

from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
			import tensorflow as tf

			from transformers.models.distilbert.modeling_tf_distilbert import (
			    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFDistilBertForMaskedLM,
			    TFDistilBertForMultipleChoice,
			    TFDistilBertForQuestionAnswering,
			    TFDistilBertForSequenceClassification,
			    TFDistilBertForTokenClassification,
			    TFDistilBertModel,
			)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(	self							:       Tuple					,					a							:       str					,					)							->				Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Tuple    =				parent
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				13
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				7
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				True
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				True
				SCREAMING_SNAKE_CASE      :					int    =				False
				SCREAMING_SNAKE_CASE      :					Dict    =				True
				SCREAMING_SNAKE_CASE      :					Tuple    =				99
				SCREAMING_SNAKE_CASE      :					Dict    =				32
				SCREAMING_SNAKE_CASE      :					List[Any]    =				2
				SCREAMING_SNAKE_CASE      :					Dict    =				4
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				37
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				"gelu"
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				0.1
				SCREAMING_SNAKE_CASE      :					Any    =				0.1
				SCREAMING_SNAKE_CASE      :					Any    =				512
				SCREAMING_SNAKE_CASE      :					Any    =				16
				SCREAMING_SNAKE_CASE      :					List[str]    =				2
				SCREAMING_SNAKE_CASE      :					Dict    =				0.02
				SCREAMING_SNAKE_CASE      :					List[Any]    =				3
				SCREAMING_SNAKE_CASE      :					Dict    =				4
				SCREAMING_SNAKE_CASE      :					List[Any]    =				None
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				int:

				"""simple docstring"""

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

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

				SCREAMING_SNAKE_CASE      :					Any    =				None
				SCREAMING_SNAKE_CASE      :					List[Any]    =				None
				SCREAMING_SNAKE_CASE      :					int    =				None
				if self.use_labels:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor([self.batch_size]					,					self.type_sequence_label_size		)
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor([self.batch_size, self.seq_length]					,					self.num_labels		)
					SCREAMING_SNAKE_CASE      :					Dict    =				ids_tensor([self.batch_size]					,					self.num_choices		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				DistilBertConfig(
				    vocab_size=self.vocab_size					,					dim=self.hidden_size					,					n_layers=self.num_hidden_layers					,					n_heads=self.num_attention_heads					,					hidden_dim=self.intermediate_size					,					hidden_act=self.hidden_act					,					dropout=self.hidden_dropout_prob					,					attention_dropout=self.attention_probs_dropout_prob					,					max_position_embeddings=self.max_position_embeddings					,					initializer_range=self.initializer_range					,					)

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       List[Any]					,					a							:       Optional[int]					,					a							:       Optional[int]					,					a							:       Dict					,					a							:       Any					,					a							:       int		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				TFDistilBertModel(config=a		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				{"input_ids": input_ids, "attention_mask": input_mask}

				SCREAMING_SNAKE_CASE      :					Dict    =				model(a		)

				SCREAMING_SNAKE_CASE      :					int    =				[input_ids, input_mask]

				SCREAMING_SNAKE_CASE      :					int    =				model(a		)

				self.parent.assertEqual(result.last_hidden_state.shape					,					(self.batch_size, self.seq_length, self.hidden_size)		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       Dict					,					a							:       Tuple					,					a							:       str					,					a							:       int					,					a							:       List[str]					,					a							:       Union[str, Any]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				TFDistilBertForMaskedLM(config=a		)
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				{"input_ids": input_ids, "attention_mask": input_mask}
				SCREAMING_SNAKE_CASE      :					List[Any]    =				model(a		)
				self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.vocab_size)		)
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       List[Any]					,					a							:       str					,					a							:       Union[str, Any]					,					a							:       Dict					,					a							:       Optional[int]					,					a							:       List[Any]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				TFDistilBertForQuestionAnswering(config=a		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				{
				    "input_ids": input_ids,
				    "attention_mask": input_mask,
				}
				SCREAMING_SNAKE_CASE      :					List[str]    =				model(a		)
				self.parent.assertEqual(result.start_logits.shape					,					(self.batch_size, self.seq_length)		)
				self.parent.assertEqual(result.end_logits.shape					,					(self.batch_size, self.seq_length)		)
			def 							__UpperCamelCase       (	self							:       int					,					a							:       List[Any]					,					a							:       Dict					,					a							:       Tuple					,					a							:       Tuple					,					a							:       Tuple					,					a							:       Optional[Any]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				self.num_labels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				TFDistilBertForSequenceClassification(a		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				{"input_ids": input_ids, "attention_mask": input_mask}
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				model(a		)
				self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)		)
			def 							__UpperCamelCase       (	self							:       Any					,					a							:       Any					,					a							:       Optional[int]					,					a							:       Any					,					a							:       int					,					a							:       int					,					a							:       Optional[int]		)							->				Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				self.num_choices
				SCREAMING_SNAKE_CASE      :					Dict    =				TFDistilBertForMultipleChoice(a		)
				SCREAMING_SNAKE_CASE      :					Dict    =				tf.tile(tf.expand_dims(a					,					1		)					,					(1, self.num_choices, 1)		)
				SCREAMING_SNAKE_CASE      :					Dict    =				tf.tile(tf.expand_dims(a					,					1		)					,					(1, self.num_choices, 1)		)
				SCREAMING_SNAKE_CASE      :					int    =				{
				    "input_ids": multiple_choice_inputs_ids,
				    "attention_mask": multiple_choice_input_mask,
				}
				SCREAMING_SNAKE_CASE      :					Dict    =				model(a		)
				self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_choices)		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]					,					a							:       List[str]					,					a							:       Dict					,					a							:       List[Any]					,					a							:       List[Any]					,					a							:       Union[str, Any]					,					a							:       Dict		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.num_labels
				SCREAMING_SNAKE_CASE      :					Any    =				TFDistilBertForTokenClassification(a		)
				SCREAMING_SNAKE_CASE      :					int    =				{"input_ids": input_ids, "attention_mask": input_mask}
				SCREAMING_SNAKE_CASE      :					str    =				model(a		)
				self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.num_labels)		)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.prepare_config_and_inputs()
				((SCREAMING_SNAKE_CASE)   ,(SCREAMING_SNAKE_CASE)   ,(SCREAMING_SNAKE_CASE)   ,(SCREAMING_SNAKE_CASE)   ,(SCREAMING_SNAKE_CASE)   ,(SCREAMING_SNAKE_CASE))      :					Optional[Any]    =				config_and_inputs
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_tf
class _UpperCamelCase							(							__A							,		__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=(
			    (
			        TFDistilBertModel,
			        TFDistilBertForMaskedLM,
			        TFDistilBertForQuestionAnswering,
			        TFDistilBertForSequenceClassification,
			        TFDistilBertForTokenClassification,
			        TFDistilBertForMultipleChoice,
			    )
			    if is_tf_available()
			    else None
			)
			lowerCamelCase__							=(
			    {
			        'feature-extraction': TFDistilBertModel,
			        'fill-mask': TFDistilBertForMaskedLM,
			        'question-answering': TFDistilBertForQuestionAnswering,
			        'text-classification': TFDistilBertForSequenceClassification,
			        'token-classification': TFDistilBertForTokenClassification,
			        'zero-shot': TFDistilBertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			lowerCamelCase__							=False
			lowerCamelCase__							=False
			def 							__UpperCamelCase       (	self							:       int		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				TFDistilBertModelTester(self		)
				SCREAMING_SNAKE_CASE      :					int    =				ConfigTester(self					,					config_class=a					,					dim=37		)
			def 							__UpperCamelCase       (	self							:       str		)							->				Any:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_model(*a		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Dict    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_masked_lm(*a		)
			def 							__UpperCamelCase       (	self							:       List[Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_question_answering(*a		)
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_sequence_classification(*a		)
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_multiple_choice(*a		)
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_token_classification(*a		)






			@slow
			def 							__UpperCamelCase       (	self							:       Dict		)							->				Any:

				"""simple docstring"""

				for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]		):
					SCREAMING_SNAKE_CASE      :					List[str]    =				TFDistilBertModel.from_pretrained(a		)
					self.assertIsNotNone(a		)






@require_tf
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase       (	self							:       Optional[int]		)							->				str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[Any]    =				TFDistilBertModel.from_pretrained("distilbert-base-uncased"		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				tf.constant([[0, 1, 2, 3, 4, 5]]		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				model(a		)[0]

				SCREAMING_SNAKE_CASE      :					int    =				[1, 6, 768]
				self.assertEqual(output.shape					,					a		)

				SCREAMING_SNAKE_CASE      :					str    =				tf.constant(
				    [
				        [
				            [0.1926_1885, -0.1373_2955, 0.411_9799],
				            [0.2215_0156, -0.0742_2661, 0.3903_7204],
				            [0.2275_6018, -0.089_6414, 0.370_1467],
				        ]
				    ]		)
				tf.debugging.assert_near(output[:, :3, :3]					,					a					,					atol=1e-4		)

'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)






@flax.struct.dataclass
class  lowerCAmelCase__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Tuple													=       CommonSchedulerState.create(self		)

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, Any]													=       jnp.array(1.0				,							dtype=self.dtype		)

											lowercase_	:     List[Any]													=       jnp.arange(0				,							self.config.num_train_timesteps		).round()[::-1]

											return DDPMSchedulerState.create(
											    common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self.config.num_train_timesteps // num_inference_steps
											# creates integer timesteps by multiplying by ratio
											# rounding to avoid issues when num_inference_step is power of 3
											lowercase_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

											return state.replace(
											    num_inference_steps=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     str													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)

											# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
											# and sample from it to get previous sample
											# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
											lowercase_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

											if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowercase_						, lowercase_	:     Optional[Any]													=       jnp.split(__SCREAMING_SNAKE_CASE				,							sample.shape[1]				,							axis=1		)
											else:
																	lowercase_	:     int													=       None

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[int]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     int													=       1 - alpha_prod_t
											lowercase_	:     str													=       1 - alpha_prod_t_prev

											# 2. compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
											if self.config.prediction_type == "epsilon":
																	lowercase_	:     Tuple													=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											elif self.config.prediction_type == "sample":
																	lowercase_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											else:
																	raise ValueError(
																	    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
																	    ''' for the FlaxDDPMScheduler.'''		)

											# 3. Clip "predicted x_0"
											if self.config.clip_sample:
																	lowercase_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-1				,							1		)

											# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[Any]													=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

											# 5. Compute predicted previous sample µ_t
											# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
											lowercase_	:     Optional[Any]													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																	lowercase_	:     str													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							num=1		)
																	lowercase_	:     List[Any]													=       jax.random.normal(__SCREAMING_SNAKE_CASE				,							shape=model_output.shape				,							dtype=self.dtype		)
																	return (self._get_variance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							predicted_variance=__SCREAMING_SNAKE_CASE		) ** 0.5) * noise

											lowercase_	:     Optional[Any]													=       jnp.where(t > 0				,							random_variance()				,							jnp.zeros(model_output.shape				,							dtype=self.dtype		)		)

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return add_noise_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return get_velocity_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



