





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


__lowercase  :   Optional[int]						      =logging.get_logger(__name__)

__lowercase  :   Optional[Any]						      ={
    """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      A       (	__lowercase						):
      _snake_case							     ='''bart'''
      _snake_case							     =['''past_key_values''']
      _snake_case							     ={'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



      def __init__(	self:						List[Any]		,  _lowerCAmelCase:						List[Any]=5_0265		,  _lowerCAmelCase:						List[Any]=1024		,  _lowerCAmelCase:						Optional[Any]=12		,  _lowerCAmelCase:						List[Any]=4096		,  _lowerCAmelCase:						str=16		,  _lowerCAmelCase:						Union[str, Any]=12		,  _lowerCAmelCase:						str=4096		,  _lowerCAmelCase:						Tuple=16		,  _lowerCAmelCase:						int=0.0		,  _lowerCAmelCase:						Optional[Any]=0.0		,  _lowerCAmelCase:						Dict="gelu"		,  _lowerCAmelCase:						Tuple=1024		,  _lowerCAmelCase:						Optional[Any]=0.1		,  _lowerCAmelCase:						List[Any]=0.0		,  _lowerCAmelCase:						int=0.0		,  _lowerCAmelCase:						int=0.02		,  _lowerCAmelCase:						Optional[int]=0.0		,  _lowerCAmelCase:						Union[str, Any]=False		,  _lowerCAmelCase:						List[Any]=True		,  _lowerCAmelCase:						str=3		,  _lowerCAmelCase:						List[str]=1		,  _lowerCAmelCase:						Union[str, Any]=0		,  _lowerCAmelCase:						str=2		,  _lowerCAmelCase:						List[Any]=True		,  _lowerCAmelCase:						int=2		,  _lowerCAmelCase:						List[Any]=2		,  **_lowerCAmelCase:						Optional[int]		,  )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=vocab_size
         UpperCAmelCase_											=max_position_embeddings
         UpperCAmelCase_											=d_model
         UpperCAmelCase_											=encoder_ffn_dim
         UpperCAmelCase_											=encoder_layers
         UpperCAmelCase_											=encoder_attention_heads
         UpperCAmelCase_											=decoder_ffn_dim
         UpperCAmelCase_											=decoder_layers
         UpperCAmelCase_											=decoder_attention_heads
         UpperCAmelCase_											=dropout
         UpperCAmelCase_											=attention_dropout
         UpperCAmelCase_											=activation_dropout
         UpperCAmelCase_											=activation_function
         UpperCAmelCase_											=init_std
         UpperCAmelCase_											=encoder_layerdrop
         UpperCAmelCase_											=decoder_layerdrop
         UpperCAmelCase_											=classifier_dropout
         UpperCAmelCase_											=use_cache
         UpperCAmelCase_											=encoder_layers
         UpperCAmelCase_											=scale_embedding  # scale factor will be sqrt(d_model) if True

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

         # ensure backward compatibility for BART CNN models
         if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=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      A       (	__lowercase						):



      @property
      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Mapping[str, Mapping[int, str]]:
         '''simple docstring'''

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

            if self.use_past:
               UpperCAmelCase_											={0: "batch"}
               UpperCAmelCase_											={0: "batch", 1: "past_decoder_sequence + sequence"}
            else:
               UpperCAmelCase_											={0: "batch", 1: "decoder_sequence"}
               UpperCAmelCase_											={0: "batch", 1: "decoder_sequence"}

            if self.use_past:
               self.fill_with_past_key_values_(_lowerCAmelCase		,  direction="inputs"       )
         elif self.task == "causal-lm":
            # TODO: figure this case out.
            UpperCAmelCase_											=OrderedDict(
                [
                    ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                    ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ]       )
            if self.use_past:
               UpperCAmelCase_						,       UpperCAmelCase_											=self.num_layers
               for i in range(_lowerCAmelCase       ):
                  UpperCAmelCase_											={0: "batch", 2: "past_sequence + sequence"}
                  UpperCAmelCase_											={0: "batch", 2: "past_sequence + sequence"}
         else:
            UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Dict       )						->    Mapping[str, Mapping[int, str]]:
         '''simple docstring'''

         if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_											=super().outputs
         else:
            UpperCAmelCase_											=super(_lowerCAmelCase		,  self       ).outputs
            if self.use_past:
               UpperCAmelCase_						,       UpperCAmelCase_											=self.num_layers
               for i in range(_lowerCAmelCase       ):
                  UpperCAmelCase_											={0: "batch", 2: "past_sequence + sequence"}
                  UpperCAmelCase_											={0: "batch", 2: "past_sequence + sequence"}
         return common_outputs



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						PreTrainedTokenizer		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						Optional[TensorType] = None		,  )						->    Mapping[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

         # Generate decoder inputs
         UpperCAmelCase_											=seq_length if not self.use_past else 1
         UpperCAmelCase_											=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											={F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
         UpperCAmelCase_											=dict(**_lowerCAmelCase		,  **_lowerCAmelCase       )

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

            UpperCAmelCase_											=torch.cat(
                [common_inputs["decoder_attention_mask"], torch.ones(_lowerCAmelCase		,  _lowerCAmelCase       )]		,  dim=1       )

            UpperCAmelCase_											=[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            UpperCAmelCase_						,       UpperCAmelCase_											=self.num_layers
            UpperCAmelCase_											=min(_lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=max(_lowerCAmelCase		,  _lowerCAmelCase       ) - min_num_layers
            UpperCAmelCase_											="encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						PreTrainedTokenizer		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						Optional[TensorType] = None		,  )						->    Mapping[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )

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

            UpperCAmelCase_											=common_inputs["attention_mask"].dtype
            UpperCAmelCase_											=torch.cat(
                [common_inputs["attention_mask"], torch.ones(_lowerCAmelCase		,  _lowerCAmelCase		,  dtype=_lowerCAmelCase       )]		,  dim=1       )
            UpperCAmelCase_											=[
                (torch.zeros(_lowerCAmelCase       ), torch.zeros(_lowerCAmelCase       )) for _ in range(_lowerCAmelCase       )
            ]
         return common_inputs



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						PreTrainedTokenizer		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						Optional[TensorType] = None		,  )						->    Mapping[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=compute_effective_axis_dimension(
             _lowerCAmelCase		,  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
         UpperCAmelCase_											=tokenizer.num_special_tokens_to_add(_lowerCAmelCase       )
         UpperCAmelCase_											=compute_effective_axis_dimension(
             _lowerCAmelCase		,  fixed_dimension=OnnxConfig.default_fixed_sequence		,  num_token_to_add=_lowerCAmelCase       )

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



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						PreTrainedTokenizer		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						int = -1		,  _lowerCAmelCase:						bool = False		,  _lowerCAmelCase:						Optional[TensorType] = None		,  )						->    Mapping[str, Any]:
         '''simple docstring'''

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

         elif self.task == "causal-lm":
            UpperCAmelCase_											=self._generate_dummy_inputs_for_causal_lm(
                _lowerCAmelCase		,  batch_size=_lowerCAmelCase		,  seq_length=_lowerCAmelCase		,  is_pair=_lowerCAmelCase		,  framework=_lowerCAmelCase       )
         else:
            UpperCAmelCase_											=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                _lowerCAmelCase		,  batch_size=_lowerCAmelCase		,  seq_length=_lowerCAmelCase		,  is_pair=_lowerCAmelCase		,  framework=_lowerCAmelCase       )

         return common_inputs






      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Tuple       )						->    int:
         '''simple docstring'''

         if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_											=super()._flatten_past_key_values_(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )
         else:
            UpperCAmelCase_											=super(_lowerCAmelCase		,  self       )._flatten_past_key_values_(
                _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       )











import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDIMParallelScheduler,)
      _snake_case							     =(('''eta''', 0.0), ('''num_inference_steps''', 50))



      def 	lowerCAmelCase__    (	self:						str		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						int		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(**_lowerCAmelCase       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter

         scheduler.set_timesteps(_lowerCAmelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       ).prev_sample

         return sample



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

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

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(steps_offset=1       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps		,  torch.LongTensor([801, 601, 401, 201, 1]       )       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''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 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''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 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         for t, num_inference_steps in zip([1, 10, 50]		,  [10, 50, 500]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  num_inference_steps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Tuple:
         '''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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=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(420		,  400       ) - 0.1_47_71       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980		,  960       ) - 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(487		,  486       ) - 0.0_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		,  998       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0
         scheduler.set_timesteps(_lowerCAmelCase       )

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

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

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

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop()

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(prediction_type="v_prediction"       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=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 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=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




