




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 						_A (  _lowerCamelCase				):
		_UpperCamelCase       :      Tuple			=      (PNDMScheduler,)
		_UpperCamelCase       :      str			=      (('''num_inference_steps''', 5_0),)






		def  __a      (  self		:     Any				,						**_A		:     Dict				)		->   Any:






							"""simple docstring"""







							lowercase :       Dict       =			{
							    '''num_train_timesteps''': 1_000,
							    '''beta_start''': 0.0_001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**_lowerCAmelCase				)
							return config






		def  __a      (  self		:     Tuple				,						_A		:     List[Any]=0				,						**_A		:     str				)		->   str:






							"""simple docstring"""







							lowercase :       str       =			dict(self.forward_default_kwargs				)
							lowercase :       str       =			kwargs.pop('''num_inference_steps'''				,						_lowerCAmelCase				)
							lowercase :       Dict       =			self.dummy_sample
							lowercase :       Union[str, Any]       =			0.1 * sample
							lowercase :       List[str]       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
												lowercase :       Optional[Any]       =			self.get_scheduler_config(**_lowerCAmelCase				)
												lowercase :       Optional[int]       =			scheduler_class(**_lowerCAmelCase				)
												scheduler.set_timesteps(_lowerCAmelCase				)
												# copy over dummy past residuals
												lowercase :       List[str]       =			dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(_lowerCAmelCase				)
																	lowercase :       Tuple       =			scheduler_class.from_pretrained(_lowerCAmelCase				)
																	new_scheduler.set_timesteps(_lowerCAmelCase				)
																	# copy over dummy past residuals
																	lowercase :       Dict       =			dummy_past_residuals[:]

												lowercase :       Dict       =			scheduler.step_prk(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample
												lowercase :       List[str]       =			new_scheduler.step_prk(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample

												assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

												lowercase :       Optional[int]       =			scheduler.step_plms(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample
												lowercase :       int       =			new_scheduler.step_plms(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample

												assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"






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






							"""simple docstring"""







							pass






		def  __a      (  self		:     Optional[int]				,						_A		:     str=0				,						**_A		:     Dict				)		->   Optional[Any]:






							"""simple docstring"""







							lowercase :       Tuple       =			dict(self.forward_default_kwargs				)
							lowercase :       int       =			kwargs.pop('''num_inference_steps'''				,						_lowerCAmelCase				)
							lowercase :       int       =			self.dummy_sample
							lowercase :       Optional[int]       =			0.1 * sample
							lowercase :       Optional[Any]       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
												lowercase :       str       =			self.get_scheduler_config()
												lowercase :       Optional[int]       =			scheduler_class(**_lowerCAmelCase				)
												scheduler.set_timesteps(_lowerCAmelCase				)

												# copy over dummy past residuals (must be after setting timesteps)
												lowercase :       Optional[int]       =			dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(_lowerCAmelCase				)
																	lowercase :       Union[str, Any]       =			scheduler_class.from_pretrained(_lowerCAmelCase				)
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(_lowerCAmelCase				)

																	# copy over dummy past residual (must be after setting timesteps)
																	lowercase :       str       =			dummy_past_residuals[:]

												lowercase :       Any       =			scheduler.step_prk(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample
												lowercase :       Union[str, Any]       =			new_scheduler.step_prk(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample

												assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

												lowercase :       Any       =			scheduler.step_plms(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample
												lowercase :       Optional[int]       =			new_scheduler.step_plms(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample

												assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"






		def  __a      (  self		:     Union[str, Any]				,						**_A		:     str				)		->   Any:






							"""simple docstring"""







							lowercase :       List[str]       =			self.scheduler_classes[0]
							lowercase :       Dict       =			self.get_scheduler_config(**_lowerCAmelCase				)
							lowercase :       int       =			scheduler_class(**_lowerCAmelCase				)

							lowercase :       str       =			10
							lowercase :       int       =			self.dummy_model()
							lowercase :       Union[str, Any]       =			self.dummy_sample_deter
							scheduler.set_timesteps(_lowerCAmelCase				)

							for i, t in enumerate(scheduler.prk_timesteps				):
												lowercase :       Dict       =			model(_lowerCAmelCase				,						_lowerCAmelCase				)
												lowercase :       int       =			scheduler.step_prk(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				).prev_sample

							for i, t in enumerate(scheduler.plms_timesteps				):
												lowercase :       str       =			model(_lowerCAmelCase				,						_lowerCAmelCase				)
												lowercase :       Tuple       =			scheduler.step_plms(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				).prev_sample

							return sample






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






							"""simple docstring"""







							lowercase :       Dict       =			dict(self.forward_default_kwargs				)

							lowercase :       Optional[Any]       =			kwargs.pop('''num_inference_steps'''				,						_lowerCAmelCase				)

							for scheduler_class in self.scheduler_classes:
												lowercase :       Dict       =			self.get_scheduler_config()
												lowercase :       Dict       =			scheduler_class(**_lowerCAmelCase				)

												lowercase :       Optional[Any]       =			self.dummy_sample
												lowercase :       Union[str, Any]       =			0.1 * sample

												if num_inference_steps is not None and hasattr(_lowerCAmelCase				,						'''set_timesteps'''				):
																	scheduler.set_timesteps(_lowerCAmelCase				)
												elif num_inference_steps is not None and not hasattr(_lowerCAmelCase				,						'''set_timesteps'''				):
																	lowercase :       List[str]       =			num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												lowercase :       List[str]       =			[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												lowercase :       List[str]       =			dummy_past_residuals[:]

												lowercase :       Any       =			scheduler.step_prk(_lowerCAmelCase				,						0				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample
												lowercase :       Tuple       =			scheduler.step_prk(_lowerCAmelCase				,						1				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample

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

												lowercase :       Union[str, Any]       =			scheduler.step_plms(_lowerCAmelCase				,						0				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample
												lowercase :       Optional[int]       =			scheduler.step_plms(_lowerCAmelCase				,						1				,						_lowerCAmelCase				,						**_lowerCAmelCase				).prev_sample

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






		def  __a      (  self		:     Dict				)		->   List[str]:






							"""simple docstring"""







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






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






							"""simple docstring"""







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

							lowercase :       Optional[int]       =			self.scheduler_classes[0]
							lowercase :       Optional[Any]       =			self.get_scheduler_config(steps_offset=1				)
							lowercase :       List[str]       =			scheduler_class(**_lowerCAmelCase				)
							scheduler.set_timesteps(10				)
							assert torch.equal(
							    scheduler.timesteps				,						torch.LongTensor(
							        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]				)				,						)






		def  __a      (  self		:     Any				)		->   int:






							"""simple docstring"""







							for beta_start, beta_end in zip([0.0_001, 0.001]				,						[0.002, 0.02]				):
												self.check_over_configs(beta_start=_lowerCAmelCase				,						beta_end=_lowerCAmelCase				)






		def  __a      (  self		:     Dict				)		->   Dict:






							"""simple docstring"""







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






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






							"""simple docstring"""







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






		def  __a      (  self		:     Dict				)		->   Optional[Any]:






							"""simple docstring"""







							for t in [1, 5, 10]:
												self.check_over_forward(time_step=_lowerCAmelCase				)






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






							"""simple docstring"""







							for t, num_inference_steps in zip([1, 5, 10]				,						[10, 50, 100]				):
												self.check_over_forward(num_inference_steps=_lowerCAmelCase				)






		def  __a      (  self		:     Dict				)		->   Tuple:






							"""simple docstring"""







							lowercase :       int       =			27

							for scheduler_class in self.scheduler_classes:
												lowercase :       Tuple       =			self.dummy_sample
												lowercase :       Union[str, Any]       =			0.1 * sample

												lowercase :       Optional[int]       =			self.get_scheduler_config()
												lowercase :       Union[str, Any]       =			scheduler_class(**_lowerCAmelCase				)

												scheduler.set_timesteps(_lowerCAmelCase				)

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]				):
																	lowercase :       List[str]       =			scheduler.step_prk(_lowerCAmelCase				,						_lowerCAmelCase				,						_lowerCAmelCase				).prev_sample






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






							"""simple docstring"""







							with self.assertRaises(_lowerCAmelCase				):
												lowercase :       int       =			self.scheduler_classes[0]
												lowercase :       Optional[Any]       =			self.get_scheduler_config()
												lowercase :       Tuple       =			scheduler_class(**_lowerCAmelCase				)

												scheduler.step_plms(self.dummy_sample				,						1				,						self.dummy_sample				).prev_sample






		def  __a      (  self		:     List[Any]				)		->   List[str]:






							"""simple docstring"""







							lowercase :       Optional[int]       =			self.full_loop()
							lowercase :       int       =			torch.sum(torch.abs(_lowerCAmelCase				)				)
							lowercase :       Optional[Any]       =			torch.mean(torch.abs(_lowerCAmelCase				)				)

							assert abs(result_sum.item() - 198.1_318				) < 1E-2
							assert abs(result_mean.item() - 0.2_580				) < 1E-3






		def  __a      (  self		:     Dict				)		->   str:






							"""simple docstring"""







							lowercase :       Any       =			self.full_loop(prediction_type='''v_prediction'''				)
							lowercase :       Tuple       =			torch.sum(torch.abs(_lowerCAmelCase				)				)
							lowercase :       List[str]       =			torch.mean(torch.abs(_lowerCAmelCase				)				)

							assert abs(result_sum.item() - 67.3_986				) < 1E-2
							assert abs(result_mean.item() - 0.0_878				) < 1E-3






		def  __a      (  self		:     int				)		->   Any:






							"""simple docstring"""







							lowercase :       Optional[int]       =			self.full_loop(set_alpha_to_one=_lowerCAmelCase				,						beta_start=0.01				)
							lowercase :       Union[str, Any]       =			torch.sum(torch.abs(_lowerCAmelCase				)				)
							lowercase :       Union[str, Any]       =			torch.mean(torch.abs(_lowerCAmelCase				)				)

							assert abs(result_sum.item() - 230.0_399				) < 1E-2
							assert abs(result_mean.item() - 0.2_995				) < 1E-3






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






							"""simple docstring"""







							lowercase :       int       =			self.full_loop(set_alpha_to_one=_lowerCAmelCase				,						beta_start=0.01				)
							lowercase :       int       =			torch.sum(torch.abs(_lowerCAmelCase				)				)
							lowercase :       Optional[Any]       =			torch.mean(torch.abs(_lowerCAmelCase				)				)

							assert abs(result_sum.item() - 186.9_482				) < 1E-2
							assert abs(result_mean.item() - 0.2_434				) < 1E-3





import os
import sys
import unittest


lowerCAmelCase_						=			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
lowerCAmelCase_						=			os.path.join(git_repo_path, 'src', 'diffusers')
class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     Any				)		->   str:






       """simple docstring"""







       lowercase :       List[str]       =			find_backend('''    if not is_torch_available():'''				)
       self.assertEqual(_A				,						'''torch'''				)

       # backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
       # self.assertEqual(backend_with_underscore, "tensorflow_text")

       lowercase :       str       =			find_backend('''    if not (is_torch_available() and is_transformers_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers'''				)

       # double_backend_with_underscore = find_backend(
       #    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
       # )
       # self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

       lowercase :       str       =			find_backend(
           '''    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):'''				)
       self.assertEqual(_A				,						'''torch_and_transformers_and_onnx'''				)






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






       """simple docstring"""







       lowercase :       Dict       =			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('''torch_and_transformers'''				,						_A				)
       self.assertIn('''flax_and_transformers'''				,						_A				)
       self.assertIn('''torch_and_transformers_and_onnx'''				,						_A				)

       # Likewise, we can't assert on the exact content of a key
       self.assertIn('''UNet2DModel'''				,						objects['''torch''']				)
       self.assertIn('''FlaxUNet2DConditionModel'''				,						objects['''flax''']				)
       self.assertIn('''StableDiffusionPipeline'''				,						objects['''torch_and_transformers''']				)
       self.assertIn('''FlaxStableDiffusionPipeline'''				,						objects['''flax_and_transformers''']				)
       self.assertIn('''LMSDiscreteScheduler'''				,						objects['''torch_and_scipy''']				)
       self.assertIn('''OnnxStableDiffusionPipeline'''				,						objects['''torch_and_transformers_and_onnx''']				)






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






       """simple docstring"""







       lowercase :       List[str]       =			create_dummy_object('''CONSTANT'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						'''\nCONSTANT = None\n'''				)

       lowercase :       List[Any]       =			create_dummy_object('''function'''				,						'''\'torch\''''				)
       self.assertEqual(
           _A				,						'''\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'''				)

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

    def __init__(self, *args, **kwargs):
        requires_backends(self, \'torch\')

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, \'torch\')
'''
       lowercase :       List[Any]       =			create_dummy_object('''FakeClass'''				,						'''\'torch\''''				)
       self.assertEqual(_A				,						_A				)






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






       """simple docstring"""







       lowercase :       Optional[int]       =			'''# 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"])

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, ["torch"])
'''
       lowercase :       int       =			create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']}				)
       self.assertEqual(dummy_files['''torch''']				,						_A				)