

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_            = get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
@require_tokenizers
class 			a					(		__lowerCamelCase							,	unittest.TestCase			):
						_lowercase        =     ReformerTokenizer
						_lowercase        =     ReformerTokenizerFast
						_lowercase        =     True
						_lowercase        =     False
						_lowercase        =     True


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										super().setUp()

										_UpperCAmelCase    :						List[str]							=      ReformerTokenizer(__lowercase  ,   keep_accents=__lowercase		)
										tokenizer.save_pretrained(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      '''<s>'''
										_UpperCAmelCase    :						Tuple							=      1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase		)  ,   __lowercase		)
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase		)  ,   __lowercase		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      list(self.get_tokenizer().get_vocab().keys()		)

										self.assertEqual(vocab_keys[0]  ,   "<unk>"		)
										self.assertEqual(vocab_keys[1]  ,   "<s>"		)
										self.assertEqual(vocab_keys[-1]  ,   "j"		)
										self.assertEqual(len(__lowercase		)  ,   1000		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.assertEqual(self.get_tokenizer().vocab_size  ,   1000		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not self.test_rust_tokenizer:
														return

										_UpperCAmelCase    :						List[str]							=      self.get_tokenizer()
										_UpperCAmelCase    :						Tuple							=      self.get_rust_tokenizer()

										_UpperCAmelCase    :						Union[str, Any]							=      '''I was born in 92000, and this is falsé.'''

										_UpperCAmelCase    :						List[Any]							=      tokenizer.tokenize(__lowercase		)
										_UpperCAmelCase    :						Optional[Any]							=      rust_tokenizer.tokenize(__lowercase		)
										self.assertListEqual(__lowercase  ,   __lowercase		)

										_UpperCAmelCase    :						int							=      tokenizer.encode(__lowercase  ,   add_special_tokens=__lowercase		)
										_UpperCAmelCase    :						List[Any]							=      rust_tokenizer.encode(__lowercase  ,   add_special_tokens=__lowercase		)
										self.assertListEqual(__lowercase  ,   __lowercase		)

										_UpperCAmelCase    :						Optional[int]							=      self.get_rust_tokenizer()
										_UpperCAmelCase    :						Any							=      tokenizer.encode(__lowercase		)
										_UpperCAmelCase    :						List[str]							=      rust_tokenizer.encode(__lowercase		)
										self.assertListEqual(__lowercase  ,   __lowercase		)


						def     _UpperCAmelCase			(    self  ,   A_=15		):







										'''simple docstring'''


										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																		_UpperCAmelCase    :						Optional[int]							=      self.rust_tokenizer_class.from_pretrained(__lowercase  ,   **__lowercase		)

																		# Simple input
																		_UpperCAmelCase    :						List[str]							=      '''This is a simple input'''
																		_UpperCAmelCase    :						str							=      ['''This is a simple input 1''', '''This is a simple input 2''']
																		_UpperCAmelCase    :						Dict							=      ('''This is a simple input''', '''This is a pair''')
																		_UpperCAmelCase    :						Optional[int]							=      [
																		    ('''This is a simple input 1''', '''This is a simple input 2'''),
																		    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																		]

																		# Simple input tests
																		self.assertRaises(__lowercase  ,   tokenizer_r.encode  ,   __lowercase  ,   max_length=__lowercase  ,   padding="max_length"		)

																		# Simple input
																		self.assertRaises(__lowercase  ,   tokenizer_r.encode_plus  ,   __lowercase  ,   max_length=__lowercase  ,   padding="max_length"		)

																		# Simple input
																		self.assertRaises(
																		    __lowercase  ,   tokenizer_r.batch_encode_plus  ,   __lowercase  ,   max_length=__lowercase  ,   padding="max_length"  ,   )

																		# Pair input
																		self.assertRaises(__lowercase  ,   tokenizer_r.encode  ,   __lowercase  ,   max_length=__lowercase  ,   padding="max_length"		)

																		# Pair input
																		self.assertRaises(__lowercase  ,   tokenizer_r.encode_plus  ,   __lowercase  ,   max_length=__lowercase  ,   padding="max_length"		)

																		# Pair input
																		self.assertRaises(
																		    __lowercase  ,   tokenizer_r.batch_encode_plus  ,   __lowercase  ,   max_length=__lowercase  ,   padding="max_length"  ,   )


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      ReformerTokenizer(__lowercase  ,   keep_accents=__lowercase		)

										_UpperCAmelCase    :						Union[str, Any]							=      tokenizer.tokenize("This is a test"		)
										self.assertListEqual(__lowercase  ,   ["▁This", "▁is", "▁a", "▁t", "est"]		)

										self.assertListEqual(
										    tokenizer.convert_tokens_to_ids(__lowercase		)  ,   [285, 46, 10, 170, 382]  ,   )

										_UpperCAmelCase    :						List[Any]							=      tokenizer.tokenize("I was born in 92000, and this is falsé."		)
										self.assertListEqual(
										    __lowercase  ,   [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "9",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "é",
										        ".",
										    ]  ,   )
										_UpperCAmelCase    :						Any							=      tokenizer.convert_tokens_to_ids(__lowercase		)
										self.assertListEqual(
										    __lowercase  ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]  ,   )

										_UpperCAmelCase    :						str							=      tokenizer.convert_ids_to_tokens(__lowercase		)
										self.assertListEqual(
										    __lowercase  ,   [
										        SPIECE_UNDERLINE + "I",
										        SPIECE_UNDERLINE + "was",
										        SPIECE_UNDERLINE + "b",
										        "or",
										        "n",
										        SPIECE_UNDERLINE + "in",
										        SPIECE_UNDERLINE + "",
										        "<unk>",
										        "2",
										        "0",
										        "0",
										        "0",
										        ",",
										        SPIECE_UNDERLINE + "and",
										        SPIECE_UNDERLINE + "this",
										        SPIECE_UNDERLINE + "is",
										        SPIECE_UNDERLINE + "f",
										        "al",
										        "s",
										        "<unk>",
										        ".",
										    ]  ,   )


						@cached_property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      '''Hello World!'''
										_UpperCAmelCase    :						int							=      [126, 32, 262, 152, 38, 72, 287]

										self.assertListEqual(__lowercase  ,   self.big_tokenizer.encode(__lowercase		)		)


						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      (
										    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
										    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
										)
										_UpperCAmelCase    :						List[Any]							=      [
										    108,
										    265,
										    24,
										    111,
										    4,
										    258,
										    156,
										    35,
										    28,
										    275,
										    3,
										    259,
										    297,
										    260,
										    84,
										    4,
										    35,
										    110,
										    44,
										    8,
										    259,
										    91,
										    268,
										    21,
										    11,
										    209,
										    274,
										    109,
										    266,
										    277,
										    117,
										    86,
										    93,
										    315,
										    258,
										    278,
										    258,
										    277,
										    258,
										    0,
										    258,
										    288,
										    258,
										    319,
										    258,
										    0,
										    258,
										    0,
										    258,
										    0,
										    258,
										    0,
										    258,
										    287,
										    258,
										    315,
										    258,
										    289,
										    258,
										    278,
										    99,
										    269,
										    266,
										    262,
										    8,
										    259,
										    241,
										    4,
										    217,
										    230,
										    268,
										    266,
										    55,
										    168,
										    106,
										    75,
										    193,
										    266,
										    223,
										    27,
										    49,
										    26,
										    282,
										    25,
										    264,
										    299,
										    19,
										    26,
										    0,
										    258,
										    277,
										    117,
										    86,
										    93,
										    176,
										    183,
										    270,
										    11,
										    262,
										    42,
										    61,
										    265,
										]

										self.assertListEqual(__lowercase  ,   self.big_tokenizer.encode(__lowercase		)		)


						@require_torch
						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										import torch

										from transformers import ReformerConfig, ReformerModel

										# Build sequence
										_UpperCAmelCase    :						int							=      list(self.big_tokenizer.get_vocab().keys()		)[:10]
										_UpperCAmelCase    :						Tuple							=      ''' '''.join(__lowercase		)
										_UpperCAmelCase    :						Optional[Any]							=      self.big_tokenizer.encode_plus(__lowercase  ,   return_tensors="pt"		)
										_UpperCAmelCase    :						Tuple							=      self.big_tokenizer.batch_encode_plus([sequence, sequence]  ,   return_tensors="pt"		)

										_UpperCAmelCase    :						List[Any]							=      ReformerConfig()
										# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
										_UpperCAmelCase    :						Optional[int]							=      encoded_sequence['''input_ids'''].shape
										_UpperCAmelCase    :						Optional[Any]							=      ReformerModel(__lowercase		)

										# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
										assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

										with torch.no_grad():
														model(**__lowercase		)
														model(**__lowercase		)





						@slow
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      {'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
										# fmt: on

										# This tokenizer does not know some characters like ")".
										# That is the reason why we use very simple texts here.
										# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
										_UpperCAmelCase    :						List[Any]							=      [
										    '''This is a very simple sentence.''',
										    '''The quick brown fox jumps over the lazy dog.''',
										]

										self.tokenizer_integration_test_util(
										    expected_encoding=__lowercase  ,   model_name="google/reformer-crime-and-punishment"  ,   revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"  ,   padding=__lowercase  ,   sequences=__lowercase  ,   )





import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 			a					(		UpperCAmelCase			):
						_lowercase        =     (PNDMScheduler,)
						_lowercase        =     (("num_inference_steps", 5_0),)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      {
										    "num_train_timesteps": 1000,
										    "beta_start": 0.00_01,
										    "beta_end": 0.02,
										    "beta_schedule": "linear",
										}

										config.update(**A_		)
										return config


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


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

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config(**A_		)
														_UpperCAmelCase    :						Any							=      scheduler_class(**A_		)
														scheduler.set_timesteps(A_		)
														# copy over dummy past residuals
														_UpperCAmelCase    :						Union[str, Any]							=      dummy_past_residuals[:]

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

														_UpperCAmelCase    :						Tuple							=      scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Any							=      new_scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

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

														_UpperCAmelCase    :						Union[str, Any]							=      scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Optional[Any]							=      new_scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										pass


						def     _UpperCAmelCase			(    self  ,   A_=0  ,   **A_		):







										'''simple docstring'''


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

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

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

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

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

														_UpperCAmelCase    :						Tuple							=      scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						Union[str, Any]							=      new_scheduler.step_prk(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

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

														_UpperCAmelCase    :						Optional[Any]							=      scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample
														_UpperCAmelCase    :						List[str]							=      new_scheduler.step_plms(A_  ,   A_  ,   A_  ,   **A_		).prev_sample

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


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Union[str, Any]							=      self.get_scheduler_config(**A_		)
										_UpperCAmelCase    :						List[str]							=      scheduler_class(**A_		)

										_UpperCAmelCase    :						List[str]							=      10
										_UpperCAmelCase    :						Optional[int]							=      self.dummy_model()
										_UpperCAmelCase    :						List[Any]							=      self.dummy_sample_deter
										scheduler.set_timesteps(A_		)

										for i, t in enumerate(scheduler.prk_timesteps		):
														_UpperCAmelCase    :						Dict							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						int							=      scheduler.step_prk(A_  ,   A_  ,   A_		).prev_sample

										for i, t in enumerate(scheduler.plms_timesteps		):
														_UpperCAmelCase    :						Any							=      model(A_  ,   A_		)
														_UpperCAmelCase    :						Any							=      scheduler.step_plms(A_  ,   A_  ,   A_		).prev_sample

										return sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      dict(self.forward_default_kwargs		)

										_UpperCAmelCase    :						str							=      kwargs.pop("num_inference_steps"  ,   A_		)

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						int							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Optional[Any]							=      scheduler_class(**A_		)

														_UpperCAmelCase    :						int							=      self.dummy_sample
														_UpperCAmelCase    :						str							=      0.1 * sample

														if num_inference_steps is not None and hasattr(A_  ,   "set_timesteps"		):
																		scheduler.set_timesteps(A_		)
														elif num_inference_steps is not None and not hasattr(A_  ,   "set_timesteps"		):
																		_UpperCAmelCase    :						Union[str, Any]							=      num_inference_steps

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

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

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

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

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						List[str]							=      self.scheduler_classes[0]
										_UpperCAmelCase    :						Optional[int]							=      self.get_scheduler_config(steps_offset=1		)
										_UpperCAmelCase    :						str							=      scheduler_class(**A_		)
										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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										for beta_start, beta_end in zip([0.00_01, 0.0_01]  ,   [0.0_02, 0.02]		):
														self.check_over_configs(beta_start=A_  ,   beta_end=A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      27

										for scheduler_class in self.scheduler_classes:
														_UpperCAmelCase    :						str							=      self.dummy_sample
														_UpperCAmelCase    :						str							=      0.1 * sample

														_UpperCAmelCase    :						str							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Tuple							=      scheduler_class(**A_		)

														scheduler.set_timesteps(A_		)

														# 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]		):
																		_UpperCAmelCase    :						Dict							=      scheduler.step_prk(A_  ,   A_  ,   A_		).prev_sample


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										with self.assertRaises(A_		):
														_UpperCAmelCase    :						Union[str, Any]							=      self.scheduler_classes[0]
														_UpperCAmelCase    :						Dict							=      self.get_scheduler_config()
														_UpperCAmelCase    :						Union[str, Any]							=      scheduler_class(**A_		)

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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.full_loop()
										_UpperCAmelCase    :						int							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 1_98.13_18		) < 1e-2
										assert abs(result_mean.item() - 0.25_80		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.full_loop(prediction_type="v_prediction"		)
										_UpperCAmelCase    :						Union[str, Any]							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						List[Any]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 67.39_86		) < 1e-2
										assert abs(result_mean.item() - 0.08_78		) < 1e-3


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self.full_loop(set_alpha_to_one=A_  ,   beta_start=0.01		)
										_UpperCAmelCase    :						Dict							=      torch.sum(torch.abs(A_		)		)
										_UpperCAmelCase    :						Optional[int]							=      torch.mean(torch.abs(A_		)		)

										assert abs(result_sum.item() - 2_30.03_99		) < 1e-2
										assert abs(result_mean.item() - 0.29_95		) < 1e-3





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										assert abs(result_sum.item() - 1_86.94_82		) < 1e-2
										assert abs(result_mean.item() - 0.24_34		) < 1e-3


