ,1  )       ,timesteps.flatten(0       ,1  )  )
         UpperCAmelCase_      :							Union[str, Any]       =      scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE       ,timesteps.flatten(0       ,1  )       ,samples.flatten(0       ,1  )       ,_SCREAMING_SNAKE_CASE  )

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

         assert abs(result_sum.item() - 11_47.79_04  ) < 1e-2
         assert abs(result_mean.item() - 0.49_82  ) < 1e-3





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							int       =      self.full_loop()

         UpperCAmelCase_      :							Optional[int]       =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							Optional[int]       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_sum.item() - 1_72.00_67  ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67  ) < 1e-3





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[str]       =      self.full_loop(prediction_type='''v_prediction'''  )

         UpperCAmelCase_      :							Tuple       =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							List[str]       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_sum.item() - 52.53_02  ) < 1e-2
         assert abs(result_mean.item() - 0.06_84  ) < 1e-3





    def 							a__				(				self  )			->		List[Any]:
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_      :							str       =      self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,beta_start=0.01  )
         UpperCAmelCase_      :							str       =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							List[Any]       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_sum.item() - 1_49.82_95  ) < 1e-2
         assert abs(result_mean.item() - 0.19_51  ) < 1e-3





    def 							a__				(				self  )			->		Union[str, Any]:
         # We specify different beta, so that the first alpha is 0.99
         UpperCAmelCase_      :							List[Any]       =      self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,beta_start=0.01  )
         UpperCAmelCase_      :							List[Any]       =      torch.sum(torch.abs(_SCREAMING_SNAKE_CASE  )  )
         UpperCAmelCase_      :							List[str]       =      torch.mean(torch.abs(_SCREAMING_SNAKE_CASE  )  )

         assert abs(result_sum.item() - 1_49.07_84  ) < 1e-2
         assert abs(result_mean.item() - 0.19_41  ) < 1e-3





import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							str       =      r'''\w+[.]\d+'''
     UpperCAmelCase_      :							int       =      re.findall(_lowercase			,						_lowercase				)
     for pat in pats:
          UpperCAmelCase_      :							List[Any]       =      key.replace(_lowercase			,						'''_'''.join(pat.split('''.'''				)				)				)
     return key







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''scale''',)
     if (
         any('''norm''' in str_ for str_ in pt_tuple_key				)
         and (pt_tuple_key[-1] == "bias")
         and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
         and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
     ):
          UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor
     elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
          UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''scale''',)
          return renamed_pt_tuple_key, pt_tensor

     # embedding
     if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
          UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''embedding''',)
          return renamed_pt_tuple_key, pt_tensor

     # conv layer
     UpperCAmelCase_      :							List[Any]       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
          UpperCAmelCase_      :							List[Any]       =      pt_tensor.transpose(2			,						3			,						1			,						0				)
          return renamed_pt_tuple_key, pt_tensor

     # linear layer
     UpperCAmelCase_      :							Dict       =      pt_tuple_key[:-1] + ('''kernel''',)
     if pt_tuple_key[-1] == "weight":
          UpperCAmelCase_      :							Tuple       =      pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     UpperCAmelCase_      :							Any       =      pt_tuple_key[:-1] + ('''weight''',)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     UpperCAmelCase_      :							Tuple       =      pt_tuple_key[:-1] + ('''bias''',)
     if pt_tuple_key[-1] == "beta":
          return renamed_pt_tuple_key, pt_tensor

     return pt_tuple_key, pt_tensor







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=42				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     UpperCAmelCase_      :							str       =      flax_model.init_weights(PRNGKey(_lowercase				)				)

     UpperCAmelCase_      :							List[Any]       =      flatten_dict(_lowercase				)
     UpperCAmelCase_      :							int       =      {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          UpperCAmelCase_      :							Optional[int]       =      rename_key(_lowercase				)
          UpperCAmelCase_      :							List[str]       =      tuple(renamed_pt_key.split('''.'''				)				)

          # Correctly rename weight parameters
          UpperCAmelCase_,   UpperCAmelCase_      :							Any       =      rename_key_and_reshape_tensor(_lowercase			,						_lowercase			,						_lowercase				)

          if flax_key in random_flax_state_dict:
               if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                    raise ValueError(
                        f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
                        f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''				)

        # also add unexpected weight so that warning is thrown
          UpperCAmelCase_      :							int       =      jnp.asarray(_lowercase				)

     return unflatten_dict(_lowercase				)