
import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

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


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

							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)
							from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
							import torch





@require_flax
class 						lowercase       :
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=14 ,	SCREAMING_SNAKE_CASE__=7 ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=99 ,	SCREAMING_SNAKE_CASE__=32 ,	SCREAMING_SNAKE_CASE__=5 ,	SCREAMING_SNAKE_CASE__=4 ,	SCREAMING_SNAKE_CASE__=37 ,	SCREAMING_SNAKE_CASE__="gelu" ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__=0.1 ,	SCREAMING_SNAKE_CASE__=512 ,	SCREAMING_SNAKE_CASE__=0.02 ,	SCREAMING_SNAKE_CASE__=None ,	):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			parent
								lowerCAmelCase__ :    List[str]				      =			batch_size
								lowerCAmelCase__ :    int				      =			seq_length
								lowerCAmelCase__ :    Optional[Any]				      =			is_training
								lowerCAmelCase__ :    Optional[Any]				      =			use_input_mask
								lowerCAmelCase__ :    Optional[Any]				      =			use_labels
								lowerCAmelCase__ :    Dict				      =			vocab_size
								lowerCAmelCase__ :    Dict				      =			d_model
								lowerCAmelCase__ :    int				      =			num_hidden_layers
								lowerCAmelCase__ :    List[str]				      =			num_attention_heads
								lowerCAmelCase__ :    str				      =			ffn_dim
								lowerCAmelCase__ :    Any				      =			activation_function
								lowerCAmelCase__ :    List[str]				      =			activation_dropout
								lowerCAmelCase__ :    int				      =			attention_dropout
								lowerCAmelCase__ :    str				      =			max_position_embeddings
								lowerCAmelCase__ :    List[Any]				      =			initializer_range
								lowerCAmelCase__ :    Optional[Any]				      =			None
								lowerCAmelCase__ :    Optional[Any]				      =			0
								lowerCAmelCase__ :    Union[str, Any]				      =			2
								lowerCAmelCase__ :    Dict				      =			1
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			np.clip(ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size    ) ,	3 ,	self.vocab_size    )

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

								lowerCAmelCase__ :    List[Any]				      =			XGLMConfig(
								    vocab_size=self.vocab_size ,	d_model=self.hidden_size ,	num_layers=self.num_hidden_layers ,	attention_heads=self.num_attention_heads ,	ffn_dim=self.ffn_dim ,	activation_function=self.activation_function ,	activation_dropout=self.activation_dropout ,	attention_dropout=self.attention_dropout ,	max_position_embeddings=self.max_position_embeddings ,	initializer_range=self.initializer_range ,	use_cache=SCREAMING_SNAKE_CASE_ ,	bos_token_id=self.bos_token_id ,	eos_token_id=self.eos_token_id ,	pad_token_id=self.pad_token_id ,	)

								return (config, input_ids, input_mask)
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			self.prepare_config_and_inputs()
								lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[Any]				      =			config_and_inputs
								lowerCAmelCase__ :    Union[str, Any]				      =			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    Tuple				      =			self.prepare_config_and_inputs()

								lowerCAmelCase__ :    int				      =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								lowerCAmelCase__ :    Tuple				      =			ids_tensor([self.batch_size, self.seq_length] ,	vocab_size=2    )

								return (
								    config,
								    input_ids,
								    attention_mask,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			20
								lowerCAmelCase__ :    Optional[int]				      =			model_class_name(SCREAMING_SNAKE_CASE_    )

								lowerCAmelCase__ :    Any				      =			model.init_cache(input_ids.shape[0] ,	SCREAMING_SNAKE_CASE_    )
								lowerCAmelCase__ :    Any				      =			jnp.ones((input_ids.shape[0], max_decoder_length) ,	dtype='''i4'''    )

								lowerCAmelCase__ :    Optional[Any]				      =			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1)    )
								lowerCAmelCase__ :    Tuple				      =			model(
								    input_ids[:, :-1] ,	attention_mask=SCREAMING_SNAKE_CASE_ ,	past_key_values=SCREAMING_SNAKE_CASE_ ,	position_ids=SCREAMING_SNAKE_CASE_ ,	)

								lowerCAmelCase__ :    Optional[int]				      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''    )
								lowerCAmelCase__ :    Any				      =			model(
								    input_ids[:, -1:] ,	attention_mask=SCREAMING_SNAKE_CASE_ ,	past_key_values=outputs_cache.past_key_values ,	position_ids=SCREAMING_SNAKE_CASE_ ,	)

								lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE_    )

								lowerCAmelCase__ :    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 lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Tuple				      =			20
								lowerCAmelCase__ :    List[Any]				      =			model_class_name(SCREAMING_SNAKE_CASE_    )

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

								lowerCAmelCase__ :    int				      =			model.init_cache(input_ids.shape[0] ,	SCREAMING_SNAKE_CASE_    )
								lowerCAmelCase__ :    List[str]				      =			jnp.broadcast_to(
								    jnp.arange(input_ids.shape[-1] - 1    )[None, :] ,	(input_ids.shape[0], input_ids.shape[-1] - 1)    )

								lowerCAmelCase__ :    Optional[Any]				      =			model(
								    input_ids[:, :-1] ,	attention_mask=SCREAMING_SNAKE_CASE_ ,	past_key_values=SCREAMING_SNAKE_CASE_ ,	position_ids=SCREAMING_SNAKE_CASE_ ,	)
								lowerCAmelCase__ :    List[Any]				      =			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,	dtype='''i4'''    )
								lowerCAmelCase__ :    Union[str, Any]				      =			model(
								    input_ids[:, -1:] ,	past_key_values=outputs_cache.past_key_values ,	attention_mask=SCREAMING_SNAKE_CASE_ ,	position_ids=SCREAMING_SNAKE_CASE_ ,	)

								lowerCAmelCase__ :    int				      =			model(SCREAMING_SNAKE_CASE_ ,	attention_mask=SCREAMING_SNAKE_CASE_    )
								lowerCAmelCase__ :    int				      =			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_sentencepiece
@require_flax
class 						lowercase       (  lowerCamelCase__	,		lowerCamelCase__	,		unittest.TestCase					):
					__a					=    (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					__a					=    (FlaxXGLMForCausalLM,) if is_flax_available() else ()
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			FlaxXGLMModelTester(self    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
											lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    List[str]				      =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
											lowerCAmelCase__      ,				lowerCAmelCase__      ,				lowerCAmelCase__ :    List[Any]				      =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    str				      =			XGLMTokenizer.from_pretrained('''XGLM''' ,	padding_side='''left'''    )
								lowerCAmelCase__ :    Dict				      =			tokenizer(['''Hello this is a long string''', '''Hey'''] ,	return_tensors='''np''' ,	padding=SCREAMING_SNAKE_CASE_ ,	truncation=SCREAMING_SNAKE_CASE_    )

								lowerCAmelCase__ :    Tuple				      =			FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
								lowerCAmelCase__ :    Tuple				      =			1
								lowerCAmelCase__ :    Optional[Any]				      =			False

								lowerCAmelCase__ :    Union[str, Any]				      =			jax.jit(model.generate    )

								lowerCAmelCase__ :    Union[str, Any]				      =			jit_generate(inputs['''input_ids'''] ,	attention_mask=inputs['''attention_mask''']    ).sequences

								lowerCAmelCase__ :    str				      =			tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ ,	skip_special_tokens=SCREAMING_SNAKE_CASE_    )

								lowerCAmelCase__ :    Union[str, Any]				      =			[
								    '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
								    '''Hey, I\'m a newbie to the forum and I\'''',
								]

								self.assertListEqual(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    )
					@is_pt_flax_cross_test
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    int				      =			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
														lowerCAmelCase__ :    Dict				      =			self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    )
														lowerCAmelCase__ :    Optional[Any]				      =			{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

														lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[Any]				      =			pt_inputs['''input_ids'''].shape
														lowerCAmelCase__ :    Optional[int]				      =			np.random.randint(0 ,	seq_length - 1 ,	size=(batch_size,)    )
														for batch_idx, start_index in enumerate(SCREAMING_SNAKE_CASE_    ):
																	lowerCAmelCase__ :    Union[str, Any]				      =			0
																	lowerCAmelCase__ :    List[str]				      =			1
																	lowerCAmelCase__ :    Dict				      =			0
																	lowerCAmelCase__ :    List[Any]				      =			1
														lowerCAmelCase__ :    List[Any]				      =			pt_model_class(SCREAMING_SNAKE_CASE_    ).eval()
														# Flax models don't use the `use_cache` option and cache is not returned as a default.
														# So we disable `use_cache` here for PyTorch model.
														lowerCAmelCase__ :    List[str]				      =			False
														lowerCAmelCase__ :    Optional[int]				      =			model_class(SCREAMING_SNAKE_CASE_ ,	dtype=jnp.floataa    )
														lowerCAmelCase__ :    int				      =			convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,	SCREAMING_SNAKE_CASE_    )
														lowerCAmelCase__ :    Dict				      =			fx_state

														with torch.no_grad():
																	lowerCAmelCase__ :    List[Any]				      =			pt_model(**SCREAMING_SNAKE_CASE_    ).to_tuple()

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																	pt_model.save_pretrained(SCREAMING_SNAKE_CASE_    )
																	lowerCAmelCase__ :    Dict				      =			model_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,	from_pt=SCREAMING_SNAKE_CASE_    )

														lowerCAmelCase__ :    Optional[Any]				      =			fx_model_loaded(**SCREAMING_SNAKE_CASE_    ).to_tuple()
														self.assertEqual(
														    len(SCREAMING_SNAKE_CASE_    ) ,	len(SCREAMING_SNAKE_CASE_    ) ,	'''Output lengths differ between Flax and PyTorch'''    )
														for fx_output_loaded, pt_output in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    ):
																	self.assert_almost_equals(fx_output_loaded[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2    )
					@is_pt_flax_cross_test
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__      ,				lowerCAmelCase__ :    Optional[int]				      =			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
														lowerCAmelCase__ :    str				      =			self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    )
														lowerCAmelCase__ :    Dict				      =			{k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

														# load corresponding PyTorch class
														lowerCAmelCase__ :    Any				      =			model_class.__name__[4:]  # Skip the "Flax" at the beginning
														lowerCAmelCase__ :    Optional[int]				      =			getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    )

														lowerCAmelCase__ :    Any				      =			pt_model_class(SCREAMING_SNAKE_CASE_    ).eval()
														lowerCAmelCase__ :    Tuple				      =			False
														lowerCAmelCase__ :    Union[str, Any]				      =			model_class(SCREAMING_SNAKE_CASE_ ,	dtype=jnp.floataa    )

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

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

														with torch.no_grad():
																	lowerCAmelCase__ :    Union[str, Any]				      =			pt_model(**SCREAMING_SNAKE_CASE_    ).to_tuple()

														lowerCAmelCase__ :    Dict				      =			fx_model(**SCREAMING_SNAKE_CASE_    ).to_tuple()
														self.assertEqual(len(SCREAMING_SNAKE_CASE_    ) ,	len(SCREAMING_SNAKE_CASE_    ) ,	'''Output lengths differ between Flax and PyTorch'''    )
														for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    ):
																	self.assert_almost_equals(fx_output[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2    )

														with tempfile.TemporaryDirectory() as tmpdirname:
																	fx_model.save_pretrained(SCREAMING_SNAKE_CASE_    )
																	lowerCAmelCase__ :    Union[str, Any]				      =			pt_model_class.from_pretrained(SCREAMING_SNAKE_CASE_ ,	from_flax=SCREAMING_SNAKE_CASE_    )

														with torch.no_grad():
																	lowerCAmelCase__ :    Tuple				      =			pt_model_loaded(**SCREAMING_SNAKE_CASE_    ).to_tuple()

														self.assertEqual(
														    len(SCREAMING_SNAKE_CASE_    ) ,	len(SCREAMING_SNAKE_CASE_    ) ,	'''Output lengths differ between Flax and PyTorch'''    )
														for fx_output, pt_output in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_    ):
																	self.assert_almost_equals(fx_output[:, -1] ,	pt_output[:, -1].numpy() ,	4E-2    )
					@slow
					def lowercase_			(		self    ):
								"""simple docstring"""







								for model_class_name in self.all_model_classes:
											lowerCAmelCase__ :    List[str]				      =			model_class_name.from_pretrained('''facebook/xglm-564M'''    )
											lowerCAmelCase__ :    Union[str, Any]				      =			model(np.ones((1, 1)    )    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE_    )





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						lowercase       (  __UpperCamelCase					):
					__a					=    (HeunDiscreteScheduler,)
					__a					=    10
					def lowercase_			(		self ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**SCREAMING_SNAKE_CASE__    )
								return config
					def lowercase_			(		self    ):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ ,	beta_end=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ ,	clip_sample=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Optional[Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Optional[int]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    Optional[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    List[Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Tuple				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Any				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    str				      =			self.get_scheduler_config(prediction_type='''v_prediction'''    )
								lowerCAmelCase__ :    Any				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    int				      =			self.dummy_model()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[int]				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			output.prev_sample

								lowerCAmelCase__ :    List[Any]				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Union[str, Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps ,	device=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Optional[Any]				      =			self.dummy_model()
								lowerCAmelCase__ :    Dict				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Dict				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''cpu'''    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								elif str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002    ) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    str				      =			scheduler_class(**SCREAMING_SNAKE_CASE__ ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps ,	device=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Any				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    Union[str, Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    int				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Dict				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								assert abs(result_sum.item() - 0.00_015    ) < 1E-2
								assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07    ) < 1E-2



