




import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME




def 							_UpperCAmelCase  (UpperCamelCase_ : Any       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Optional[Any]										=      [
        '''encoder.version''',
        '''decoder.version''',
        '''model.encoder.version''',
        '''model.decoder.version''',
        '''decoder.output_projection.weight''',
        '''_float_tensor''',
        '''encoder.embed_positions._float_tensor''',
        '''decoder.embed_positions._float_tensor''',
    ]
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase_      ,    UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : List[str]       ):
    '''simple docstring'''
    _lowerCAmelCase    :    int										=      emb.weight.shape
    _lowerCAmelCase    :    str										=      nn.Linear(UpperCamelCase_      ,    UpperCamelCase_      ,    bias=UpperCamelCase_       )
    _lowerCAmelCase    :    List[Any]										=      emb.weight.data
    return lin_layer




def 							_UpperCAmelCase  (UpperCamelCase_ : Any      ,    UpperCamelCase_ : Dict=None       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Dict										=      {}
    for old_key in state_dict.keys():
        _lowerCAmelCase    :    List[Any]										=      old_key
        if "moe_layer.experts." in key:
            if expert_idx is not None:
                _lowerCAmelCase    :    Tuple										=      key.replace("""moe_layer.experts.0"""      ,    F"ffn.experts.expert_{expert_idx}"       )
            else:
                _lowerCAmelCase    :    List[str]										=      key.replace("""moe_layer.experts."""      ,    """ffn.experts.expert_"""       )
        if "gate" in key:
            _lowerCAmelCase    :    Tuple										=      key.replace(""".moe_layer.gate.wg"""      ,    """.ffn.router.classifier"""       )
        if "fc2" and "experts" not in key:
            _lowerCAmelCase    :    int										=      key.replace(""".fc2."""      ,    """.ffn.fc2."""       )
        if "fc1" and "experts" not in key:
            _lowerCAmelCase    :    Union[str, Any]										=      key.replace(""".fc1."""      ,    """.ffn.fc1."""       )
        if ".encoder_attn." in key:
            _lowerCAmelCase    :    Any										=      key.replace(""".encoder_attn."""      ,    """.cross_attention."""       )
        if "encoder_attn_layer_norm" in key:
            _lowerCAmelCase    :    Optional[int]										=      key.replace("""encoder_attn_layer_norm"""      ,    """cross_attention_layer_norm"""       )
        if "final_layer_norm" in key:
            _lowerCAmelCase    :    Optional[Any]										=      key.replace("""final_layer_norm"""      ,    """ff_layer_norm"""       )
        _lowerCAmelCase    :    Optional[int]										=      state_dict[old_key]
    return new_dict




def 							_UpperCAmelCase  (UpperCamelCase_ : Tuple      ,    UpperCamelCase_ : List[str]      ,    UpperCamelCase_ : Optional[int]      ,    UpperCamelCase_ : Optional[Any]      ,    UpperCamelCase_ : str = WEIGHTS_NAME       ):
    '''simple docstring'''
    _lowerCAmelCase    :    Tuple										=      []
    _lowerCAmelCase    :    Union[str, Any]										=      0
    os.makedirs(UpperCamelCase_      ,    exist_ok=UpperCamelCase_       )

    for expert in range(UpperCamelCase_       ):
        _lowerCAmelCase    :    List[Any]										=      switch_checkpoint_path + F"-rank-{expert}.pt"
        if os.path.isfile(UpperCamelCase_       ):
            _lowerCAmelCase    :    str										=      torch.load(UpperCamelCase_       )['''model''']
            remove_ignore_keys_(UpperCamelCase_       )
            _lowerCAmelCase    :    Union[str, Any]										=      rename_fairseq_keys(UpperCamelCase_      ,    UpperCamelCase_       )
            _lowerCAmelCase    :    Any										=      os.path.join(
                UpperCamelCase_      ,    weights_name.replace(""".bin"""      ,    F"-{len(UpperCamelCase_       )+1:05d}-of-???.bin"       )       )
            torch.save(UpperCamelCase_      ,    UpperCamelCase_       )
            sharded_state_dicts.append(expert_state.keys()       )
            total_size += sum([value.numel() for key, value in expert_state.items()]       ) * dtype_byte_size(
                expert_state[list(UpperCamelCase_       )[0]].dtype       )

    # Add the last block
    _lowerCAmelCase    :    Any										=      os.path.join(UpperCamelCase_      ,    weights_name.replace(""".bin"""      ,    F"-{len(UpperCamelCase_       )+1:05d}-of-???.bin"       )       )
    _lowerCAmelCase    :    Tuple										=      torch.load(switch_checkpoint_path + """-shared.pt"""       )['''model''']
    remove_ignore_keys_(UpperCamelCase_       )
    _lowerCAmelCase    :    Union[str, Any]										=      rename_fairseq_keys(UpperCamelCase_      ,    UpperCamelCase_       )
    _lowerCAmelCase    :    Any										=      shared_weights['''decoder.embed_tokens.weight''']
    sharded_state_dicts.append(shared_weights.keys()       )

    # If we only have the shared weights (dummy model/experts saved on the same file)
    if len(UpperCamelCase_       ) == 1:
        _lowerCAmelCase    :    int										=      os.path.join(UpperCamelCase_      ,    UpperCamelCase_       )
        torch.save(UpperCamelCase_      ,    UpperCamelCase_       )
        return {weights_name: sharded_state_dicts[0]}, None
    else:
        torch.save(UpperCamelCase_      ,    UpperCamelCase_       )
    # Otherwise, let's build the index
    _lowerCAmelCase    :    Optional[Any]										=      {}
    for idx, shard in enumerate(UpperCamelCase_       ):
        _lowerCAmelCase    :    str										=      weights_name.replace(""".bin"""      ,    F"-{idx+1:05d}-of-{len(UpperCamelCase_       ):05d}.bin"       )
        _lowerCAmelCase    :    List[Any]										=      os.path.join(UpperCamelCase_      ,    weights_name.replace(""".bin"""      ,    F"-{idx+1:05d}-of-???.bin"       )       )
        os.rename(UpperCamelCase_      ,    os.path.join(UpperCamelCase_      ,    UpperCamelCase_       )       )
        for key in shard:
            _lowerCAmelCase    :    Optional[int]										=      shard_file

    # Add the metadata
    _lowerCAmelCase    :    Optional[int]										=      {'''total_size''': total_size}
    _lowerCAmelCase    :    str										=      {'''metadata''': metadata, '''weight_map''': weight_map}

    with open(os.path.join(UpperCamelCase_      ,    UpperCamelCase_       )      ,    """w"""      ,    encoding="""utf-8"""       ) as f:
        _lowerCAmelCase    :    Union[str, Any]										=      json.dumps(UpperCamelCase_      ,    indent=2      ,    sort_keys=UpperCamelCase_       ) + '''\n'''
        f.write(UpperCamelCase_       )

    return metadata, index


if __name__ == "__main__":
 _lowerCamelCase      :  Any      						=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--nllb_moe_checkpoint_path",
     default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
     type=str,
     required=False,
     help="Path to a directory containing a folder per layer. Follows the original Google format.",
 )
 parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
     type=str,
     required=False,
     help="Path to the output pytorch model.",
 )
 _lowerCamelCase      :  int      						=						parser.parse_args()
 _lowerCamelCase						, _lowerCamelCase      :  int      						=						shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     1_2_8,
     args.dtype,
 )

 _lowerCamelCase      :  Optional[int]      						=						NllbMoeConfig.from_pretrained(
     "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
 )
 config.save_pretrained(args.pytorch_dump_folder_path)
 _lowerCamelCase      :  Optional[int]      						=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]=0		,	**_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    str										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    List[str]										=      0.1 * sample
								_lowerCAmelCase    :    Tuple										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    str										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																new_scheduler.set_timesteps(_UpperCAmelCase      )
																# copy over dummy past residuals
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"



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





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

												# copy over dummy past residuals (must be after setting timesteps)
												_lowerCAmelCase    :    str										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    List[Any]										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(_UpperCAmelCase      )

																# copy over dummy past residual (must be after setting timesteps)
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



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





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      dict(self.forward_default_kwargs      )

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      0.1 * sample

												if num_inference_steps is not None and hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																scheduler.set_timesteps(_UpperCAmelCase      )
												elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																_lowerCAmelCase    :    Tuple										=      num_inference_steps

												# copy over dummy past residuals (must be done after set_timesteps)
												_lowerCAmelCase    :    List[Any]										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
												_lowerCAmelCase    :    List[str]										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												self.assertEqual(output_a.shape		,	sample.shape      )
												self.assertEqual(output_a.shape		,	output_a.shape      )

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

												self.assertEqual(output_a.shape		,	sample.shape      )
												self.assertEqual(output_a.shape		,	output_a.shape      )



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





								'''simple docstring'''




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



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





								'''simple docstring'''




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

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								scheduler.set_timesteps(10      )
								assert torch.equal(
								    scheduler.timesteps		,	torch.LongTensor(
								        [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]      )		,	)



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								for beta_start, beta_end in zip([0.0001, 0.001]		,	[0.002, 0.02]      ):
												self.check_over_configs(beta_start=_UpperCAmelCase		,	beta_end=_UpperCAmelCase      )



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





								'''simple docstring'''




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



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





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								for t in [1, 5, 10]:
												self.check_over_forward(time_step=_UpperCAmelCase      )



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





								'''simple docstring'''




								for t, num_inference_steps in zip([1, 5, 10]		,	[10, 50, 100]      ):
												self.check_over_forward(num_inference_steps=_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# before power of 3 fix, would error on first step, so we only need to do two
												for i, t in enumerate(scheduler.prk_timesteps[:2]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



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





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.step_plms(self.dummy_sample		,	1		,	self.dummy_sample      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 198.1318      ) < 1E-2
								assert abs(result_mean.item() - 0.2580      ) < 1E-3



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





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

								assert abs(result_sum.item() - 67.3986      ) < 1E-2
								assert abs(result_mean.item() - 0.0878      ) < 1E-3



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





								'''simple docstring'''




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

								assert abs(result_sum.item() - 230.0399      ) < 1E-2
								assert abs(result_mean.item() - 0.2995      ) < 1E-3



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





								'''simple docstring'''




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

								assert abs(result_sum.item() - 186.9482      ) < 1E-2
								assert abs(result_mean.item() - 0.2434      ) < 1E-3

