





import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
	import torch

	from transformers import (
	    MraForMaskedLM,
	    MraForMultipleChoice,
	    MraForQuestionAnswering,
	    MraForSequenceClassification,
	    MraForTokenClassification,
	    MraModel,
	)
	from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST



class  __lowerCAmelCase	:






			def __init__(self ,							__magic_name__ ,							__magic_name__=2 ,							__magic_name__=8 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=99 ,							__magic_name__=16 ,							__magic_name__=5 ,							__magic_name__=2 ,							__magic_name__=36 ,							__magic_name__="gelu" ,							__magic_name__=0.0 ,							__magic_name__=0.0 ,							__magic_name__=512 ,							__magic_name__=16 ,							__magic_name__=2 ,							__magic_name__=0.02 ,							__magic_name__=3 ,							__magic_name__=4 ,							__magic_name__=None ,							)					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  parent
							snake_case_						:   Optional[int]           =  batch_size
							snake_case_						:   int           =  seq_length
							snake_case_						:   Union[str, Any]           =  is_training
							snake_case_						:   Optional[Any]           =  use_input_mask
							snake_case_						:   Tuple           =  use_token_type_ids
							snake_case_						:   Dict           =  use_labels
							snake_case_						:   Union[str, Any]           =  vocab_size
							snake_case_						:   Union[str, Any]           =  hidden_size
							snake_case_						:   Any           =  num_hidden_layers
							snake_case_						:   List[Any]           =  num_attention_heads
							snake_case_						:   int           =  intermediate_size
							snake_case_						:   List[Any]           =  hidden_act
							snake_case_						:   Optional[Any]           =  hidden_dropout_prob
							snake_case_						:   Dict           =  attention_probs_dropout_prob
							snake_case_						:   Dict           =  max_position_embeddings
							snake_case_						:   int           =  type_vocab_size
							snake_case_						:   List[str]           =  type_sequence_label_size
							snake_case_						:   Tuple           =  initializer_range
							snake_case_						:   List[str]           =  num_labels
							snake_case_						:   List[str]           =  num_choices
							snake_case_						:   Optional[int]           =  scope






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  ids_tensor([self.batch_size, self.seq_length] ,							self.vocab_size   )

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

							snake_case_						:   List[str]           =  None
							if self.use_token_type_ids:
											snake_case_						:   Dict           =  ids_tensor([self.batch_size, self.seq_length] ,							self.type_vocab_size   )

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

							snake_case_						:   Tuple           =  self.get_config()

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






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


							'''simple docstring'''



							return MraConfig(
							    vocab_size=self.vocab_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							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 ,							is_decoder=__magic_name__ ,							initializer_range=self.initializer_range ,							)






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


							'''simple docstring'''



							snake_case_						:   Any           =  self.get_config()
							snake_case_						:   List[str]           =  300
							return config






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							(
							    snake_case_						
							)						:   int           =  self.prepare_config_and_inputs()

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

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  MraModel(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Dict           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__   )
							snake_case_						:   int           =  model(__magic_name__ ,							token_type_ids=__magic_name__   )
							snake_case_						:   List[str]           =  model(__magic_name__   )
							self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Any           =  True
							snake_case_						:   Optional[Any]           =  MraModel(__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Optional[Any]           =  model(
							    __magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							encoder_hidden_states=__magic_name__ ,							encoder_attention_mask=__magic_name__ ,							)
							snake_case_						:   str           =  model(
							    __magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							encoder_hidden_states=__magic_name__ ,							)
							snake_case_						:   List[Any]           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__   )
							self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  MraForMaskedLM(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Dict           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							labels=__magic_name__   )
							self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.vocab_size)   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  MraForQuestionAnswering(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Any           =  model(
							    __magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							start_positions=__magic_name__ ,							end_positions=__magic_name__ ,							)
							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       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   str           =  self.num_labels
							snake_case_						:   List[Any]           =  MraForSequenceClassification(__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Optional[int]           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							labels=__magic_name__   )
							self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_labels)   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.num_labels
							snake_case_						:   Tuple           =  MraForTokenClassification(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   Dict           =  model(__magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							labels=__magic_name__   )
							self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.seq_length, self.num_labels)   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       str:


							'''simple docstring'''



							snake_case_						:   int           =  self.num_choices
							snake_case_						:   int           =  MraForMultipleChoice(config=__magic_name__   )
							model.to(__magic_name__   )
							model.eval()
							snake_case_						:   str           =  input_ids.unsqueeze(1   ).expand(-1 ,							self.num_choices ,							-1   ).contiguous()
							snake_case_						:   Optional[int]           =  token_type_ids.unsqueeze(1   ).expand(-1 ,							self.num_choices ,							-1   ).contiguous()
							snake_case_						:   Optional[Any]           =  input_mask.unsqueeze(1   ).expand(-1 ,							self.num_choices ,							-1   ).contiguous()
							snake_case_						:   Union[str, Any]           =  model(
							    __magic_name__ ,							attention_mask=__magic_name__ ,							token_type_ids=__magic_name__ ,							labels=__magic_name__ ,							)
							self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.num_choices)   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Union[str, Any]           =  self.prepare_config_and_inputs()
							(
							    snake_case_						
							)						:   str           =  config_and_inputs
							snake_case_						:   Optional[Any]           =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict




@require_torch
class  __lowerCAmelCase	(      __UpperCamelCase,					unittest.TestCase					):
			lowerCamelCase_					:					Optional[Any]	      =				(
			    (
			        MraModel,
			        MraForMaskedLM,
			        MraForMultipleChoice,
			        MraForQuestionAnswering,
			        MraForSequenceClassification,
			        MraForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase_					:					List[Any]	      =				False
			lowerCamelCase_					:					List[Any]	      =				False
			lowerCamelCase_					:					Optional[Any]	      =				False
			lowerCamelCase_					:					Union[str, Any]	      =				False

			lowerCamelCase_					:					Optional[Any]	      =				()






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


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  MraModelTester(self   )
							snake_case_						:   Optional[int]           =  ConfigTester(self ,							config_class=__magic_name__ ,							hidden_size=37   )






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
											snake_case_						:   Dict           =  type
											self.model_tester.create_and_check_model(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_multiple_choice(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Tuple           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__magic_name__   )






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


							'''simple docstring'''



							snake_case_						:   int           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__magic_name__   )






			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							snake_case_						:   Any           =  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__magic_name__   )






			@slow
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_						:   Tuple           =  MraModel.from_pretrained(__magic_name__   )
											self.assertIsNotNone(__magic_name__   )






			@unittest.skip(reason='''MRA does not output attentions'''   )
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							return




@require_torch
class  __lowerCAmelCase	(      unittest.TestCase					):






			@slow
			def       lowerCamelCase  (self   )					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  MraModel.from_pretrained('''uw-madison/mra-base-512-4'''   )
							snake_case_						:   Optional[Any]           =  torch.arange(256   ).unsqueeze(0   )

							with torch.no_grad():
											snake_case_						:   int           =  model(__magic_name__   )[0]

							snake_case_						:   str           =  torch.Size((1, 256, 768)   )
							self.assertEqual(output.shape ,							__magic_name__   )

							snake_case_						:   List[Any]           =  torch.tensor(
							    [[[-0.0_140, 0.0_830, -0.0_381], [0.1_546, 0.1_402, 0.0_220], [0.1_162, 0.0_851, 0.0_165]]]   )

							self.assertTrue(torch.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=1e-4   )   )






			@slow
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Dict           =  MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''   )
							snake_case_						:   Union[str, Any]           =  torch.arange(256   ).unsqueeze(0   )

							with torch.no_grad():
											snake_case_						:   Tuple           =  model(__magic_name__   )[0]

							snake_case_						:   int           =  5_0265

							snake_case_						:   int           =  torch.Size((1, 256, vocab_size)   )
							self.assertEqual(output.shape ,							__magic_name__   )

							snake_case_						:   Union[str, Any]           =  torch.tensor(
							    [[[9.2_595, -3.6_038, 11.8_819], [9.3_869, -3.2_693, 11.0_956], [11.8_524, -3.4_938, 13.1_210]]]   )

							self.assertTrue(torch.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=1e-4   )   )






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


							'''simple docstring'''



							snake_case_						:   Dict           =  MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''   )
							snake_case_						:   List[str]           =  torch.arange(4096   ).unsqueeze(0   )

							with torch.no_grad():
											snake_case_						:   List[str]           =  model(__magic_name__   )[0]

							snake_case_						:   List[Any]           =  5_0265

							snake_case_						:   Dict           =  torch.Size((1, 4096, vocab_size)   )
							self.assertEqual(output.shape ,							__magic_name__   )

							snake_case_						:   Optional[int]           =  torch.tensor(
							    [[[5.4_789, -2.3_564, 7.5_064], [7.9_067, -1.3_369, 9.9_668], [9.0_712, -1.8_106, 7.0_380]]]   )

							self.assertTrue(torch.allclose(output[:, :3, :3] ,							__magic_name__ ,							atol=1e-4   )   )







# 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_					:					CommonSchedulerState

			# setable values
			lowerCamelCase_					:					jnp.ndarray
			lowerCamelCase_					:					jnp.ndarray
			lowerCamelCase_					:					Optional[int]	      =				None






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



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




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



							if common is None:
											snake_case_						:   Any           =  CommonSchedulerState.create(self   )

							# standard deviation of the initial noise distribution
							snake_case_						:   Any           =  jnp.array(1.0 ,							dtype=self.dtype   )

							snake_case_						:   Optional[int]           =  jnp.arange(0 ,							self.config.num_train_timesteps   ).round()[::-1]

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  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
							snake_case_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__=None ,							__magic_name__=None   )					->       int:


							'''simple docstring'''



							snake_case_						:   Any           =  state.common.alphas_cumprod[t]
							snake_case_						:   Dict           =  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
							snake_case_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											snake_case_						:   Any           =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											snake_case_						:   int           =  jnp.clip(__magic_name__ ,							a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											snake_case_						:   List[str]           =  jnp.log(jnp.clip(__magic_name__ ,							a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											snake_case_						:   str           =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											snake_case_						:   int           =  jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											snake_case_						:   str           =  variance
											snake_case_						:   Optional[Any]           =  state.common.betas[t]
											snake_case_						:   Optional[Any]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[Any]           =  frac * max_log + (1 - frac) * min_log

							return variance






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None ,							__magic_name__ = True ,							)					->       Union[FlaxDDPMSchedulerOutput, Tuple]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[Any]           =  jax.random.PRNGKey(0   )

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

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   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":
											snake_case_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[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:
											snake_case_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-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
							snake_case_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  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
							snake_case_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

							snake_case_						:   List[str]           =  jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype   )   )

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



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






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
