
"""simple docstring"""

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   SCREAMING_SNAKE_CASE_    					=						'''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import BlenderbotTokenizer
   from transformers.models.blenderbot.modeling_flax_blenderbot import (
       FlaxBlenderbotForConditionalGeneration,
       FlaxBlenderbotModel,
       shift_tokens_right,
   )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					str=None ,				lowerCAmelCase				:					Dict=None ,				lowerCAmelCase				:					Optional[Any]=None ,				lowerCAmelCase				:					Tuple=None ,				lowerCAmelCase				:					Optional[int]=None ,				lowerCAmelCase				:					Optional[int]=None ,				) ->      Union[str, Any]:






    """simple docstring"""




    if attention_mask is None:
        UpperCAmelCase									=					np.where(input_ids != config.pad_token_id ,				1 ,				0 )
    if decoder_attention_mask is None:
        UpperCAmelCase									=					np.where(decoder_input_ids != config.pad_token_id ,				1 ,				0 )
    if head_mask is None:
        UpperCAmelCase									=					np.ones((config.encoder_layers, config.encoder_attention_heads) )
    if decoder_head_mask is None:
        UpperCAmelCase									=					np.ones((config.decoder_layers, config.decoder_attention_heads) )
    if cross_attn_head_mask is None:
        UpperCAmelCase									=					np.ones((config.decoder_layers, config.decoder_attention_heads) )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }
class      _UpperCAmelCase							:




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=False   ,       lowercase_=9_9   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=4   ,       lowercase_=4   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=3_2   ,       lowercase_=2   ,       lowercase_=1   ,       lowercase_=0   ,       lowercase_=0.0_2   ,       )						->					Any:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					max_position_embeddings
          UpperCAmelCase									=					eos_token_id
          UpperCAmelCase									=					pad_token_id
          UpperCAmelCase									=					bos_token_id
          UpperCAmelCase									=					initializer_range




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,       self.vocab_size							)   ,       3   ,       self.vocab_size							)
          UpperCAmelCase									=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,       dtype=np.intaa							))   ,       -1							)

          UpperCAmelCase									=					shift_tokens_right(lowercase_   ,       1   ,       2							)

          UpperCAmelCase									=					BlenderbotConfig(
              vocab_size=self.vocab_size   ,       d_model=self.hidden_size   ,       encoder_layers=self.num_hidden_layers   ,       decoder_layers=self.num_hidden_layers   ,       encoder_attention_heads=self.num_attention_heads   ,       decoder_attention_heads=self.num_attention_heads   ,       encoder_ffn_dim=self.intermediate_size   ,       decoder_ffn_dim=self.intermediate_size   ,       dropout=self.hidden_dropout_prob   ,       attention_dropout=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       eos_token_id=self.eos_token_id   ,       bos_token_id=self.bos_token_id   ,       pad_token_id=self.pad_token_id   ,       initializer_range=self.initializer_range   ,       use_cache=lowercase_   ,       )
          UpperCAmelCase									=					prepare_blenderbot_inputs_dict(lowercase_   ,       lowercase_   ,       lowercase_							)
          return config, inputs_dict




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.prepare_config_and_inputs()
          return config, inputs_dict




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Optional[int]:
          UpperCAmelCase									=					2_0
          UpperCAmelCase									=					model_class_name(lowercase_							)

          UpperCAmelCase									=					model.encode(inputs_dict['input_ids']							)

          UpperCAmelCase			,							UpperCAmelCase									=					(
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCAmelCase									=					model.init_cache(decoder_input_ids.shape[0]   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,       dtype='i4'							)

          UpperCAmelCase									=					jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]   ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,       )
          UpperCAmelCase									=					model.decode(
              decoder_input_ids[:, :-1]   ,       lowercase_   ,       decoder_attention_mask=lowercase_   ,       past_key_values=lowercase_   ,       decoder_position_ids=lowercase_   ,       )

          UpperCAmelCase									=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype='i4'							)
          UpperCAmelCase									=					model.decode(
              decoder_input_ids[:, -1:]   ,       lowercase_   ,       decoder_attention_mask=lowercase_   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_position_ids=lowercase_   ,       )

          UpperCAmelCase									=					model.decode(lowercase_   ,       lowercase_							)

          UpperCAmelCase									=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
          self.parent.assertTrue(diff < 1E-3   ,       msg=F"Max diff is {diff}"							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					Tuple:
          UpperCAmelCase									=					2_0
          UpperCAmelCase									=					model_class_name(lowercase_							)

          UpperCAmelCase									=					model.encode(inputs_dict['input_ids']							)

          UpperCAmelCase			,							UpperCAmelCase									=					(
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCAmelCase									=					jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
              ]   ,       axis=-1   ,       )

          UpperCAmelCase									=					model.init_cache(decoder_input_ids.shape[0]   ,       lowercase_   ,       lowercase_							)
          UpperCAmelCase									=					jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]   ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,       )

          UpperCAmelCase									=					model.decode(
              decoder_input_ids[:, :-1]   ,       lowercase_   ,       decoder_attention_mask=lowercase_   ,       past_key_values=lowercase_   ,       decoder_position_ids=lowercase_   ,       )
          UpperCAmelCase									=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,       dtype='i4'							)
          UpperCAmelCase									=					model.decode(
              decoder_input_ids[:, -1:]   ,       lowercase_   ,       past_key_values=outputs_cache.past_key_values   ,       decoder_attention_mask=lowercase_   ,       decoder_position_ids=lowercase_   ,       )

          UpperCAmelCase									=					model.decode(lowercase_   ,       lowercase_   ,       decoder_attention_mask=lowercase_							)

          UpperCAmelCase									=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])							)							)
          self.parent.assertTrue(diff < 1E-3   ,       msg=F"Max diff is {diff}"							)

@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Dict							      =	9_9




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					np.array(
              [
                  [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                  [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                  [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                  [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                  [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                  [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                  [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                  [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                  [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                  [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                  [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                  [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                  [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
              ]   ,       dtype=np.intaa   ,       )

          UpperCAmelCase									=					input_ids.shape[0]
          UpperCAmelCase									=					BlenderbotConfig(
              vocab_size=self.vocab_size   ,       d_model=2_4   ,       encoder_layers=2   ,       decoder_layers=2   ,       encoder_attention_heads=2   ,       decoder_attention_heads=2   ,       encoder_ffn_dim=3_2   ,       decoder_ffn_dim=3_2   ,       max_position_embeddings=4_8   ,       eos_token_id=2   ,       pad_token_id=1   ,       bos_token_id=0   ,       )
          return config, input_ids, batch_size




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					self._get_config_and_data()
          UpperCAmelCase									=					FlaxBlenderbotForConditionalGeneration(lowercase_							)
          UpperCAmelCase									=					lm_model(input_ids=lowercase_							)
          UpperCAmelCase									=					(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape   ,       lowercase_							)




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					BlenderbotConfig(
              vocab_size=self.vocab_size   ,       d_model=1_4   ,       encoder_layers=2   ,       decoder_layers=2   ,       encoder_attention_heads=2   ,       decoder_attention_heads=2   ,       encoder_ffn_dim=8   ,       decoder_ffn_dim=8   ,       max_position_embeddings=4_8   ,       )
          UpperCAmelCase									=					FlaxBlenderbotForConditionalGeneration(lowercase_							)
          UpperCAmelCase									=					np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]   ,       dtype=np.intaa							)
          UpperCAmelCase									=					np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]   ,       dtype=np.intaa							)
          UpperCAmelCase									=					lm_model(input_ids=lowercase_   ,       decoder_input_ids=lowercase_							)
          UpperCAmelCase									=					(*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape   ,       lowercase_							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]   ,       dtype=np.intaa							)
          UpperCAmelCase									=					shift_tokens_right(lowercase_   ,       1   ,       2							)
          UpperCAmelCase									=					np.equal(lowercase_   ,       1							).astype(np.floataa							).sum()
          UpperCAmelCase									=					np.equal(lowercase_   ,       1							).astype(np.floataa							).sum()
          self.assertEqual(shifted.shape   ,       input_ids.shape							)
          self.assertEqual(lowercase_   ,       n_pad_before - 1							)
          self.assertTrue(np.equal(shifted[:, 0]   ,       2							).all()							)

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase     ,			SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Dict							      =	True
      __SCREAMING_SNAKE_CASE   :							Any							      =	(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE   :							str							      =	(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxBlenderbotModelTester(self							)




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(lowercase_   ,       lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Any:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase									=					self._prepare_for_class(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

                  @jax.jit
                  def encode_jitted(lowercase_   ,       lowercase_=None   ,       **lowercase_							):
                      return model.encode(input_ids=lowercase_   ,       attention_mask=lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					encode_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					encode_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_   ,       lowercase_							):
                      self.assertEqual(jitted_output.shape   ,       output.shape							)




      def     a_      (							self							)						->					Any:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase									=					model_class(lowercase_							)
                  UpperCAmelCase									=					model.encode(inputs_dict['input_ids']   ,       inputs_dict['attention_mask']							)

                  UpperCAmelCase									=					{
                      'decoder_input_ids': inputs_dict['decoder_input_ids'],
                      'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                      'encoder_outputs': encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(lowercase_   ,       lowercase_   ,       lowercase_							):
                      return model.decode(
                          decoder_input_ids=lowercase_   ,       decoder_attention_mask=lowercase_   ,       encoder_outputs=lowercase_   ,       )

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					decode_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					decode_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_   ,       lowercase_							):
                      self.assertEqual(jitted_output.shape   ,       output.shape							)




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('facebook/blenderbot-400M-distill'							)
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              UpperCAmelCase									=					np.ones((1, 1)							) * model.config.eos_token_id
              UpperCAmelCase									=					model(lowercase_							)
              self.assertIsNotNone(lowercase_							)




      @unittest.skipUnless(jax_device != 'cpu'   ,       '3B test too slow on CPU.'							)
      @slow
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					{'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
          UpperCAmelCase									=					{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

          UpperCAmelCase									=					FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'   ,       from_pt=lowercase_							)
          UpperCAmelCase									=					BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'							)

          UpperCAmelCase									=					['Sam']
          UpperCAmelCase									=					tokenizer(lowercase_   ,       return_tensors='jax'							)

          UpperCAmelCase									=					model.generate(**lowercase_   ,       **lowercase_							)
          UpperCAmelCase									=					'Sam is a great name. It means "sun" in Gaelic.'

          UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       **lowercase_							)
          assert generated_txt[0].strip() == tgt_text





"""simple docstring"""

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							str							      =	(DDPMScheduler,)




      def     a_      (							self   ,       **lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					{
              'num_train_timesteps': 1_0_0_0,
              'beta_start': 0.0_0_0_1,
              'beta_end': 0.0_2,
              'beta_schedule': 'linear',
              'variance_type': 'fixed_small',
              'clip_sample': True,
          }

          config.update(**lowercase_							)
          return config




      def     a_      (							self							)						->					int:
          for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
              self.check_over_configs(num_train_timesteps=lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]   ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
              self.check_over_configs(beta_start=lowercase_   ,       beta_end=lowercase_							)




      def     a_      (							self							)						->					Dict:
          for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          for clip_sample in [True, False]:
              self.check_over_configs(clip_sample=lowercase_							)




      def     a_      (							self							)						->					Tuple:
          self.check_over_configs(thresholding=lowercase_							)
          for threshold in [0.5, 1.0, 2.0]:
              for prediction_type in ["epsilon", "sample", "v_prediction"]:
                  self.check_over_configs(
                      thresholding=lowercase_   ,       prediction_type=lowercase_   ,       sample_max_value=lowercase_   ,       )




      def     a_      (							self							)						->					Any:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(prediction_type=lowercase_							)




      def     a_      (							self							)						->					Union[str, Any]:
          for t in [0, 5_0_0, 9_9_9]:
              self.check_over_forward(time_step=lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.0_2							)							) < 1E-5




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

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

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config(prediction_type='v_prediction'							)
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					len(lowercase_							)

          UpperCAmelCase									=					self.dummy_model()
          UpperCAmelCase									=					self.dummy_sample_deter
          UpperCAmelCase									=					torch.manual_seed(0							)

          for t in reversed(range(lowercase_							)							):
              # 1. predict noise residual
              UpperCAmelCase									=					model(lowercase_   ,       lowercase_							)

              # 2. predict previous mean of sample x_t-1
              UpperCAmelCase									=					scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       generator=lowercase_							).prev_sample

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

          UpperCAmelCase									=					torch.sum(torch.abs(lowercase_							)							)
          UpperCAmelCase									=					torch.mean(torch.abs(lowercase_							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]

          scheduler.set_timesteps(timesteps=lowercase_							)

          UpperCAmelCase									=					scheduler.timesteps

          for i, timestep in enumerate(lowercase_							):
              if i == len(lowercase_							) - 1:
                  UpperCAmelCase									=					-1
              else:
                  UpperCAmelCase									=					timesteps[i + 1]

              UpperCAmelCase									=					scheduler.previous_timestep(lowercase_							)
              UpperCAmelCase									=					prev_t.item()

              self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 5_1, 0]

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




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[1_0_0, 8_7, 5_0, 1, 0]
          UpperCAmelCase									=					len(lowercase_							)

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




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.scheduler_classes[0]
          UpperCAmelCase									=					self.get_scheduler_config()
          UpperCAmelCase									=					scheduler_class(**lowercase_							)

          UpperCAmelCase									=					[scheduler.config.num_train_timesteps]

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



