
from __future__ import annotations

import unittest

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

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


if is_tf_available():
							import tensorflow as tf

							from transformers import TFLEDForConditionalGeneration, TFLEDModel






@require_tf
class 			lowerCAmelCase__       :
					lowerCAmelCase_					       =					LEDConfig
					lowerCAmelCase_					       =					{}
					lowerCAmelCase_					       =					'''gelu'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=13    ,							__SCREAMING_SNAKE_CASE=7    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=99    ,							__SCREAMING_SNAKE_CASE=32    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=4    ,							__SCREAMING_SNAKE_CASE=37    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=20    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=1    ,							__SCREAMING_SNAKE_CASE=0    ,							__SCREAMING_SNAKE_CASE=4    ,							):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	parent
											lowercase_     :					Dict													=	batch_size
											lowercase_     :					List[Any]													=	seq_length
											lowercase_     :					Union[str, Any]													=	is_training
											lowercase_     :					Any													=	use_labels
											lowercase_     :					Union[str, Any]													=	vocab_size
											lowercase_     :					Union[str, Any]													=	hidden_size
											lowercase_     :					int													=	num_hidden_layers
											lowercase_     :					Any													=	num_attention_heads
											lowercase_     :					Union[str, Any]													=	intermediate_size

											lowercase_     :					List[str]													=	hidden_dropout_prob
											lowercase_     :					int													=	attention_probs_dropout_prob
											lowercase_     :					Optional[Any]													=	max_position_embeddings
											lowercase_     :					Union[str, Any]													=	eos_token_id
											lowercase_     :					Optional[Any]													=	pad_token_id
											lowercase_     :					Optional[Any]													=	bos_token_id
											lowercase_     :					Optional[int]													=	attention_window

											# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
											# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
											# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
											# because its local attention only attends to `self.attention_window` and one before and one after
											lowercase_     :					Tuple													=	self.attention_window + 2

											# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
											# the `test_attention_outputs` and `test_hidden_states_output` tests
											lowercase_     :					str													=	(
											    self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
											)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size		)
											lowercase_     :					str													=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)    ,							1		)
											lowercase_     :					List[Any]													=	tf.concat([input_ids, eos_tensor]    ,							axis=1		)

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

											lowercase_     :					Union[str, Any]													=	self.config_cls(
											    vocab_size=self.vocab_size    ,							d_model=self.hidden_size    ,							encoder_layers=self.num_hidden_layers    ,							decoder_layers=self.num_hidden_layers    ,							encoder_attention_heads=self.num_attention_heads    ,							decoder_attention_heads=self.num_attention_heads    ,							encoder_ffn_dim=self.intermediate_size    ,							decoder_ffn_dim=self.intermediate_size    ,							dropout=self.hidden_dropout_prob    ,							attention_dropout=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							eos_token_ids=[2]    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							decoder_start_token_id=self.pad_token_id    ,							attention_window=self.attention_window    ,							**self.config_updates    ,							)
											lowercase_     :					Optional[Any]													=	prepare_led_inputs_dict(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Tuple													=	tf.concat(
											    [tf.zeros_like(__SCREAMING_SNAKE_CASE		)[:, :-1], tf.ones_like(__SCREAMING_SNAKE_CASE		)[:, -1:]]    ,							axis=-1    ,							)
											lowercase_     :					Optional[Any]													=	global_attention_mask
											return config, inputs_dict






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	TFLEDModel(config=__SCREAMING_SNAKE_CASE		).get_decoder()
											lowercase_     :					List[Any]													=	inputs_dict['''input_ids''']

											lowercase_     :					List[str]													=	input_ids[:1, :]
											lowercase_     :					Optional[int]													=	inputs_dict['''attention_mask'''][:1, :]
											lowercase_     :					Dict													=	1

											# first forward pass
											lowercase_     :					Tuple													=	model(__SCREAMING_SNAKE_CASE    ,							attention_mask=__SCREAMING_SNAKE_CASE    ,							use_cache=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Union[str, Any]													=	outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											lowercase_     :					Optional[Any]													=	ids_tensor((self.batch_size, 3)    ,							config.vocab_size		)
											lowercase_     :					Any													=	tf.cast(ids_tensor((self.batch_size, 3)    ,							2		)    ,							tf.inta		)

											# append to next input_ids and
											lowercase_     :					Optional[Any]													=	tf.concat([input_ids, next_tokens]    ,							axis=-1		)
											lowercase_     :					List[str]													=	tf.concat([attention_mask, next_attn_mask]    ,							axis=-1		)

											lowercase_     :					Tuple													=	model(__SCREAMING_SNAKE_CASE    ,							attention_mask=__SCREAMING_SNAKE_CASE		)[0]
											lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							attention_mask=__SCREAMING_SNAKE_CASE    ,							past_key_values=__SCREAMING_SNAKE_CASE		)[0]

											self.parent.assertEqual(next_tokens.shape[1]    ,							output_from_past.shape[1]		)

											# select random slice
											lowercase_     :					Tuple													=	int(ids_tensor((1,)    ,							output_from_past.shape[-1]		)		)
											lowercase_     :					List[str]													=	output_from_no_past[:, -3:, random_slice_idx]
											lowercase_     :					Dict													=	output_from_past[:, :, random_slice_idx]

											# test that outputs are equal for slice
											tf.debugging.assert_near(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							rtol=1E-3		)







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Any				,				__SCREAMING_SNAKE_CASE :			int				,				__SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			Tuple=None				,				__SCREAMING_SNAKE_CASE :			Optional[int]=None				,				__SCREAMING_SNAKE_CASE :			List[str]=None				,				__SCREAMING_SNAKE_CASE :			Any=None				,				):



						"""simple docstring"""





						if attention_mask is None:
												lowercase_     :					int													=	tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE				,				config.pad_token_id							)				,				tf.inta							)
						if decoder_attention_mask is None:
												lowercase_     :					Any													=	tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape				,				dtype=tf.inta							),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]				,				config.pad_token_id							)				,				tf.inta							),
												    ]				,				axis=-1				,				)
						if head_mask is None:
												lowercase_     :					Any													=	tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
						if decoder_head_mask is None:
												lowercase_     :					Dict													=	tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						    "decoder_input_ids": decoder_input_ids,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						}






@require_tf
class 			lowerCAmelCase__       (  lowerCamelCase_   ,						lowerCamelCase_   ,						unittest.TestCase					):
					lowerCAmelCase_					       =					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
					lowerCAmelCase_					       =					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
					lowerCAmelCase_					       =					(
					    {
					        '''conversational''': TFLEDForConditionalGeneration,
					        '''feature-extraction''': TFLEDModel,
					        '''summarization''': TFLEDForConditionalGeneration,
					        '''text2text-generation''': TFLEDForConditionalGeneration,
					        '''translation''': TFLEDForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_					       =					True
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False
					lowerCAmelCase_					       =					False






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	TFLEDModelTester(self		)
											lowercase_     :					Optional[int]													=	ConfigTester(self    ,							config_class=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()
											lowercase_     :					Optional[int]													=	tf.zeros_like(inputs_dict['''attention_mask''']		)
											lowercase_     :					Tuple													=	2
											lowercase_     :					Optional[int]													=	tf.where(
											    tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices    ,							1    ,							inputs_dict['''global_attention_mask''']    ,							)

											lowercase_     :					Dict													=	True
											lowercase_     :					Any													=	self.model_tester.seq_length
											lowercase_     :					List[str]													=	self.model_tester.encoder_seq_length

											def check_decoder_attentions_output(__SCREAMING_SNAKE_CASE		):
																	lowercase_     :					Optional[int]													=	outputs.decoder_attentions
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE		)    ,							self.model_tester.num_hidden_layers		)
																	self.assertListEqual(
																	    list(decoder_attentions[0].shape[-3:]		)    ,							[self.model_tester.num_attention_heads, seq_length, seq_length]    ,							)

											def check_encoder_attentions_output(__SCREAMING_SNAKE_CASE		):
																	lowercase_     :					Optional[int]													=	[t.numpy() for t in outputs.encoder_attentions]
																	lowercase_     :					Union[str, Any]													=	[t.numpy() for t in outputs.encoder_global_attentions]
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE		)    ,							self.model_tester.num_hidden_layers		)
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE		)    ,							self.model_tester.num_hidden_layers		)
																	self.assertListEqual(
																	    list(attentions[0].shape[-3:]		)    ,							[self.model_tester.num_attention_heads, seq_length, seq_length]    ,							)
																	self.assertListEqual(
																	    list(global_attentions[0].shape[-3:]		)    ,							[self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]    ,							)

											for model_class in self.all_model_classes:
																	lowercase_     :					str													=	True
																	lowercase_     :					Tuple													=	False
																	lowercase_     :					Dict													=	False
																	lowercase_     :					Optional[int]													=	model_class(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Optional[Any]													=	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)		)
																	lowercase_     :					Dict													=	len(__SCREAMING_SNAKE_CASE		)
																	self.assertEqual(config.output_hidden_states    ,							__SCREAMING_SNAKE_CASE		)
																	check_encoder_attentions_output(__SCREAMING_SNAKE_CASE		)

																	if self.is_encoder_decoder:
																							lowercase_     :					Optional[Any]													=	model_class(__SCREAMING_SNAKE_CASE		)
																							lowercase_     :					Dict													=	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)		)
																							self.assertEqual(config.output_hidden_states    ,							__SCREAMING_SNAKE_CASE		)
																							check_decoder_attentions_output(__SCREAMING_SNAKE_CASE		)

																	# Check that output attentions can also be changed via the config
																	del inputs_dict["output_attentions"]
																	lowercase_     :					Tuple													=	True
																	lowercase_     :					str													=	model_class(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Optional[int]													=	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)		)
																	self.assertEqual(config.output_hidden_states    ,							__SCREAMING_SNAKE_CASE		)
																	check_encoder_attentions_output(__SCREAMING_SNAKE_CASE		)

																	# Check attention is always last and order is fine
																	lowercase_     :					str													=	True
																	lowercase_     :					str													=	True
																	lowercase_     :					str													=	model_class(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Union[str, Any]													=	model(self._prepare_for_class(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)		)

																	self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)    ,							len(__SCREAMING_SNAKE_CASE		)		)
																	self.assertEqual(model.config.output_hidden_states    ,							__SCREAMING_SNAKE_CASE		)
																	check_encoder_attentions_output(__SCREAMING_SNAKE_CASE		)






					@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''		)
					def      _snake_case   (       self		):


											"""simple docstring"""





											pass






					def      _snake_case   (       self		):


											"""simple docstring"""





											pass







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Tuple							):



						"""simple docstring"""





						return tf.constant(__SCREAMING_SNAKE_CASE				,				dtype=tf.intaa							)


_lowercase			:  Optional[int]   							=	1E-4






@slow
@require_tf
class 			lowerCAmelCase__       (  unittest.TestCase					):






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		).led

											# change to intended input here
											lowercase_     :					Any													=	_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]		)
											lowercase_     :					Tuple													=	_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]		)
											lowercase_     :					int													=	prepare_led_inputs_dict(model.config    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Tuple													=	model(**__SCREAMING_SNAKE_CASE		)[0]
											lowercase_     :					Optional[int]													=	(1, 10_24, 7_68)
											self.assertEqual(output.shape    ,							__SCREAMING_SNAKE_CASE		)
											# change to expected output here
											lowercase_     :					Optional[int]													=	tf.convert_to_tensor(
											    [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]    ,							)
											tf.debugging.assert_near(output[:, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=1E-3		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Any													=	TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		)

											# change to intended input here
											lowercase_     :					Dict													=	_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]		)
											lowercase_     :					Tuple													=	_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]		)
											lowercase_     :					str													=	prepare_led_inputs_dict(model.config    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					str													=	model(**__SCREAMING_SNAKE_CASE		)[0]
											lowercase_     :					Tuple													=	(1, 10_24, model.config.vocab_size)
											self.assertEqual(output.shape    ,							__SCREAMING_SNAKE_CASE		)
											# change to expected output here
											lowercase_     :					Optional[Any]													=	tf.convert_to_tensor(
											    [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]    ,							)
											tf.debugging.assert_near(output[:, :3, :3]    ,							__SCREAMING_SNAKE_CASE    ,							atol=1E-3    ,							rtol=1E-3		)





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for schedule in ["linear", "squaredcos_cap_v2"]:
																	self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for clip_sample in [True, False]:
																	self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE		)
											for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample", "v_prediction"]:
																							self.check_over_configs(
																							    thresholding=__SCREAMING_SNAKE_CASE    ,							prediction_type=__SCREAMING_SNAKE_CASE    ,							sample_max_value=__SCREAMING_SNAKE_CASE    ,							)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





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

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					int													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''`custom_timesteps` must be in descending order.'''		):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

											with self.assertRaises(__SCREAMING_SNAKE_CASE    ,							msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''		):
																	scheduler.set_timesteps(num_inference_steps=__SCREAMING_SNAKE_CASE    ,							timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    __SCREAMING_SNAKE_CASE    ,							msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''    ,							):
																	scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)



