



'''simple docstring'''

import os
import sys
import unittest


a							   =				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
a							   =				os.path.join(git_repo_path, 'src', 'transformers')

a							   =				'\n{0} = None\n'

a							   =				'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


a							   =				'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						Dict			)			->					Dict:
										snake_case:    Union[str, Any]      							=find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'			)
										self.assertIsNone(a_			)

										snake_case:    Optional[Any]      							=find_backend('    if not is_tokenizers_available():'			)
										self.assertEqual(a_      ,						'tokenizers'			)

										snake_case:    Tuple      							=find_backend('    if not is_tensorflow_text_available():'			)
										self.assertEqual(a_      ,						'tensorflow_text'			)

										snake_case:    Union[str, Any]      							=find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'			)
										self.assertEqual(a_      ,						'sentencepiece_and_tokenizers'			)

										snake_case:    Optional[Any]      							=find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'			)
										self.assertEqual(a_      ,						'sentencepiece_and_tensorflow_text'			)

										snake_case:    Optional[int]      							=find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'			)
										self.assertEqual(a_      ,						'sentencepiece_and_tokenizers_and_vision'			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Any:
										snake_case:    int      							=read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'      ,						a_			)
										self.assertIn('tensorflow_text'      ,						a_			)
										self.assertIn('sentencepiece_and_tokenizers'      ,						a_			)

										# 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    UpperCamelCase     (				self   :						Optional[Any]			)			->					Union[str, Any]:
										snake_case:    List[Any]      							=create_dummy_object('CONSTANT'      ,						'\'torch\''			)
										self.assertEqual(a_      ,						'\nCONSTANT = None\n'			)

										snake_case:    Tuple      							=create_dummy_object('function'      ,						'\'torch\''			)
										self.assertEqual(
										    a_      ,						'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'			)

										snake_case:    List[Any]      							='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case:    Tuple      							=create_dummy_object('FakeClass'      ,						'\'torch\''			)
										self.assertEqual(a_      ,						a_			)




							def    UpperCamelCase     (				self   :						Dict			)			->					Any:
										snake_case:    Union[str, Any]      							='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case:    Union[str, Any]      							=create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}			)
										self.assertEqual(dummy_files['torch']      ,						a_			)









'''simple docstring'''

import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest






class 						a_		(     snake_case    ):
							UpperCAmelCase			:		str        =				(CMStochasticIterativeScheduler,)
							UpperCAmelCase			:		int        =				10


							def    UpperCamelCase     (				self   :						Dict      ,						**a_   :						List[str]			)			->					Any:
										snake_case:    Any      							={
										    'num_train_timesteps': 2_0_1,
										    'sigma_min': 0.0_0_2,
										    'sigma_max': 8_0.0,
										}

										config.update(**a_			)
										return config


							def    UpperCamelCase     (				self   :						List[Any]			)			->					List[Any]:
										snake_case:    Any      							=1_0

										snake_case:    List[str]      							=self.get_scheduler_config()
										snake_case:    List[Any]      							=self.scheduler_classes[0](**a_			)

										scheduler.set_timesteps(a_			)

										snake_case:    Dict      							=scheduler.timesteps[0]
										snake_case:    Union[str, Any]      							=scheduler.timesteps[1]

										snake_case:    List[str]      							=self.dummy_sample
										snake_case:    List[str]      							=0.1 * sample

										snake_case:    int      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample
										snake_case:    Optional[Any]      							=scheduler.step(a_      ,						a_      ,						a_			).prev_sample

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


							def    UpperCamelCase     (				self   :						int			)			->					int:
										for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=a_			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Dict:
										for clip_denoised in [True, False]:
													self.check_over_configs(clip_denoised=a_			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					List[str]:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    List[Any]      							=self.get_scheduler_config()
										snake_case:    Any      							=scheduler_class(**a_			)

										snake_case:    Dict      							=1
										scheduler.set_timesteps(a_			)
										snake_case:    List[Any]      							=scheduler.timesteps

										snake_case:    Optional[Any]      							=torch.manual_seed(0			)

										snake_case:    Optional[Any]      							=self.dummy_model()
										snake_case:    List[Any]      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for i, t in enumerate(a_			):
													# 1. scale model input
													snake_case:    Any      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    List[str]      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    Dict      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    List[Any]      							=pred_prev_sample

										snake_case:    Optional[Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Optional[Any]      							=torch.mean(torch.abs(a_			)			)

										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    UpperCamelCase     (				self   :						Dict			)			->					Union[str, Any]:
										snake_case:    Dict      							=self.scheduler_classes[0]
										snake_case:    Tuple      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[1_0_6, 0]
										scheduler.set_timesteps(timesteps=a_			)
										snake_case:    Optional[Any]      							=scheduler.timesteps

										snake_case:    Dict      							=torch.manual_seed(0			)

										snake_case:    Optional[int]      							=self.dummy_model()
										snake_case:    Any      							=self.dummy_sample_deter * scheduler.init_noise_sigma

										for t in timesteps:
													# 1. scale model input
													snake_case:    List[Any]      							=scheduler.scale_model_input(a_      ,						a_			)

													# 2. predict noise residual
													snake_case:    Any      							=model(a_      ,						a_			)

													# 3. predict previous sample x_t-1
													snake_case:    List[str]      							=scheduler.step(a_      ,						a_      ,						a_      ,						generator=a_			).prev_sample

													snake_case:    Optional[Any]      							=pred_prev_sample

										snake_case:    Union[str, Any]      							=torch.sum(torch.abs(a_			)			)
										snake_case:    Tuple      							=torch.mean(torch.abs(a_			)			)

										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    UpperCamelCase     (				self   :						int			)			->					Tuple:
										snake_case:    List[Any]      							=self.scheduler_classes[0]
										snake_case:    Union[str, Any]      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    str      							=[3_9, 3_0, 1_2, 1_5, 0]

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


							def    UpperCamelCase     (				self   :						Dict			)			->					Optional[int]:
										snake_case:    Optional[Any]      							=self.scheduler_classes[0]
										snake_case:    Dict      							=self.get_scheduler_config()
										snake_case:    str      							=scheduler_class(**a_			)

										snake_case:    Any      							=[3_9, 3_0, 1_2, 1, 0]
										snake_case:    List[Any]      							=len(a_			)

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




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Tuple:
										snake_case:    Any      							=self.scheduler_classes[0]
										snake_case:    int      							=self.get_scheduler_config()
										snake_case:    Optional[Any]      							=scheduler_class(**a_			)

										snake_case:    List[Any]      							=[scheduler.config.num_train_timesteps]

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




