

'''simple docstring'''






import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    (CMStochasticIterativeScheduler,)
  SCREAMING_SNAKE_CASE					:						int           =    1_0






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,**lowercase__     :  str    ):
   __lowercase													=				{
       '''num_train_timesteps''': 2_0_1,
       '''sigma_min''': 0.0_0_2,
       '''sigma_max''': 8_0.0,
   }

   config.update(**_SCREAMING_SNAKE_CASE    )
   return config






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				1_0

   __lowercase													=				self.get_scheduler_config()
   __lowercase													=				self.scheduler_classes[0](**_SCREAMING_SNAKE_CASE    )

   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )

   __lowercase													=				scheduler.timesteps[0]
   __lowercase													=				scheduler.timesteps[1]

   __lowercase													=				self.dummy_sample
   __lowercase													=				0.1 * sample

   __lowercase													=				scheduler.step(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE    ).prev_sample
   __lowercase													=				scheduler.step(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE    ).prev_sample

   self.assertEqual(output_a.shape      ,sample.shape    )
   self.assertEqual(output_a.shape      ,output_a.shape    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):






   for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
    self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):






   for clip_denoised in [True, False]:
    self.check_over_configs(clip_denoised=_SCREAMING_SNAKE_CASE    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.scheduler_classes[0]
   __lowercase													=				self.get_scheduler_config()
   __lowercase													=				scheduler_class(**_SCREAMING_SNAKE_CASE    )

   __lowercase													=				1
   scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
   __lowercase													=				scheduler.timesteps

   __lowercase													=				torch.manual_seed(0    )

   __lowercase													=				self.dummy_model()
   __lowercase													=				self.dummy_sample_deter * scheduler.init_noise_sigma

   for i, t in enumerate(_SCREAMING_SNAKE_CASE    ):
    # 1. scale model input
    __lowercase													=				scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE    )

    # 2. predict noise residual
    __lowercase													=				model(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE    )

    # 3. predict previous sample x_t-1
    __lowercase													=				scheduler.step(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,generator=_SCREAMING_SNAKE_CASE    ).prev_sample

    __lowercase													=				pred_prev_sample

   __lowercase													=				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
   __lowercase													=				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

   assert abs(result_sum.item() - 1_9_2.7_6_1_4    ) < 1e-2
   assert abs(result_mean.item() - 0.2_5_1_0    ) < 1e-3






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.scheduler_classes[0]
   __lowercase													=				self.get_scheduler_config()
   __lowercase													=				scheduler_class(**_SCREAMING_SNAKE_CASE    )

   __lowercase													=				[1_0_6, 0]
   scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE    )
   __lowercase													=				scheduler.timesteps

   __lowercase													=				torch.manual_seed(0    )

   __lowercase													=				self.dummy_model()
   __lowercase													=				self.dummy_sample_deter * scheduler.init_noise_sigma

   for t in timesteps:
    # 1. scale model input
    __lowercase													=				scheduler.scale_model_input(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE    )

    # 2. predict noise residual
    __lowercase													=				model(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE    )

    # 3. predict previous sample x_t-1
    __lowercase													=				scheduler.step(_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,_SCREAMING_SNAKE_CASE      ,generator=_SCREAMING_SNAKE_CASE    ).prev_sample

    __lowercase													=				pred_prev_sample

   __lowercase													=				torch.sum(torch.abs(_SCREAMING_SNAKE_CASE    )    )
   __lowercase													=				torch.mean(torch.abs(_SCREAMING_SNAKE_CASE    )    )

   assert abs(result_sum.item() - 3_4_7.6_3_5_7    ) < 1e-2
   assert abs(result_mean.item() - 0.4_5_2_7    ) < 1e-3






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   __lowercase													=				self.scheduler_classes[0]
   __lowercase													=				self.get_scheduler_config()
   __lowercase													=				scheduler_class(**_SCREAMING_SNAKE_CASE    )

   __lowercase													=				[3_9, 3_0, 1_2, 1_5, 0]

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.scheduler_classes[0]
   __lowercase													=				self.get_scheduler_config()
   __lowercase													=				scheduler_class(**_SCREAMING_SNAKE_CASE    )

   __lowercase													=				[3_9, 3_0, 1_2, 1, 0]
   __lowercase													=				len(_SCREAMING_SNAKE_CASE    )

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.scheduler_classes[0]
   __lowercase													=				self.get_scheduler_config()
   __lowercase													=				scheduler_class(**_SCREAMING_SNAKE_CASE    )

   __lowercase													=				[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'''






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def __init__(   self     :  Optional[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]=None      ,lowercase__     :  Any=True      ,lowercase__     :  Union[str, Any]=None      ,**lowercase__     :  Dict    ):
   __lowercase													=				parent
   __lowercase													=				config_class
   __lowercase													=				has_text_modality
   __lowercase													=				kwargs
   __lowercase													=				common_properties






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )
   __lowercase													=				(
       ['''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(lowercase__      ,lowercase__    )      ,msg=F"`{prop}` does not exist"    )

   # Test that config has the common properties as setter
   for idx, name in enumerate(lowercase__    ):
    try:
     setattr(lowercase__      ,lowercase__      ,lowercase__    )
     self.parent.assertEqual(
         getattr(lowercase__      ,lowercase__    )      ,lowercase__      ,msg=F"`{name} value {idx} expected, but was {getattr(lowercase__      ,lowercase__    )}"    )
    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(lowercase__    ):
    try:
     __lowercase													=				self.config_class(**{name: idx}    )
     self.parent.assertEqual(
         getattr(lowercase__      ,lowercase__    )      ,lowercase__      ,msg=F"`{name} value {idx} expected, but was {getattr(lowercase__      ,lowercase__    )}"    )
    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     :  str    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )
   __lowercase													=				json.loads(config.to_json_string()    )
   for key, value in self.inputs_dict.items():
    self.parent.assertEqual(obj[key]      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase													=				os.path.join(lowercase__      ,'''config.json'''    )
    config_first.to_json_file(lowercase__    )
    __lowercase													=				self.config_class.from_json_file(lowercase__    )

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )

   with tempfile.TemporaryDirectory() as tmpdirname:
    config_first.save_pretrained(lowercase__    )
    __lowercase													=				self.config_class.from_pretrained(lowercase__    )

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.config_class(**self.inputs_dict    )

   __lowercase													=				'''test'''
   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase													=				os.path.join(lowercase__      ,lowercase__    )
    config_first.save_pretrained(lowercase__    )
    __lowercase													=				self.config_class.from_pretrained(lowercase__      ,subfolder=lowercase__    )

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				self.config_class(**self.inputs_dict      ,num_labels=5    )
   self.parent.assertEqual(len(config.idalabel    )      ,5    )
   self.parent.assertEqual(len(config.labelaid    )      ,5    )

   __lowercase													=				3
   self.parent.assertEqual(len(config.idalabel    )      ,3    )
   self.parent.assertEqual(len(config.labelaid    )      ,3    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   if self.config_class.is_composition:
    return
   __lowercase													=				self.config_class()
   self.parent.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				copy.deepcopy(lowercase__    )
   __lowercase													=				self.config_class(**lowercase__    )
   __lowercase													=				[]
   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(lowercase__      ,lowercase__    ) != value:
     wrong_values.append((key, getattr(lowercase__      ,lowercase__    ), value)    )

   if len(lowercase__    ) > 0:
    __lowercase													=				'''\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     :  Optional[int]    ):
   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()

