code: 
  
  
  
  import os
  import unittest
  
  from transformers import RealmTokenizerFast
  from transformers.models.bert.tokenization_bert import (
      VOCAB_FILES_NAMES,
      BasicTokenizer,
      WordpieceTokenizer,
      _is_control,
      _is_punctuation,
      _is_whitespace,
  )
  from transformers.models.realm.tokenization_realm import RealmTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
  
  
  
  
  
  
  @require_tokenizers
  class 	lowercase		(				snake_case__				,					unittest.TestCase):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							RealmTokenizer
  	a__   :      Optional[Any]	     =							RealmTokenizerFast
  	a__   :      List[Any]	     =							True
  	a__   :      Dict	     =							True
  	a__   :      Optional[int]	     =							filter_non_english
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Tuple:
  							super().setUp()
  
  							UpperCAmelCase_=     [
  							    """[UNK]""",
  							    """[CLS]""",
  							    """[SEP]""",
  							    """[PAD]""",
  							    """[MASK]""",
  							    """want""",
  							    """##want""",
  							    """##ed""",
  							    """wa""",
  							    """un""",
  							    """runn""",
  							    """##ing""",
  							    """,""",
  							    """low""",
  							    """lowest""",
  							]
  							UpperCAmelCase_=     os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["""vocab_file"""]  )
  							with open(self.vocab_file		,						"""w"""		,						encoding="""utf-8"""  ) as vocab_writer:
  													vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		str  )    ->				str:
  							UpperCAmelCase_=     """UNwant\u00E9d,running"""
  							UpperCAmelCase_=     """unwanted, running"""
  							return input_text, output_text
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Tuple:
  							UpperCAmelCase_=     self.tokenizer_class(self.vocab_file  )
  
  							UpperCAmelCase_=     tokenizer.tokenize("""UNwant\u00E9d,running"""  )
  							self.assertListEqual(__UpperCAmelCase		,						["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]  )
  							self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase  )		,						[9, 6, 7, 12, 10, 11]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Dict:
  							if not self.test_rust_tokenizer:
  													return
  
  							UpperCAmelCase_=     self.get_tokenizer()
  							UpperCAmelCase_=     self.get_rust_tokenizer()
  
  							UpperCAmelCase_=     """UNwant\u00E9d,running"""
  
  							UpperCAmelCase_=     tokenizer.tokenize(__UpperCAmelCase  )
  							UpperCAmelCase_=     rust_tokenizer.tokenize(__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     tokenizer.encode(__UpperCAmelCase		,						add_special_tokens=__UpperCAmelCase  )
  							UpperCAmelCase_=     rust_tokenizer.encode(__UpperCAmelCase		,						add_special_tokens=__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     self.get_rust_tokenizer()
  							UpperCAmelCase_=     tokenizer.encode(__UpperCAmelCase  )
  							UpperCAmelCase_=     rust_tokenizer.encode(__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							# With lower casing
  							UpperCAmelCase_=     self.get_tokenizer(do_lower_case=__UpperCAmelCase  )
  							UpperCAmelCase_=     self.get_rust_tokenizer(do_lower_case=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     """UNwant\u00E9d,running"""
  
  							UpperCAmelCase_=     tokenizer.tokenize(__UpperCAmelCase  )
  							UpperCAmelCase_=     rust_tokenizer.tokenize(__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     tokenizer.encode(__UpperCAmelCase		,						add_special_tokens=__UpperCAmelCase  )
  							UpperCAmelCase_=     rust_tokenizer.encode(__UpperCAmelCase		,						add_special_tokens=__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     self.get_rust_tokenizer()
  							UpperCAmelCase_=     tokenizer.encode(__UpperCAmelCase  )
  							UpperCAmelCase_=     rust_tokenizer.encode(__UpperCAmelCase  )
  							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[Any]:
  							UpperCAmelCase_=     BasicTokenizer()
  
  							self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""  )		,						["""ah""", """\u535A""", """\u63A8""", """zz"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				List[str]:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """  )		,						["""hello""", """!""", """how""", """are""", """you""", """?"""]  )
  							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )		,						["""hello"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]  )    ->				List[Any]:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase		,						strip_accents=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )		,						["""hällo""", """!""", """how""", """are""", """you""", """?"""]  )
  							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )		,						["""h\u00E9llo"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Optional[Any]:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase		,						strip_accents=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )		,						["""hallo""", """!""", """how""", """are""", """you""", """?"""]  )
  							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )		,						["""hello"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				List[str]:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )		,						["""hallo""", """!""", """how""", """are""", """you""", """?"""]  )
  							self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""  )		,						["""hello"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Tuple:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """  )		,						["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				str:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase		,						strip_accents=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )		,						["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase		,						strip_accents=__UpperCAmelCase  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """  )		,						["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Any:
  							UpperCAmelCase_=     BasicTokenizer(do_lower_case=__UpperCAmelCase		,						never_split=["""[UNK]"""]  )
  
  							self.assertListEqual(
  							    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""  )		,						["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]
  
  							UpperCAmelCase_=     {}
  							for i, token in enumerate(__UpperCAmelCase  ):
  													UpperCAmelCase_=     i
  							UpperCAmelCase_=     WordpieceTokenizer(vocab=__UpperCAmelCase		,						unk_token="""[UNK]"""  )
  
  							self.assertListEqual(tokenizer.tokenize(""""""  )		,						[]  )
  
  							self.assertListEqual(tokenizer.tokenize("""unwanted running"""  )		,						["""un""", """##want""", """##ed""", """runn""", """##ing"""]  )
  
  							self.assertListEqual(tokenizer.tokenize("""unwantedX running"""  )		,						["""[UNK]""", """runn""", """##ing"""]  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				str:
  							self.assertTrue(_is_whitespace(""" """  )  )
  							self.assertTrue(_is_whitespace("""\t"""  )  )
  							self.assertTrue(_is_whitespace("""\r"""  )  )
  							self.assertTrue(_is_whitespace("""\n"""  )  )
  							self.assertTrue(_is_whitespace("""\u00A0"""  )  )
  
  							self.assertFalse(_is_whitespace("""A"""  )  )
  							self.assertFalse(_is_whitespace("""-"""  )  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Tuple:
  							self.assertTrue(_is_control("""\u0005"""  )  )
  
  							self.assertFalse(_is_control("""A"""  )  )
  							self.assertFalse(_is_control(""" """  )  )
  							self.assertFalse(_is_control("""\t"""  )  )
  							self.assertFalse(_is_control("""\r"""  )  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				int:
  							self.assertTrue(_is_punctuation("""-"""  )  )
  							self.assertTrue(_is_punctuation("""$"""  )  )
  							self.assertTrue(_is_punctuation("""`"""  )  )
  							self.assertTrue(_is_punctuation("""."""  )  )
  
  							self.assertFalse(_is_punctuation("""A"""  )  )
  							self.assertFalse(_is_punctuation(""" """  )  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				str:
  							UpperCAmelCase_=     self.get_tokenizer()
  
  							# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
  							self.assertListEqual([tokenizer.tokenize(__UpperCAmelCase  ) for t in ["""Test""", """\xad""", """test"""]]		,						[["""[UNK]"""], [], ["""[UNK]"""]]  )
  
  							if self.test_rust_tokenizer:
  													UpperCAmelCase_=     self.get_rust_tokenizer()
  													self.assertListEqual(
  													    [rust_tokenizer.tokenize(__UpperCAmelCase  ) for t in ["""Test""", """\xad""", """test"""]]		,						[["""[UNK]"""], [], ["""[UNK]"""]]  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Union[str, Any]:
  							UpperCAmelCase_=     self.tokenizer_class.from_pretrained("""bert-base-uncased"""  )
  
  							UpperCAmelCase_=     tokenizer.encode("""sequence builders"""		,						add_special_tokens=__UpperCAmelCase  )
  							UpperCAmelCase_=     tokenizer.encode("""multi-sequence build"""		,						add_special_tokens=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase  )
  							UpperCAmelCase_=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase		,						__UpperCAmelCase  )
  
  							assert encoded_sentence == [101] + text + [102]
  							assert encoded_pair == [101] + text + [102] + text_a + [102]
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Tuple:
  							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
  																			UpperCAmelCase_=     self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,						**__UpperCAmelCase  )
  
  																			UpperCAmelCase_=     F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
  																			UpperCAmelCase_=     tokenizer_r.encode_plus(
  																			    __UpperCAmelCase		,						return_attention_mask=__UpperCAmelCase		,						return_token_type_ids=__UpperCAmelCase		,						return_offsets_mapping=__UpperCAmelCase		,						add_special_tokens=__UpperCAmelCase		,						)
  
  																			UpperCAmelCase_=     tokenizer_r.do_lower_case if hasattr(__UpperCAmelCase		,						"""do_lower_case"""  ) else False
  																			UpperCAmelCase_=     (
  																			    [
  																			        ((0, 0), tokenizer_r.cls_token),
  																			        ((0, 1), """A"""),
  																			        ((1, 2), ""","""),
  																			        ((3, 5), """na"""),
  																			        ((5, 6), """##ï"""),
  																			        ((6, 8), """##ve"""),
  																			        ((9, 15), tokenizer_r.mask_token),
  																			        ((16, 21), """Allen"""),
  																			        ((21, 23), """##NL"""),
  																			        ((23, 24), """##P"""),
  																			        ((25, 33), """sentence"""),
  																			        ((33, 34), """."""),
  																			        ((0, 0), tokenizer_r.sep_token),
  																			    ]
  																			    if not do_lower_case
  																			    else [
  																			        ((0, 0), tokenizer_r.cls_token),
  																			        ((0, 1), """a"""),
  																			        ((1, 2), ""","""),
  																			        ((3, 8), """naive"""),
  																			        ((9, 15), tokenizer_r.mask_token),
  																			        ((16, 21), """allen"""),
  																			        ((21, 23), """##nl"""),
  																			        ((23, 24), """##p"""),
  																			        ((25, 33), """sentence"""),
  																			        ((33, 34), """."""),
  																			        ((0, 0), tokenizer_r.sep_token),
  																			    ]
  																			)
  
  																			self.assertEqual(
  																			    [e[1] for e in expected_results]		,						tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]  )  )
  																			self.assertEqual([e[0] for e in expected_results]		,						tokens["""offset_mapping"""]  )
  
  
  
  
  	@slow
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Any:
  							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
  																			UpperCAmelCase_=     self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase		,						**__UpperCAmelCase  )
  																			UpperCAmelCase_=     self.tokenizer_class.from_pretrained(__UpperCAmelCase		,						**__UpperCAmelCase  )
  																			UpperCAmelCase_=     [["""Hello world!""", """Nice to meet you!"""], ["""The cute cat.""", """The adorable dog."""]]
  
  																			UpperCAmelCase_=     tokenizer_r.batch_encode_candidates(__UpperCAmelCase		,						max_length=10		,						return_tensors="""np"""  )
  																			UpperCAmelCase_=     tokenizer_p.batch_encode_candidates(__UpperCAmelCase		,						max_length=10		,						return_tensors="""np"""  )
  
  																			UpperCAmelCase_=     (2, 2, 10)
  
  																			self.assertEqual(encoded_sentence_r["""input_ids"""].shape		,						__UpperCAmelCase  )
  																			self.assertEqual(encoded_sentence_r["""attention_mask"""].shape		,						__UpperCAmelCase  )
  																			self.assertEqual(encoded_sentence_r["""token_type_ids"""].shape		,						__UpperCAmelCase  )
  
  																			self.assertEqual(encoded_sentence_p["""input_ids"""].shape		,						__UpperCAmelCase  )
  																			self.assertEqual(encoded_sentence_p["""attention_mask"""].shape		,						__UpperCAmelCase  )
  																			self.assertEqual(encoded_sentence_p["""token_type_ids"""].shape		,						__UpperCAmelCase  )
  
  
  
  
  
style_context: 
  
  
  
  # DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
  # and https://github.com/hojonathanho/diffusion
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      add_noise_common,
      get_velocity_common,
  )
  
  
  
  
  
  
  @flax.struct.dataclass
  class 	lowercase		:
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      CommonSchedulerState
  	a__   :      jnp.ndarray
  
  	# setable values
  	a__   :      jnp.ndarray
  	a__   :      jnp.ndarray
  	a__   :      Optional[int]	     =							None
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
  							return cls(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  
  
  @dataclass
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      DDIMSchedulerState
  
  
  
  
  
  
  class 	lowercase		(				snake_case__				,					snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  	a__   :      jnp.dtype
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  							return True
  
  
  
  
  	@register_to_config
  	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
  							UpperCAmelCase_=     dtype
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
  							if common is None:
  													UpperCAmelCase_=     CommonSchedulerState.create(self  )
  
  							# At every step in ddim, we are looking into the previous alphas_cumprod
  							# For the final step, there is no previous alphas_cumprod because we are already at 0
  							# `set_alpha_to_one` decides whether we set this parameter simply to one or
  							# whether we use the final alpha of the "non-previous" one.
  							UpperCAmelCase_=     (
  							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
  							)
  
  							# standard deviation of the initial noise distribution
  							UpperCAmelCase_=     jnp.array(1.0		,						dtype=self.dtype  )
  
  							UpperCAmelCase_=     jnp.arange(0		,						self.config.num_train_timesteps  ).round()[::-1]
  
  							return DDIMSchedulerState.create(
  							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
  							return sample
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
  							UpperCAmelCase_=     self.config.num_train_timesteps // num_inference_steps
  							# creates integer timesteps by multiplying by ratio
  							# rounding to avoid issues when num_inference_step is power of 3
  							UpperCAmelCase_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset
  
  							return state.replace(
  							    num_inference_steps=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
  							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(
  							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
  							UpperCAmelCase_=     1 - alpha_prod_t
  							UpperCAmelCase_=     1 - alpha_prod_t_prev
  
  							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  							return variance
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
  							if state.num_inference_steps is None:
  													raise ValueError(
  													    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )
  
  							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  							# Ideally, read DDIM paper in-detail understanding
  
  							# Notation (<variable name> -> <name in paper>
  							# - pred_noise_t -> e_theta(x_t, t)
  							# - pred_original_sample -> f_theta(x_t, t) or x_0
  							# - std_dev_t -> sigma_t
  							# - eta -> η
  							# - pred_sample_direction -> "direction pointing to x_t"
  							# - pred_prev_sample -> "x_t-1"
  
  							# 1. get previous step value (=t-1)
  							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  							UpperCAmelCase_=     state.common.alphas_cumprod
  							UpperCAmelCase_=     state.final_alpha_cumprod
  
  							# 2. compute alphas, betas
  							UpperCAmelCase_=     alphas_cumprod[timestep]
  							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )
  
  							UpperCAmelCase_=     1 - alpha_prod_t
  
  							# 3. compute predicted original sample from predicted noise also called
  							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							if self.config.prediction_type == "epsilon":
  													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  													UpperCAmelCase_=     model_output
  							elif self.config.prediction_type == "sample":
  													UpperCAmelCase_=     model_output
  													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  							elif self.config.prediction_type == "v_prediction":
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
  							else:
  													raise ValueError(
  													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
  													    """ `v_prediction`"""  )
  
  							# 4. compute variance: "sigma_t(η)" -> see formula (16)
  							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  							UpperCAmelCase_=     eta * variance ** 0.5
  
  							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon
  
  							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
  
  							if not return_dict:
  													return (prev_sample, state)
  
  							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
  							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
  
  
  
  
  	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
  							return self.config.num_train_timesteps
  
  
  
  
  