




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

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left


@flax.struct.dataclass
class     __SCREAMING_SNAKE_CASE							:
					# setable values
					_lowerCamelCase		  =			None
					_lowerCamelCase		  =			None
					_lowerCamelCase		  =			None

					@classmethod
					def      lowercase_							(    cls ):







						'''simple docstring'''




						return cls()


@dataclass
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			None


class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase  ):

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return True

					@register_to_config
					def __init__(    self			,	__lowercase = 2_000			,	__lowercase = 0.15			,	__lowercase = 0.01			,	__lowercase = 13_48.0			,	__lowercase = 1E-5			,	__lowercase = 1			,	):







						'''simple docstring'''




						pass

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							ScoreSdeVeSchedulerState.create()
						return self.set_sigmas(
						    __lowercase			,	self.config.num_train_timesteps			,	self.config.sigma_min			,	self.config.sigma_max			,	self.config.sampling_eps			,	)

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase = ()			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							sampling_eps if sampling_eps is not None else self.config.sampling_eps

						lowerCamelCase_							:		str							       =							jnp.linspace(1			,	__lowercase			,	__lowercase )
						return state.replace(timesteps=__lowercase )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase = None			,	__lowercase = None			,	__lowercase = None			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							sigma_min if sigma_min is not None else self.config.sigma_min
						lowerCamelCase_							:		Any							       =							sigma_max if sigma_max is not None else self.config.sigma_max
						lowerCamelCase_							:		Optional[Any]							       =							sampling_eps if sampling_eps is not None else self.config.sampling_eps
						if state.timesteps is None:
							lowerCamelCase_							:		Union[str, Any]							       =							self.set_timesteps(__lowercase			,	__lowercase			,	__lowercase )

						lowerCamelCase_							:		Optional[Any]							       =							jnp.exp(jnp.linspace(jnp.log(__lowercase )			,	jnp.log(__lowercase )			,	__lowercase ) )
						lowerCamelCase_							:		List[Any]							       =							jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps] )

						return state.replace(discrete_sigmas=__lowercase			,	sigmas=__lowercase )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						return jnp.where(timesteps == 0			,	jnp.zeros_like(__lowercase )			,	state.discrete_sigmas[timesteps - 1] )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase = True			,	):







						'''simple docstring'''




						if state.timesteps is None:
							raise ValueError(
							    '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler' )

						lowerCamelCase_							:		List[Any]							       =							timestep * jnp.ones(
						    sample.shape[0]			,	)
						lowerCamelCase_							:		Dict							       =							(timestep * (len(state.timesteps ) - 1)).long()

						lowerCamelCase_							:		Tuple							       =							state.discrete_sigmas[timesteps]
						lowerCamelCase_							:		str							       =							self.get_adjacent_sigma(__lowercase			,	__lowercase			,	__lowercase )
						lowerCamelCase_							:		Optional[Any]							       =							jnp.zeros_like(__lowercase )
						lowerCamelCase_							:		str							       =							(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
						lowerCamelCase_							:		List[Any]							       =							diffusion.flatten()
						lowerCamelCase_							:		int							       =							broadcast_to_shape_from_left(__lowercase			,	sample.shape )
						lowerCamelCase_							:		int							       =							drift - diffusion**2 * model_output

						#  equation 6: sample noise for the diffusion term of
						lowerCamelCase_							:		Optional[int]							       =							random.split(__lowercase			,	num=1 )
						lowerCamelCase_							:		Any							       =							random.normal(key=__lowercase			,	shape=sample.shape )
						lowerCamelCase_							:		List[str]							       =							sample - drift  # subtract because `dt` is a small negative timestep
						# TODO is the variable diffusion the correct scaling term for the noise?
						lowerCamelCase_							:		List[Any]							       =							prev_sample_mean + diffusion * noise  # add impact of diffusion field g

						if not return_dict:
							return (prev_sample, prev_sample_mean, state)

						return FlaxSdeVeOutput(prev_sample=__lowercase			,	prev_sample_mean=__lowercase			,	state=__lowercase )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase = True			,	):







						'''simple docstring'''




						if state.timesteps is None:
							raise ValueError(
							    '`state.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
						lowerCamelCase_							:		str							       =							random.split(__lowercase			,	num=1 )
						lowerCamelCase_							:		Dict							       =							random.normal(key=__lowercase			,	shape=sample.shape )

						# compute step size from the model_output, the noise, and the snr
						lowerCamelCase_							:		str							       =							jnp.linalg.norm(__lowercase )
						lowerCamelCase_							:		Any							       =							jnp.linalg.norm(__lowercase )
						lowerCamelCase_							:		Any							       =							(self.config.snr * noise_norm / grad_norm) ** 2 * 2
						lowerCamelCase_							:		List[Any]							       =							step_size * jnp.ones(sample.shape[0] )

						# compute corrected sample: model_output term and noise term
						lowerCamelCase_							:		Dict							       =							step_size.flatten()
						lowerCamelCase_							:		Any							       =							broadcast_to_shape_from_left(__lowercase			,	sample.shape )
						lowerCamelCase_							:		Optional[Any]							       =							sample + step_size * model_output
						lowerCamelCase_							:		List[str]							       =							prev_sample_mean + ((step_size * 2) ** 0.5) * noise

						if not return_dict:
							return (prev_sample, state)

						return FlaxSdeVeOutput(prev_sample=__lowercase			,	state=__lowercase )





					def __len__(    self ):







						'''simple docstring'''




						return self.config.num_train_timesteps






from __future__ import annotations

import unittest

from transformers import MPNetConfig, 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.mpnet.modeling_tf_mpnet import (
							    TFMPNetForMaskedLM,
							    TFMPNetForMultipleChoice,
							    TFMPNetForQuestionAnswering,
							    TFMPNetForSequenceClassification,
							    TFMPNetForTokenClassification,
							    TFMPNetModel,
							)


class     __SCREAMING_SNAKE_CASE							:

					def __init__(    self			,	__lowercase			,	__lowercase=13			,	__lowercase=7			,	__lowercase=True			,	__lowercase=True			,	__lowercase=False			,	__lowercase=True			,	__lowercase=99			,	__lowercase=64			,	__lowercase=2			,	__lowercase=4			,	__lowercase=64			,	__lowercase="gelu"			,	__lowercase=0.1			,	__lowercase=0.1			,	__lowercase=512			,	__lowercase=16			,	__lowercase=2			,	__lowercase=0.02			,	__lowercase=3			,	__lowercase=4			,	__lowercase=None			,	):







						'''simple docstring'''




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

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							ids_tensor([self.batch_size, self.seq_length]			,	self.vocab_size )

						lowerCamelCase_							:		Optional[int]							       =							None
						if self.use_input_mask:
							lowerCamelCase_							:		List[Any]							       =							random_attention_mask([self.batch_size, self.seq_length] )

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

						lowerCamelCase_							:		Any							       =							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			,	)
						return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							TFMPNetModel(config=__lowercase )
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						lowerCamelCase_							:		Dict							       =							[input_ids, input_mask]
						lowerCamelCase_							:		Dict							       =							model(__lowercase )
						self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							TFMPNetForMaskedLM(config=__lowercase )
						lowerCamelCase_							:		List[Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		int							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.vocab_size) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							TFMPNetForQuestionAnswering(config=__lowercase )
						lowerCamelCase_							:		Tuple							       =							{
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						}
						lowerCamelCase_							:		Optional[Any]							       =							model(__lowercase )
						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      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							self.num_labels
						lowerCamelCase_							:		Union[str, Any]							       =							TFMPNetForSequenceClassification(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		List[Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_labels) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.num_choices
						lowerCamelCase_							:		Tuple							       =							TFMPNetForMultipleChoice(__lowercase )
						lowerCamelCase_							:		Dict							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Any							       =							tf.tile(tf.expand_dims(__lowercase			,	1 )			,	(1, self.num_choices, 1) )
						lowerCamelCase_							:		Optional[Any]							       =							{
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						}
						lowerCamelCase_							:		Union[str, Any]							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.num_choices) )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.num_labels
						lowerCamelCase_							:		str							       =							TFMPNetForTokenClassification(__lowercase )
						lowerCamelCase_							:		Any							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						lowerCamelCase_							:		str							       =							model(__lowercase )
						self.parent.assertEqual(result.logits.shape			,	(self.batch_size, self.seq_length, self.num_labels) )





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.prepare_config_and_inputs()
						((lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_)       ,      (lowerCamelCase_))							:		Tuple							       =							config_and_inputs
						lowerCamelCase_							:		str							       =							{'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict


@require_tf
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			(
					    (
					        TFMPNetForMaskedLM,
					        TFMPNetForMultipleChoice,
					        TFMPNetForQuestionAnswering,
					        TFMPNetForSequenceClassification,
					        TFMPNetForTokenClassification,
					        TFMPNetModel,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCamelCase		  =			(
					    {
					        """feature-extraction""": TFMPNetModel,
					        """fill-mask""": TFMPNetForMaskedLM,
					        """question-answering""": TFMPNetForQuestionAnswering,
					        """text-classification""": TFMPNetForSequenceClassification,
					        """token-classification""": TFMPNetForTokenClassification,
					        """zero-shot""": TFMPNetForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					_lowerCamelCase		  =			False
					_lowerCamelCase		  =			False

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							TFMPNetModelTester(self )
						lowerCamelCase_							:		str							       =							ConfigTester(self			,	config_class=__lowercase			,	hidden_size=37 )

					def      lowercase_							(    self ):







						'''simple docstring'''




						self.config_tester.run_common_tests()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_model(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_masked_lm(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_question_answering(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Dict							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowercase )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_mpnet_for_token_classification(*__lowercase )





					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						for model_name in ["microsoft/mpnet-base"]:
							lowerCamelCase_							:		str							       =							TFMPNetModel.from_pretrained(__lowercase )
							self.assertIsNotNone(__lowercase )


@require_tf
class     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					@slow
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							TFMPNetModel.from_pretrained('microsoft/mpnet-base' )
						lowerCamelCase_							:		Optional[int]							       =							tf.constant([[0, 1, 2, 3, 4, 5]] )
						lowerCamelCase_							:		str							       =							model(__lowercase )[0]

						lowerCamelCase_							:		List[Any]							       =							[1, 6, 768]
						self.assertEqual(output.shape			,	__lowercase )

						lowerCamelCase_							:		Any							       =							tf.constant(
						    [
						        [
						            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
						            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
						            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
						        ]
						    ] )
						tf.debugging.assert_near(output[:, :3, :3]			,	__lowercase			,	atol=1E-4 )
