




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      A     :
					"""simple docstring"""

					def __init__(      self					:				List[Any]		,					A__					:				Optional[int]		,					A__					:				Optional[Any]=1_4		,					A__					:				Any=7		,					A__					:				Tuple=True		,					A__					:				List[Any]=True		,					A__					:				List[str]=True		,					A__					:				Dict=9_9		,					A__					:				int=3_2		,					A__					:				Union[str, Any]=5		,					A__					:				List[str]=4		,					A__					:				Any=3_7		,					A__					:				Tuple="gelu"		,					A__					:				int=0.1		,					A__					:				Any=0.1		,					A__					:				Any=5_1_2		,					A__					:				Any=0.02		,					A__					:				Tuple=None		,					)			->    Tuple:
											lowerCamelCase__ 					=			parent
											lowerCamelCase__ 					=			batch_size
											lowerCamelCase__ 					=			seq_length
											lowerCamelCase__ 					=			is_training
											lowerCamelCase__ 					=			use_input_mask
											lowerCamelCase__ 					=			use_labels
											lowerCamelCase__ 					=			vocab_size
											lowerCamelCase__ 					=			d_model
											lowerCamelCase__ 					=			num_hidden_layers
											lowerCamelCase__ 					=			num_attention_heads
											lowerCamelCase__ 					=			ffn_dim
											lowerCamelCase__ 					=			activation_function
											lowerCamelCase__ 					=			activation_dropout
											lowerCamelCase__ 					=			attention_dropout
											lowerCamelCase__ 					=			max_position_embeddings
											lowerCamelCase__ 					=			initializer_range
											lowerCamelCase__ 					=			None
											lowerCamelCase__ 					=			0
											lowerCamelCase__ 					=			2
											lowerCamelCase__ 					=			1




					def 	__snake_case				(      self					:				Optional[Any]     )			->    Union[str, Any]:
											lowerCamelCase__ 					=			np.clip(ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size     )		,					3		,					self.vocab_size     )

											lowerCamelCase__ 					=			None
											if self.use_input_mask:
																	lowerCamelCase__ 					=			random_attention_mask([self.batch_size, self.seq_length]     )

											lowerCamelCase__ 					=			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=A__		,					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 	__snake_case				(      self					:				Tuple     )			->    str:
											lowerCamelCase__ 					=			self.prepare_config_and_inputs()
											lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			config_and_inputs
											lowerCamelCase__ 					=			{'input_ids': input_ids, 'attention_mask': attention_mask}
											return config, inputs_dict




					def 	__snake_case				(      self					:				Optional[int]     )			->    List[Any]:
											lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			self.prepare_config_and_inputs()

											lowerCamelCase__ 					=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
											lowerCamelCase__ 					=			ids_tensor([self.batch_size, self.seq_length]		,					vocab_size=2     )

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)




					def 	__snake_case				(      self					:				Optional[int]		,					A__					:				List[str]		,					A__					:				List[Any]		,					A__					:				Union[str, Any]		,					A__					:				Union[str, Any]     )			->    Dict:
											lowerCamelCase__ 					=			2_0
											lowerCamelCase__ 					=			model_class_name(A__     )

											lowerCamelCase__ 					=			model.init_cache(input_ids.shape[0]		,					A__     )
											lowerCamelCase__ 					=			jnp.ones((input_ids.shape[0], max_decoder_length)		,					dtype='i4'     )

											lowerCamelCase__ 					=			jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1     )[None, :]		,					(input_ids.shape[0], input_ids.shape[-1] - 1)     )
											lowerCamelCase__ 					=			model(
											    input_ids[:, :-1]		,					attention_mask=A__		,					past_key_values=A__		,					position_ids=A__		,					)

											lowerCamelCase__ 					=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,					dtype='i4'     )
											lowerCamelCase__ 					=			model(
											    input_ids[:, -1:]		,					attention_mask=A__		,					past_key_values=outputs_cache.past_key_values		,					position_ids=A__		,					)

											lowerCamelCase__ 					=			model(A__     )

											lowerCamelCase__ 					=			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 	__snake_case				(      self					:				List[str]		,					A__					:				List[Any]		,					A__					:				Dict		,					A__					:				Tuple		,					A__					:				Any     )			->    str:
											lowerCamelCase__ 					=			2_0
											lowerCamelCase__ 					=			model_class_name(A__     )

											lowerCamelCase__ 					=			jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]		,					axis=-1		,					)

											lowerCamelCase__ 					=			model.init_cache(input_ids.shape[0]		,					A__     )
											lowerCamelCase__ 					=			jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1     )[None, :]		,					(input_ids.shape[0], input_ids.shape[-1] - 1)     )

											lowerCamelCase__ 					=			model(
											    input_ids[:, :-1]		,					attention_mask=A__		,					past_key_values=A__		,					position_ids=A__		,					)
											lowerCamelCase__ 					=			jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]		,					dtype='i4'     )
											lowerCamelCase__ 					=			model(
											    input_ids[:, -1:]		,					past_key_values=outputs_cache.past_key_values		,					attention_mask=A__		,					position_ids=A__		,					)

											lowerCamelCase__ 					=			model(A__		,					attention_mask=A__     )
											lowerCamelCase__ 					=			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      A     (						_lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
					"""simple docstring"""

					_snake_case:		Union[str, Any]       =(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					_snake_case:		List[Any]       =(FlaxXGLMForCausalLM,) if is_flax_available() else ()




					def 	__snake_case				(      self					:				str     )			->    Union[str, Any]:
											lowerCamelCase__ 					=			FlaxXGLMModelTester(self     )




					def 	__snake_case				(      self					:				Tuple     )			->    Tuple:
											for model_class_name in self.all_model_classes:
																	lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(A__		,					A__		,					A__		,					A__     )




					def 	__snake_case				(      self					:				List[Any]     )			->    Optional[Any]:
											for model_class_name in self.all_model_classes:
																	lowerCamelCase__					,						lowerCamelCase__					,						lowerCamelCase__ 					=			self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    A__		,					A__		,					A__		,					A__     )




					@slow
					def 	__snake_case				(      self					:				str     )			->    Union[str, Any]:
											lowerCamelCase__ 					=			XGLMTokenizer.from_pretrained('XGLM'		,					padding_side='left'     )
											lowerCamelCase__ 					=			tokenizer(['Hello this is a long string', 'Hey']		,					return_tensors='np'		,					padding=A__		,					truncation=A__     )

											lowerCamelCase__ 					=			FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'     )
											lowerCamelCase__ 					=			1
											lowerCamelCase__ 					=			False

											lowerCamelCase__ 					=			jax.jit(model.generate     )

											lowerCamelCase__ 					=			jit_generate(inputs['input_ids']		,					attention_mask=inputs['attention_mask']     ).sequences

											lowerCamelCase__ 					=			tokenizer.batch_decode(A__		,					skip_special_tokens=A__     )

											lowerCamelCase__ 					=			[
											    '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(A__		,					A__     )




					@is_pt_flax_cross_test
					def 	__snake_case				(      self					:				List[str]     )			->    Any:
											lowerCamelCase__					,						lowerCamelCase__ 					=			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__ 					=			self._prepare_for_class(A__		,					A__     )
																							lowerCamelCase__ 					=			{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																							lowerCamelCase__					,						lowerCamelCase__ 					=			pt_inputs['input_ids'].shape
																							lowerCamelCase__ 					=			np.random.randint(0		,					seq_length - 1		,					size=(batch_size,)     )
																							for batch_idx, start_index in enumerate(A__     ):
																													lowerCamelCase__ 					=			0
																													lowerCamelCase__ 					=			1
																													lowerCamelCase__ 					=			0
																													lowerCamelCase__ 					=			1
																							lowerCamelCase__ 					=			pt_model_class(A__     ).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__ 					=			False
																							lowerCamelCase__ 					=			model_class(A__		,					dtype=jnp.floataa     )
																							lowerCamelCase__ 					=			convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,					A__     )
																							lowerCamelCase__ 					=			fx_state

																							with torch.no_grad():
																													lowerCamelCase__ 					=			pt_model(**A__     ).to_tuple()

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

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													pt_model.save_pretrained(A__     )
																													lowerCamelCase__ 					=			model_class.from_pretrained(A__		,					from_pt=A__     )

																							lowerCamelCase__ 					=			fx_model_loaded(**A__     ).to_tuple()
																							self.assertEqual(
																							    len(A__     )		,					len(A__     )		,					'Output lengths differ between Flax and PyTorch'     )
																							for fx_output_loaded, pt_output in zip(A__		,					A__     ):
																													self.assert_almost_equals(fx_output_loaded[:, -1]		,					pt_output[:, -1].numpy()		,					4E-2     )




					@is_pt_flax_cross_test
					def 	__snake_case				(      self					:				Any     )			->    int:
											lowerCamelCase__					,						lowerCamelCase__ 					=			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__ 					=			self._prepare_for_class(A__		,					A__     )
																							lowerCamelCase__ 					=			{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

																							lowerCamelCase__ 					=			pt_model_class(A__     ).eval()
																							lowerCamelCase__ 					=			False
																							lowerCamelCase__ 					=			model_class(A__		,					dtype=jnp.floataa     )

																							lowerCamelCase__ 					=			load_flax_weights_in_pytorch_model(A__		,					fx_model.params     )
																							lowerCamelCase__					,						lowerCamelCase__ 					=			pt_inputs['input_ids'].shape
																							lowerCamelCase__ 					=			np.random.randint(0		,					seq_length - 1		,					size=(batch_size,)     )
																							for batch_idx, start_index in enumerate(A__     ):
																													lowerCamelCase__ 					=			0
																													lowerCamelCase__ 					=			1
																													lowerCamelCase__ 					=			0
																													lowerCamelCase__ 					=			1

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

																							with torch.no_grad():
																													lowerCamelCase__ 					=			pt_model(**A__     ).to_tuple()

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

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													fx_model.save_pretrained(A__     )
																													lowerCamelCase__ 					=			pt_model_class.from_pretrained(A__		,					from_flax=A__     )

																							with torch.no_grad():
																													lowerCamelCase__ 					=			pt_model_loaded(**A__     ).to_tuple()

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




					@slow
					def 	__snake_case				(      self					:				Tuple     )			->    List[str]:
											for model_class_name in self.all_model_classes:
																	lowerCamelCase__ 					=			model_class_name.from_pretrained('facebook/xglm-564M'     )
																	lowerCamelCase__ 					=			model(np.ones((1, 1)     )     )
																	self.assertIsNotNone(A__     )





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Optional[int]       =(DPMSolverMultistepScheduler,)
     _snake_case:		Union[str, Any]       =(("num_inference_steps", 25),)




     def 	__snake_case				(      self					:				str		,					**A__					:				Union[str, Any]     )			->    str:
           lowerCamelCase__ 					=			{
               'num_train_timesteps': 1_0_0_0,
               'beta_start': 0.0001,
               'beta_end': 0.02,
               'beta_schedule': 'linear',
               'solver_order': 2,
               'prediction_type': 'epsilon',
               'thresholding': False,
               'sample_max_value': 1.0,
               'algorithm_type': 'dpmsolver++',
               'solver_type': 'midpoint',
               'lower_order_final': False,
               'lambda_min_clipped': -float('inf'     ),
               'variance_type': None,
           }

           config.update(**A__     )
           return config




     def 	__snake_case				(      self					:				Dict		,					A__					:				Dict=0		,					**A__					:				Dict     )			->    Union[str, Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )
                 # copy over dummy past residuals
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       new_scheduler.set_timesteps(A__     )
                       # copy over dummy past residuals
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__					,						lowerCamelCase__ 					=			sample, sample
                 for t in range(A__		,					time_step + scheduler.config.solver_order + 1     ):
                       lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                       lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




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




     def 	__snake_case				(      self					:				Optional[Any]		,					A__					:				List[str]=0		,					**A__					:				Optional[Any]     )			->    Optional[Any]:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )
           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )
           lowerCamelCase__ 					=			self.dummy_sample
           lowerCamelCase__ 					=			0.1 * sample
           lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )
                 scheduler.set_timesteps(A__     )

                 # copy over dummy past residuals (must be after setting timesteps)
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       scheduler.save_config(A__     )
                       lowerCamelCase__ 					=			scheduler_class.from_pretrained(A__     )
                       # copy over dummy past residuals
                       new_scheduler.set_timesteps(A__     )

                       # copy over dummy past residual (must be after setting timesteps)
                       lowerCamelCase__ 					=			dummy_past_residuals[: new_scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			new_scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Tuple		,					A__					:				Any=None		,					**A__					:				Any     )			->    List[str]:
           if scheduler is None:
                 lowerCamelCase__ 					=			self.scheduler_classes[0]
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           return sample




     def 	__snake_case				(      self					:				Dict     )			->    Any:
           lowerCamelCase__ 					=			dict(self.forward_default_kwargs     )

           lowerCamelCase__ 					=			kwargs.pop('num_inference_steps'		,					A__     )

           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config()
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 lowerCamelCase__ 					=			self.dummy_sample
                 lowerCamelCase__ 					=			0.1 * sample

                 if num_inference_steps is not None and hasattr(A__		,					'set_timesteps'     ):
                       scheduler.set_timesteps(A__     )
                 elif num_inference_steps is not None and not hasattr(A__		,					'set_timesteps'     ):
                       lowerCamelCase__ 					=			num_inference_steps

                 # copy over dummy past residuals (must be done after set_timesteps)
                 lowerCamelCase__ 					=			[residual + 0.2, residual + 0.15, residual + 0.10]
                 lowerCamelCase__ 					=			dummy_past_residuals[: scheduler.config.solver_order]

                 lowerCamelCase__ 					=			scheduler.timesteps[5]
                 lowerCamelCase__ 					=			scheduler.timesteps[6]

                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__		,					**A__     ).prev_sample

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




     def 	__snake_case				(      self					:				Dict     )			->    Tuple:
           for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=A__     )




     def 	__snake_case				(      self					:				int     )			->    str:
           self.check_over_configs(thresholding=A__     )
           for order in [1, 2, 3]:
                 for solver_type in ["midpoint", "heun"]:
                       for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                   self.check_over_configs(
                                       thresholding=A__		,					prediction_type=A__		,					sample_max_value=A__		,					algorithm_type='dpmsolver++'		,					solver_order=A__		,					solver_type=A__		,					)




     def 	__snake_case				(      self					:				Any     )			->    Optional[Any]:
           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=A__     )




     def 	__snake_case				(      self					:				Union[str, Any]     )			->    str:
           for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                 for solver_type in ["midpoint", "heun"]:
                       for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                   if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                         if order == 3:
                                               continue
                                   else:
                                         self.check_over_configs(
                                             solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   lowerCamelCase__ 					=			self.full_loop(
                                       solver_order=A__		,					solver_type=A__		,					prediction_type=A__		,					algorithm_type=A__		,					)
                                   assert not torch.isnan(A__     ).any(), "Samples have nan numbers"




     def 	__snake_case				(      self					:				List[Any]     )			->    int:
           self.check_over_configs(lower_order_final=A__     )
           self.check_over_configs(lower_order_final=A__     )




     def 	__snake_case				(      self					:				List[Any]     )			->    Dict:
           self.check_over_configs(lambda_min_clipped=-float('inf'     )     )
           self.check_over_configs(lambda_min_clipped=-5.1     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    int:
           self.check_over_configs(variance_type=A__     )
           self.check_over_configs(variance_type='learned_range'     )




     def 	__snake_case				(      self					:				Optional[Any]     )			->    List[str]:
           for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
                 self.check_over_forward(num_inference_steps=A__		,					time_step=0     )




     def 	__snake_case				(      self					:				List[Any]     )			->    str:
           lowerCamelCase__ 					=			self.full_loop()
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    int:
           lowerCamelCase__ 					=			self.full_loop(thresholding=A__		,					dynamic_thresholding_ratio=0.87		,					sample_max_value=0.5     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 1.1364     ) < 1E-3




     def 	__snake_case				(      self					:				Optional[int]     )			->    List[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2251     ) < 1E-3




     def 	__snake_case				(      self					:				Tuple     )			->    Optional[Any]:
           lowerCamelCase__ 					=			self.full_loop(prediction_type='v_prediction'		,					use_karras_sigmas=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.2096     ) < 1E-3




     def 	__snake_case				(      self					:				Any     )			->    Optional[int]:
           # make sure that iterating over schedulers with same config names gives same results
           # for defaults
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler(**self.get_scheduler_config()     )
           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3

           lowerCamelCase__ 					=			DPMSolverSinglestepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			UniPCMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DEISMultistepScheduler.from_config(scheduler.config     )
           lowerCamelCase__ 					=			DPMSolverMultistepScheduler.from_config(scheduler.config     )

           lowerCamelCase__ 					=			self.full_loop(scheduler=A__     )
           lowerCamelCase__ 					=			torch.mean(torch.abs(A__     )     )

           assert abs(result_mean.item() - 0.3301     ) < 1E-3




     def 	__snake_case				(      self					:				int     )			->    Optional[int]:
           lowerCamelCase__ 					=			self.scheduler_classes[0]
           lowerCamelCase__ 					=			self.get_scheduler_config(thresholding=A__		,					dynamic_thresholding_ratio=0     )
           lowerCamelCase__ 					=			scheduler_class(**A__     )

           lowerCamelCase__ 					=			1_0
           lowerCamelCase__ 					=			self.dummy_model()
           lowerCamelCase__ 					=			self.dummy_sample_deter.half()
           scheduler.set_timesteps(A__     )

           for i, t in enumerate(scheduler.timesteps     ):
                 lowerCamelCase__ 					=			model(A__		,					A__     )
                 lowerCamelCase__ 					=			scheduler.step(A__		,					A__		,					A__     ).prev_sample

           assert sample.dtype == torch.floataa




     def 	__snake_case				(      self					:				List[str]		,					**A__					:				int     )			->    Optional[int]:
           for scheduler_class in self.scheduler_classes:
                 lowerCamelCase__ 					=			self.get_scheduler_config(**A__     )
                 lowerCamelCase__ 					=			scheduler_class(**A__     )

                 scheduler.set_timesteps(scheduler.config.num_train_timesteps     )
                 assert len(scheduler.timesteps.unique()     ) == scheduler.num_inference_steps