


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       (IPNDMScheduler,)
		_a									=       (('num_inference_steps', 50),)




		def 					lowerCAmelCase__       (	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				{'''num_train_timesteps''': 1000}
							config.update(**UpperCamelCase_							)
							return config




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0					,     **UpperCamelCase_							):

							'''simple docstring'''


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

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

												if time_step is None:
																	UpperCamelCase__     :Tuple							      =				scheduler.timesteps[len(scheduler.timesteps							) // 2]

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

												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Union[str, Any]							      =				new_scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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

												UpperCamelCase__     :List[Any]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Dict							      =				new_scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0					,     **UpperCamelCase_							):

							'''simple docstring'''


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

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__     :Optional[Any]							      =				self.get_scheduler_config()
												UpperCamelCase__     :Any							      =				scheduler_class(**UpperCamelCase_							)
												scheduler.set_timesteps(UpperCamelCase_							)

												# copy over dummy past residuals (must be after setting timesteps)
												UpperCamelCase__     :List[str]							      =				dummy_past_residuals[:]

												if time_step is None:
																	UpperCamelCase__     :str							      =				scheduler.timesteps[len(scheduler.timesteps							) // 2]

												with tempfile.TemporaryDirectory() as tmpdirname:
																	scheduler.save_config(UpperCamelCase_							)
																	UpperCamelCase__     :Optional[Any]							      =				scheduler_class.from_pretrained(UpperCamelCase_							)
																	# copy over dummy past residuals
																	new_scheduler.set_timesteps(UpperCamelCase_							)

																	# copy over dummy past residual (must be after setting timesteps)
																	UpperCamelCase__     :Optional[Any]							      =				dummy_past_residuals[:]

												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Union[str, Any]							      =				new_scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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

												UpperCamelCase__     :List[Any]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :Tuple							      =				new_scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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




		def 					lowerCAmelCase__       (	self					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.scheduler_classes[0]
							UpperCamelCase__     :Dict							      =				self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__     :Dict							      =				10
							UpperCamelCase__     :int							      =				self.dummy_model()
							UpperCamelCase__     :str							      =				self.dummy_sample_deter
							scheduler.set_timesteps(UpperCamelCase_							)

							for i, t in enumerate(scheduler.timesteps							):
												UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :Any							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							).prev_sample

							for i, t in enumerate(scheduler.timesteps							):
												UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_					,     UpperCamelCase_							)
												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							).prev_sample

							return sample




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				dict(self.forward_default_kwargs							)

							UpperCamelCase__     :int							      =				kwargs.pop('''num_inference_steps'''					,     UpperCamelCase_							)

							for scheduler_class in self.scheduler_classes:
												UpperCamelCase__     :int							      =				self.get_scheduler_config()
												UpperCamelCase__     :Dict							      =				scheduler_class(**UpperCamelCase_							)

												UpperCamelCase__     :List[Any]							      =				self.dummy_sample
												UpperCamelCase__     :Tuple							      =				0.1 * sample

												if num_inference_steps is not None and hasattr(UpperCamelCase_					,     '''set_timesteps'''							):
																	scheduler.set_timesteps(UpperCamelCase_							)
												elif num_inference_steps is not None and not hasattr(UpperCamelCase_					,     '''set_timesteps'''							):
																	UpperCamelCase__     :Dict							      =				num_inference_steps

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

												UpperCamelCase__     :str							      =				scheduler.timesteps[5]
												UpperCamelCase__     :List[str]							      =				scheduler.timesteps[6]

												UpperCamelCase__     :List[Any]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :int							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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

												UpperCamelCase__     :Optional[int]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample
												UpperCamelCase__     :List[str]							      =				scheduler.step(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							).prev_sample

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for timesteps in [100, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_					,     time_step=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.full_loop()
							UpperCamelCase__     :Optional[Any]							      =				torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_mean.item() - 2540529							) < 10



'''simple docstring'''
import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
		import torch
@require_torch
@require_optimum
@slow
class      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :int							      =				AutoTokenizer.from_pretrained(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :int							      =				tokenizer('''This is me'''					,     return_tensors='''pt'''							)

							UpperCamelCase__     :Optional[int]							      =				model.to_bettertransformer()

							self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							UpperCamelCase__     :int							      =				model.generate(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				model.reverse_bettertransformer()

							self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()							)							)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

												self.assertFalse(
												    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()							)							)

												UpperCamelCase__     :Tuple							      =				model_reloaded.generate(**UpperCamelCase_							)
												self.assertTrue(torch.allclose(UpperCamelCase_					,     UpperCamelCase_							)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				'''hf-internal-testing/tiny-random-t5'''
							UpperCamelCase__     :Dict							      =				AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_							)

							UpperCamelCase__     :Union[str, Any]							      =				model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
												with self.assertRaises(UpperCamelCase_							):
																	model.save_pretrained(UpperCamelCase_							)

												UpperCamelCase__     :Tuple							      =				model.reverse_bettertransformer()
												model.save_pretrained(UpperCamelCase_							)