
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "bart"
   A__					:		str						     =     ["past_key_values"]
   A__					:		Dict						     =     {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



   def __init__(self		:					List[Any]    ,						snake_case__		:					Optional[Any]=5_02_65    ,						snake_case__		:					Dict=10_24    ,						snake_case__		:					List[str]=12    ,						snake_case__		:					int=40_96    ,						snake_case__		:					int=16    ,						snake_case__		:					Optional[int]=12    ,						snake_case__		:					Any=40_96    ,						snake_case__		:					int=16    ,						snake_case__		:					str=0.0    ,						snake_case__		:					List[str]=0.0    ,						snake_case__		:					Optional[int]="gelu"    ,						snake_case__		:					Any=10_24    ,						snake_case__		:					Union[str, Any]=0.1    ,						snake_case__		:					Dict=0.0    ,						snake_case__		:					int=0.0    ,						snake_case__		:					int=0.02    ,						snake_case__		:					int=0.0    ,						snake_case__		:					List[str]=False    ,						snake_case__		:					Tuple=True    ,						snake_case__		:					str=3    ,						snake_case__		:					Any=1    ,						snake_case__		:					Optional[Any]=0    ,						snake_case__		:					int=2    ,						snake_case__		:					Tuple=True    ,						snake_case__		:					List[Any]=2    ,						snake_case__		:					Optional[Any]=2    ,						**snake_case__		:					int    ,						)		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      vocab_size
          snake_case       :   Tuple							=      max_position_embeddings
          snake_case       :   List[Any]							=      d_model
          snake_case       :   Optional[Any]							=      encoder_ffn_dim
          snake_case       :   Dict							=      encoder_layers
          snake_case       :   str							=      encoder_attention_heads
          snake_case       :   str							=      decoder_ffn_dim
          snake_case       :   List[Any]							=      decoder_layers
          snake_case       :   Optional[Any]							=      decoder_attention_heads
          snake_case       :   Tuple							=      dropout
          snake_case       :   Any							=      attention_dropout
          snake_case       :   List[Any]							=      activation_dropout
          snake_case       :   Optional[Any]							=      activation_function
          snake_case       :   List[str]							=      init_std
          snake_case       :   Optional[Any]							=      encoder_layerdrop
          snake_case       :   Any							=      decoder_layerdrop
          snake_case       :   Any							=      classifier_dropout
          snake_case       :   Dict							=      use_cache
          snake_case       :   int							=      encoder_layers
          snake_case       :   Optional[Any]							=      scale_embedding  # scale factor will be sqrt(d_model) if True

          super().__init__(
              num_labels=snake_case__    ,						pad_token_id=snake_case__    ,						bos_token_id=snake_case__    ,						eos_token_id=snake_case__    ,						is_encoder_decoder=snake_case__    ,						decoder_start_token_id=snake_case__    ,						forced_eos_token_id=snake_case__    ,						**snake_case__    ,						)

          # ensure backward compatibility for BART CNN models
          if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"    ,						snake_case__ ):
                 snake_case       :   Optional[Any]							=      self.bos_token_id
                 warnings.warn(
                     f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
                     "The config can simply be saved and uploaded again to be fixed." )






class   UpperCAmelCase		(						A_ ):



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
                 snake_case       :   List[Any]							=      OrderedDict(
                     [
                         ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                         ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ] )

                 if self.use_past:
                        snake_case       :   int							=      {0: "batch"}
                        snake_case       :   Dict							=      {0: "batch", 1: "past_decoder_sequence + sequence"}
                 else:
                        snake_case       :   Tuple							=      {0: "batch", 1: "decoder_sequence"}
                        snake_case       :   str							=      {0: "batch", 1: "decoder_sequence"}

                 if self.use_past:
                        self.fill_with_past_key_values_(snake_case__    ,						direction="inputs" )
          elif self.task == "causal-lm":
                 # TODO: figure this case out.
                 snake_case       :   int							=      OrderedDict(
                     [
                         ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                         ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                     ] )
                 if self.use_past:
                        snake_case				,					snake_case       :   Dict							=      self.num_layers
                        for i in range(snake_case__ ):
                               snake_case       :   List[Any]							=      {0: "batch", 2: "past_sequence + sequence"}
                               snake_case       :   Optional[int]							=      {0: "batch", 2: "past_sequence + sequence"}
          else:
                 snake_case       :   Union[str, Any]							=      OrderedDict(
                     [
                         ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                         ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                         ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                         ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
                     ] )

          return common_inputs



   @property
   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Mapping[str, Mapping[int, str]]:


          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
                 snake_case       :   Optional[Any]							=      super().outputs
          else:
                 snake_case       :   List[str]							=      super(snake_case__    ,						self ).outputs
                 if self.use_past:
                        snake_case				,					snake_case       :   int							=      self.num_layers
                        for i in range(snake_case__ ):
                               snake_case       :   Dict							=      {0: "batch", 2: "past_sequence + sequence"}
                               snake_case       :   int							=      {0: "batch", 2: "past_sequence + sequence"}
          return common_outputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					PreTrainedTokenizer    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[TensorType] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )

          # Generate decoder inputs
          snake_case       :   Any							=      seq_length if not self.use_past else 1
          snake_case       :   str							=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )
          snake_case       :   Optional[int]							=      {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
          snake_case       :   int							=      dict(**snake_case__    ,						**snake_case__ )

          if self.use_past:
                 if not is_torch_available():
                        raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
                 else:
                        import torch
                 snake_case				,					snake_case       :   Optional[int]							=      common_inputs["input_ids"].shape
                 snake_case       :   Any							=      common_inputs["decoder_input_ids"].shape[1]
                 snake_case				,					snake_case       :   Tuple							=      self.num_attention_heads
                 snake_case       :   Union[str, Any]							=      (
                     batch,
                     num_encoder_attention_heads,
                     encoder_seq_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )
                 snake_case       :   Any							=      decoder_seq_length + 3
                 snake_case       :   List[Any]							=      (
                     batch,
                     num_decoder_attention_heads,
                     decoder_past_length,
                     self._config.hidden_size // num_decoder_attention_heads,
                 )

                 snake_case       :   Tuple							=      torch.cat(
                     [common_inputs["decoder_attention_mask"], torch.ones(snake_case__    ,						snake_case__ )]    ,						dim=1 )

                 snake_case       :   Union[str, Any]							=      []
                 # If the number of encoder and decoder layers are present in the model configuration, both are considered
                 snake_case				,					snake_case       :   Optional[int]							=      self.num_layers
                 snake_case       :   Optional[int]							=      min(snake_case__    ,						snake_case__ )
                 snake_case       :   int							=      max(snake_case__    ,						snake_case__ ) - min_num_layers
                 snake_case       :   Optional[Any]							=      "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

                 for _ in range(snake_case__ ):
                        common_inputs["past_key_values"].append(
                            (
                                torch.zeros(snake_case__ ),
                                torch.zeros(snake_case__ ),
                                torch.zeros(snake_case__ ),
                                torch.zeros(snake_case__ ),
                            ) )
                 # TODO: test this.
                 snake_case       :   Union[str, Any]							=      encoder_shape if remaining_side_name == "encoder" else decoder_shape
                 for _ in range(snake_case__    ,						snake_case__ ):
                        common_inputs["past_key_values"].append((torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) )
          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					PreTrainedTokenizer    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[TensorType] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
              snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )

          if self.use_past:
                 if not is_torch_available():
                        raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
                 else:
                        import torch
                 snake_case				,					snake_case       :   Optional[int]							=      common_inputs["input_ids"].shape
                 # Not using the same length for past_key_values
                 snake_case       :   Optional[Any]							=      seqlen + 2
                 snake_case				,					snake_case       :   Union[str, Any]							=      self.num_layers
                 snake_case				,					snake_case       :   Tuple							=      self.num_attention_heads
                 snake_case       :   Tuple							=      (
                     batch,
                     num_encoder_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // num_encoder_attention_heads,
                 )

                 snake_case       :   Any							=      common_inputs["attention_mask"].dtype
                 snake_case       :   int							=      torch.cat(
                     [common_inputs["attention_mask"], torch.ones(snake_case__    ,						snake_case__    ,						dtype=snake_case__ )]    ,						dim=1 )
                 snake_case       :   Dict							=      [
                     (torch.zeros(snake_case__ ), torch.zeros(snake_case__ )) for _ in range(snake_case__ )
                 ]
          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any]    ,						snake_case__		:					PreTrainedTokenizer    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[TensorType] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          snake_case       :   int							=      compute_effective_axis_dimension(
              snake_case__    ,						fixed_dimension=OnnxConfig.default_fixed_batch    ,						num_token_to_add=0 )

          # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
          snake_case       :   Optional[Any]							=      tokenizer.num_special_tokens_to_add(snake_case__ )
          snake_case       :   int							=      compute_effective_axis_dimension(
              snake_case__    ,						fixed_dimension=OnnxConfig.default_fixed_sequence    ,						num_token_to_add=snake_case__ )

          # Generate dummy inputs according to compute batch and sequence
          snake_case       :   Dict							=      [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
          snake_case       :   int							=      dict(tokenizer(snake_case__    ,						return_tensors=snake_case__ ) )
          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					PreTrainedTokenizer    ,						snake_case__		:					int = -1    ,						snake_case__		:					int = -1    ,						snake_case__		:					bool = False    ,						snake_case__		:					Optional[TensorType] = None    ,						)		->	Mapping[str, Any]:


          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
                 snake_case       :   int							=      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                     snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=snake_case__ )

          elif self.task == "causal-lm":
                 snake_case       :   str							=      self._generate_dummy_inputs_for_causal_lm(
                     snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=snake_case__ )
          else:
                 snake_case       :   Tuple							=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                     snake_case__    ,						batch_size=snake_case__    ,						seq_length=snake_case__    ,						is_pair=snake_case__    ,						framework=snake_case__ )

          return common_inputs



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					str    ,						snake_case__		:					List[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Optional[int] )		->	List[Any]:


          '''simple docstring'''


          if self.task in ["default", "seq2seq-lm"]:
                 snake_case       :   str							=      super()._flatten_past_key_values_(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )
          else:
                 snake_case       :   Tuple							=      super(snake_case__    ,						self )._flatten_past_key_values_(
                     snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ )







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class   UpperCAmelCase		(						A_ ):
   A__					:		Dict						     =     (DDIMParallelScheduler,)
   A__					:		Tuple						     =     (("eta", 0.0), ("num_inference_steps", 50))



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						**snake_case__		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**snake_case__ )
          return config



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						**snake_case__		:					Optional[int] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.scheduler_classes[0]
          snake_case       :   Any							=      self.get_scheduler_config(**snake_case__ )
          snake_case       :   Any							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Union[str, Any]							=      10, 0.0

          snake_case       :   List[Any]							=      self.dummy_model()
          snake_case       :   Any							=      self.dummy_sample_deter

          scheduler.set_timesteps(snake_case__ )

          for t in scheduler.timesteps:
                 snake_case       :   Optional[int]							=      model(snake_case__    ,						snake_case__ )
                 snake_case       :   List[str]							=      scheduler.step(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ ).prev_sample

          return sample



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


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


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

          snake_case       :   Optional[int]							=      self.scheduler_classes[0]
          snake_case       :   Optional[int]							=      self.get_scheduler_config(steps_offset=1 )
          snake_case       :   Union[str, Any]							=      scheduler_class(**snake_case__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Tuple:


          '''simple docstring'''


          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=snake_case__    ,						beta_end=snake_case__ )



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


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


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



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


          '''simple docstring'''


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



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


          '''simple docstring'''


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



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


          '''simple docstring'''


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



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


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


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



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


          '''simple docstring'''


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



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


          '''simple docstring'''


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



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


          '''simple docstring'''


          snake_case       :   Dict							=      self.scheduler_classes[0]
          snake_case       :   Tuple							=      self.get_scheduler_config()
          snake_case       :   Dict							=      scheduler_class(**snake_case__ )

          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 ) - 0.14771 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,						9_60 ) - 0.32460 ) ) < 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 ) - 0.00979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,						9_98 ) - 0.02 ) ) < 1e-5



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


          '''simple docstring'''


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

          snake_case				,					snake_case       :   Any							=      10, 0.0
          scheduler.set_timesteps(snake_case__ )

          snake_case       :   Optional[Any]							=      self.dummy_model()
          snake_case       :   str							=      self.dummy_sample_deter
          snake_case       :   Dict							=      self.dummy_sample_deter + 0.1
          snake_case       :   Dict							=      self.dummy_sample_deter - 0.1

          snake_case       :   Optional[Any]							=      samplea.shape[0]
          snake_case       :   str							=      torch.stack([samplea, samplea, samplea]    ,						dim=0 )
          snake_case       :   Tuple							=      torch.arange(snake_case__ )[0:3, None].repeat(1    ,						snake_case__ )

          snake_case       :   Tuple							=      model(samples.flatten(0    ,						1 )    ,						timesteps.flatten(0    ,						1 ) )
          snake_case       :   List[str]							=      scheduler.batch_step_no_noise(snake_case__    ,						timesteps.flatten(0    ,						1 )    ,						samples.flatten(0    ,						1 )    ,						snake_case__ )

          snake_case       :   Dict							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 1147.7904 ) < 1e-2
          assert abs(result_mean.item() - 0.4982 ) < 1e-3



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


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.full_loop()

          snake_case       :   Optional[Any]							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1e-2
          assert abs(result_mean.item() - 0.223967 ) < 1e-3



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


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(prediction_type="v_prediction" )

          snake_case       :   int							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[int]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1e-2
          assert abs(result_mean.item() - 0.0684 ) < 1e-3



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


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   str							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1e-2
          assert abs(result_mean.item() - 0.1951 ) < 1e-3



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


          '''simple docstring'''


          snake_case       :   int							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   Tuple							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1e-2
          assert abs(result_mean.item() - 0.1941 ) < 1e-3





