





"""simple docstring"""
import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest






class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   Optional[int]					=						(DDIMScheduler,)
       SCREAMING_SNAKE_CASE_     :   Any					=						(('''eta''', 0.0), ('''num_inference_steps''', 50))




       def 					_UpperCamelCase  (		self	,**SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :int	       =						{
                 '''num_train_timesteps''': 10_00,
                 '''beta_start''': 0.0_0_0_1,
                 '''beta_end''': 0.0_2,
                 '''beta_schedule''': '''linear''',
                 '''clip_sample''': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__							)
             return config




       def 					_UpperCamelCase  (		self	,**SCREAMING_SNAKE_CASE__							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE     :Any	       =						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						scheduler_class(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						10, 0.0

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.dummy_model()
             __SCREAMING_SNAKE_CASE     :str	       =						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							)

             for t in scheduler.timesteps:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						scheduler.step(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							).prev_sample

             return sample




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             for timesteps in [1_00, 5_00, 10_00]:
                   self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             for steps_offset in [0, 1]:
                   self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :List[str]	       =						self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.get_scheduler_config(steps_offset=1							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						scheduler_class(**SCREAMING_SNAKE_CASE__							)
             scheduler.set_timesteps(5							)
             assert torch.equal(scheduler.timesteps	,torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]							)							)




       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]	,[0.0_0_2, 0.0_2, 0.2, 2]							):
                   self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__	,beta_end=SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






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




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





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






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




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





             """simple docstring"""






             for timestep_spacing in ["trailing", "leading"]:
                   self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             for rescale_betas_zero_snr in [True, False]:
                   self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             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 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             for t in [1, 10, 49]:
                   self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             for t, num_inference_steps in zip([1, 10, 50]	,[10, 50, 5_00]							):
                   self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__	,num_inference_steps=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             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 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						self.scheduler_classes[0]
             __SCREAMING_SNAKE_CASE     :Any	       =						self.get_scheduler_config()
             __SCREAMING_SNAKE_CASE     :List[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(4_20	,4_00							) - 0.1_4_7_7_1							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_80	,9_60							) - 0.3_2_4_6_0							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(0	,0							) - 0.0							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(4_87	,4_86							) - 0.0_0_9_7_9							)							) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_99	,9_98							) - 0.0_2							)							) < 1E-5




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.full_loop()

             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE     :Any	       =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

             assert abs(result_sum.item() - 1_7_2.0_0_6_7							) < 1E-2
             assert abs(result_mean.item() - 0.2_2_3_9_6_7							) < 1E-3




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.full_loop(prediction_type='''v_prediction'''							)

             __SCREAMING_SNAKE_CASE     :str	       =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

             assert abs(result_sum.item() - 5_2.5_3_0_2							) < 1E-2
             assert abs(result_mean.item() - 0.0_6_8_4							) < 1E-3




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__	,beta_start=0.0_1							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

             assert abs(result_sum.item() - 1_4_9.8_2_9_5							) < 1E-2
             assert abs(result_mean.item() - 0.1_9_5_1							) < 1E-3




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__	,beta_start=0.0_1							)
             __SCREAMING_SNAKE_CASE     :str	       =						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__							)							)
             __SCREAMING_SNAKE_CASE     :int	       =						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__							)							)

             assert abs(result_sum.item() - 1_4_9.0_7_8_4							) < 1E-2
             assert abs(result_mean.item() - 0.1_9_4_1							) < 1E-3






"""simple docstring"""
# Lint as: python3

import hashlib
from typing import Union



def   __lowerCamelCase     (							a_  :	Union[str, int, bytes]							)    ->   bytes:
      if isinstance(a_				,     a_							):
            # Data already in bytes, returns as it as
            return hash_data
      elif isinstance(a_				,     a_							):
            # We keep the data as it as for it ot be later encoded to UTF-8
            # However replace `\\` with `/` for Windows compatibility
            __SCREAMING_SNAKE_CASE     :str	       =						hash_data.replace('''\\'''				,     '''/'''							)
      elif isinstance(a_				,     a_							):
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						str(a_							)
      else:
            # If data is not of the required type, raise error
            raise InvalidKeyError(a_							)

      return hash_data.encode('''utf-8'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						'''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
             __SCREAMING_SNAKE_CASE     :int	       =						f'''\nFound Key {hash_data} of type {type(SCREAMING_SNAKE_CASE__							)}'''
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''\nKeys should be either str, int or bytes type'''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=""							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						key
             __SCREAMING_SNAKE_CASE     :str	       =						duplicate_key_indices
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						fix_msg
             __SCREAMING_SNAKE_CASE     :int	       =						'''Found multiple examples generated with the same key'''
             if len(SCREAMING_SNAKE_CASE__							) <= 20:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						f'''\nThe examples at index {", ".join(SCREAMING_SNAKE_CASE__							)} have the key {key}'''
             else:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						f'''\nThe examples at index {", ".join(duplicate_key_indices[:20]							)}... ({len(SCREAMING_SNAKE_CASE__							) - 20} more) have the key {key}'''
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						'''\n''' + fix_msg if fix_msg else ''''''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE:




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						hashlib.mda(_as_bytes(SCREAMING_SNAKE_CASE__							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self._split_mda.copy()
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						_as_bytes(SCREAMING_SNAKE_CASE__							)
             mda.update(SCREAMING_SNAKE_CASE__							)
             # Convert to integer with hexadecimal conversion
             return int(mda.hexdigest()	,16							)