"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				__lowerCamelCase									=	"platform"

				import jax
				import jax.numpy as jnp

				from transformers import MarianTokenizer
				from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=None							,     UpperCamelCase__=None							,     UpperCamelCase__=None							,     UpperCamelCase__=None							,     UpperCamelCase__=None							,     UpperCamelCase__=None							,     ):






							"""simple docstring"""





							if attention_mask is None:
														A__						  =				np.where(input_ids != config.pad_token_id							,     1							,     0					)
							if decoder_attention_mask is None:
														A__						  =				np.where(decoder_input_ids != config.pad_token_id							,     1							,     0					)
							if head_mask is None:
														A__						  =				np.ones((config.encoder_layers, config.encoder_attention_heads)					)
							if decoder_head_mask is None:
														A__						  =				np.ones((config.decoder_layers, config.decoder_attention_heads)					)
							if cross_attn_head_mask is None:
														A__						  =				np.ones((config.decoder_layers, config.decoder_attention_heads)					)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							}
class      UpperCamelCase__:





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase=13		,__UpperCAmelCase=7		,__UpperCAmelCase=True		,__UpperCAmelCase=False		,__UpperCAmelCase=99		,__UpperCAmelCase=16		,__UpperCAmelCase=2		,__UpperCAmelCase=4		,__UpperCAmelCase=4		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.1		,__UpperCAmelCase=0.1		,__UpperCAmelCase=32		,__UpperCAmelCase=2		,__UpperCAmelCase=1		,__UpperCAmelCase=0		,__UpperCAmelCase=0.0_2		,)       ->					Optional[Any]:
													A__						  =				parent
													A__						  =				batch_size
													A__						  =				seq_length
													A__						  =				is_training
													A__						  =				use_labels
													A__						  =				vocab_size
													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				max_position_embeddings
													A__						  =				eos_token_id
													A__						  =				pad_token_id
													A__						  =				bos_token_id
													A__						  =				initializer_range





						def 					snake_case__						(							self  )       ->					List[str]:
													A__						  =				np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,self.vocab_size  )		,3		,self.vocab_size  )
													A__						  =				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,dtype=np.intaa  ))		,-1  )

													A__						  =				shift_tokens_right(__UpperCAmelCase		,1		,2  )

													A__						  =				MarianConfig(
													    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_id=self.eos_token_id		,bos_token_id=self.bos_token_id		,pad_token_id=self.pad_token_id		,initializer_range=self.initializer_range		,use_cache=__UpperCAmelCase		,)
													A__						  =				prepare_marian_inputs_dict(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )
													return config, inputs_dict





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__  ,	A__						  =				self.prepare_config_and_inputs()
													return config, inputs_dict





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Dict:
													A__						  =				20
													A__						  =				model_class_name(__UpperCAmelCase  )

													A__						  =				model.encode(inputs_dict['input_ids']  )

													A__  ,	A__						  =				(
													    inputs_dict['decoder_input_ids'],
													    inputs_dict['decoder_attention_mask'],
													)

													A__						  =				model.init_cache(decoder_input_ids.shape[0]		,__UpperCAmelCase		,__UpperCAmelCase  )
													A__						  =				jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,dtype='i4'  )

													A__						  =				jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]		,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,)
													A__						  =				model.decode(
													    decoder_input_ids[:, :-1]		,__UpperCAmelCase		,decoder_attention_mask=__UpperCAmelCase		,past_key_values=__UpperCAmelCase		,decoder_position_ids=__UpperCAmelCase		,)

													A__						  =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,dtype='i4'  )
													A__						  =				model.decode(
													    decoder_input_ids[:, -1:]		,__UpperCAmelCase		,decoder_attention_mask=__UpperCAmelCase		,past_key_values=outputs_cache.past_key_values		,decoder_position_ids=__UpperCAmelCase		,)

													A__						  =				model.decode(__UpperCAmelCase		,__UpperCAmelCase  )

													A__						  =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
													self.parent.assertTrue(diff < 1e-3		,msg=f'''Max diff is {diff}'''  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					int:
													A__						  =				20
													A__						  =				model_class_name(__UpperCAmelCase  )

													A__						  =				model.encode(inputs_dict['input_ids']  )

													A__  ,	A__						  =				(
													    inputs_dict['decoder_input_ids'],
													    inputs_dict['decoder_attention_mask'],
													)

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

													A__						  =				model.init_cache(decoder_input_ids.shape[0]		,__UpperCAmelCase		,__UpperCAmelCase  )
													A__						  =				jnp.broadcast_to(
													    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]		,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,)

													A__						  =				model.decode(
													    decoder_input_ids[:, :-1]		,__UpperCAmelCase		,decoder_attention_mask=__UpperCAmelCase		,past_key_values=__UpperCAmelCase		,decoder_position_ids=__UpperCAmelCase		,)
													A__						  =				jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,dtype='i4'  )
													A__						  =				model.decode(
													    decoder_input_ids[:, -1:]		,__UpperCAmelCase		,past_key_values=outputs_cache.past_key_values		,decoder_attention_mask=__UpperCAmelCase		,decoder_position_ids=__UpperCAmelCase		,)

													A__						  =				model.decode(__UpperCAmelCase		,__UpperCAmelCase		,decoder_attention_mask=__UpperCAmelCase  )

													A__						  =				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
													self.parent.assertTrue(diff < 1e-3		,msg=f'''Max diff is {diff}'''  )






@require_flax
class      UpperCamelCase__(      __A		,    unittest.TestCase		,    __A			):
						lowerCAmelCase__	:    List[Any]    				=		True
						lowerCAmelCase__	:    Optional[int]    				=		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
						lowerCAmelCase__	:    Dict    				=		(FlaxMarianMTModel,) if is_flax_available() else ()





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				FlaxMarianModelTester(self  )





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Tuple:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					int:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__  ):
																											A__						  =				self._prepare_for_class(__UpperCAmelCase		,__UpperCAmelCase  )
																											A__						  =				model_class(__UpperCAmelCase  )

																											@jax.jit
																											def encode_jitted(__UpperCAmelCase		,__UpperCAmelCase=None		,**__UpperCAmelCase  ):
																																		return model.encode(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase  )

																											with self.subTest('JIT Enabled'  ):
																																		A__						  =				encode_jitted(**__UpperCAmelCase  ).to_tuple()

																											with self.subTest('JIT Disabled'  ):
																																		with jax.disable_jit():
																																									A__						  =				encode_jitted(**__UpperCAmelCase  ).to_tuple()

																											self.assertEqual(len(__UpperCAmelCase  )		,len(__UpperCAmelCase  )  )
																											for jitted_output, output in zip(__UpperCAmelCase		,__UpperCAmelCase  ):
																																		self.assertEqual(jitted_output.shape		,output.shape  )





						def 					snake_case__						(							self  )       ->					Tuple:
													A__  ,	A__						  =				self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__  ):
																											A__						  =				model_class(__UpperCAmelCase  )
																											A__						  =				model.encode(inputs_dict['input_ids']		,inputs_dict['attention_mask']  )

																											A__						  =				{
																											    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																											    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																											    'encoder_outputs': encoder_outputs,
																											}

																											@jax.jit
																											def decode_jitted(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ):
																																		return model.decode(
																																		    decoder_input_ids=__UpperCAmelCase		,decoder_attention_mask=__UpperCAmelCase		,encoder_outputs=__UpperCAmelCase		,)

																											with self.subTest('JIT Enabled'  ):
																																		A__						  =				decode_jitted(**__UpperCAmelCase  ).to_tuple()

																											with self.subTest('JIT Disabled'  ):
																																		with jax.disable_jit():
																																									A__						  =				decode_jitted(**__UpperCAmelCase  ).to_tuple()

																											self.assertEqual(len(__UpperCAmelCase  )		,len(__UpperCAmelCase  )  )
																											for jitted_output, output in zip(__UpperCAmelCase		,__UpperCAmelCase  ):
																																		self.assertEqual(jitted_output.shape		,output.shape  )





						@slow
						def 					snake_case__						(							self  )       ->					Optional[int]:
													for model_class_name in self.all_model_classes:
																				A__						  =				model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'  )
																				# FlaxMarianForSequenceClassification expects eos token in input_ids
																				A__						  =				np.ones((1, 1)  ) * model.config.eos_token_id
																				A__						  =				model(__UpperCAmelCase  )
																				self.assertIsNotNone(__UpperCAmelCase  )






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      unittest.TestCase			):
						lowerCAmelCase__	:    List[str]    				=		None
						lowerCAmelCase__	:    int    				=		None





						@classmethod
						def 					snake_case__						(							cls  )       ->					None:
													A__						  =				f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
													return cls





						@cached_property
						def 					snake_case__						(							self  )       ->					Optional[int]:
													return MarianTokenizer.from_pretrained(self.model_name  )





						@property
						def 					snake_case__						(							self  )       ->					int:
													return self.tokenizer.eos_token_id





						@cached_property
						def 					snake_case__						(							self  )       ->					int:
													A__						  =				FlaxMarianMTModel.from_pretrained(self.model_name  )
													self.assertEqual(model.config.decoder_start_token_id		,model.config.pad_token_id  )
													return model





						def 					snake_case__						(							self		,**__UpperCAmelCase  )       ->					Optional[Any]:
													A__						  =				self.translate_src_text(**__UpperCAmelCase  )
													self.assertListEqual(self.expected_text		,__UpperCAmelCase  )





						def 					snake_case__						(							self		,**__UpperCAmelCase  )       ->					int:
													A__						  =				self.tokenizer(self.src_text		,padding=__UpperCAmelCase		,return_tensors='np'		,**__UpperCAmelCase  )
													A__						  =				self.model.generate(
													    model_inputs.input_ids		,attention_mask=model_inputs.attention_mask		,num_beams=2		,max_length=1_28		,).sequences
													A__						  =				self.tokenizer.batch_decode(__UpperCAmelCase		,skip_special_tokens=__UpperCAmelCase  )
													return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    List[str]    				=		'en'
						lowerCAmelCase__	:    Union[str, Any]    				=		'fr'
						lowerCAmelCase__	:    Optional[int]    				=		[
						    'I am a small frog.',
						    'Now I can forget the 100 words of german that I know.',
						]
						lowerCAmelCase__	:    Optional[int]    				=		[
						    'Je suis une petite grenouille.',
						    'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
						]





						@slow
						def 					snake_case__						(							self  )       ->					int:
													self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    List[Any]    				=		'fr'
						lowerCAmelCase__	:    Any    				=		'en'
						lowerCAmelCase__	:    str    				=		[
						    'Donnez moi le micro.',
						    'Tom et Mary étaient assis à une table.',  # Accents
						]
						lowerCAmelCase__	:    Any    				=		[
						    'Give me the microphone.',
						    'Tom and Mary were sitting at a table.',
						]





						@slow
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Tuple    				=		'mt'
						lowerCAmelCase__	:    Tuple    				=		'en'
						lowerCAmelCase__	:    Optional[int]    				=		['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
						lowerCAmelCase__	:    Dict    				=		['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']





						@slow
						def 					snake_case__						(							self  )       ->					List[str]:
													self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Dict    				=		'en'
						lowerCAmelCase__	:    Union[str, Any]    				=		'de'
						lowerCAmelCase__	:    Optional[int]    				=		[
						    'I am a small frog.',
						    'Now I can forget the 100 words of german that I know.',
						    'Tom asked his teacher for advice.',
						    'That\'s how I would do it.',
						    'Tom really admired Mary\'s courage.',
						    'Turn around and close your eyes.',
						]
						lowerCAmelCase__	:    Optional[int]    				=		[
						    'Ich bin ein kleiner Frosch.',
						    'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
						    'Tom bat seinen Lehrer um Rat.',
						    'So würde ich das machen.',
						    'Tom bewunderte Marias Mut wirklich.',
						    'Drehen Sie sich um und schließen Sie die Augen.',
						]





						@slow
						def 					snake_case__						(							self  )       ->					Tuple:
													self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Dict    				=		'en'
						lowerCAmelCase__	:    List[Any]    				=		'zh'
						lowerCAmelCase__	:    Union[str, Any]    				=		['My name is Wolfgang and I live in Berlin']
						lowerCAmelCase__	:    Tuple    				=		['我叫沃尔夫冈 我住在柏林']





						@slow
						def 					snake_case__						(							self  )       ->					List[str]:
													self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    int    				=		'ru'
						lowerCAmelCase__	:    int    				=		'fr'
						lowerCAmelCase__	:    Dict    				=		['Он показал мне рукопись своей новой пьесы.']
						lowerCAmelCase__	:    List[Any]    				=		['Il m\'a montré le manuscrit de sa nouvelle pièce.']





						@slow
						def 					snake_case__						(							self  )       ->					Any:
													self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    Dict    				=		'en'
						lowerCAmelCase__	:    Optional[Any]    				=		'ROMANCE'
						lowerCAmelCase__	:    List[Any]    				=		[
						    '>>fr<< Don\'t spend so much time watching TV.',
						    '>>pt<< Your message has been sent.',
						    '>>es<< He\'s two years older than me.',
						]
						lowerCAmelCase__	:    Optional[Any]    				=		[
						    'Ne passez pas autant de temps à regarder la télé.',
						    'A sua mensagem foi enviada.',
						    'Es dos años más viejo que yo.',
						]





						@slow
						def 					snake_case__						(							self  )       ->					Optional[int]:
													self._assert_generated_batch_equal_expected()






"""simple docstring"""




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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class      UpperCamelCase__:
						lowerCAmelCase__	:    CommonSchedulerState

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

						# running values
						lowerCAmelCase__	:    Optional[jnp.ndarray]    				=		None





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[Any]:
													return cls(common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase  )






@dataclass
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    LMSDiscreteSchedulerState






class      UpperCamelCase__(      __A		,    __A			):
						lowerCAmelCase__	:    List[str]    				=		[e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase__	:    jnp.dtype





						@property
						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													return True





						@register_to_config
						def __init__(							self		,__UpperCAmelCase = 10_00		,__UpperCAmelCase = 0.0_0_0_1		,__UpperCAmelCase = 0.0_2		,__UpperCAmelCase = "linear"		,__UpperCAmelCase = None		,__UpperCAmelCase = "epsilon"		,__UpperCAmelCase = jnp.floataa		,)       ->					str:
													A__						  =				dtype





						def 					snake_case__						(							self		,__UpperCAmelCase = None  )       ->					LMSDiscreteSchedulerState:
													if common is None:
																				A__						  =				CommonSchedulerState.create(self  )

													A__						  =				jnp.arange(0		,self.config.num_train_timesteps  ).round()[::-1]
													A__						  =				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													A__						  =				sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=__UpperCAmelCase		,init_noise_sigma=__UpperCAmelCase		,timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					jnp.ndarray:
													((A__)  ,	)						  =				jnp.where(state.timesteps == timestep		,size=1  )
													A__						  =				step_index[0]

													A__						  =				state.sigmas[step_index]
													A__						  =				sample / ((sigma**2 + 1) ** 0.5)
													return sample





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Optional[int]:

													def lms_derivative(__UpperCAmelCase  ):
																				A__						  =				1.0
																				for k in range(__UpperCAmelCase  ):
																											if current_order == k:
																																		continue
																											prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																				return prod

													A__						  =				integrate.quad(__UpperCAmelCase		,state.sigmas[t]		,state.sigmas[t + 1]		,epsrel=1e-4  )[0]

													return integrated_coeff





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = ()  )       ->					LMSDiscreteSchedulerState:
													A__						  =				jnp.linspace(self.config.num_train_timesteps - 1		,0		,__UpperCAmelCase		,dtype=self.dtype  )

													A__						  =				jnp.floor(__UpperCAmelCase  ).astype(jnp.intaa  )
													A__						  =				jnp.ceil(__UpperCAmelCase  ).astype(jnp.intaa  )

													A__						  =				jnp.mod(__UpperCAmelCase		,1.0  )

													A__						  =				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													A__						  =				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													A__						  =				jnp.concatenate([sigmas, jnp.array([0.0]		,dtype=self.dtype  )]  )

													A__						  =				timesteps.astype(jnp.intaa  )

													# initial running values
													A__						  =				jnp.zeros((0,) + shape		,dtype=self.dtype  )

													return state.replace(
													    timesteps=__UpperCAmelCase		,sigmas=__UpperCAmelCase		,num_inference_steps=__UpperCAmelCase		,derivatives=__UpperCAmelCase		,)





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = 4		,__UpperCAmelCase = True		,)       ->					Union[FlaxLMSSchedulerOutput, Tuple]:
													if state.num_inference_steps is None:
																				raise ValueError(
																				    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'  )

													A__						  =				state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																				A__						  =				sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																				# * c_out + input * c_skip
																				A__						  =				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																				raise ValueError(
																				    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''  )

													# 2. Convert to an ODE derivative
													A__						  =				(sample - pred_original_sample) / sigma
													A__						  =				state.replace(derivatives=jnp.append(state.derivatives		,__UpperCAmelCase  )  )
													if len(state.derivatives  ) > order:
																				A__						  =				state.replace(derivatives=jnp.delete(state.derivatives		,0  )  )

													# 3. Compute linear multistep coefficients
													A__						  =				min(timestep + 1		,__UpperCAmelCase  )
													A__						  =				[self.get_lms_coefficient(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase  ) for curr_order in range(__UpperCAmelCase  )]

													# 4. Compute previous sample based on the derivatives path
													A__						  =				sample + sum(
													    coeff * derivative for coeff, derivative in zip(__UpperCAmelCase		,reversed(state.derivatives  )  )  )

													if not return_dict:
																				return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=__UpperCAmelCase		,state=__UpperCAmelCase  )





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,)       ->					jnp.ndarray:
													A__						  =				state.sigmas[timesteps].flatten()
													A__						  =				broadcast_to_shape_from_left(__UpperCAmelCase		,noise.shape  )

													A__						  =				original_samples + noise * sigma

													return noisy_samples





						def __len__(							self  )       ->					Optional[Any]:
													return self.config.num_train_timesteps





