
'''simple docstring'''






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		(UnCLIPScheduler,)



    def 	lowerCamelCase_	(						self   :							str,**__A   :							List[str]  ):
          _lowerCamelCase					: Optional[int]		    = {
              "num_train_timesteps": 1_0_0_0,
              "variance_type": "fixed_small_log",
              "clip_sample": True,
              "clip_sample_range": 1.0,
              "prediction_type": "epsilon",
          }

          config.update(**a_  )
          return config



    def 	lowerCamelCase_	(						self   :							Any  ):
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
                self.check_over_configs(num_train_timesteps=a_  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          for variance in ["fixed_small_log", "learned_range"]:
                self.check_over_configs(variance_type=a_  )



    def 	lowerCamelCase_	(						self   :							int  ):
          for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=a_  )



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          for clip_sample_range in [1, 5, 1_0, 2_0]:
                self.check_over_configs(clip_sample_range=a_  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          for prediction_type in ["epsilon", "sample"]:
                self.check_over_configs(prediction_type=a_  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          for time_step in [0, 5_0_0, 9_9_9]:
                for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
                      if prev_timestep is not None and prev_timestep >= time_step:
                            continue

                      self.check_over_forward(time_step=a_,prev_timestep=a_  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: List[str]		    = self.scheduler_classes[0]
          _lowerCamelCase					: Dict		    = self.get_scheduler_config(variance_type="fixed_small_log"  )
          _lowerCamelCase					: List[str]		    = scheduler_class(**a_  )

          assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 1.00_00e-10  )  ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7  ) - 0.0549625  )  ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ) - 0.9994987  )  ) < 1e-5



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: str		    = self.scheduler_classes[0]
          _lowerCamelCase					: Dict		    = self.get_scheduler_config(variance_type="learned_range"  )
          _lowerCamelCase					: Tuple		    = scheduler_class(**a_  )

          _lowerCamelCase					: List[Any]		    = 0.5

          assert scheduler._get_variance(1,predicted_variance=a_  ) - -10.1712790 < 1e-5
          assert scheduler._get_variance(4_8_7,predicted_variance=a_  ) - -5.7998052 < 1e-5
          assert scheduler._get_variance(9_9_9,predicted_variance=a_  ) - -0.0010011 < 1e-5



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Dict		    = self.scheduler_classes[0]
          _lowerCamelCase					: Optional[int]		    = self.get_scheduler_config()
          _lowerCamelCase					: Optional[int]		    = scheduler_class(**a_  )

          _lowerCamelCase					: str		    = scheduler.timesteps

          _lowerCamelCase					: Any		    = self.dummy_model()
          _lowerCamelCase					: List[Any]		    = self.dummy_sample_deter
          _lowerCamelCase					: Tuple		    = torch.manual_seed(0  )

          for i, t in enumerate(a_  ):
                # 1. predict noise residual
                _lowerCamelCase					: Dict		    = model(a_,a_  )

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase					: List[str]		    = scheduler.step(a_,a_,a_,generator=a_  ).prev_sample

                _lowerCamelCase					: int		    = pred_prev_sample

          _lowerCamelCase					: List[str]		    = torch.sum(torch.abs(a_  )  )
          _lowerCamelCase					: Optional[Any]		    = torch.mean(torch.abs(a_  )  )

          assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5  ) < 1e-2
          assert abs(result_mean.item() - 0.3284743  ) < 1e-3



    def 	lowerCamelCase_	(						self   :							Tuple  ):
          _lowerCamelCase					: Optional[Any]		    = self.scheduler_classes[0]
          _lowerCamelCase					: Any		    = self.get_scheduler_config()
          _lowerCamelCase					: Union[str, Any]		    = scheduler_class(**a_  )

          scheduler.set_timesteps(2_5  )

          _lowerCamelCase					: List[Any]		    = scheduler.timesteps

          _lowerCamelCase					: str		    = self.dummy_model()
          _lowerCamelCase					: int		    = self.dummy_sample_deter
          _lowerCamelCase					: Dict		    = torch.manual_seed(0  )

          for i, t in enumerate(a_  ):
                # 1. predict noise residual
                _lowerCamelCase					: Tuple		    = model(a_,a_  )

                if i + 1 == timesteps.shape[0]:
                      _lowerCamelCase					: Union[str, Any]		    = None
                else:
                      _lowerCamelCase					: List[Any]		    = timesteps[i + 1]

                # 2. predict previous mean of sample x_t-1
                _lowerCamelCase					: Union[str, Any]		    = scheduler.step(
                    a_,a_,a_,prev_timestep=a_,generator=a_  ).prev_sample

                _lowerCamelCase					: int		    = pred_prev_sample

          _lowerCamelCase					: Dict		    = torch.sum(torch.abs(a_  )  )
          _lowerCamelCase					: Optional[int]		    = torch.mean(torch.abs(a_  )  )

          assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3  ) < 1e-2
          assert abs(result_mean.item() - 0.3362038  ) < 1e-3



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          pass



    def 	lowerCamelCase_	(						self   :							int  ):
          pass



'''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        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_=1_3							,      a_=7							,      a_=True							,      a_=True							,      a_=True							,      a_=True							,      a_=9_9							,      a_=3_2							,      a_=5							,      a_=4							,      a_=3_7							,      a_="gelu"							,      a_=0.1							,      a_=0.1							,      a_=5_1_2							,      a_=1_6							,      a_=2							,      a_=0.02							,      a_=4							,      )    ->  Tuple:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		int   					=		batch_size
					lowercase						:		int   					=		seq_length
					lowercase						:		List[str]   					=		is_training
					lowercase						:		str   					=		use_attention_mask
					lowercase						:		List[str]   					=		use_token_type_ids
					lowercase						:		Optional[Any]   					=		use_labels
					lowercase						:		Dict   					=		vocab_size
					lowercase						:		Union[str, Any]   					=		hidden_size
					lowercase						:		int   					=		num_hidden_layers
					lowercase						:		Optional[Any]   					=		num_attention_heads
					lowercase						:		Optional[Any]   					=		intermediate_size
					lowercase						:		int   					=		hidden_act
					lowercase						:		Dict   					=		hidden_dropout_prob
					lowercase						:		Optional[Any]   					=		attention_probs_dropout_prob
					lowercase						:		Dict   					=		max_position_embeddings
					lowercase						:		Tuple   					=		type_vocab_size
					lowercase						:		int   					=		type_sequence_label_size
					lowercase						:		Optional[Any]   					=		initializer_range
					lowercase						:		Union[str, Any]   					=		num_choices

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size							)

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

					lowercase						:		Optional[Any]   					=		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 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase					,		lowercase					,		lowercase						:		Dict   					=		config_and_inputs
					lowercase						:		Any   					=		{"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




@require_flax
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				(
				    (
				        FlaxDistilBertModel,
				        FlaxDistilBertForMaskedLM,
				        FlaxDistilBertForMultipleChoice,
				        FlaxDistilBertForQuestionAnswering,
				        FlaxDistilBertForSequenceClassification,
				        FlaxDistilBertForTokenClassification,
				        FlaxDistilBertForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)

				def 	a__		(     self							)    ->  Dict:
					lowercase						:		Optional[int]   					=		FlaxDistilBertModelTester(self							)


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					for model_class_name in self.all_model_classes:
						lowercase						:		int   					=		model_class_name.from_pretrained("distilbert-base-uncased"							)
						lowercase						:		Tuple   					=		model(np.ones((1, 1)							)							)
						self.assertIsNotNone(a_							)




@require_flax
class        _UpperCamelCase	(    unittest.TestCase):


				'''simple docstring'''


				@slow
				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		str   					=		FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"							)
					lowercase						:		int   					=		np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
					lowercase						:		int   					=		np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
					lowercase						:		List[str]   					=		model(a_							,      attention_mask=a_							)[0]
					lowercase						:		int   					=		(1, 1_1, 7_6_8)
					self.assertEqual(output.shape							,      a_							)
					lowercase						:		List[str]   					=		np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]							)

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


