
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 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def __init__(      self		:       List[str]				,       _snake_case		:       Optional[int]				,       _snake_case		:       str=13				,       _snake_case		:       Tuple=7				,       _snake_case		:       int=True				,       _snake_case		:       Optional[Any]=True				,       _snake_case		:       Optional[int]=True				,       _snake_case		:       int=True				,       _snake_case		:       List[Any]=99				,       _snake_case		:       Tuple=32				,       _snake_case		:       Optional[int]=5				,       _snake_case		:       List[str]=4				,       _snake_case		:       int=37				,       _snake_case		:       int="gelu"				,       _snake_case		:       Tuple=0.1				,       _snake_case		:       Dict=0.1				,       _snake_case		:       Optional[int]=512				,       _snake_case		:       Dict=16				,       _snake_case		:       Dict=2				,       _snake_case		:       int=0.02				,       _snake_case		:       Any=4				,       ):
          __lowercase		:			Optional[int]	    =       parent
          __lowercase		:			List[str]	    =       batch_size
          __lowercase		:			Tuple	    =       seq_length
          __lowercase		:			Optional[Any]	    =       is_training
          __lowercase		:			Union[str, Any]	    =       use_attention_mask
          __lowercase		:			int	    =       use_token_type_ids
          __lowercase		:			List[Any]	    =       use_labels
          __lowercase		:			Optional[Any]	    =       vocab_size
          __lowercase		:			Optional[Any]	    =       hidden_size
          __lowercase		:			Any	    =       num_hidden_layers
          __lowercase		:			List[str]	    =       num_attention_heads
          __lowercase		:			List[str]	    =       intermediate_size
          __lowercase		:			Optional[int]	    =       hidden_act
          __lowercase		:			Tuple	    =       hidden_dropout_prob
          __lowercase		:			Optional[Any]	    =       attention_probs_dropout_prob
          __lowercase		:			str	    =       max_position_embeddings
          __lowercase		:			Any	    =       type_vocab_size
          __lowercase		:			Optional[int]	    =       type_sequence_label_size
          __lowercase		:			int	    =       initializer_range
          __lowercase		:			str	    =       num_choices




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			int	    =       ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size				)

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

          __lowercase		:			Optional[int]	    =       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_=_snake_case				,       )

          return config, input_ids, attention_mask




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			Any	    =       self.prepare_config_and_inputs()
          __lowercase    ,      __lowercase    ,      __lowercase		:			List[str]	    =       config_and_inputs
          __lowercase		:			str	    =       {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict






@require_flax
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			str        =    (
          (
              FlaxDistilBertModel,
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			Optional[Any]	    =       FlaxDistilBertModelTester(self				)




      @slow
      def 		snake_case_			(      self		:       List[Any]				):
          for model_class_name in self.all_model_classes:
              __lowercase		:			int	    =       model_class_name.from_pretrained('''distilbert-base-uncased'''				)
              __lowercase		:			List[Any]	    =       model(np.ones((1, 1)				)				)
              self.assertIsNotNone(_snake_case				)






@require_flax
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @slow
      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Union[str, Any]	    =       FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''				)
          __lowercase		:			Optional[Any]	    =       np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]				)
          __lowercase		:			Tuple	    =       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
          __lowercase		:			Any	    =       model(_snake_case				,       attention_mask=_snake_case				)[0]
          __lowercase		:			Dict	    =       (1, 11, 768)
          self.assertEqual(output.shape				,       _snake_case				)
          __lowercase		:			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]				,       _snake_case				,       atol=1E-4				)				)




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      A__	:			Optional[Any]        =    (DDPMScheduler,)




      def 		snake_case_			(      self		:       Dict				,       **_snake_case		:       List[str]				):
          __lowercase		:			Tuple	    =       {
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
              '''variance_type''': '''fixed_small''',
              '''clip_sample''': True,
          }

          config.update(**_snake_case				)
          return config




      def 		snake_case_			(      self		:       Union[str, Any]				):
          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]				,       [0.0_02, 0.02, 0.2, 2]				):
              self.check_over_configs(beta_start=_snake_case				,       beta_end=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          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 		snake_case_			(      self		:       List[str]				):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          for t in [0, 500, 999]:
              self.check_over_forward(time_step=_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, 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_09_79				)				) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999				) - 0.02				)				) < 1E-5




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			Dict	    =       self.dummy_model()
          __lowercase		:			Tuple	    =       self.dummy_sample_deter
          __lowercase		:			List[str]	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Optional[int]	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			int	    =       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
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			str	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			Any	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_58.96_06				) < 1E-2
          assert abs(result_mean.item() - 0.33_72				) < 1E-3




      def 		snake_case_			(      self		:       Tuple				):
          __lowercase		:			str	    =       self.scheduler_classes[0]
          __lowercase		:			str	    =       self.get_scheduler_config(prediction_type='''v_prediction'''				)
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			List[Any]	    =       len(_snake_case				)

          __lowercase		:			List[Any]	    =       self.dummy_model()
          __lowercase		:			str	    =       self.dummy_sample_deter
          __lowercase		:			Dict	    =       torch.manual_seed(0				)

          for t in reversed(range(_snake_case				)				):
              # 1. predict noise residual
              __lowercase		:			Tuple	    =       model(_snake_case				,       _snake_case				)

              # 2. predict previous mean of sample x_t-1
              __lowercase		:			Optional[int]	    =       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
              __lowercase		:			Any	    =       pred_prev_sample

          __lowercase		:			int	    =       torch.sum(torch.abs(_snake_case				)				)
          __lowercase		:			List[Any]	    =       torch.mean(torch.abs(_snake_case				)				)

          assert abs(result_sum.item() - 2_02.02_96				) < 1E-2
          assert abs(result_mean.item() - 0.26_31				) < 1E-3




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			int	    =       self.scheduler_classes[0]
          __lowercase		:			List[str]	    =       self.get_scheduler_config()
          __lowercase		:			List[str]	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[Any]	    =       [100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_snake_case				)

          __lowercase		:			int	    =       scheduler.timesteps

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

              __lowercase		:			Optional[Any]	    =       scheduler.previous_timestep(_snake_case				)
              __lowercase		:			str	    =       prev_t.item()

              self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Tuple	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[Any]	    =       self.get_scheduler_config()
          __lowercase		:			Union[str, Any]	    =       scheduler_class(**_snake_case				)

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

          __lowercase		:			Dict	    =       [100, 87, 50, 1, 0]
          __lowercase		:			str	    =       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 		snake_case_			(      self		:       str				):
          __lowercase		:			List[str]	    =       self.scheduler_classes[0]
          __lowercase		:			Optional[int]	    =       self.get_scheduler_config()
          __lowercase		:			int	    =       scheduler_class(**_snake_case				)

          __lowercase		:			Optional[int]	    =       [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				)


