





'''simple docstring'''


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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.models.blenderbot_small.modeling_flax_blenderbot_small import (
           FlaxBlenderbotSmallForConditionalGeneration,
           FlaxBlenderbotSmallModel,
           shift_tokens_right,
       )


def 						__a(SCREAMING_SNAKE_CASE_	:       Optional[Any]		,							SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=None		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=None		,							SCREAMING_SNAKE_CASE_	:       Optional[int]=None		,							SCREAMING_SNAKE_CASE_	:       List[str]=None		,							SCREAMING_SNAKE_CASE_	:       Union[str, Any]=None		,							SCREAMING_SNAKE_CASE_	:       Dict=None		,							):



    '''simple docstring'''





    if attention_mask is None:
        _lowerCAmelCase						      =   np.where(input_ids != config.pad_token_id		,							1		,							0     )
    if decoder_attention_mask is None:
        _lowerCAmelCase						      =   np.where(decoder_input_ids != config.pad_token_id		,							1		,							0     )
    if head_mask is None:
        _lowerCAmelCase						      =   np.ones((config.encoder_layers, config.encoder_attention_heads)     )
    if decoder_head_mask is None:
        _lowerCAmelCase						      =   np.ones((config.decoder_layers, config.decoder_attention_heads)     )
    if cross_attn_head_mask is None:
        _lowerCAmelCase						      =   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  lowerCAmelCase_		:




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=13      ,  _lowerCAmelCase=7      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=99      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase=4      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=0.02      ,  )     ->				Optional[int]:
     _lowerCAmelCase						      =   parent
     _lowerCAmelCase						      =   batch_size
     _lowerCAmelCase						      =   seq_length
     _lowerCAmelCase						      =   is_training
     _lowerCAmelCase						      =   use_labels
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   max_position_embeddings
     _lowerCAmelCase						      =   eos_token_id
     _lowerCAmelCase						      =   pad_token_id
     _lowerCAmelCase						      =   bos_token_id
     _lowerCAmelCase						      =   initializer_range




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

     _lowerCAmelCase						      =   shift_tokens_right(_lowerCAmelCase      ,  1      ,  2		)

     _lowerCAmelCase						      =   BlenderbotSmallConfig(
         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=_lowerCAmelCase      ,  )
     _lowerCAmelCase						      =   prepare_blenderbot_inputs_dict(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     return config, inputs_dict




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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Optional[int]:
     _lowerCAmelCase						      =   20
     _lowerCAmelCase						      =   model_class_name(_lowerCAmelCase		)

     _lowerCAmelCase						      =   model.encode(inputs_dict["input_ids"]		)

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     _lowerCAmelCase						      =   model.init_cache(decoder_input_ids.shape[0]      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)
     _lowerCAmelCase						      =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,  dtype="i4"		)

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

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

     _lowerCAmelCase						      =   model.decode(_lowerCAmelCase      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   20
     _lowerCAmelCase						      =   model_class_name(_lowerCAmelCase		)

     _lowerCAmelCase						      =   model.encode(inputs_dict["input_ids"]		)

     _lowerCAmelCase      ,      _lowerCAmelCase						      =   (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

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

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

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

     _lowerCAmelCase						      =   model.decode(_lowerCAmelCase      ,  _lowerCAmelCase      ,  decoder_attention_mask=_lowerCAmelCase		)

     _lowerCAmelCase						      =   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  lowerCAmelCase_		(		unittest.TestCase		):
 __lowerCamelCase    :				str								=       99




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]      ,  dtype=np.intaa      ,  )

     _lowerCAmelCase						      =   input_ids.shape[0]
     _lowerCAmelCase						      =   BlenderbotSmallConfig(
         vocab_size=self.vocab_size      ,  d_model=24      ,  encoder_layers=2      ,  decoder_layers=2      ,  encoder_attention_heads=2      ,  decoder_attention_heads=2      ,  encoder_ffn_dim=32      ,  decoder_ffn_dim=32      ,  max_position_embeddings=48      ,  eos_token_id=2      ,  pad_token_id=1      ,  bos_token_id=0      ,  )
     return config, input_ids, batch_size




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase						      =   self._get_config_and_data()
     _lowerCAmelCase						      =   FlaxBlenderbotSmallForConditionalGeneration(_lowerCAmelCase		)
     _lowerCAmelCase						      =   lm_model(input_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase						      =   BlenderbotSmallConfig(
         vocab_size=self.vocab_size      ,  d_model=14      ,  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=48      ,  )
     _lowerCAmelCase						      =   FlaxBlenderbotSmallForConditionalGeneration(_lowerCAmelCase		)
     _lowerCAmelCase						      =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,  dtype=np.intaa		)
     _lowerCAmelCase						      =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,  dtype=np.intaa		)
     _lowerCAmelCase						      =   lm_model(input_ids=_lowerCAmelCase      ,  decoder_input_ids=_lowerCAmelCase		)
     _lowerCAmelCase						      =   (*summary.shape, config.vocab_size)
     self.assertEqual(outputs["logits"].shape      ,  _lowerCAmelCase		)





 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,  dtype=np.intaa		)
     _lowerCAmelCase						      =   shift_tokens_right(_lowerCAmelCase      ,  1      ,  2		)
     _lowerCAmelCase						      =   np.equal(_lowerCAmelCase      ,  1		).astype(np.floataa		).sum()
     _lowerCAmelCase						      =   np.equal(_lowerCAmelCase      ,  1		).astype(np.floataa		).sum()
     self.assertEqual(shifted.shape      ,  input_ids.shape		)
     self.assertEqual(_lowerCAmelCase      ,  n_pad_before - 1		)
     self.assertTrue(np.equal(shifted[:, 0]      ,  2		).all()		)







@require_flax
class  lowerCAmelCase_		(		__magic_name__    ,unittest.TestCase    ,__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       True
 __lowerCamelCase    :				List[str]								=       (
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 __lowerCamelCase    :				Optional[Any]								=       (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




 def 				_snake_case (						self		)     ->				List[Any]:
     _lowerCAmelCase						      =   FlaxBlenderbotSmallModelTester(self		)




 def 				_snake_case (						self		)     ->				List[str]:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   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(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

             with self.subTest("JIT Enabled"		):
                 _lowerCAmelCase						      =   encode_jitted(**_lowerCAmelCase		).to_tuple()

             with self.subTest("JIT Disabled"		):
                 with jax.disable_jit():
                     _lowerCAmelCase						      =   encode_jitted(**_lowerCAmelCase		).to_tuple()

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




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase      ,      _lowerCAmelCase						      =   self.model_tester.prepare_config_and_inputs_for_common()

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

             _lowerCAmelCase						      =   {
                 "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(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		):
                 return model.decode(
                     decoder_input_ids=_lowerCAmelCase      ,  decoder_attention_mask=_lowerCAmelCase      ,  encoder_outputs=_lowerCAmelCase      ,  )

             with self.subTest("JIT Enabled"		):
                 _lowerCAmelCase						      =   decode_jitted(**_lowerCAmelCase		).to_tuple()

             with self.subTest("JIT Disabled"		):
                 with jax.disable_jit():
                     _lowerCAmelCase						      =   decode_jitted(**_lowerCAmelCase		).to_tuple()

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





 @slow
 def 				_snake_case (						self		)     ->				Dict:
     for model_class_name in self.all_model_classes:
         _lowerCAmelCase						      =   model_class_name.from_pretrained("facebook/blenderbot_small-90M"		)
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         _lowerCAmelCase						      =   np.ones((1, 1)		) * model.config.eos_token_id
         _lowerCAmelCase						      =   model(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)









'''simple docstring'''


import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Any								=       (DDPMParallelScheduler,)




 def 				_snake_case (						self      ,  **_lowerCAmelCase		)     ->				int:
     _lowerCAmelCase						      =   {
         "num_train_timesteps": 1000,
         "beta_start": 0.0001,
         "beta_end": 0.02,
         "beta_schedule": "linear",
         "variance_type": "fixed_small",
         "clip_sample": True,
     }

     config.update(**_lowerCAmelCase		)
     return config




 def 				_snake_case (						self		)     ->				List[Any]:
     for timesteps in [1, 5, 100, 1000]:
         self.check_over_configs(num_train_timesteps=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[Any]:
     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=_lowerCAmelCase      ,  beta_end=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				Optional[Any]:
     for variance in ["fixed_small", "fixed_large", "other"]:
         self.check_over_configs(variance_type=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Optional[int]:
     for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				List[str]:
     self.check_over_configs(thresholding=_lowerCAmelCase		)
     for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "sample", "v_prediction"]:
             self.check_over_configs(
                 thresholding=_lowerCAmelCase      ,  prediction_type=_lowerCAmelCase      ,  sample_max_value=_lowerCAmelCase      ,  )




 def 				_snake_case (						self		)     ->				int:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
         self.check_over_configs(prediction_type=_lowerCAmelCase		)




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




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.00979		)		) < 1E-5
     assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1E-5




 def 				_snake_case (						self		)     ->				Tuple:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

     _lowerCAmelCase						      =   self.dummy_model()
     _lowerCAmelCase						      =   self.dummy_sample_deter
     _lowerCAmelCase						      =   self.dummy_sample_deter + 0.1
     _lowerCAmelCase						      =   self.dummy_sample_deter - 0.1

     _lowerCAmelCase						      =   samplea.shape[0]
     _lowerCAmelCase						      =   torch.stack([samplea, samplea, samplea]      ,  dim=0		)
     _lowerCAmelCase						      =   torch.arange(_lowerCAmelCase		)[0:3, None].repeat(1      ,  _lowerCAmelCase		)

     _lowerCAmelCase						      =   model(samples.flatten(0      ,  1		)      ,  timesteps.flatten(0      ,  1		)		)
     _lowerCAmelCase						      =   scheduler.batch_step_no_noise(_lowerCAmelCase      ,  timesteps.flatten(0      ,  1		)      ,  samples.flatten(0      ,  1		)		)

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

     assert abs(result_sum.item() - 1153.1833		) < 1E-2
     assert abs(result_mean.item() - 0.5005		) < 1E-3




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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

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

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

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

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




 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config(prediction_type="v_prediction"		)
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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

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

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

         _lowerCAmelCase						      =   pred_prev_sample

     _lowerCAmelCase						      =   torch.sum(torch.abs(_lowerCAmelCase		)		)
     _lowerCAmelCase						      =   torch.mean(torch.abs(_lowerCAmelCase		)		)

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




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]

     scheduler.set_timesteps(timesteps=_lowerCAmelCase		)

     _lowerCAmelCase						      =   scheduler.timesteps

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

         _lowerCAmelCase						      =   scheduler.previous_timestep(_lowerCAmelCase		)
         _lowerCAmelCase						      =   prev_t.item()

         self.assertEqual(_lowerCAmelCase      ,  _lowerCAmelCase		)




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 51, 0]

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




 def 				_snake_case (						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [100, 87, 50, 1, 0]
     _lowerCAmelCase						      =   len(_lowerCAmelCase		)

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





 def 				_snake_case (						self		)     ->				Optional[int]:
     _lowerCAmelCase						      =   self.scheduler_classes[0]
     _lowerCAmelCase						      =   self.get_scheduler_config()
     _lowerCAmelCase						      =   scheduler_class(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   [scheduler.config.num_train_timesteps]

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


