





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''

 def __init__(self				:   Dict       , UpperCAmelCase_				:   List[str]       , UpperCAmelCase_				:   Tuple=None       , UpperCAmelCase_				:   int=True       , UpperCAmelCase_				:   int=None       , **UpperCAmelCase_				:   List[str])       ->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__:      Any			  =parent
   lowerCamelCase__:      int			  =config_class
   lowerCamelCase__:      List[str]			  =has_text_modality
   lowerCamelCase__:      str			  =kwargs
   lowerCamelCase__:      Any			  =common_properties

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[int])       ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.config_class(**self.inputs_dict)
   lowerCamelCase__:      int			  =(
       ["hidden_size", "num_attention_heads", "num_hidden_layers"]
       if self.common_properties is None
       else self.common_properties
   )

   # Add common fields for text models
   if self.has_text_modality:
     common_properties.extend(["vocab_size"])

   # Test that config has the common properties as getters
   for prop in common_properties:
     self.parent.assertTrue(hasattr(UpperCAmelCase_       , UpperCAmelCase_)       , msg=F"""`{prop}` does not exist""")

   # Test that config has the common properties as setter
   for idx, name in enumerate(UpperCAmelCase_):
     try:
       setattr(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_)
       self.parent.assertEqual(
           getattr(UpperCAmelCase_       , UpperCAmelCase_)       , UpperCAmelCase_       , msg=F"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase_       , UpperCAmelCase_)}""")
     except NotImplementedError:
       # Some models might not be able to implement setters for common_properties
       # In that case, a NotImplementedError is raised
       pass

        # Test if config class can be called with Config(prop_name=..)
   for idx, name in enumerate(UpperCAmelCase_):
     try:
       lowerCamelCase__:      Optional[int]			  =self.config_class(**{name: idx})
       self.parent.assertEqual(
           getattr(UpperCAmelCase_       , UpperCAmelCase_)       , UpperCAmelCase_       , msg=F"""`{name} value {idx} expected, but was {getattr(UpperCAmelCase_       , UpperCAmelCase_)}""")
     except NotImplementedError:
       # Some models might not be able to implement setters for common_properties
       # In that case, a NotImplementedError is raised
       pass

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



   '''simple docstring'''



   lowerCamelCase__:      str			  =self.config_class(**self.inputs_dict)
   lowerCamelCase__:      List[str]			  =json.loads(config.to_json_string())
   for key, value in self.inputs_dict.items():
     self.parent.assertEqual(obj[key]       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =self.config_class(**self.inputs_dict)

   with tempfile.TemporaryDirectory() as tmpdirname:
     lowerCamelCase__:      List[str]			  =os.path.join(UpperCAmelCase_       , "config.json")
     config_first.to_json_file(UpperCAmelCase_)
     lowerCamelCase__:      Tuple			  =self.config_class.from_json_file(UpperCAmelCase_)

   self.parent.assertEqual(config_second.to_dict()       , config_first.to_dict())

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



   '''simple docstring'''



   lowerCamelCase__:      Optional[Any]			  =self.config_class(**self.inputs_dict)

   with tempfile.TemporaryDirectory() as tmpdirname:
     config_first.save_pretrained(UpperCAmelCase_)
     lowerCamelCase__:      Any			  =self.config_class.from_pretrained(UpperCAmelCase_)

   self.parent.assertEqual(config_second.to_dict()       , config_first.to_dict())

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



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.config_class(**self.inputs_dict)

   lowerCamelCase__:      List[str]			  ="test"
   with tempfile.TemporaryDirectory() as tmpdirname:
     lowerCamelCase__:      List[str]			  =os.path.join(UpperCAmelCase_       , UpperCAmelCase_)
     config_first.save_pretrained(UpperCAmelCase_)
     lowerCamelCase__:      Any			  =self.config_class.from_pretrained(UpperCAmelCase_       , subfolder=UpperCAmelCase_)

   self.parent.assertEqual(config_second.to_dict()       , config_first.to_dict())

 def 							SCREAMING_SNAKE_CASE_					(self				:   str)       ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.config_class(**self.inputs_dict       , num_labels=5)
   self.parent.assertEqual(len(config.idalabel)       , 5)
   self.parent.assertEqual(len(config.labelaid)       , 5)

   lowerCamelCase__:      Any			  =3
   self.parent.assertEqual(len(config.idalabel)       , 3)
   self.parent.assertEqual(len(config.labelaid)       , 3)

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->int:



   '''simple docstring'''



   if self.config_class.is_composition:
     return
   lowerCamelCase__:      Tuple			  =self.config_class()
   self.parent.assertIsNotNone(UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =copy.deepcopy(UpperCAmelCase_)
   lowerCamelCase__:      str			  =self.config_class(**UpperCAmelCase_)
   lowerCamelCase__:      Optional[int]			  =[]
   for key, value in config_common_kwargs.items():
     if key == "torch_dtype":
       if not is_torch_available():
         continue
       else:
         import torch

         if config.torch_dtype != torch.floataa:
           wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa))
     elif getattr(UpperCAmelCase_       , UpperCAmelCase_) != value:
       wrong_values.append((key, getattr(UpperCAmelCase_       , UpperCAmelCase_), value))

   if len(UpperCAmelCase_) > 0:
     lowerCamelCase__:      List[Any]			  ="\n".join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values])
     raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""")







 def 							SCREAMING_SNAKE_CASE_					(self				:   List[str])       ->Tuple:



   '''simple docstring'''



   self.create_and_test_config_common_properties()
   self.create_and_test_config_to_json_string()
   self.create_and_test_config_to_json_file()
   self.create_and_test_config_from_and_save_pretrained()
   self.create_and_test_config_from_and_save_pretrained_subfolder()
   self.create_and_test_config_with_num_labels()
   self.check_config_can_be_init_without_params()
   self.check_config_arguments_init()








import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			(DDPMParallelScheduler,)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any       , **UpperCAmelCase_				:   Any)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Any			  ={
       "num_train_timesteps": 1_000,
       "beta_start": 0.0001,
       "beta_end": 0.02,
       "beta_schedule": "linear",
       "variance_type": "fixed_small",
       "clip_sample": True,
   }

   config.update(**UpperCAmelCase_)
   return config

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Dict:



   '''simple docstring'''



   for timesteps in [1, 5, 100, 1_000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''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=UpperCAmelCase_       , beta_end=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Optional[int]:



   '''simple docstring'''



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

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->Tuple:



   '''simple docstring'''



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

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



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->int:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->str:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   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 							SCREAMING_SNAKE_CASE_					(self				:   Any)       ->str:



   '''simple docstring'''



   lowerCamelCase__:      int			  =self.scheduler_classes[0]
   lowerCamelCase__:      Tuple			  =self.get_scheduler_config()
   lowerCamelCase__:      Tuple			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =self.dummy_model()
   lowerCamelCase__:      int			  =self.dummy_sample_deter
   lowerCamelCase__:      Union[str, Any]			  =self.dummy_sample_deter + 0.1
   lowerCamelCase__:      Optional[Any]			  =self.dummy_sample_deter - 0.1

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

   lowerCamelCase__:      Optional[int]			  =model(samples.flatten(0       , 1)       , timesteps.flatten(0       , 1))
   lowerCamelCase__:      Tuple			  =scheduler.batch_step_no_noise(UpperCAmelCase_       , timesteps.flatten(0       , 1)       , samples.flatten(0       , 1))

   lowerCamelCase__:      List[str]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Any			  =torch.mean(torch.abs(UpperCAmelCase_))

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

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



   '''simple docstring'''



   lowerCamelCase__:      Any			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[int]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =len(UpperCAmelCase_)

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

   for t in reversed(range(UpperCAmelCase_)):
     # 1. predict noise residual
     lowerCamelCase__:      Tuple			  =model(UpperCAmelCase_       , UpperCAmelCase_)

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Optional[Any]			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      Any			  =pred_prev_sample

   lowerCamelCase__:      Any			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      List[str]			  =torch.mean(torch.abs(UpperCAmelCase_))

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   int)       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config(prediction_type="v_prediction")
   lowerCamelCase__:      Any			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      str			  =len(UpperCAmelCase_)

   lowerCamelCase__:      str			  =self.dummy_model()
   lowerCamelCase__:      str			  =self.dummy_sample_deter
   lowerCamelCase__:      Dict			  =torch.manual_seed(0)

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

     # 2. predict previous mean of sample x_t-1
     lowerCamelCase__:      Dict			  =scheduler.step(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , generator=UpperCAmelCase_).prev_sample

     lowerCamelCase__:      List[str]			  =pred_prev_sample

   lowerCamelCase__:      List[Any]			  =torch.sum(torch.abs(UpperCAmelCase_))
   lowerCamelCase__:      Tuple			  =torch.mean(torch.abs(UpperCAmelCase_))

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple)       ->Optional[int]:



   '''simple docstring'''



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

   lowerCamelCase__:      List[Any]			  =[100, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=UpperCAmelCase_)

   lowerCamelCase__:      Union[str, Any]			  =scheduler.timesteps

   for i, timestep in enumerate(UpperCAmelCase_):
     if i == len(UpperCAmelCase_) - 1:
       lowerCamelCase__:      Dict			  =-1
     else:
       lowerCamelCase__:      Union[str, Any]			  =timesteps[i + 1]

     lowerCamelCase__:      Tuple			  =scheduler.previous_timestep(UpperCAmelCase_)
     lowerCamelCase__:      str			  =prev_t.item()

     self.assertEqual(UpperCAmelCase_       , UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      List[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Dict			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[Any]			  =[100, 87, 50, 51, 0]

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

 def 							SCREAMING_SNAKE_CASE_					(self				:   List[Any])       ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:      Dict			  =self.scheduler_classes[0]
   lowerCamelCase__:      Any			  =self.get_scheduler_config()
   lowerCamelCase__:      int			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =[100, 87, 50, 1, 0]
   lowerCamelCase__:      int			  =len(UpperCAmelCase_)

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







 def 							SCREAMING_SNAKE_CASE_					(self				:   Optional[Any])       ->Any:



   '''simple docstring'''



   lowerCamelCase__:      Tuple			  =self.scheduler_classes[0]
   lowerCamelCase__:      Optional[Any]			  =self.get_scheduler_config()
   lowerCamelCase__:      Optional[Any]			  =scheduler_class(**UpperCAmelCase_)

   lowerCamelCase__:      Dict			  =[scheduler.config.num_train_timesteps]

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

