
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 	_snake_case						:




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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size					)

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

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

       a		:Dict											= 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  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:List[Any]											= TFMPNetModel(config=lowerCAmelCase_					)
       a		:Optional[int]											= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       a		:List[Any]											= model(lowerCAmelCase_					)
       a		:List[str]											= [input_ids, input_mask]
       a		:Optional[Any]											= model(lowerCAmelCase_					)
       self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:List[str]											= TFMPNetForMaskedLM(config=lowerCAmelCase_					)
       a		:List[Any]											= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       a		:int											= model(lowerCAmelCase_					)
       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Dict											= TFMPNetForQuestionAnswering(config=lowerCAmelCase_					)
       a		:List[str]											= {
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
       }
       a		:int											= model(lowerCAmelCase_					)
       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  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Tuple											= self.num_labels
       a		:Any											= TFMPNetForSequenceClassification(lowerCAmelCase_					)
       a		:int											= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       a		:Optional[Any]											= model(lowerCAmelCase_					)
       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_labels)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:int											= self.num_choices
       a		:str											= TFMPNetForMultipleChoice(lowerCAmelCase_					)
       a		:str											= tf.tile(tf.expand_dims(lowerCAmelCase_      ,						1					)      ,						(1, self.num_choices, 1)					)
       a		:Union[str, Any]											= tf.tile(tf.expand_dims(lowerCAmelCase_      ,						1					)      ,						(1, self.num_choices, 1)					)
       a		:List[str]											= {
           '''input_ids''': multiple_choice_inputs_ids,
           '''attention_mask''': multiple_choice_input_mask,
       }
       a		:Dict											= model(lowerCAmelCase_					)
       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_choices)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Optional[Any]											= self.num_labels
       a		:str											= TFMPNetForTokenClassification(lowerCAmelCase_					)
       a		:Dict											= {'''input_ids''': input_ids, '''attention_mask''': input_mask}
       a		:Tuple											= model(lowerCAmelCase_					)
       self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.num_labels)					)




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







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




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= TFMPNetModelTester(self					)
       a		:List[Any]											= ConfigTester(self      ,						config_class=lowerCAmelCase_      ,						hidden_size=37					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       self.config_tester.run_common_tests()




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase_					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[Any]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCAmelCase_					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase_					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Any											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase_					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase_					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Optional[int]											= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase_					)




 @slow
 def  SCREAMING_SNAKE_CASE__       (	self					):






       for model_name in ["microsoft/mpnet-base"]:
             a		:int											= TFMPNetModel.from_pretrained(lowerCAmelCase_					)
             self.assertIsNotNone(lowerCAmelCase_					)







@require_tf
class 	_snake_case						(   unittest.TestCase							):




 @slow
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= TFMPNetModel.from_pretrained('''microsoft/mpnet-base'''					)
       a		:Any											= tf.constant([[0, 1, 2, 3, 4, 5]]					)
       a		:List[Any]											= model(lowerCAmelCase_					)[0]

       a		:Optional[int]											= [1, 6, 768]
       self.assertEqual(output.shape      ,						lowerCAmelCase_					)

       a		:Optional[int]											= tf.constant(
           [
               [
                   [-0.106_7172, 0.0821_6473, 0.002_4543],
                   [-0.0346_5879, 0.835_4118, -0.0325_2288],
                   [-0.0656_9476, -0.1242_4111, -0.049_4436],
               ]
           ]					)
       tf.debugging.assert_near(output[:, :3, :3]      ,						lowerCAmelCase_      ,						atol=1e-4					)









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





