


'''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 	UpperCamelCase__					:



     """simple docstring"""






     def __init__( self      ,	snake_case__      ,	snake_case__=13      ,	snake_case__=7      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=False      ,	snake_case__=True      ,	snake_case__=99      ,	snake_case__=64      ,	snake_case__=5      ,	snake_case__=4      ,	snake_case__=64      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=16      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=3      ,	snake_case__=4      ,	snake_case__=None      ,	):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            return MPNetConfig.from_pretrained('microsoft/mpnet-base'   )







     def a	( self   ):

            '''simple docstring'''

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

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

            _lowerCAmelCase    :							int   			=       None
            _lowerCAmelCase    :							Optional[Any]   			=       None
            _lowerCAmelCase    :							List[Any]   			=       None
            if self.use_labels:
                   _lowerCAmelCase    :							str   			=       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    :							List[Any]   			=       ids_tensor([self.batch_size]      ,	self.num_choices   )

            _lowerCAmelCase    :							List[Any]   			=       self.get_config()
            return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







     def a	( self   ):

            '''simple docstring'''

            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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       MPNetModel(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							List[str]   			=       model(snake_case__      ,	snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       MPNetForQuestionAnswering(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Optional[int]   			=       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 a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.num_labels
            _lowerCAmelCase    :							Optional[Any]   			=       MPNetForSequenceClassification(snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Optional[int]   			=       model(snake_case__      ,	attention_mask=snake_case__      ,	labels=snake_case__   )
            self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_labels)   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.num_choices
            _lowerCAmelCase    :							Union[str, Any]   			=       MPNetForMultipleChoice(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							Optional[int]   			=       input_ids.unsqueeze(1   ).expand(-1      ,	self.num_choices      ,	-1   ).contiguous()
            _lowerCAmelCase    :							Any   			=       input_mask.unsqueeze(1   ).expand(-1      ,	self.num_choices      ,	-1   ).contiguous()
            _lowerCAmelCase    :							Optional[Any]   			=       model(
                snake_case__      ,	attention_mask=snake_case__      ,	labels=snake_case__      ,	)
            self.parent.assertEqual(result.logits.shape      ,	(self.batch_size, self.num_choices)   )







     def a	( self      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.num_labels
            _lowerCAmelCase    :							str   			=       MPNetForTokenClassification(config=snake_case__   )
            model.to(snake_case__   )
            model.eval()
            _lowerCAmelCase    :							List[str]   			=       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 a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.prepare_config_and_inputs()
            ((_lowerCAmelCase)			,     (_lowerCAmelCase)			,     (_lowerCAmelCase)			,     (_lowerCAmelCase)			,     (_lowerCAmelCase)			,     (_lowerCAmelCase))    :							Tuple   			=       config_and_inputs
            _lowerCAmelCase    :							Any   			=       {'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict
@require_torch
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       MPNetModelTester(self   )
            _lowerCAmelCase    :							List[str]   			=       ConfigTester(self      ,	config_class=snake_case__      ,	hidden_size=37   )







     def a	( self   ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_model(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							str   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case__   )
@require_torch
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       MPNetModel.from_pretrained('microsoft/mpnet-base'   )
            _lowerCAmelCase    :							Dict   			=       torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
            _lowerCAmelCase    :							Any   			=       model(snake_case__   )[0]
            _lowerCAmelCase    :							Optional[Any]   			=       torch.Size((1, 11, 768)   )
            self.assertEqual(output.shape      ,	snake_case__   )
            _lowerCAmelCase    :							Dict   			=       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'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,	[0.002, 0.02, 0.2, 2]   ):
                   self.check_over_configs(beta_start=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for schedule in ["linear", "squaredcos_cap_v2"]:
                   self.check_over_configs(beta_schedule=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for variance in ["fixed_small", "fixed_large", "other"]:
                   self.check_over_configs(variance_type=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            self.check_over_configs(thresholding=snake_case__   )
            for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "sample", "v_prediction"]:
                          self.check_over_configs(
                              thresholding=snake_case__      ,	prediction_type=snake_case__      ,	sample_max_value=snake_case__      ,	)







     def a	( self   ):

            '''simple docstring'''

            for prediction_type in ["epsilon", "sample", "v_prediction"]:
                   self.check_over_configs(prediction_type=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for t in [0, 500, 999]:
                   self.check_over_forward(time_step=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0979   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							str   			=       self.dummy_model()
            _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Dict   			=       pred_prev_sample

            _lowerCAmelCase    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 258.9606   ) < 1E-2
            assert abs(result_mean.item() - 0.3372   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							Any   			=       self.dummy_model()
            _lowerCAmelCase    :							Tuple   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							Union[str, Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Dict   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

                   # if t > 0:
                   #     noise = self.dummy_sample_deter
                   #     variance = scheduler.get_variance(t) ** (0.5) * noise
                   #
                   # sample = pred_prev_sample + variance
                   _lowerCAmelCase    :							Tuple   			=       pred_prev_sample

            _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 202.0296   ) < 1E-2
            assert abs(result_mean.item() - 0.2631   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.timesteps

            for i, timestep in enumerate(snake_case__   ):
                   if i == len(snake_case__   ) - 1:
                          _lowerCAmelCase    :							str   			=       -1
                   else:
                          _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

                   _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
                   _lowerCAmelCase    :							int   			=       prev_t.item()

                   self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

            with self.assertRaises(snake_case__      ,	msg='`custom_timesteps` must be in descending order.'   ):
                   scheduler.set_timesteps(timesteps=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
            _lowerCAmelCase    :							int   			=       len(snake_case__   )

            with self.assertRaises(snake_case__      ,	msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'   ):
                   scheduler.set_timesteps(num_inference_steps=snake_case__      ,	timesteps=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Any   			=       [scheduler.config.num_train_timesteps]

            with self.assertRaises(
                snake_case__      ,	msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      ,	):
                   scheduler.set_timesteps(timesteps=snake_case__   )

