"""simple docstring"""






import unittest

from transformers import MPNetConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MPNetForMaskedLM,
						    MPNetForMultipleChoice,
						    MPNetForQuestionAnswering,
						    MPNetForSequenceClassification,
						    MPNetForTokenClassification,
						    MPNetModel,
						)

class lowerCamelCase		:






					'''simple docstring'''


					def __init__(						self			:				List[Any]   , _snake_case			:				int   , _snake_case			:				List[str]=13   , _snake_case			:				List[str]=7   , _snake_case			:				List[str]=True   , _snake_case			:				List[Any]=True   , _snake_case			:				List[str]=False   , _snake_case			:				List[str]=True   , _snake_case			:				Union[str, Any]=99   , _snake_case			:				Tuple=64   , _snake_case			:				List[str]=5   , _snake_case			:				Tuple=4   , _snake_case			:				Optional[int]=64   , _snake_case			:				int="gelu"   , _snake_case			:				int=0.1   , _snake_case			:				Any=0.1   , _snake_case			:				List[Any]=512   , _snake_case			:				Union[str, Any]=16   , _snake_case			:				Optional[Any]=2   , _snake_case			:				Dict=0.02   , _snake_case			:				Optional[Any]=3   , _snake_case			:				Optional[int]=4   , _snake_case			:				List[Any]=None   , )							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				seq_length
									SCREAMING_SNAKE_CASE__		  =				is_training
									SCREAMING_SNAKE_CASE__		  =				use_input_mask
									SCREAMING_SNAKE_CASE__		  =				use_token_type_ids
									SCREAMING_SNAKE_CASE__		  =				use_labels
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				hidden_size
									SCREAMING_SNAKE_CASE__		  =				num_hidden_layers
									SCREAMING_SNAKE_CASE__		  =				num_attention_heads
									SCREAMING_SNAKE_CASE__		  =				intermediate_size
									SCREAMING_SNAKE_CASE__		  =				hidden_act
									SCREAMING_SNAKE_CASE__		  =				hidden_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				type_vocab_size
									SCREAMING_SNAKE_CASE__		  =				type_sequence_label_size
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				num_labels
									SCREAMING_SNAKE_CASE__		  =				num_choices
									SCREAMING_SNAKE_CASE__		  =				scope



					def 			lowerCAmelCase_					(						self			:				str		)							->       Tuple:
									return MPNetConfig.from_pretrained("microsoft/mpnet-base"		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size		)

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

									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									if self.use_labels:
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.type_sequence_label_size		)
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size, self.seq_length]   , self.num_labels		)
													SCREAMING_SNAKE_CASE__		  =				ids_tensor([self.batch_size]   , self.num_choices		)

									SCREAMING_SNAKE_CASE__		  =				self.get_config()
									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       int:
									return MPNetConfig(
									    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   , initializer_range=self.initializer_range   , )



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				str   , _snake_case			:				Dict   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				Any		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				MPNetModel(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)
									self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size)		)
									self.parent.assertEqual(result.pooler_output.shape   , (self.batch_size, self.hidden_size)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				Optional[int]   , _snake_case			:				int   , _snake_case			:				Any   , _snake_case			:				str   , _snake_case			:				str   , _snake_case			:				Any		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				MPNetForQuestionAnswering(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , start_positions=_snake_case   , end_positions=_snake_case   , )
									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			:				Union[str, Any]   , _snake_case			:				List[str]   , _snake_case			:				str   , _snake_case			:				Optional[int]   , _snake_case			:				List[Any]   , _snake_case			:				str   , _snake_case			:				str		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				MPNetForSequenceClassification(_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				List[Any]   , _snake_case			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict   , _snake_case			:				Union[str, Any]   , _snake_case			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				self.num_choices
									SCREAMING_SNAKE_CASE__		  =				MPNetForMultipleChoice(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				input_ids.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				input_mask.unsqueeze(1		).expand(-1   , self.num_choices   , -1		).contiguous()
									SCREAMING_SNAKE_CASE__		  =				model(
									    _snake_case   , attention_mask=_snake_case   , labels=_snake_case   , )
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.num_choices)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				str   , _snake_case			:				List[str]   , _snake_case			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Union[str, Any]   , _snake_case			:				List[str]		)							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				self.num_labels
									SCREAMING_SNAKE_CASE__		  =				MPNetForTokenClassification(config=_snake_case		)
									model.to(_snake_case		)
									model.eval()
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case   , attention_mask=_snake_case   , labels=_snake_case		)
									self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.seq_length, self.num_labels)		)



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.prepare_config_and_inputs()
									((SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__)  ,       (SCREAMING_SNAKE_CASE__))		  =				config_and_inputs
									SCREAMING_SNAKE_CASE__		  =				{"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE    ,    unittest.TestCase      ):






					'''simple docstring'''


					a   	=		(
					    (
					        MPNetForMaskedLM,
					        MPNetForMultipleChoice,
					        MPNetForQuestionAnswering,
					        MPNetForSequenceClassification,
					        MPNetForTokenClassification,
					        MPNetModel,
					    )
					    if is_torch_available()
					    else ()
					)
					a   	=		(
					    {
					        "feature-extraction": MPNetModel,
					        "fill-mask": MPNetForMaskedLM,
					        "question-answering": MPNetForQuestionAnswering,
					        "text-classification": MPNetForSequenceClassification,
					        "token-classification": MPNetForTokenClassification,
					        "zero-shot": MPNetForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					a   	=		False
					a   	=		True



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				MPNetModelTester(self		)
									SCREAMING_SNAKE_CASE__		  =				ConfigTester(self   , config_class=_snake_case   , hidden_size=37		)



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



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_model(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_sequence_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_multiple_choice(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_token_classification(*_snake_case		)



					def 			lowerCAmelCase_					(						self			:				str		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_mpnet_for_question_answering(*_snake_case		)



@require_torch
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					@slow
					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				MPNetModel.from_pretrained("microsoft/mpnet-base"		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
									SCREAMING_SNAKE_CASE__		  =				model(_snake_case		)[0]
									SCREAMING_SNAKE_CASE__		  =				torch.Size((1, 11, 768)		)
									self.assertEqual(output.shape   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(
									    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]		)
									# compare the actual values for a slice.
									self.assertTrue(torch.allclose(output[:, :3, :3]   , _snake_case   , atol=1e-4		)		)





"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		42
					a   	=		42



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		1



					@register_to_config
					def __init__(						self			:				Tuple   , _snake_case			:				int = 2000   , _snake_case			:				float = 0.15   , _snake_case			:				float = 0.01   , _snake_case			:				float = 1348.0   , _snake_case			:				float = 1e-5   , _snake_case			:				int = 1   , )							->       str:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None

									self.set_sigmas(_snake_case   , _snake_case   , _snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				Union[str, torch.device] = None		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps

									SCREAMING_SNAKE_CASE__		  =				torch.linspace(1   , _snake_case   , _snake_case   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				int   , _snake_case			:				float = None   , _snake_case			:				float = None   , _snake_case			:				float = None		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				sigma_min if sigma_min is not None else self.config.sigma_min
									SCREAMING_SNAKE_CASE__		  =				sigma_max if sigma_max is not None else self.config.sigma_max
									SCREAMING_SNAKE_CASE__		  =				sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if self.timesteps is None:
													self.set_timesteps(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
									SCREAMING_SNAKE_CASE__		  =				torch.exp(torch.linspace(math.log(_snake_case		)   , math.log(_snake_case		)   , _snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[Any]   , _snake_case			:				List[str]		)							->       List[Any]:
									return torch.where(
									    timesteps == 0   , torch.zeros_like(t.to(timesteps.device		)		)   , self.discrete_sigmas[timesteps - 1].to(timesteps.device		)   , )



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				torch.FloatTensor   , _snake_case			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SdeVeOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									SCREAMING_SNAKE_CASE__		  =				timestep * torch.ones(
									    sample.shape[0]   , device=sample.device		)  # torch.repeat_interleave(timestep, sample.shape[0])
									SCREAMING_SNAKE_CASE__		  =				(timestep * (len(self.timesteps		) - 1)).long()

									# mps requires indices to be in the same device, so we use cpu as is the default with cuda
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(self.discrete_sigmas.device		)

									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas[timesteps].to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				self.get_adjacent_sigma(_snake_case   , _snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				torch.zeros_like(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				(sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									SCREAMING_SNAKE_CASE__		  =				diffusion.flatten()
									while len(diffusion.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				diffusion.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(
									    sample.shape   , layout=sample.layout   , generator=_snake_case   , device=sample.device   , dtype=sample.dtype		)
									SCREAMING_SNAKE_CASE__		  =				sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
													return (prev_sample, prev_sample_mean)

									return SdeVeOutput(prev_sample=_snake_case   , prev_sample_mean=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[Any]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[torch.Generator] = None   , _snake_case			:				bool = True   , )							->       Union[SchedulerOutput, Tuple]:
									if self.timesteps is None:
													raise ValueError(
													    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									SCREAMING_SNAKE_CASE__		  =				randn_tensor(sample.shape   , layout=sample.layout   , generator=_snake_case		).to(sample.device		)

									# compute step size from the model_output, the noise, and the snr
									SCREAMING_SNAKE_CASE__		  =				torch.norm(model_output.reshape(model_output.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				torch.norm(noise.reshape(noise.shape[0]   , -1		)   , dim=-1		).mean()
									SCREAMING_SNAKE_CASE__		  =				(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									SCREAMING_SNAKE_CASE__		  =				step_size * torch.ones(sample.shape[0]		).to(sample.device		)
									# self.repeat_scalar(step_size, sample.shape[0])

									# compute corrected sample: model_output term and noise term
									SCREAMING_SNAKE_CASE__		  =				step_size.flatten()
									while len(step_size.shape		) < len(sample.shape		):
													SCREAMING_SNAKE_CASE__		  =				step_size.unsqueeze(-1		)
									SCREAMING_SNAKE_CASE__		  =				sample + step_size * model_output
									SCREAMING_SNAKE_CASE__		  =				prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
													return (prev_sample,)

									return SchedulerOutput(prev_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , )							->       torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									SCREAMING_SNAKE_CASE__		  =				timesteps.to(original_samples.device		)
									SCREAMING_SNAKE_CASE__		  =				self.discrete_sigmas.to(original_samples.device		)[timesteps]
									SCREAMING_SNAKE_CASE__		  =				(
									    noise * sigmas[:, None, None, None]
									    if noise is not None
									    else torch.randn_like(_snake_case		) * sigmas[:, None, None, None]
									)
									SCREAMING_SNAKE_CASE__		  =				noise + original_samples
									return noisy_samples



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




