




'''simple docstring'''





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 				_snake_case	:




			def __init__(      self       ,							a__       ,							a__=14       ,							a__=7       ,							a__=True       ,							a__=True       ,							a__=True       ,							a__=99       ,							a__=32       ,							a__=5       ,							a__=4       ,							a__=37       ,							a__="gelu"       ,							a__=0.1       ,							a__=0.1       ,							a__=512       ,							a__=0.0_2       ,							a__=None       ,							)       ->       List[Any]:

										'''simple docstring'''




										snake_case_			      =   parent
										snake_case_			      =   batch_size
										snake_case_			      =   seq_length
										snake_case_			      =   is_training
										snake_case_			      =   use_input_mask
										snake_case_			      =   use_labels
										snake_case_			      =   vocab_size
										snake_case_			      =   d_model
										snake_case_			      =   num_hidden_layers
										snake_case_			      =   num_attention_heads
										snake_case_			      =   ffn_dim
										snake_case_			      =   activation_function
										snake_case_			      =   activation_dropout
										snake_case_			      =   attention_dropout
										snake_case_			      =   max_position_embeddings
										snake_case_			      =   initializer_range
										snake_case_			      =   None
										snake_case_			      =   0
										snake_case_			      =   2
										snake_case_			      =   1




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   np.clip(ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size							)       ,							3       ,							self.vocab_size							)

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

										snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   self.prepare_config_and_inputs()
										snake_case_      ,   snake_case_      ,   snake_case_			      =   config_and_inputs
										snake_case_			      =   {"input_ids": input_ids, "attention_mask": attention_mask}
										return config, inputs_dict




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

										'''simple docstring'''




										snake_case_      ,   snake_case_      ,   snake_case_			      =   self.prepare_config_and_inputs()

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

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




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   20
										snake_case_			      =   model_class_name(a__							)

										snake_case_			      =   model.init_cache(input_ids.shape[0]       ,							a__							)
										snake_case_			      =   jnp.ones((input_ids.shape[0], max_decoder_length)       ,							dtype="i4"							)

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

										snake_case_			      =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,							dtype="i4"							)
										snake_case_			      =   model(
										    input_ids[:, -1:]       ,							attention_mask=a__       ,							past_key_values=outputs_cache.past_key_values       ,							position_ids=a__       ,							)

										snake_case_			      =   model(a__							)

										snake_case_			      =   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       ,							a__       ,							a__       ,							a__       ,							a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   20
										snake_case_			      =   model_class_name(a__							)

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

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

										snake_case_			      =   model(
										    input_ids[:, :-1]       ,							attention_mask=a__       ,							past_key_values=a__       ,							position_ids=a__       ,							)
										snake_case_			      =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,							dtype="i4"							)
										snake_case_			      =   model(
										    input_ids[:, -1:]       ,							past_key_values=outputs_cache.past_key_values       ,							attention_mask=a__       ,							position_ids=a__       ,							)

										snake_case_			      =   model(a__       ,							attention_mask=a__							)
										snake_case_			      =   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 				_snake_case	(			lowercase_		,		lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   Union[str, Any]		 =					(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			lowerCAmelCase_  :   Dict		 =					(FlaxXGLMForCausalLM,) if is_flax_available() else ()




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

										'''simple docstring'''




										snake_case_			      =   FlaxXGLMModelTester(self							)




			def      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																	snake_case_      ,   snake_case_      ,   snake_case_			      =   self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(a__       ,							a__       ,							a__       ,							a__							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																	snake_case_      ,   snake_case_      ,   snake_case_			      =   self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    a__       ,							a__       ,							a__       ,							a__							)




			@slow
			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   XGLMTokenizer.from_pretrained("XGLM"       ,							padding_side="left"							)
										snake_case_			      =   tokenizer(["Hello this is a long string", "Hey"]       ,							return_tensors="np"       ,							padding=a__       ,							truncation=a__							)

										snake_case_			      =   FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"							)
										snake_case_			      =   1
										snake_case_			      =   False

										snake_case_			      =   jax.jit(model.generate							)

										snake_case_			      =   jit_generate(inputs["input_ids"]       ,							attention_mask=inputs["attention_mask"]							).sequences

										snake_case_			      =   tokenizer.batch_decode(a__       ,							skip_special_tokens=a__							)

										snake_case_			      =   [
										    "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      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										snake_case_      ,   snake_case_			      =   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
																								snake_case_			      =   self._prepare_for_class(a__       ,							a__							)
																								snake_case_			      =   {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

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

																								with torch.no_grad():
																															snake_case_			      =   pt_model(**a__							).to_tuple()

																								snake_case_			      =   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__							)
																															snake_case_			      =   model_class.from_pretrained(a__       ,							from_pt=a__							)

																								snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										snake_case_      ,   snake_case_			      =   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
																								snake_case_			      =   self._prepare_for_class(a__       ,							a__							)
																								snake_case_			      =   {k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

																								snake_case_			      =   pt_model_class(a__							).eval()
																								snake_case_			      =   False
																								snake_case_			      =   model_class(a__       ,							dtype=jnp.floataa							)

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

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

																								with torch.no_grad():
																															snake_case_			      =   pt_model(**a__							).to_tuple()

																								snake_case_			      =   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__							)
																															snake_case_			      =   pt_model_class.from_pretrained(a__       ,							from_flax=a__							)

																								with torch.no_grad():
																															snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       Optional[int]:

										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																	snake_case_			      =   model_class_name.from_pretrained("facebook/xglm-564M"							)
																	snake_case_			      =   model(np.ones((1, 1)							)							)
																	self.assertIsNotNone(a__							)










'''simple docstring'''





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Optional[int]		 =					(DPMSolverMultistepScheduler,)
			lowerCAmelCase_  :   int		 =					(("num_inference_steps", 25),)




			def      lowerCAmelCase__       (      self       ,							**a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   {
										    "num_train_timesteps": 1_000,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "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      lowerCAmelCase__       (      self       ,							a__=0       ,							**a__							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)
										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)
										snake_case_			      =   self.dummy_sample
										snake_case_			      =   0.1 * sample
										snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

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

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

																	snake_case_      ,   snake_case_			      =   sample, sample
																	for t in range(a__       ,							time_step + scheduler.config.solver_order + 1							):
																								snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																								snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										pass




			def      lowerCAmelCase__       (      self       ,							a__=0       ,							**a__							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)
										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)
										snake_case_			      =   self.dummy_sample
										snake_case_			      =   0.1 * sample
										snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config()
																	snake_case_			      =   scheduler_class(**a__							)
																	scheduler.set_timesteps(a__							)

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

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								scheduler.save_config(a__							)
																								snake_case_			      =   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)
																								snake_case_			      =   dummy_past_residuals[: new_scheduler.config.solver_order]

																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																	snake_case_			      =   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      lowerCAmelCase__       (      self       ,							a__=None       ,							**a__							)       ->       Tuple:

										'''simple docstring'''




										if scheduler is None:
																	snake_case_			      =   self.scheduler_classes[0]
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)

										snake_case_			      =   10
										snake_case_			      =   self.dummy_model()
										snake_case_			      =   self.dummy_sample_deter
										scheduler.set_timesteps(a__							)

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

										return sample




			def      lowerCAmelCase__       (      self							)       ->       Dict:

										'''simple docstring'''




										snake_case_			      =   dict(self.forward_default_kwargs							)

										snake_case_			      =   kwargs.pop("num_inference_steps"       ,							a__							)

										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config()
																	snake_case_			      =   scheduler_class(**a__							)

																	snake_case_			      =   self.dummy_sample
																	snake_case_			      =   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"							):
																								snake_case_			      =   num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	snake_case_			      =   [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
																	snake_case_			      =   dummy_past_residuals[: scheduler.config.solver_order]

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

																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample
																	snake_case_			      =   scheduler.step(a__       ,							a__       ,							a__       ,							**a__							).prev_sample

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




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

										'''simple docstring'''




										for timesteps in [25, 50, 100, 999, 1_000]:
																	self.check_over_configs(num_train_timesteps=a__							)




			def      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




										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      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




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




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

										'''simple docstring'''




										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__       ,							)
																																						snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       Union[str, Any]:

										'''simple docstring'''




										self.check_over_configs(lower_order_final=a__							)
										self.check_over_configs(lower_order_final=a__							)




			def      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




										self.check_over_configs(lambda_min_clipped=-float("inf"							)							)
										self.check_over_configs(lambda_min_clipped=-5.1							)




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										self.check_over_configs(variance_type=a__							)
										self.check_over_configs(variance_type="learned_range"							)




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

										'''simple docstring'''




										for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
																	self.check_over_forward(num_inference_steps=a__       ,							time_step=0							)




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

										'''simple docstring'''




										snake_case_			      =   self.full_loop()
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3




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

										'''simple docstring'''




										snake_case_			      =   self.full_loop(thresholding=a__       ,							dynamic_thresholding_ratio=0.8_7       ,							sample_max_value=0.5							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 1.1_3_6_4							) < 1e-3




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

										'''simple docstring'''




										snake_case_			      =   self.full_loop(prediction_type="v_prediction"							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_2_5_1							) < 1e-3




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

										'''simple docstring'''




										snake_case_			      =   self.full_loop(prediction_type="v_prediction"       ,							use_karras_sigmas=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.2_0_9_6							) < 1e-3




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

										'''simple docstring'''




										snake_case_			      =   DPMSolverMultistepScheduler(**self.get_scheduler_config()							)
										snake_case_			      =   self.full_loop(scheduler=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3

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

										snake_case_			      =   self.full_loop(scheduler=a__							)
										snake_case_			      =   torch.mean(torch.abs(a__							)							)

										assert abs(result_mean.item() - 0.3_3_0_1							) < 1e-3




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   self.scheduler_classes[0]
										snake_case_			      =   self.get_scheduler_config(thresholding=a__       ,							dynamic_thresholding_ratio=0							)
										snake_case_			      =   scheduler_class(**a__							)

										snake_case_			      =   10
										snake_case_			      =   self.dummy_model()
										snake_case_			      =   self.dummy_sample_deter.half()
										scheduler.set_timesteps(a__							)

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

										assert sample.dtype == torch.floataa




			def      lowerCAmelCase__       (      self       ,							**a__							)       ->       Dict:

										'''simple docstring'''




										for scheduler_class in self.scheduler_classes:
																	snake_case_			      =   self.get_scheduler_config(**a__							)
																	snake_case_			      =   scheduler_class(**a__							)

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




