




"""simple docstring"""






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.distilbert.modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				)




class   a__			(       unittest.TestCase  ):





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




											"""simple docstring"""







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





						def       __UpperCamelCase     (							self      :	List[str])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:int									=  ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

											_lowerCAmelCase:Dict									=  DistilBertConfig(
											    vocab_size=self.vocab_size     ,dim=self.hidden_size     ,n_layers=self.num_hidden_layers     ,n_heads=self.num_attention_heads     ,hidden_dim=self.intermediate_size     ,hidden_act=self.hidden_act     ,dropout=self.hidden_dropout_prob     ,attention_dropout=self.attention_probs_dropout_prob     ,max_position_embeddings=self.max_position_embeddings     ,initializer_range=self.initializer_range     ,tie_weights_=a__     ,)

											return config, input_ids, attention_mask







						def       __UpperCamelCase     (							self      :	Any)						->       Any:




											"""simple docstring"""







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




@require_flax
class   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = (
						    (
						        FlaxDistilBertModel,
						        FlaxDistilBertForMaskedLM,
						        FlaxDistilBertForMultipleChoice,
						        FlaxDistilBertForQuestionAnswering,
						        FlaxDistilBertForSequenceClassification,
						        FlaxDistilBertForTokenClassification,
						        FlaxDistilBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def       __UpperCamelCase     (							self      :	str)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:str									=  FlaxDistilBertModelTester(self)







						@slow
						def       __UpperCamelCase     (							self      :	List[str])						->       int:




											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																_lowerCAmelCase:List[str]									=  model_class_name.from_pretrained('''distilbert-base-uncased''')
																_lowerCAmelCase:Tuple									=  model(np.ones((1, 1)))
																self.assertIsNotNone(a__)




@require_flax
class   a__			(       unittest.TestCase  ):





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




											"""simple docstring"""







											_lowerCAmelCase:Any									=  FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''')
											_lowerCAmelCase:Optional[int]									=  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
											_lowerCAmelCase:Tuple									=  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
											_lowerCAmelCase:List[str]									=  model(a__     ,attention_mask=a__)[0]
											_lowerCAmelCase:List[str]									=  (1, 11, 768)
											self.assertEqual(output.shape     ,a__)
											_lowerCAmelCase:int									=  np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,a__     ,atol=1E-4))












"""simple docstring"""






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

import math
from typing import Union

import torch

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




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







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

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






