


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  Union[str, Any]       =		PegasusConfig
      snake_case__       :  Optional[int]       =		{}
      snake_case__       :  Tuple       =		'''gelu'''
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[str]=1_3  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=7  ,							SCREAMING_SNAKE_CASE__       : Dict=True  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=False  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=9_9  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=3_2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=2  ,							SCREAMING_SNAKE_CASE__       : List[Any]=4  ,							SCREAMING_SNAKE_CASE__       : Any=3_7  ,							SCREAMING_SNAKE_CASE__       : Any=0.1  ,							SCREAMING_SNAKE_CASE__       : Dict=0.1  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=4_0  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=2  ,							SCREAMING_SNAKE_CASE__       : Any=1  ,							SCREAMING_SNAKE_CASE__       : List[str]=0  ,							)	->    Tuple:
             a_      :	List[str]							=						parent
             a_      :	Tuple							=						batch_size
             a_      :	List[Any]							=						seq_length
             a_      :	Optional[int]							=						is_training
             a_      :	Any							=						use_labels
             a_      :	Dict							=						vocab_size
             a_      :	Tuple							=						hidden_size
             a_      :	List[str]							=						num_hidden_layers
             a_      :	Dict							=						num_attention_heads
             a_      :	int							=						intermediate_size

             a_      :	Tuple							=						hidden_dropout_prob
             a_      :	List[Any]							=						attention_probs_dropout_prob
             a_      :	Dict							=						max_position_embeddings
             a_      :	Union[str, Any]							=						eos_token_id
             a_      :	Union[str, Any]							=						pad_token_id
             a_      :	Dict							=						bos_token_id
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    str:
             a_      :	Optional[Any]							=						ids_tensor([self.batch_size, self.seq_length - 1]  ,							self.vocab_size  )
             a_      :	Optional[int]							=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )  ,							1  )
             a_      :	str							=						tf.concat([input_ids, eos_tensor]  ,							axis=1  )

             a_      :	List[str]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

             a_      :	List[str]							=						self.config_cls(
                 vocab_size=self.vocab_size  ,							d_model=self.hidden_size  ,							encoder_layers=self.num_hidden_layers  ,							decoder_layers=self.num_hidden_layers  ,							encoder_attention_heads=self.num_attention_heads  ,							decoder_attention_heads=self.num_attention_heads  ,							encoder_ffn_dim=self.intermediate_size  ,							decoder_ffn_dim=self.intermediate_size  ,							dropout=self.hidden_dropout_prob  ,							attention_dropout=self.attention_probs_dropout_prob  ,							max_position_embeddings=self.max_position_embeddings  ,							eos_token_ids=[2]  ,							bos_token_id=self.bos_token_id  ,							pad_token_id=self.pad_token_id  ,							decoder_start_token_id=self.pad_token_id  ,							**self.config_updates  ,							)
             a_      :	List[str]							=						prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             return config, inputs_dict

      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							SCREAMING_SNAKE_CASE__       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    List[str]:
             a_      :	Any							=						TFPegasusModel(config=SCREAMING_SNAKE_CASE__  ).get_decoder()
             a_      :	str							=						inputs_dict['input_ids']

             a_      :	Any							=						input_ids[:1, :]
             a_      :	List[Any]							=						inputs_dict['attention_mask'][:1, :]
             a_      :	Dict							=						inputs_dict['head_mask']
             a_      :	str							=						1

             # first forward pass
             a_      :	Dict							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							head_mask=SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						outputs.to_tuple()

             # create hypothetical next token and extent to next_input_ids
             a_      :	Dict							=						ids_tensor((self.batch_size, 3)  ,							config.vocab_size  )
             a_      :	Tuple							=						tf.cast(ids_tensor((self.batch_size, 3)  ,							2  )  ,							tf.inta  )

             # append to next input_ids and
             a_      :	Optional[Any]							=						tf.concat([input_ids, next_tokens]  ,							axis=-1  )
             a_      :	Optional[int]							=						tf.concat([attention_mask, next_attn_mask]  ,							axis=-1  )

             a_      :	List[str]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )[0]
             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  )[0]

             self.parent.assertEqual(next_tokens.shape[1]  ,							output_from_past.shape[1]  )

             # select random slice
             a_      :	int							=						int(ids_tensor((1,)  ,							output_from_past.shape[-1]  )  )
             a_      :	Any							=						output_from_no_past[:, -3:, random_slice_idx]
             a_      :	Any							=						output_from_past[:, :, random_slice_idx]

             # test that outputs are equal for slice
             tf.debugging.assert_near(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							rtol=1E-3  )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    int						,					__A				:    Optional[int]						,					__A				:    Optional[int]=None						,					__A				:    Optional[int]=None						,					__A				:    Dict=None						,					__A				:    Dict=None						,					__A				:    int=None						,					)			->	Any:

       """simple docstring"""

       if attention_mask is None:
              a_      :	Optional[Any]							=						tf.cast(tf.math.not_equal(__A						,					config.pad_token_id       )						,					tf.inta       )
       if decoder_attention_mask is None:
              a_      :	Optional[Any]							=						tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape						,					dtype=tf.inta       ),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]						,					config.pad_token_id       )						,					tf.inta       ),
                  ]						,					axis=-1						,					)
       if head_mask is None:
              a_      :	List[Any]							=						tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
       if decoder_head_mask is None:
              a_      :	Optional[int]							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       if cross_attn_head_mask is None:
              a_      :	List[Any]							=						tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }
@require_tf
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[Any]       =		(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
      snake_case__       :  Optional[Any]       =		(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
      snake_case__       :  Dict       =		(
          {
              '''conversational''': TFPegasusForConditionalGeneration,
              '''feature-extraction''': TFPegasusModel,
              '''summarization''': TFPegasusForConditionalGeneration,
              '''text2text-generation''': TFPegasusForConditionalGeneration,
              '''translation''': TFPegasusForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      snake_case__       :  List[Any]       =		True
      snake_case__       :  List[Any]       =		False
      snake_case__       :  List[Any]       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Union[str, Any]:
             a_      :	Optional[int]							=						TFPegasusModelTester(self  )
             a_      :	str							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             self.config_tester.run_common_tests()

      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    List[str]:
             a_      :	List[str]							=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__  )
@require_sentencepiece
@require_tokenizers
@require_tf
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      snake_case__       :  Any       =		[
          ''' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.''',
          ''' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ''',
      ]
      snake_case__       :  List[Any]       =		[
          '''California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'''
          ''' reduce the risk of wildfires.''',
          '''N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.''',
      ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
      snake_case__       :  int       =		'''google/pegasus-xsum'''
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[int]:
             return AutoTokenizer.from_pretrained(self.model_name  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Any:
             a_      :	Optional[int]							=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
             return model
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							**SCREAMING_SNAKE_CASE__       : List[str]  )	->    List[str]:
             a_      :	Optional[int]							=						self.translate_src_text(**SCREAMING_SNAKE_CASE__  )
             assert self.expected_text == generated_words
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							**SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    Tuple:
             a_      :	List[Any]							=						self.tokenizer(self.src_text  ,							**SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='tf'  )
             a_      :	int							=						self.model.generate(
                 model_inputs.input_ids  ,							attention_mask=model_inputs.attention_mask  ,							num_beams=2  ,							use_cache=SCREAMING_SNAKE_CASE__  ,							)
             a_      :	List[Any]							=						self.tokenizer.batch_decode(generated_ids.numpy()  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             return generated_words

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             self._assert_generated_batch_equal_expected()



import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      snake_case__       :  Optional[Any]       =		(DDIMParallelScheduler,)
      snake_case__       :  Union[str, Any]       =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             a_      :	Union[str, Any]							=						{
                 'num_train_timesteps': 1_0_0_0,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
                 'clip_sample': True,
             }

             config.update(**SCREAMING_SNAKE_CASE__  )
             return config
      def  SCREAMING_SNAKE_CASE							( self       : str  ,							**SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Any							=						self.scheduler_classes[0]
             a_      :	str							=						self.get_scheduler_config(**SCREAMING_SNAKE_CASE__  )
             a_      :	Optional[Any]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						1_0, 0.0

             a_      :	str							=						self.dummy_model()
             a_      :	Dict							=						self.dummy_sample_deter

             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             for t in scheduler.timesteps:
                    a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[Any]							=						scheduler.step(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ).prev_sample

             return sample
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Any:
             for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                    self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    List[Any]:
             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=SCREAMING_SNAKE_CASE__  )

             a_      :	Tuple							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config(steps_offset=1  )
             a_      :	str							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )
             scheduler.set_timesteps(5  )
             assert torch.equal(scheduler.timesteps  ,							torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[str]:
             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=SCREAMING_SNAKE_CASE__  ,							beta_end=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    List[str]:
             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    Tuple:
             for clip_sample in [True, False]:
                    self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Union[str, Any]:
             for timestep_spacing in ["trailing", "leading"]:
                    self.check_over_configs(timestep_spacing=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    int:
             for rescale_betas_zero_snr in [True, False]:
                    self.check_over_configs(rescale_betas_zero_snr=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE__  )
             for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "v_prediction"]:
                           self.check_over_configs(
                               thresholding=SCREAMING_SNAKE_CASE__  ,							prediction_type=SCREAMING_SNAKE_CASE__  ,							sample_max_value=SCREAMING_SNAKE_CASE__  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             for t in [1, 1_0, 4_9]:
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Dict:
             for t, num_inference_steps in zip([1, 1_0, 5_0]  ,							[1_0, 5_0, 5_0_0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							num_inference_steps=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Optional[Any]:
             for t, eta in zip([1, 1_0, 4_9]  ,							[0.0, 0.5, 1.0]  ):
                    self.check_over_forward(time_step=SCREAMING_SNAKE_CASE__  ,							eta=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Union[str, Any]							=						self.scheduler_classes[0]
             a_      :	List[Any]							=						self.get_scheduler_config()
             a_      :	Dict							=						scheduler_class(**SCREAMING_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_2_0  ,							4_0_0  ) - 0.14771  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_8_0  ,							9_6_0  ) - 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_8_7  ,							4_8_6  ) - 0.00979  )  ) < 1E-5
             assert torch.sum(torch.abs(scheduler._get_variance(9_9_9  ,							9_9_8  ) - 0.02  )  ) < 1E-5
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Dict:
             a_      :	Dict							=						self.scheduler_classes[0]
             a_      :	int							=						self.get_scheduler_config()
             a_      :	List[str]							=						scheduler_class(**SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	int							=						1_0, 0.0
             scheduler.set_timesteps(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						self.dummy_model()
             a_      :	Optional[int]							=						self.dummy_sample_deter
             a_      :	Optional[Any]							=						self.dummy_sample_deter + 0.1
             a_      :	Any							=						self.dummy_sample_deter - 0.1

             a_      :	List[Any]							=						samplea.shape[0]
             a_      :	List[str]							=						torch.stack([samplea, samplea, samplea]  ,							dim=0  )
             a_      :	Union[str, Any]							=						torch.arange(SCREAMING_SNAKE_CASE__  )[0:3, None].repeat(1  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	int							=						model(samples.flatten(0  ,							1  )  ,							timesteps.flatten(0  ,							1  )  )
             a_      :	Optional[Any]							=						scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE__  ,							timesteps.flatten(0  ,							1  )  ,							samples.flatten(0  ,							1  )  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[int]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Optional[int]							=						torch.mean(torch.abs(SCREAMING_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]  )	->    Tuple:
             a_      :	Tuple							=						self.full_loop()

             a_      :	List[str]							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Any							=						torch.mean(torch.abs(SCREAMING_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       : int  )	->    List[str]:
             a_      :	Optional[int]							=						self.full_loop(prediction_type='v_prediction'  )

             a_      :	str							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	Union[str, Any]							=						torch.mean(torch.abs(SCREAMING_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       : str  )	->    Optional[Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Dict							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	int							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_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       : Any  )	->    Union[str, Any]:
             # We specify different beta, so that the first alpha is 0.99
             a_      :	Tuple							=						self.full_loop(set_alpha_to_one=SCREAMING_SNAKE_CASE__  ,							beta_start=0.01  )
             a_      :	Dict							=						torch.sum(torch.abs(SCREAMING_SNAKE_CASE__  )  )
             a_      :	str							=						torch.mean(torch.abs(SCREAMING_SNAKE_CASE__  )  )

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


