
import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
 import os

 # The slow tests are often failing with OOM error on GPU
 # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
 # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
 _lowercase											=    """platform"""
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class 			lowercase_		:
       __lowerCamelCase				       =						PegasusConfig
       __lowerCamelCase				       =						{}
       __lowerCamelCase				       =						"gelu"


       def __init__(    self						,       __A						,       __A=13						,       __A=7						,       __A=True						,       __A=False						,       __A=99						,       __A=32						,       __A=5						,       __A=4						,       __A=37						,       __A=0.1						,       __A=0.1						,       __A=20						,       __A=2						,       __A=1						,       __A=0						,       )						->    Any:
              SCREAMING_SNAKE_CASE_							:				Any		=parent
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=batch_size
              SCREAMING_SNAKE_CASE_							:				Dict		=seq_length
              SCREAMING_SNAKE_CASE_							:				str		=is_training
              SCREAMING_SNAKE_CASE_							:				int		=use_labels
              SCREAMING_SNAKE_CASE_							:				int		=vocab_size
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				Dict		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				List[str]		=intermediate_size

              SCREAMING_SNAKE_CASE_							:				Any		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Any		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				Dict		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				int		=eos_token_id
              SCREAMING_SNAKE_CASE_							:				List[str]		=pad_token_id
              SCREAMING_SNAKE_CASE_							:				str		=bos_token_id


       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				int		=ids_tensor([self.batch_size, self.seq_length - 1]						,       self.vocab_size				).clip(3						,       self.vocab_size				)
              SCREAMING_SNAKE_CASE_							:				Any		=np.expand_dims(np.array([self.eos_token_id] * self.batch_size				)						,       1				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=np.concatenate([input_ids, eos_tensor]						,       axis=1				)

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

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=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						,       )
              SCREAMING_SNAKE_CASE_							:				List[Any]		=prepare_pegasus_inputs_dict(__A						,       __A						,       __A				)
              return config, inputs_dict


       def 	_snake_case				(    self						,       __A						,       __A						,       __A				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				int		=20
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model_class_name(__A				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model.encode(inputs_dict['''input_ids''']				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[int]		=(
                  inputs_dict['''decoder_input_ids'''],
                  inputs_dict['''decoder_attention_mask'''],
              )

              SCREAMING_SNAKE_CASE_							:				Tuple		=model.init_cache(decoder_input_ids.shape[0]						,       __A						,       __A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,       dtype='''i4'''				)

              SCREAMING_SNAKE_CASE_							:				str		=jnp.broadcast_to(
                  jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]						,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,       )
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model.decode(
                  decoder_input_ids[:, :-1]						,       __A						,       decoder_attention_mask=__A						,       past_key_values=__A						,       decoder_position_ids=__A						,       )

              SCREAMING_SNAKE_CASE_							:				Any		=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,       dtype='''i4'''				)
              SCREAMING_SNAKE_CASE_							:				int		=model.decode(
                  decoder_input_ids[:, -1:]						,       __A						,       decoder_attention_mask=__A						,       past_key_values=outputs_cache.past_key_values						,       decoder_position_ids=__A						,       )

              SCREAMING_SNAKE_CASE_							:				Tuple		=model.decode(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Any		=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
              self.parent.assertTrue(diff < 1e-3						,       msg=F'Max diff is {diff}'				)





       def 	_snake_case				(    self						,       __A						,       __A						,       __A				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=20
              SCREAMING_SNAKE_CASE_							:				List[str]		=model_class_name(__A				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=model.encode(inputs_dict['''input_ids''']				)

              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=(
                  inputs_dict['''decoder_input_ids'''],
                  inputs_dict['''decoder_attention_mask'''],
              )

              SCREAMING_SNAKE_CASE_							:				Tuple		=jnp.concatenate(
                  [
                      decoder_attention_mask,
                      jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])				),
                  ]						,       axis=-1						,       )

              SCREAMING_SNAKE_CASE_							:				Dict		=model.init_cache(decoder_input_ids.shape[0]						,       __A						,       __A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=jnp.broadcast_to(
                  jnp.arange(decoder_input_ids.shape[-1] - 1				)[None, :]						,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,       )

              SCREAMING_SNAKE_CASE_							:				List[str]		=model.decode(
                  decoder_input_ids[:, :-1]						,       __A						,       decoder_attention_mask=__A						,       past_key_values=__A						,       decoder_position_ids=__A						,       )
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,       dtype='''i4'''				)
              SCREAMING_SNAKE_CASE_							:				Dict		=model.decode(
                  decoder_input_ids[:, -1:]						,       __A						,       past_key_values=outputs_cache.past_key_values						,       decoder_attention_mask=__A						,       decoder_position_ids=__A						,       )

              SCREAMING_SNAKE_CASE_							:				int		=model.decode(__A						,       __A						,       decoder_attention_mask=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])				)				)
              self.parent.assertTrue(diff < 1e-3						,       msg=F'Max diff is {diff}'				)






def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     List[Any]=None			,							UpperCAmelCase_	:     int=None			,							)			->						Optional[Any]:
       if attention_mask is None:
              SCREAMING_SNAKE_CASE_							:				Tuple		=np.not_equal(UpperCAmelCase_			,							config.pad_token_id				).astype(np.inta				)
       if decoder_attention_mask is None:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=np.concatenate(
                  [
                      np.ones(decoder_input_ids[:, :1].shape			,							dtype=np.inta				),
                      np.not_equal(decoder_input_ids[:, 1:]			,							config.pad_token_id				).astype(np.inta				),
                  ]			,							axis=-1			,							)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
       }


@require_flax
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						(
           (
               FlaxPegasusForConditionalGeneration,
               FlaxPegasusModel,
           )
           if is_flax_available()
           else ()
       )
       __lowerCamelCase				       =						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				str		=FlaxPegasusModelTester(self				)
              SCREAMING_SNAKE_CASE_							:				Dict		=ConfigTester(self						,       config_class=__A				)


       def 	_snake_case				(    self				)						->    List[Any]:
              self.config_tester.run_common_tests()


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Tuple		=self.model_tester.prepare_config_and_inputs_for_common()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward(__A						,       __A						,       __A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.model_tester.prepare_config_and_inputs_for_common()
              for model_class in self.all_model_classes:
                     self.model_tester.check_use_cache_forward_with_attn_mask(__A						,       __A						,       __A				)


       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__				):
                            SCREAMING_SNAKE_CASE_							:				List[str]		=self._prepare_for_class(__A						,       __A				)
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model_class(__A				)

                            @jax.jit
                            def encode_jitted(__A						,       __A=None						,       **__A				):
                                   return model.encode(input_ids=__A						,       attention_mask=__A				)

                            with self.subTest('''JIT Enabled'''				):
                                   SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=encode_jitted(**__A				).to_tuple()

                            with self.subTest('''JIT Disabled'''				):
                                   with jax.disable_jit():
                                          SCREAMING_SNAKE_CASE_							:				Any		=encode_jitted(**__A				).to_tuple()

                            self.assertEqual(len(__A				)						,       len(__A				)				)
                            for jitted_output, output in zip(__A						,       __A				):
                                   self.assertEqual(jitted_output.shape						,       output.shape				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				str		=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__				):
                            SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model_class(__A				)
                            SCREAMING_SNAKE_CASE_							:				str		=model.encode(inputs_dict['''input_ids''']						,       inputs_dict['''attention_mask''']				)

                            SCREAMING_SNAKE_CASE_							:				str		={
                                '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                                '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                                '''encoder_outputs''': encoder_outputs,
                            }

                            @jax.jit
                            def decode_jitted(__A						,       __A						,       __A				):
                                   return model.decode(
                                       decoder_input_ids=__A						,       decoder_attention_mask=__A						,       encoder_outputs=__A						,       )

                            with self.subTest('''JIT Enabled'''				):
                                   SCREAMING_SNAKE_CASE_							:				int		=decode_jitted(**__A				).to_tuple()

                            with self.subTest('''JIT Disabled'''				):
                                   with jax.disable_jit():
                                          SCREAMING_SNAKE_CASE_							:				Tuple		=decode_jitted(**__A				).to_tuple()

                            self.assertEqual(len(__A				)						,       len(__A				)				)
                            for jitted_output, output in zip(__A						,       __A				):
                                   self.assertEqual(jitted_output.shape						,       output.shape				)


       @slow
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for model_class_name in self.all_model_classes:
                     SCREAMING_SNAKE_CASE_							:				Any		=model_class_name.from_pretrained('''google/pegasus-large'''						,       from_pt=__A				)
                     SCREAMING_SNAKE_CASE_							:				Dict		=np.ones((1, 1)				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=model(__A				)
                     self.assertIsNotNone(__A				)





       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				List[Any]		=FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''				)

              SCREAMING_SNAKE_CASE_							:				Dict		=[
                  ''' 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!" ''',
              ]

              SCREAMING_SNAKE_CASE_							:				Any		=[
                  '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
                  '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
              ]

              SCREAMING_SNAKE_CASE_							:				Tuple		=tokenizer(__A						,       return_tensors='''np'''						,       truncation=__A						,       max_length=512						,       padding=__A				)
              SCREAMING_SNAKE_CASE_							:				int		=model.generate(**__A						,       num_beams=2				).sequences
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.batch_decode(__A						,       skip_special_tokens=__A				)
              assert tgt_text == decoded


import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						(DPMSolverSDEScheduler,)
       __lowerCamelCase				       =						1_0


       def 	_snake_case				(    self						,       **__A				)						->    Union[str, Any]:
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={
                  '''num_train_timesteps''': 1_100,
                  '''beta_start''': 0.0_001,
                  '''beta_end''': 0.02,
                  '''beta_schedule''': '''linear''',
                  '''noise_sampler_seed''': 0,
              }

              config.update(**__A				)
              return config


       def 	_snake_case				(    self				)						->    Any:
              for timesteps in [10, 50, 100, 1_000]:
                     self.check_over_configs(num_train_timesteps=__A				)


       def 	_snake_case				(    self				)						->    Union[str, Any]:
              for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,       [0.0_002, 0.002, 0.02]				):
                     self.check_over_configs(beta_start=__A						,       beta_end=__A				)


       def 	_snake_case				(    self				)						->    Tuple:
              for schedule in ["linear", "scaled_linear"]:
                     self.check_over_configs(beta_schedule=__A				)


       def 	_snake_case				(    self				)						->    List[str]:
              for prediction_type in ["epsilon", "v_prediction"]:
                     self.check_over_configs(prediction_type=__A				)


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				int		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Tuple		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				str		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.47_821_044_921_875				) < 1e-2
                     assert abs(result_mean.item() - 0.2_178_705_964_565_277				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_352_111_816_406				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_906_892_299_652				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3


       def 	_snake_case				(    self				)						->    Dict:
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.get_scheduler_config(prediction_type='''v_prediction'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.dummy_sample_deter * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				Tuple		=sample.to(__A				)

              for i, t in enumerate(scheduler.timesteps				):
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				str		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				Tuple		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 124.77_149_200_439_453				) < 1e-2
                     assert abs(result_mean.item() - 0.16_226_289_014_816_284				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 128.1_663_360_595_703				) < 1e-2
                     assert abs(result_mean.item() - 0.16_688_326_001_167_297				) < 1e-3
              else:
                     assert abs(result_sum.item() - 119.8_487_548_828_125				) < 1e-2
                     assert abs(result_mean.item() - 0.1_560_530_662_536_621				) < 1e-3


       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				List[str]		=scheduler_class(**__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				int		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Dict		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				int		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 167.46_957_397_460_938				) < 1e-2
                     assert abs(result_mean.item() - 0.21_805_934_607_982_635				) < 1e-3
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 171.59_353_637_695_312				) < 1e-2
                     assert abs(result_mean.item() - 0.22_342_908_382_415_771				) < 1e-3
              else:
                     assert abs(result_sum.item() - 162.52_383_422_851_562				) < 1e-2
                     assert abs(result_mean.item() - 0.211_619_570_851_326				) < 1e-3





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_							:				List[str]		=self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_							:				Dict		=scheduler_class(**__A						,       use_karras_sigmas=__A				)

              scheduler.set_timesteps(self.num_inference_steps						,       device=__A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.dummy_model()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.dummy_sample_deter.to(__A				) * scheduler.init_noise_sigma
              SCREAMING_SNAKE_CASE_							:				str		=sample.to(__A				)

              for t in scheduler.timesteps:
                     SCREAMING_SNAKE_CASE_							:				Any		=scheduler.scale_model_input(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=model(__A						,       __A				)

                     SCREAMING_SNAKE_CASE_							:				int		=scheduler.step(__A						,       __A						,       __A				)
                     SCREAMING_SNAKE_CASE_							:				List[str]		=output.prev_sample

              SCREAMING_SNAKE_CASE_							:				str		=torch.sum(torch.abs(__A				)				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=torch.mean(torch.abs(__A				)				)

              if torch_device in ["mps"]:
                     assert abs(result_sum.item() - 176.66_974_135_742_188				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              elif torch_device in ["cuda"]:
                     assert abs(result_sum.item() - 177.63_653_564_453_125				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
              else:
                     assert abs(result_sum.item() - 170.3_135_223_388_672				) < 1e-2
                     assert abs(result_mean.item() - 0.23_003_872_730_981_811				) < 1e-2
