





'''simple docstring'''




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
							import jax
							import jax.numpy as jnp

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
							import torch




class       UpperCamelCase__			:




					"""simple docstring"""


					def __init__(		self							:	List[str]     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	Dict=14     ,		lowerCamelCase_							:	Any=7     ,		lowerCamelCase_							:	List[Any]=True     ,		lowerCamelCase_							:	int=True     ,		lowerCamelCase_							:	int=False     ,		lowerCamelCase_							:	Any=True     ,		lowerCamelCase_							:	List[Any]=99     ,		lowerCamelCase_							:	Optional[int]=32     ,		lowerCamelCase_							:	int=4     ,		lowerCamelCase_							:	Dict=4     ,		lowerCamelCase_							:	Any=[[["global", "local"], 2]]     ,		lowerCamelCase_							:	List[Any]=37     ,		lowerCamelCase_							:	Dict="gelu"     ,		lowerCamelCase_							:	str=0.1     ,		lowerCamelCase_							:	Union[str, Any]=0.1     ,		lowerCamelCase_							:	Any=5_12     ,		lowerCamelCase_							:	Dict=7     ,		lowerCamelCase_							:	int=0.02     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	parent
							SCREAMING_SNAKE_CASE				:							Dict						=	batch_size
							SCREAMING_SNAKE_CASE				:							List[str]						=	seq_length
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	is_training
							SCREAMING_SNAKE_CASE				:							List[Any]						=	use_input_mask
							SCREAMING_SNAKE_CASE				:							List[str]						=	use_token_type_ids
							SCREAMING_SNAKE_CASE				:							Any						=	use_labels
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	vocab_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	hidden_size
							SCREAMING_SNAKE_CASE				:							str						=	num_hidden_layers
							SCREAMING_SNAKE_CASE				:							Any						=	num_attention_heads
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	attention_types
							SCREAMING_SNAKE_CASE				:							Dict						=	intermediate_size
							SCREAMING_SNAKE_CASE				:							Any						=	hidden_act
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	max_position_embeddings
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	window_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	initializer_range
							SCREAMING_SNAKE_CASE				:							Dict						=	None
							SCREAMING_SNAKE_CASE				:							List[Any]						=	vocab_size - 1
							SCREAMING_SNAKE_CASE				:							Tuple						=	vocab_size - 1
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	vocab_size - 1


					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	None
							if self.use_input_mask:
									SCREAMING_SNAKE_CASE				:							Dict						=	random_attention_mask([self.batch_size, self.seq_length]		)

							SCREAMING_SNAKE_CASE				:							int						=	GPTNeoConfig(
							    vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		num_layers=self.num_hidden_layers     ,		num_heads=self.num_attention_heads     ,		max_position_embeddings=self.max_position_embeddings     ,		use_cache=lowerCamelCase_     ,		bos_token_id=self.bos_token_id     ,		eos_token_id=self.eos_token_id     ,		pad_token_id=self.pad_token_id     ,		window_size=self.window_size     ,		attention_types=self.attention_types     ,		)

							return (config, input_ids, input_mask)


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.prepare_config_and_inputs()
							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	config_and_inputs
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{"""input_ids""": input_ids, """attention_mask""": attention_mask}
							return config, inputs_dict


					def lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	20
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model_class_name(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model.init_cache(input_ids.shape[0]     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Any						=	jnp.ones((input_ids.shape[0], max_decoder_length)     ,		dtype="""i4"""		)

							SCREAMING_SNAKE_CASE				:							List[str]						=	jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1		)[None, :]     ,		(input_ids.shape[0], input_ids.shape[-1] - 1)		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(
							    input_ids[:, :-1]     ,		attention_mask=lowerCamelCase_     ,		past_key_values=lowerCamelCase_     ,		position_ids=lowerCamelCase_     ,		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,		dtype="""i4"""		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(
							    input_ids[:, -1:]     ,		attention_mask=lowerCamelCase_     ,		past_key_values=outputs_cache.past_key_values     ,		position_ids=lowerCamelCase_     ,		)

							SCREAMING_SNAKE_CASE				:							List[str]						=	model(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
							self.parent.assertTrue(diff < 1e-3     ,		msg=f'''Max diff is {diff}'''		)



					def lowerCamelCase_	(		self							:	int     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	20
							SCREAMING_SNAKE_CASE				:							str						=	model_class_name(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	jnp.concatenate(
							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])		)]     ,		axis=-1     ,		)

							SCREAMING_SNAKE_CASE				:							List[Any]						=	model.init_cache(input_ids.shape[0]     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	jnp.broadcast_to(
							    jnp.arange(input_ids.shape[-1] - 1		)[None, :]     ,		(input_ids.shape[0], input_ids.shape[-1] - 1)		)

							SCREAMING_SNAKE_CASE				:							int						=	model(
							    input_ids[:, :-1]     ,		attention_mask=lowerCamelCase_     ,		past_key_values=lowerCamelCase_     ,		position_ids=lowerCamelCase_     ,		)
							SCREAMING_SNAKE_CASE				:							Any						=	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]     ,		dtype="""i4"""		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model(
							    input_ids[:, -1:]     ,		past_key_values=outputs_cache.past_key_values     ,		attention_mask=lowerCamelCase_     ,		position_ids=lowerCamelCase_     ,		)

							SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		attention_mask=lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
							self.parent.assertTrue(diff < 1e-3     ,		msg=f'''Max diff is {diff}'''		)







@require_flax
class       UpperCamelCase__			(			lowercase_		,			lowercase_		,			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
					SCREAMING_SNAKE_CASE__						=					(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	FlaxGPTNeoModelTester(self		)


					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)


					@slow
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	GPTaTokenizer.from_pretrained("""gpt2"""     ,		pad_token="""<|endoftext|>"""     ,		padding_side="""left"""		)
							SCREAMING_SNAKE_CASE				:							int						=	tokenizer(["""Hello this is a long string""", """Hey"""]     ,		return_tensors="""np"""     ,		padding=lowerCamelCase_     ,		truncation=lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Any						=	FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""		)
							SCREAMING_SNAKE_CASE				:							str						=	False
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	model.config.eos_token_id

							SCREAMING_SNAKE_CASE				:							Tuple						=	jax.jit(model.generate		)

							SCREAMING_SNAKE_CASE				:							Any						=	jit_generate(
							    inputs["""input_ids"""]     ,		attention_mask=inputs["""attention_mask"""]     ,		pad_token_id=tokenizer.pad_token_id		).sequences

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	tokenizer.batch_decode(lowerCamelCase_     ,		skip_special_tokens=lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							str						=	[
							    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
							    """Hey, I'm a little late to the party. I'm going to""",
							]

							self.assertListEqual(lowerCamelCase_     ,		lowerCamelCase_		)


					@is_pt_flax_cross_test
					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__		):
											# prepare inputs
											SCREAMING_SNAKE_CASE				:							Any						=	self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Dict						=	{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

											# load corresponding PyTorch class
											SCREAMING_SNAKE_CASE				:							List[Any]						=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
											SCREAMING_SNAKE_CASE				:							Tuple						=	getattr(lowerCamelCase_     ,		lowerCamelCase_		)

											SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	pt_inputs["""input_ids"""].shape
											SCREAMING_SNAKE_CASE				:							int						=	np.random.randint(0     ,		seq_length - 1     ,		size=(batch_size,)		)
											for batch_idx, start_index in enumerate(lowerCamelCase_		):
													SCREAMING_SNAKE_CASE				:							str						=	0
													SCREAMING_SNAKE_CASE				:							Any						=	1
													SCREAMING_SNAKE_CASE				:							Tuple						=	0
													SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	1
											SCREAMING_SNAKE_CASE				:							Dict						=	pt_model_class(lowerCamelCase_		).eval()
											SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(lowerCamelCase_     ,		dtype=jnp.floataa		)

											SCREAMING_SNAKE_CASE				:							List[str]						=	convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	fx_state

											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							Dict						=	pt_model(**lowerCamelCase_		).to_tuple()

											SCREAMING_SNAKE_CASE				:							int						=	fx_model(**lowerCamelCase_		).to_tuple()
											self.assertEqual(len(lowerCamelCase_		)     ,		len(lowerCamelCase_		)     ,		"""Output lengths differ between Flax and PyTorch"""		)
											for fx_output, pt_output in zip(lowerCamelCase_     ,		lowerCamelCase_		):
													self.assert_almost_equals(fx_output[:, -1]     ,		pt_output[:, -1].numpy()     ,		4e-2		)

											with tempfile.TemporaryDirectory() as tmpdirname:
													pt_model.save_pretrained(lowerCamelCase_		)
													SCREAMING_SNAKE_CASE				:							List[str]						=	model_class.from_pretrained(lowerCamelCase_     ,		from_pt=lowerCamelCase_		)

											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	fx_model_loaded(**lowerCamelCase_		).to_tuple()
											self.assertEqual(
											    len(lowerCamelCase_		)     ,		len(lowerCamelCase_		)     ,		"""Output lengths differ between Flax and PyTorch"""		)
											for fx_output_loaded, pt_output in zip(lowerCamelCase_     ,		lowerCamelCase_		):
													self.assert_almost_equals(fx_output_loaded[:, -1]     ,		pt_output[:, -1].numpy()     ,		4e-2		)


					@is_pt_flax_cross_test
					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Any						=	self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__		):
											# prepare inputs
											SCREAMING_SNAKE_CASE				:							str						=	self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Dict						=	{k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

											# load corresponding PyTorch class
											SCREAMING_SNAKE_CASE				:							Dict						=	model_class.__name__[4:]  # Skip the "Flax" at the beginning
											SCREAMING_SNAKE_CASE				:							Dict						=	getattr(lowerCamelCase_     ,		lowerCamelCase_		)

											SCREAMING_SNAKE_CASE				:							Optional[int]						=	pt_model_class(lowerCamelCase_		).eval()
											SCREAMING_SNAKE_CASE				:							List[str]						=	model_class(lowerCamelCase_     ,		dtype=jnp.floataa		)

											SCREAMING_SNAKE_CASE				:							List[Any]						=	load_flax_weights_in_pytorch_model(lowerCamelCase_     ,		fx_model.params		)
											SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[str]						=	pt_inputs["""input_ids"""].shape
											SCREAMING_SNAKE_CASE				:							Optional[int]						=	np.random.randint(0     ,		seq_length - 1     ,		size=(batch_size,)		)
											for batch_idx, start_index in enumerate(lowerCamelCase_		):
													SCREAMING_SNAKE_CASE				:							Any						=	0
													SCREAMING_SNAKE_CASE				:							int						=	1
													SCREAMING_SNAKE_CASE				:							Optional[Any]						=	0
													SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	1

											# make sure weights are tied in PyTorch
											pt_model.tie_weights()

											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							List[Any]						=	pt_model(**lowerCamelCase_		).to_tuple()

											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	fx_model(**lowerCamelCase_		).to_tuple()
											self.assertEqual(len(lowerCamelCase_		)     ,		len(lowerCamelCase_		)     ,		"""Output lengths differ between Flax and PyTorch"""		)
											for fx_output, pt_output in zip(lowerCamelCase_     ,		lowerCamelCase_		):
													self.assert_almost_equals(fx_output[:, -1]     ,		pt_output[:, -1].numpy()     ,		4e-2		)

											with tempfile.TemporaryDirectory() as tmpdirname:
													fx_model.save_pretrained(lowerCamelCase_		)
													SCREAMING_SNAKE_CASE				:							int						=	pt_model_class.from_pretrained(lowerCamelCase_     ,		from_flax=lowerCamelCase_		)

											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							int						=	pt_model_loaded(**lowerCamelCase_		).to_tuple()

											self.assertEqual(
											    len(lowerCamelCase_		)     ,		len(lowerCamelCase_		)     ,		"""Output lengths differ between Flax and PyTorch"""		)
											for fx_output, pt_output in zip(lowerCamelCase_     ,		lowerCamelCase_		):
													self.assert_almost_equals(fx_output[:, -1]     ,		pt_output[:, -1].numpy()     ,		4e-2		)



					@slow
					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							Dict						=	model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""		)
									SCREAMING_SNAKE_CASE				:							Any						=	model(np.ones((1, 1)		)		)
									self.assertIsNotNone(lowerCamelCase_		)










'''simple docstring'''




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
					SCREAMING_SNAKE_CASE__						=					(ScoreSdeVeScheduler,)
					SCREAMING_SNAKE_CASE__						=					()


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	4
							SCREAMING_SNAKE_CASE				:							int						=	3
							SCREAMING_SNAKE_CASE				:							Any						=	8
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	8

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.rand((batch_size, num_channels, height, width)		)

							return sample


					@property
					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	4
							SCREAMING_SNAKE_CASE				:							Tuple						=	3
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	8
							SCREAMING_SNAKE_CASE				:							List[str]						=	8

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	batch_size * num_channels * height * width
							SCREAMING_SNAKE_CASE				:							Dict						=	torch.arange(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	sample.reshape(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	sample / num_elems
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	sample.permute(3     ,		0     ,		1     ,		2		)

							return sample


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							def model(lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int     ,		*lowerCamelCase_							:	int		):
									return sample * t / (t + 1)

							return model


					def lowerCamelCase_	(		self							:	str     ,		**lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
							    """num_train_timesteps""": 20_00,
							    """snr""": 0.15,
							    """sigma_min""": 0.01,
							    """sigma_max""": 13_48,
							    """sampling_eps""": 1e-5,
							}

							config.update(**lowerCamelCase_		)
							return config


					def lowerCamelCase_	(		self							:	List[str]     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config(**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							int						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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

									SCREAMING_SNAKE_CASE				:							Any						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							int						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	List[Any]=0     ,		**lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	dict(self.forward_default_kwargs		)
							kwargs.update(lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							List[str]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							Tuple						=	0.1 * sample

									SCREAMING_SNAKE_CASE				:							int						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler_class(**lowerCamelCase_		)

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler_class.from_pretrained(lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Tuple						=	scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_pred(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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

									SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	new_scheduler.step_correct(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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


					def lowerCamelCase_	(		self							:	List[Any]		):

							'''simple docstring'''



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


					def lowerCamelCase_	(		self							:	Dict		):

							'''simple docstring'''



							for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]     ,		[1, 1_00, 10_00]		):
									self.check_over_configs(sigma_min=lowerCamelCase_     ,		sigma_max=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							for t in [0.1, 0.5, 0.75]:
									self.check_over_forward(time_step=lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							str						=	self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE				:							Dict						=	self.get_scheduler_config()
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler_class(**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	3

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.dummy_model()
							SCREAMING_SNAKE_CASE				:							Dict						=	self.dummy_sample_deter

							scheduler.set_sigmas(lowerCamelCase_		)
							scheduler.set_timesteps(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	torch.manual_seed(0		)

							for i, t in enumerate(scheduler.timesteps		):
									SCREAMING_SNAKE_CASE				:							int						=	scheduler.sigmas[i]

									for _ in range(scheduler.config.correct_steps		):
											with torch.no_grad():
													SCREAMING_SNAKE_CASE				:							List[Any]						=	model(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							List[str]						=	scheduler.step_correct(lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		).prev_sample

									with torch.no_grad():
											SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_     ,		lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							List[Any]						=	scheduler.step_pred(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		generator=lowerCamelCase_     ,		**lowerCamelCase_		)
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[Any]						=	output.prev_sample, output.prev_sample_mean

							SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.sum(torch.abs(lowerCamelCase_		)		)
							SCREAMING_SNAKE_CASE				:							Any						=	torch.mean(torch.abs(lowerCamelCase_		)		)

							assert np.isclose(result_sum.item()     ,		14_372_758_528.0		)
							assert np.isclose(result_mean.item()     ,		18_714_530.0		)



					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[int]						=	dict(self.forward_default_kwargs		)

							SCREAMING_SNAKE_CASE				:							Any						=	kwargs.pop("""num_inference_steps"""     ,		lowerCamelCase_		)

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE				:							Tuple						=	self.get_scheduler_config()
									SCREAMING_SNAKE_CASE				:							int						=	scheduler_class(**lowerCamelCase_		)

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.dummy_sample
									SCREAMING_SNAKE_CASE				:							List[str]						=	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"""		):
											SCREAMING_SNAKE_CASE				:							Dict						=	num_inference_steps

									SCREAMING_SNAKE_CASE				:							Optional[int]						=	scheduler.step_pred(lowerCamelCase_     ,		0     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	scheduler.step_pred(lowerCamelCase_     ,		1     ,		lowerCamelCase_     ,		generator=torch.manual_seed(0		)     ,		**lowerCamelCase_		).prev_sample

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



