
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
     lowerCamelCase__     :		Optional[Any]        = """platform"""

     import jax
     import jax.numpy as jnp

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			lowercase_=None			,			)		->						Dict:


  '''simple docstring'''






  if attention_mask is None:
    lowercase__	: Optional[Any]     		=   np.where(input_ids != config.pad_token_id			,			1			,			0 )
  if decoder_attention_mask is None:
    lowercase__	: Any     		=   np.where(decoder_input_ids != config.pad_token_id			,			1			,			0 )
  if head_mask is None:
    lowercase__	: str     		=   np.ones((config.encoder_layers, config.encoder_attention_heads) )
  if decoder_head_mask is None:
    lowercase__	: Optional[Any]     		=   np.ones((config.decoder_layers, config.decoder_attention_heads) )
  if cross_attn_head_mask is None:
    lowercase__	: int     		=   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    _snake_case :


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=7       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=False       ,	SCREAMING_SNAKE_CASE_=99       ,	SCREAMING_SNAKE_CASE_=16       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_=4       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=32       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: Dict     		=   shift_tokens_right(SCREAMING_SNAKE_CASE_       ,	1       ,	2)

   lowercase__	: str     		=   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=SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: Tuple     		=   prepare_blenderbot_inputs_dict(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return config, inputs_dict


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.prepare_config_and_inputs()
   return config, inputs_dict


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[Any]     		=   20
   lowercase__	: Tuple     		=   model_class_name(SCREAMING_SNAKE_CASE_)

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

   lowercase__		,     lowercase__	: Union[str, Any]     		=   (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   lowercase__	: str     		=   model.init_cache(decoder_input_ids.shape[0]       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[int]     		=   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,	dtype="""i4""")

   lowercase__	: Dict     		=   jnp.broadcast_to(
       jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]       ,	(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,	)
   lowercase__	: Optional[Any]     		=   model.decode(
       decoder_input_ids[:, :-1]       ,	SCREAMING_SNAKE_CASE_       ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_       ,	past_key_values=SCREAMING_SNAKE_CASE_       ,	decoder_position_ids=SCREAMING_SNAKE_CASE_       ,	)

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

   lowercase__	: List[str]     		=   model.decode(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: str     		=   20
   lowercase__	: List[Any]     		=   model_class_name(SCREAMING_SNAKE_CASE_)

   lowercase__	: Optional[int]     		=   model.encode(inputs_dict["""input_ids"""])

   lowercase__		,     lowercase__	: Dict     		=   (
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

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

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

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

   lowercase__	: str     		=   model.decode(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_)

   lowercase__	: 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    _snake_case (						unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      99


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   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       ,	)

   lowercase__	: Tuple     		=   input_ids.shape[0]
   lowercase__	: List[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   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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



 def   lowercase__  (       self):


   '''simple docstring'''




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



@require_flax
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase	,    UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[Any]	   =      True
 __lowerCAmelCase	:  List[Any]	   =      (
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 __lowerCAmelCase	:  Tuple	   =      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   FlaxBlenderbotSmallModelTester(self)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: int     		=   self.model_tester.prepare_config_and_inputs()
   for model_class in self.all_model_classes:
     self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Dict     		=   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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       lowercase__	: Union[str, Any]     		=   self._prepare_for_class(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
       lowercase__	: int     		=   model_class(SCREAMING_SNAKE_CASE_)

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

       with self.subTest("""JIT Enabled"""):
         lowercase__	: str     		=   encode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

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

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


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.model_tester.prepare_config_and_inputs_for_common()

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

       lowercase__	: Dict     		=   {
           """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(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
         return model.decode(
             decoder_input_ids=SCREAMING_SNAKE_CASE_       ,	decoder_attention_mask=SCREAMING_SNAKE_CASE_       ,	encoder_outputs=SCREAMING_SNAKE_CASE_       ,	)

       with self.subTest("""JIT Enabled"""):
         lowercase__	: int     		=   decode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

       with self.subTest("""JIT Disabled"""):
         with jax.disable_jit():
           lowercase__	: Optional[Any]     		=   decode_jitted(**SCREAMING_SNAKE_CASE_).to_tuple()

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



 @slow
 def   lowercase__  (       self):


   '''simple docstring'''




   for model_class_name in self.all_model_classes:
     lowercase__	: Any     		=   model_class_name.from_pretrained("""facebook/blenderbot_small-90M""")
     # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
     lowercase__	: Any     		=   np.ones((1, 1)) * model.config.eos_token_id
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_)
     self.assertIsNotNone(SCREAMING_SNAKE_CASE_)








import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   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=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




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

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: str     		=   self.get_scheduler_config()
   lowercase__	: Tuple     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

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

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

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_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__	: str     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_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__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   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   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: str     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

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


 def   lowercase__  (       self):


   '''simple docstring'''




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

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

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



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

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






