



from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import numpy
							import tensorflow as tf

							from transformers.models.esm.modeling_tf_esm import (
							    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFEsmForMaskedLM,
							    TFEsmForSequenceClassification,
							    TFEsmForTokenClassification,
							    TFEsmModel,
							)

class 	lowerCamelCase_    :






						'''simple docstring'''







						def __init__(	self      :			Dict	,	__lowerCamelCase      :			List[str]	,	)      ->				Optional[int]:
									A							:					List[Any]              =						parent
									A							:					str              =						13
									A							:					Optional[int]              =						7
									A							:					int              =						True
									A							:					Any              =						True
									A							:					List[Any]              =						True
									A							:					Any              =						99
									A							:					int              =						32
									A							:					Tuple              =						2
									A							:					Union[str, Any]              =						4
									A							:					int              =						37
									A							:					Optional[int]              =						"gelu"
									A							:					Tuple              =						0.1
									A							:					Dict              =						0.1
									A							:					str              =						5_12
									A							:					Tuple              =						16
									A							:					Tuple              =						2
									A							:					str              =						0.02
									A							:					int              =						3
									A							:					Optional[int]              =						4
									A							:					Dict              =						None







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									A							:					Any              =						ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size )

									A							:					Tuple              =						None
									if self.use_input_mask:
												A							:					List[Any]              =						random_attention_mask([self.batch_size, self.seq_length] )

									A							:					Any              =						None
									A							:					Dict              =						None
									A							:					Union[str, Any]              =						None
									if self.use_labels:
												A							:					Tuple              =						ids_tensor([self.batch_size]	,	self.type_sequence_label_size )
												A							:					List[Any]              =						ids_tensor([self.batch_size, self.seq_length]	,	self.num_labels )
												A							:					Optional[Any]              =						ids_tensor([self.batch_size]	,	self.num_choices )

									A							:					Optional[Any]              =						EsmConfig(
									    vocab_size=self.vocab_size	,	hidden_size=self.hidden_size	,	num_hidden_layers=self.num_hidden_layers	,	pad_token_id=1	,	num_attention_heads=self.num_attention_heads	,	intermediate_size=self.intermediate_size	,	hidden_act=self.hidden_act	,	hidden_dropout_prob=self.hidden_dropout_prob	,	attention_probs_dropout_prob=self.attention_probs_dropout_prob	,	max_position_embeddings=self.max_position_embeddings	,	type_vocab_size=self.type_vocab_size	,	initializer_range=self.initializer_range	,	)

									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				List[Any]:
									(
									    (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , 
									)							:					List[str]              =						self.prepare_config_and_inputs()

									A							:					str              =						True
									A							:					str              =						floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									A							:					Any              =						ids_tensor([self.batch_size, self.seq_length]	,	vocab_size=2 )

									return (
									    config,
									    input_ids,
									    input_mask,
									    sequence_labels,
									    token_labels,
									    choice_labels,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Optional[int] )      ->				Optional[Any]:
									A							:					Tuple              =						TFEsmModel(config=__lowerCamelCase )
									A							:					List[Any]              =						{"input_ids": input_ids, "attention_mask": input_mask}
									A							:					Optional[int]              =						model(__lowerCamelCase )

									A							:					List[Any]              =						[input_ids, input_mask]
									A							:					List[Any]              =						model(__lowerCamelCase )

									A							:					Optional[Any]              =						model(__lowerCamelCase )

									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			List[str]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[str]	,	)      ->				List[Any]:
									A							:					Dict              =						True

									A							:					Union[str, Any]              =						TFEsmModel(config=__lowerCamelCase )
									A							:					Optional[int]              =						{
									    "input_ids": input_ids,
									    "attention_mask": input_mask,
									    "encoder_hidden_states": encoder_hidden_states,
									    "encoder_attention_mask": encoder_attention_mask,
									}
									A							:					str              =						model(__lowerCamelCase )

									A							:					Tuple              =						[input_ids, input_mask]
									A							:					Union[str, Any]              =						model(__lowerCamelCase	,	encoder_hidden_states=__lowerCamelCase )

									# Also check the case where encoder outputs are not passed
									A							:					str              =						model(__lowerCamelCase	,	attention_mask=__lowerCamelCase )

									self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any]	,	__lowerCamelCase      :			Union[str, Any] )      ->				Dict:
									A							:					Dict              =						TFEsmForMaskedLM(config=__lowerCamelCase )
									A							:					Any              =						model([input_ids, input_mask] )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.vocab_size) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			int	,	__lowerCamelCase      :			int )      ->				int:
									A							:					Any              =						self.num_labels
									A							:					Tuple              =						TFEsmForTokenClassification(config=__lowerCamelCase )
									A							:					Dict              =						{"input_ids": input_ids, "attention_mask": input_mask}
									A							:					int              =						model(__lowerCamelCase )
									self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.seq_length, self.num_labels) )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									A							:					int              =						self.prepare_config_and_inputs()
									(
									    (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , (
									    A
									)       , 
									)							:					Tuple              =						config_and_inputs
									A							:					int              =						{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict







@require_tf
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						a__   =  (
						    {
						        "feature-extraction": TFEsmModel,
						        "fill-mask": TFEsmForMaskedLM,
						        "text-classification": TFEsmForSequenceClassification,
						        "token-classification": TFEsmForTokenClassification,
						        "zero-shot": TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					Any              =						TFEsmModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	hidden_size=37 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									self.config_tester.run_common_tests()







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Union[str, Any]:
									A							:					str              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[Any]:
									A							:					int              =						self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
									A							:					Optional[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[Any]:
									A							:					Dict              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Tuple              =						TFEsmModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







						@unittest.skip("Protein models do not support embedding resizing." )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									pass







						@unittest.skip("Protein models do not support embedding resizing." )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									A       , A							:					int              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Union[str, Any]              =						model_class(__lowerCamelCase )
												assert isinstance(model.get_input_embeddings()	,	tf.keras.layers.Layer )
												if model_class is TFEsmForMaskedLM:
															# Output embedding test differs from the main test because they're a matrix, not a layer
															A							:					Union[str, Any]              =						model.get_bias()
															assert isinstance(__lowerCamelCase	,	__lowerCamelCase )
															for k, v in name.items():
																		assert isinstance(__lowerCamelCase	,	tf.Variable )
												else:
															A							:					List[Any]              =						model.get_output_embeddings()
															assert x is None
															A							:					List[Any]              =						model.get_bias()
															assert name is None







@require_tf
class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[int]:
									A							:					Any              =						TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )

									A							:					List[str]              =						tf.constant([[0, 1, 2, 3, 4, 5]] )
									A							:					Any              =						model(__lowerCamelCase )[0]
									A							:					int              =						[1, 6, 33]
									self.assertEqual(list(output.numpy().shape )	,	__lowerCamelCase )
									# compare the actual values for a slice.
									A							:					Union[str, Any]              =						tf.constant(
									    [
									        [
									            [8.921518, -10.589814, -6.4671307],
									            [-6.3967156, -13.911377, -1.1211915],
									            [-7.781247, -13.951557, -3.740592],
									        ]
									    ] )
									self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()	,	expected_slice.numpy()	,	atol=1e-2 ) )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				int:
									A							:					Optional[int]              =						TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )

									A							:					int              =						tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
									A							:					Optional[Any]              =						model(__lowerCamelCase )[0]
									# compare the actual values for a slice.
									A							:					Dict              =						tf.constant(
									    [
									        [
									            [0.14443092, 0.54125327, 0.3247739],
									            [0.30340484, 0.00526676, 0.31077722],
									            [0.32278043, -0.24987096, 0.3414628],
									        ]
									    ] )
									self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()	,	expected_slice.numpy()	,	atol=1e-4 ) )




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  (PNDMScheduler,)
						a__   =  (("num_inference_steps", 50),)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	**__lowerCamelCase      :			str )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						{
									    "num_train_timesteps": 10_00,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									}

									config.update(**__lowerCamelCase )
									return config







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str]=0	,	**__lowerCamelCase      :			Any )      ->				Tuple:
									A							:					Dict              =						dict(self.forward_default_kwargs )
									A							:					Dict              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					Union[str, Any]              =						self.dummy_sample
									A							:					List[Any]              =						0.1 * sample
									A							:					List[Any]              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					int              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[str]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Optional[Any]=0	,	**__lowerCamelCase      :			Tuple )      ->				str:
									A							:					List[str]              =						dict(self.forward_default_kwargs )
									A							:					Optional[int]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )
									A							:					List[str]              =						self.dummy_sample
									A							:					Any              =						0.1 * sample
									A							:					Any              =						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

									for scheduler_class in self.scheduler_classes:
												A							:					Tuple              =						self.get_scheduler_config()
												A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
												scheduler.set_timesteps(__lowerCamelCase )

												# copy over dummy past residuals (must be after setting timesteps)
												A							:					Optional[int]              =						dummy_past_residuals[:]

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

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

												A							:					Union[str, Any]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					Dict              =						new_scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Union[str, Any]              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						new_scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple	,	**__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									A							:					Optional[Any]              =						self.scheduler_classes[0]
									A							:					List[Any]              =						self.get_scheduler_config(**__lowerCamelCase )
									A							:					str              =						scheduler_class(**__lowerCamelCase )

									A							:					List[str]              =						10
									A							:					Union[str, Any]              =						self.dummy_model()
									A							:					int              =						self.dummy_sample_deter
									scheduler.set_timesteps(__lowerCamelCase )

									for i, t in enumerate(scheduler.prk_timesteps ):
												A							:					Optional[int]              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Optional[int]              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									for i, t in enumerate(scheduler.plms_timesteps ):
												A							:					Tuple              =						model(__lowerCamelCase	,	__lowerCamelCase )
												A							:					Tuple              =						scheduler.step_plms(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample

									return sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Any:
									A							:					Union[str, Any]              =						dict(self.forward_default_kwargs )

									A							:					Union[str, Any]              =						kwargs.pop("num_inference_steps"	,	__lowerCamelCase )

									for scheduler_class in self.scheduler_classes:
												A							:					Dict              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												A							:					List[Any]              =						self.dummy_sample
												A							:					List[Any]              =						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" ):
															A							:					List[str]              =						num_inference_steps

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

												A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					List[Any]              =						scheduler.step_prk(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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

												A							:					Any              =						scheduler.step_plms(__lowerCamelCase	,	0	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample
												A							:					str              =						scheduler.step_plms(__lowerCamelCase	,	1	,	__lowerCamelCase	,	**__lowerCamelCase ).prev_sample

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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									for timesteps in [1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[str]:
									for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=__lowerCamelCase )

									A							:					Dict              =						self.scheduler_classes[0]
									A							:					Union[str, Any]              =						self.get_scheduler_config(steps_offset=1 )
									A							:					Optional[int]              =						scheduler_class(**__lowerCamelCase )
									scheduler.set_timesteps(10 )
									assert torch.equal(
									    scheduler.timesteps	,	torch.LongTensor(
									        [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1] )	,	)







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									for beta_start, beta_end in zip([0.0001, 0.001]	,	[0.002, 0.02] ):
												self.check_over_configs(beta_start=__lowerCamelCase	,	beta_end=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Union[str, Any]:
									for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				str:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									for t in [1, 5, 10]:
												self.check_over_forward(time_step=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				int:
									for t, num_inference_steps in zip([1, 5, 10]	,	[10, 50, 1_00] ):
												self.check_over_forward(num_inference_steps=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
									A							:					str              =						27

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

												A							:					List[Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.set_timesteps(__lowerCamelCase )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2] ):
															A							:					Dict              =						scheduler.step_prk(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				int:
									with self.assertRaises(__lowerCamelCase ):
												A							:					Union[str, Any]              =						self.scheduler_classes[0]
												A							:					Union[str, Any]              =						self.get_scheduler_config()
												A							:					List[str]              =						scheduler_class(**__lowerCamelCase )

												scheduler.step_plms(self.dummy_sample	,	1	,	self.dummy_sample ).prev_sample







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Dict:
									A							:					Optional[Any]              =						self.full_loop()
									A							:					Tuple              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[int]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 198.1318 ) < 1e-2
									assert abs(result_mean.item() - 0.2580 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Any:
									A							:					Any              =						self.full_loop(prediction_type="v_prediction" )
									A							:					Union[str, Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					List[str]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 67.3986 ) < 1e-2
									assert abs(result_mean.item() - 0.0878 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				List[str]:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					Dict              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Any              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 230.0399 ) < 1e-2
									assert abs(result_mean.item() - 0.2995 ) < 1e-3







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Any:
									# We specify different beta, so that the first alpha is 0.99
									A							:					Any              =						self.full_loop(set_alpha_to_one=__lowerCamelCase	,	beta_start=0.01 )
									A							:					List[Any]              =						torch.sum(torch.abs(__lowerCamelCase ) )
									A							:					Optional[Any]              =						torch.mean(torch.abs(__lowerCamelCase ) )

									assert abs(result_sum.item() - 186.9482 ) < 1e-2
									assert abs(result_mean.item() - 0.2434 ) < 1e-3