



import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel



class       UpperCAmelCase__:

   '''simple docstring'''




   def __init__(						self  :			int			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Dict=13			,				lowerCAmelCase  :			Optional[Any]=7			,				lowerCAmelCase  :			Union[str, Any]=True			,				lowerCAmelCase  :			str=True			,				lowerCAmelCase  :			int=False			,				lowerCAmelCase  :			Dict=True			,				lowerCAmelCase  :			Dict=99			,				lowerCAmelCase  :			List[str]=32			,				lowerCAmelCase  :			str=5			,				lowerCAmelCase  :			str=4			,				lowerCAmelCase  :			Tuple=37			,				lowerCAmelCase  :			Any="gelu"			,				lowerCAmelCase  :			Union[str, Any]=0.1			,				lowerCAmelCase  :			int=0.1			,				lowerCAmelCase  :			Dict=5_12			,				lowerCAmelCase  :			Optional[int]=16			,				lowerCAmelCase  :			List[Any]=2			,				lowerCAmelCase  :			Dict=0.02			,				lowerCAmelCase  :			Optional[int]=3			,				lowerCAmelCase  :			Dict=4			,				lowerCAmelCase  :			List[Any]=None			,				)	->				Optional[int]:




         """simple docstring"""

         lowercase__     =  parent
         lowercase__     =  batch_size
         lowercase__     =  seq_length
         lowercase__     =  is_training
         lowercase__     =  use_input_mask
         lowercase__     =  use_token_type_ids
         lowercase__     =  use_labels
         lowercase__     =  vocab_size
         lowercase__     =  hidden_size
         lowercase__     =  num_hidden_layers
         lowercase__     =  num_attention_heads
         lowercase__     =  intermediate_size
         lowercase__     =  hidden_act
         lowercase__     =  hidden_dropout_prob
         lowercase__     =  attention_probs_dropout_prob
         lowercase__     =  max_position_embeddings
         lowercase__     =  type_vocab_size
         lowercase__     =  type_sequence_label_size
         lowercase__     =  initializer_range
         lowercase__     =  num_labels
         lowercase__     =  num_choices
         lowercase__     =  scope


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

         lowercase__     =  None
         if self.use_input_mask:
               lowercase__     =  random_attention_mask([self.batch_size, self.seq_length])

         lowercase__     =  None
         if self.use_token_type_ids:
               lowercase__     =  ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

         lowercase__     =  None
         lowercase__     =  None
         lowercase__     =  None
         if self.use_labels:
               lowercase__     =  ids_tensor([self.batch_size]			,				self.type_sequence_label_size)
               lowercase__     =  ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels)
               lowercase__     =  ids_tensor([self.batch_size]			,				self.num_choices)

         lowercase__     =  self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Tuple:




         """simple docstring"""

         return OpenLlamaConfig(
             vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				type_vocab_size=self.type_vocab_size			,				is_decoder=lowerCAmelCase			,				initializer_range=self.initializer_range			,				use_stable_embedding=lowerCAmelCase			,				)


   def 			UpperCAmelCase		(						self  :			int			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Union[str, Any]			,				lowerCAmelCase  :			Dict			,				lowerCAmelCase  :			Optional[int])	->				Optional[Any]:




         """simple docstring"""

         lowercase__     =  OpenLlamaModel(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase)
         lowercase__     =  model(lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			List[Any]			,				lowerCAmelCase  :			List[str]			,				)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  True
         lowercase__     =  OpenLlamaModel(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(
             lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				encoder_hidden_states=lowerCAmelCase			,				encoder_attention_mask=lowerCAmelCase			,				)
         lowercase__     =  model(
             lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				encoder_hidden_states=lowerCAmelCase			,				)
         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase)
         self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size))


   def 			UpperCAmelCase		(						self  :			Optional[int]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			str			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			int			,				lowerCAmelCase  :			int			,				)	->				List[str]:




         """simple docstring"""

         lowercase__     =  OpenLlamaForCausalLM(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				labels=lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))


   def 			UpperCAmelCase		(						self  :			Dict			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			List[str]			,				lowerCAmelCase  :			Optional[Any]			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Optional[int]			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Any			,				lowerCAmelCase  :			Union[str, Any]			,				)	->				Dict:




         """simple docstring"""

         lowercase__     =  True
         lowercase__     =  True
         lowercase__     =  OpenLlamaForCausalLM(config=lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()

         # first forward pass
         lowercase__     =  model(
             lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				encoder_hidden_states=lowerCAmelCase			,				encoder_attention_mask=lowerCAmelCase			,				use_cache=lowerCAmelCase			,				)
         lowercase__     =  outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         lowercase__     =  ids_tensor((self.batch_size, 3)			,				config.vocab_size)
         lowercase__     =  ids_tensor((self.batch_size, 3)			,				vocab_size=2)

         # append to next input_ids and
         lowercase__     =  torch.cat([input_ids, next_tokens]			,				dim=-1)
         lowercase__     =  torch.cat([input_mask, next_mask]			,				dim=-1)

         lowercase__     =  model(
             lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				encoder_hidden_states=lowerCAmelCase			,				encoder_attention_mask=lowerCAmelCase			,				output_hidden_states=lowerCAmelCase			,				)['hidden_states'][0]
         lowercase__     =  model(
             lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				encoder_hidden_states=lowerCAmelCase			,				encoder_attention_mask=lowerCAmelCase			,				past_key_values=lowerCAmelCase			,				output_hidden_states=lowerCAmelCase			,				)['hidden_states'][0]

         # select random slice
         lowercase__     =  ids_tensor((1,)			,				output_from_past.shape[-1]).item()
         lowercase__     =  output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase__     =  output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=1E-3))



   def 			UpperCAmelCase		(						self  :			int)	->				int:




         """simple docstring"""

         lowercase__     =  self.prepare_config_and_inputs()
         (
             (
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	(
             lowercase__
         ),	
         )     =  config_and_inputs
         lowercase__     =  {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict



@require_torch
class       UpperCAmelCase__(							lowerCamelCase     ,							lowerCamelCase     ,							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  Dict       				=    (
       (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
   )
   A    :  int       				=    (OpenLlamaForCausalLM,) if is_torch_available() else ()
   A    :  List[str]       				=    (
       {
           "feature-extraction": OpenLlamaModel,
           "text-classification": OpenLlamaForSequenceClassification,
           "text-generation": OpenLlamaForCausalLM,
           "zero-shot": OpenLlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   A    :  str       				=    False
   A    :  Tuple       				=    False


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




         """simple docstring"""

         lowercase__     =  OpenLlamaModelTester(self)
         lowercase__     =  ConfigTester(self			,				config_class=lowerCAmelCase			,				hidden_size=37)


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




         """simple docstring"""

         self.config_tester.run_common_tests()


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
               lowercase__     =  type
               self.model_tester.create_and_check_model(*lowerCAmelCase)


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




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__     =  3
         lowercase__     =  input_dict['input_ids']
         lowercase__     =  input_ids.ne(1).to(lowerCAmelCase)
         lowercase__     =  ids_tensor([self.model_tester.batch_size]			,				self.model_tester.type_sequence_label_size)
         lowercase__     =  OpenLlamaForSequenceClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				labels=lowerCAmelCase)
         self.assertEqual(result.logits.shape			,				(self.model_tester.batch_size, self.model_tester.num_labels))


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




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__     =  3
         lowercase__     =  'single_label_classification'
         lowercase__     =  input_dict['input_ids']
         lowercase__     =  input_ids.ne(1).to(lowerCAmelCase)
         lowercase__     =  ids_tensor([self.model_tester.batch_size]			,				self.model_tester.type_sequence_label_size)
         lowercase__     =  OpenLlamaForSequenceClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				labels=lowerCAmelCase)
         self.assertEqual(result.logits.shape			,				(self.model_tester.batch_size, self.model_tester.num_labels))


   def 			UpperCAmelCase		(						self  :			Dict)	->				int:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__     =  3
         lowercase__     =  'multi_label_classification'
         lowercase__     =  input_dict['input_ids']
         lowercase__     =  input_ids.ne(1).to(lowerCAmelCase)
         lowercase__     =  ids_tensor(
             [self.model_tester.batch_size, config.num_labels]			,				self.model_tester.type_sequence_label_size).to(torch.float)
         lowercase__     =  OpenLlamaForSequenceClassification(lowerCAmelCase)
         model.to(lowerCAmelCase)
         model.eval()
         lowercase__     =  model(lowerCAmelCase			,				attention_mask=lowerCAmelCase			,				labels=lowerCAmelCase)
         self.assertEqual(result.logits.shape			,				(self.model_tester.batch_size, self.model_tester.num_labels))


   @unittest.skip('Open-Llama buffers include complex numbers, which breaks this test')
   def 			UpperCAmelCase		(						self  :			int)	->				Union[str, Any]:




         """simple docstring"""

         pass



   @parameterized.expand([('linear',), ('dynamic',)])
   def 			UpperCAmelCase		(						self  :			List[str]			,				lowerCAmelCase  :			List[str])	->				List[str]:




         """simple docstring"""

         lowercase__,	lowercase__     =  self.model_tester.prepare_config_and_inputs_for_common()
         lowercase__     =  ids_tensor([1, 10]			,				config.vocab_size)
         lowercase__     =  ids_tensor([1, int(config.max_position_embeddings * 1.5)]			,				config.vocab_size)

         set_seed(42)  # Fixed seed at init time so the two models get the same random weights
         lowercase__     =  OpenLlamaModel(lowerCAmelCase)
         original_model.to(lowerCAmelCase)
         original_model.eval()
         lowercase__     =  original_model(lowerCAmelCase).last_hidden_state
         lowercase__     =  original_model(lowerCAmelCase).last_hidden_state

         set_seed(42)  # Fixed seed at init time so the two models get the same random weights
         lowercase__     =  {'type': scaling_type, 'factor': 10.0}
         lowercase__     =  OpenLlamaModel(lowerCAmelCase)
         scaled_model.to(lowerCAmelCase)
         scaled_model.eval()
         lowercase__     =  scaled_model(lowerCAmelCase).last_hidden_state
         lowercase__     =  scaled_model(lowerCAmelCase).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
               self.assertTrue(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=1E-5))
         else:
               self.assertFalse(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=1E-5))

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(lowerCAmelCase			,				lowerCAmelCase			,				atol=1E-5))





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  str       				=    (DDIMParallelScheduler,)
   A    :  Any       				=    (("eta", 0.0), ("num_inference_steps", 50))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(**lowerCAmelCase)
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter

         scheduler.set_timesteps(lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps			,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

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


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




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

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


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




         """simple docstring"""

         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[Any]:




         """simple docstring"""

         for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase)


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




         """simple docstring"""

         for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 5_00]):
               self.check_over_forward(time_step=lowerCAmelCase			,				num_inference_steps=lowerCAmelCase)


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




         """simple docstring"""

         for t, eta in zip([1, 10, 49]			,				[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=lowerCAmelCase			,				eta=lowerCAmelCase)


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




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20			,				4_00) - 0.1_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80			,				9_60) - 0.3_24_60)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87			,				4_86) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99			,				9_98) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0
         scheduler.set_timesteps(lowerCAmelCase)

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

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

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

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 11_47.79_04) < 1E-2
         assert abs(result_mean.item() - 0.49_82) < 1E-3


   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  self.full_loop()

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3



   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3
