import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
					from transformers.models.roberta.modeling_flax_roberta import (
					    FlaxRobertaForCausalLM,
					    FlaxRobertaForMaskedLM,
					    FlaxRobertaForMultipleChoice,
					    FlaxRobertaForQuestionAnswering,
					    FlaxRobertaForSequenceClassification,
					    FlaxRobertaForTokenClassification,
					    FlaxRobertaModel,
					)





class       lowercase__		(     unittest.TestCase):


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Union[str, Any]=13					,	UpperCamelCase__			:       List[str]=7					,	UpperCamelCase__			:       List[str]=True					,	UpperCamelCase__			:       int=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=99					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       str=5					,	UpperCamelCase__			:       Optional[Any]=4					,	UpperCamelCase__			:       int=37					,	UpperCamelCase__			:       List[str]="gelu"					,	UpperCamelCase__			:       Union[str, Any]=0.1					,	UpperCamelCase__			:       str=0.1					,	UpperCamelCase__			:       str=512					,	UpperCamelCase__			:       Union[str, Any]=16					,	UpperCamelCase__			:       Tuple=2					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       Tuple=4					,	):





												'''simple docstring'''





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


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							None
												if self.use_attention_mask:
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							random_attention_mask([self.batch_size, self.seq_length]		)

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

												SCREAMING_SNAKE_CASE							:					List[Any]           =							RobertaConfig(
												    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=UpperCamelCase__					,	initializer_range=self.initializer_range					,	)

												return config, input_ids, token_type_ids, attention_mask


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





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



						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.prepare_config_and_inputs()
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							config_and_inputs

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

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)






@require_flax
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	True

						UpperCamelCase_  =	(
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							FlaxRobertaModelTester(self		)



						@slow
						def  __A ( self			:       int		):





												'''simple docstring'''





												for model_class_name in self.all_model_classes:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							model_class_name.from_pretrained('''roberta-base'''					,	from_pt=UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Tuple           =							model(np.ones((1, 1)		)		)
																		self.assertIsNotNone(UpperCamelCase__		)






import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput





class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_):
						UpperCamelCase_  =	1


						@register_to_config
						def __init__( self			:       List[str]					,	UpperCamelCase__			:       int = 1000					,	UpperCamelCase__			:       Optional[Union[np.ndarray, List[float]]] = None		):





												'''simple docstring'''





												self.set_timesteps(UpperCamelCase__		)

												# standard deviation of the initial noise distribution
												SCREAMING_SNAKE_CASE							:					str           =							1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												SCREAMING_SNAKE_CASE							:					Tuple           =							4

												# running values
												SCREAMING_SNAKE_CASE							:					int           =							[]


						def  __A ( self			:       Dict					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, torch.device] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							num_inference_steps
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.linspace(1					,	0					,	num_inference_steps + 1		)[:-1]
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cat([steps, torch.tensor([0.0]		)]		)

												if self.config.trained_betas is not None:
																		SCREAMING_SNAKE_CASE							:					int           =							torch.tensor(self.config.trained_betas					,	dtype=torch.floataa		)
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							torch.sin(steps * math.pi / 2		) ** 2

												SCREAMING_SNAKE_CASE							:					Dict           =							(1.0 - self.betas**2) ** 0.5

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(torch.atana(self.betas					,	self.alphas		) / math.pi * 2)[:-1]
												SCREAMING_SNAKE_CASE							:					List[str]           =							timesteps.to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							[]


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       torch.FloatTensor					,	UpperCamelCase__			:       bool = True					,	):





												'''simple docstring'''





												if self.num_inference_steps is None:
																		raise ValueError(
																		    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							(self.timesteps == timestep).nonzero().item()
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							timestep_index + 1

												SCREAMING_SNAKE_CASE							:					int           =							sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(UpperCamelCase__		)

												if len(self.ets		) == 1:
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.ets[-1]
												elif len(self.ets		) == 2:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							(3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets		) == 3:
																		SCREAMING_SNAKE_CASE							:					str           =							(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self._get_prev_sample(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												if not return_dict:
																		return (prev_sample,)

												return SchedulerOutput(prev_sample=UpperCamelCase__		)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       torch.FloatTensor					,	*UpperCamelCase__			:       List[str]					,	**UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												return sample


						def  __A ( self			:       Union[str, Any]					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.alphas[timestep_index]
												SCREAMING_SNAKE_CASE							:					List[str]           =							self.betas[timestep_index]

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.alphas[prev_timestep_index]
												SCREAMING_SNAKE_CASE							:					Tuple           =							self.betas[prev_timestep_index]

												SCREAMING_SNAKE_CASE							:					Dict           =							(sample - sigma * ets) / max(UpperCamelCase__					,	1E-8		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							next_alpha * pred + ets * next_sigma

												return prev_sample



						def __len__( self			:       int		):





												'''simple docstring'''





												return self.config.num_train_timesteps





