



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 					_A (			__UpperCAmelCase					):
       UpperCamelCase__						:					int		      =		(DDIMParallelScheduler,)
       UpperCamelCase__						:					Optional[Any]		      =		(('''eta''', 0.0), ('''num_inference_steps''', 50))



       def 	_lowerCamelCase    (	self							:  List[str]  ,	**__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       {
               '''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 	_lowerCamelCase    (	self							:  Dict  ,	**__SCREAMING_SNAKE_CASE							:  Dict):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config(**__SCREAMING_SNAKE_CASE)
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       10, 0.0

           __a		 =       self.dummy_model()
           __a		 =       self.dummy_sample_deter

           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

           for t in scheduler.timesteps:
               __a		 =       model(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)
               __a		 =       scheduler.step(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE).prev_sample

           return sample



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           for timesteps in [100, 500, 1_000]:
               self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

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

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config(steps_offset=1)
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)
           scheduler.set_timesteps(5)
           assert torch.equal(scheduler.timesteps  ,	torch.LongTensor([801, 601, 401, 201, 1]))



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           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 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''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 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           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 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''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 	_lowerCamelCase    (	self							:  Dict):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config()
           __a		 =       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 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.scheduler_classes[0]
           __a		 =       self.get_scheduler_config()
           __a		 =       scheduler_class(**__SCREAMING_SNAKE_CASE)

           __a ,     __a		 =       10, 0.0
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE)

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

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

           __a		 =       model(samples.flatten(0  ,	1)  ,	timesteps.flatten(0  ,	1))
           __a		 =       scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE  ,	timesteps.flatten(0  ,	1)  ,	samples.flatten(0  ,	1)  ,	__SCREAMING_SNAKE_CASE)

           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       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 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       self.full_loop()

           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       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 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a		 =       self.full_loop(prediction_type='''v_prediction''')

           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       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 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,	beta_start=0.01)
           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       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 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE  ,	beta_start=0.01)
           __a		 =       torch.sum(torch.abs(__SCREAMING_SNAKE_CASE))
           __a		 =       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 os
import sys
import unittest


__snake_case		:Union[str, Any]   	=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__snake_case		:List[str]   	=  os.path.join(git_repo_path, '''src''', '''transformers''')

__snake_case		:Any   	=  '''
{0} = None
'''

__snake_case		:Dict   	=  '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


__snake_case		:str   	=  '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''

class 					_A (			unittest.TestCase					):



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       find_backend('''    _import_structure["models.albert"].append("AlbertTokenizerFast")''')
           self.assertIsNone(__SCREAMING_SNAKE_CASE)

           __a		 =       find_backend('''    if not is_tokenizers_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tokenizers''')

           __a		 =       find_backend('''    if not is_tensorflow_text_available():''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''tensorflow_text''')

           __a		 =       find_backend('''    if not (is_sentencepiece_available() and is_tokenizers_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tensorflow_text_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tensorflow_text''')

           __a		 =       find_backend(
               '''    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''sentencepiece_and_tokenizers_and_vision''')



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       read_init()
           # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
           self.assertIn('''torch'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''tensorflow_text'''  ,	__SCREAMING_SNAKE_CASE)
           self.assertIn('''sentencepiece_and_tokenizers'''  ,	__SCREAMING_SNAKE_CASE)

           # Likewise, we can't assert on the exact content of a key
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertModel'''  ,	objects['''tf'''])
           self.assertIn('''FlaxBertModel'''  ,	objects['''flax'''])
           self.assertIn('''BertModel'''  ,	objects['''torch'''])
           self.assertIn('''TFBertTokenizer'''  ,	objects['''tensorflow_text'''])
           self.assertIn('''convert_slow_tokenizer'''  ,	objects['''sentencepiece_and_tokenizers'''])



       def 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           __a		 =       create_dummy_object('''CONSTANT'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	'''\nCONSTANT = None\n''')

           __a		 =       create_dummy_object('''function'''  ,	'''\'torch\'''')
           self.assertEqual(
               __SCREAMING_SNAKE_CASE  ,	'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n''')

           __a		 =       '''
class FakeClass(metaclass=DummyObject):
    _backends = \'torch\'

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')
'''
           __a		 =       create_dummy_object('''FakeClass'''  ,	'''\'torch\'''')
           self.assertEqual(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)





       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, ["torch"])


class FakeClass(metaclass=DummyObject):
    _backends = ["torch"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
'''
           __a		 =       create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']})
           self.assertEqual(dummy_files['''torch''']  ,	__SCREAMING_SNAKE_CASE)





