

"""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
  a					:Tuple    = "platform"

  import jax
  import jax.numpy as jnp

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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					__lowerCAmelCase=None  ,					)       ->				Any:
      if attention_mask is None:
            SCREAMING_SNAKE_CASE__   :      str      		=	np.where(input_ids != config.pad_token_id  ,					1  ,					0							)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	np.where(decoder_input_ids != config.pad_token_id  ,					1  ,					0							)
      if head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Any      		=	np.ones((config.encoder_layers, config.encoder_attention_heads)							)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Tuple      		=	np.ones((config.decoder_layers, config.decoder_attention_heads)							)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE__   :      Any      		=	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      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=False	,    _a=99	,    _a=16	,    _a=2	,    _a=4	,    _a=4	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=32	,    _a=2	,    _a=1	,    _a=0	,    _a=0.02	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	parent
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	seq_length
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	is_training
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      Dict      		=	vocab_size
           SCREAMING_SNAKE_CASE__   :      Any      		=	hidden_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_hidden_layers
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	num_attention_heads
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	intermediate_size
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	hidden_dropout_prob
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE__   :      str      		=	max_position_embeddings
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	eos_token_id
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	pad_token_id
           SCREAMING_SNAKE_CASE__   :      str      		=	bos_token_id
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	initializer_range

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size  )	,    3	,    self.vocab_size  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,    dtype=np.intaa  ))	,    -1  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	shift_tokens_right(_a	,    1	,    2  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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=_a	,    )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	prepare_blenderbot_inputs_dict(_a	,    _a	,    _a  )
           return config, inputs_dict

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.prepare_config_and_inputs()
           return config, inputs_dict

     def 					_a			(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	20
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_class_name(_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	model.encode(inputs_dict["""input_ids"""]  )

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[Any]      		=	(
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model.init_cache(decoder_input_ids.shape[0]	,    _a	,    _a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype="""i4"""  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model.decode(
               decoder_input_ids[:, :-1]	,    _a	,    decoder_attention_mask=_a	,    past_key_values=_a	,    decoder_position_ids=_a	,    )

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

           SCREAMING_SNAKE_CASE__   :      int      		=	model.decode(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	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	,    _a	,    _a	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	20
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model_class_name(_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	model.encode(inputs_dict["""input_ids"""]  )

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      int      		=	(
               inputs_dict["""decoder_input_ids"""],
               inputs_dict["""decoder_attention_mask"""],
           )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
               ]	,    axis=-1	,    )

           SCREAMING_SNAKE_CASE__   :      Any      		=	model.init_cache(decoder_input_ids.shape[0]	,    _a	,    _a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )

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

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model.decode(_a	,    _a	,    decoder_attention_mask=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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      __a							(unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Dict       = 99

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	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	,    )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	input_ids.shape[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	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 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self._get_config_and_data()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	FlaxBlenderbotSmallForConditionalGeneration(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	lm_model(input_ids=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs["""logits"""].shape	,    _a  )

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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	,    )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	FlaxBlenderbotSmallForConditionalGeneration(_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]	,    dtype=np.intaa  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]	,    dtype=np.intaa  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	lm_model(input_ids=_a	,    decoder_input_ids=_a  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	(*summary.shape, config.vocab_size)
           self.assertEqual(outputs["""logits"""].shape	,    _a  )





     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


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





@require_flax
class      __a							(UpperCamelCase_   ,	unittest.TestCase   ,	UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Any       = True
     _SCREAMING_SNAKE_CASE						:Optional[int]       = (
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	FlaxBlenderbotSmallModelTester(self  )

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 self.model_tester.check_use_cache_forward(_a	,    _a	,    _a  )

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[Any]      		=	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(_a	,    _a	,    _a  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__  ):
                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self._prepare_for_class(_a	,    _a  )
                       SCREAMING_SNAKE_CASE__   :      str      		=	model_class(_a  )

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

                       with self.subTest("""JIT Enabled"""  ):
                             SCREAMING_SNAKE_CASE__   :      str      		=	encode_jitted(**_a  ).to_tuple()

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

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

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__  ):
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model_class(_a  )
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	model.encode(inputs_dict["""input_ids"""]	,    inputs_dict["""attention_mask"""]  )

                       SCREAMING_SNAKE_CASE__   :      Any      		=	{
                           """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(_a	,    _a	,    _a  ):
                             return model.decode(
                                 decoder_input_ids=_a	,    decoder_attention_mask=_a	,    encoder_outputs=_a	,    )

                       with self.subTest("""JIT Enabled"""  ):
                             SCREAMING_SNAKE_CASE__   :      List[str]      		=	decode_jitted(**_a  ).to_tuple()

                       with self.subTest("""JIT Disabled"""  ):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	decode_jitted(**_a  ).to_tuple()

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





     @slow
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           for model_class_name in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      int      		=	model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""  )
                 # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	np.ones((1, 1)  ) * model.config.eos_token_id
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	model(_a  )
                 self.assertIsNotNone(_a  )







"""simple docstring"""


import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = (DDPMScheduler,)

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	{
               """num_train_timesteps""": 1_000,
               """beta_start""": 0.0_001,
               """beta_end""": 0.02,
               """beta_schedule""": """linear""",
               """variance_type""": """fixed_small""",
               """clip_sample""": True,
           }

           config.update(**_a  )
           return config

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]	,    [0.002, 0.02, 0.2, 2]  ):
                 self.check_over_configs(beta_start=_a	,    beta_end=_a  )

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

           assert torch.sum(torch.abs(scheduler._get_variance(0  ) - 0.0  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(487  ) - 0.00_979  )  ) < 1E-5
           assert torch.sum(torch.abs(scheduler._get_variance(999  ) - 0.02  )  ) < 1E-5

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      int      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	len(_a  )

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

           for t in reversed(range(_a  )  ):
                 # 1. predict noise residual
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a	,    _a  )

                 # 2. predict previous mean of sample x_t-1
                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.step(_a	,    _a	,    _a	,    generator=_a  ).prev_sample

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

           SCREAMING_SNAKE_CASE__   :      str      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 258.9_606  ) < 1E-2
           assert abs(result_mean.item() - 0.3_372  ) < 1E-3

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_scheduler_config(prediction_type="""v_prediction"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	len(_a  )

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

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

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

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

           SCREAMING_SNAKE_CASE__   :      Any      		=	torch.sum(torch.abs(_a  )  )
           SCREAMING_SNAKE_CASE__   :      int      		=	torch.mean(torch.abs(_a  )  )

           assert abs(result_sum.item() - 202.0_296  ) < 1E-2
           assert abs(result_mean.item() - 0.2_631  ) < 1E-3

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	scheduler_class(**_a  )

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

           scheduler.set_timesteps(timesteps=_a  )

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

           for i, timestep in enumerate(_a  ):
                 if i == len(_a  ) - 1:
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	-1
                 else:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	timesteps[i + 1]

                 SCREAMING_SNAKE_CASE__   :      int      		=	scheduler.previous_timestep(_a  )
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	prev_t.item()

                 self.assertEqual(_a	,    _a  )

     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

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

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

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	[100, 87, 50, 1, 0]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	len(_a  )

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





     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.scheduler_classes[0]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_scheduler_config()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	scheduler_class(**_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[scheduler.config.num_train_timesteps]

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




