code: 
  
  
  # 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"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  							snake_case_        =		42
  
  							# setable values
  							snake_case_        =		42
  							snake_case_        =		42
  							snake_case_        =		None
  
  
  							@classmethod
  							def 						lowercase__     (   cls			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return cls(
  													    common=_UpperCAmelCase			,    final_alpha_cumprod=_UpperCAmelCase			,    init_noise_sigma=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )
  
  
  
  
  
  @dataclass
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__			,UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		[e.name for e in FlaxKarrasDiffusionSchedulers]
  
  							snake_case_        =		42
  
  
  							@property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return True
  
  
  							@register_to_config
  							def __init__(   self			,    _UpperCAmelCase = 1_000			,    _UpperCAmelCase = 0.0_0_0_1			,    _UpperCAmelCase = 0.0_2			,    _UpperCAmelCase = "linear"			,    _UpperCAmelCase = None			,    _UpperCAmelCase = True			,    _UpperCAmelCase = 0			,    _UpperCAmelCase = "epsilon"			,    _UpperCAmelCase = jnp.floataa			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		dtype
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if common is None:
  																			a_					=		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.
  													a_					=		(
  													    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
  													a_					=		jnp.array(1.0			,    dtype=self.dtype		)
  
  													a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return sample
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = ()		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		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
  													a_					=		(jnp.arange(0			,    _UpperCAmelCase		) * step_ratio).round()[::-1] + self.config.steps_offset
  
  													return state.replace(
  													    num_inference_steps=_UpperCAmelCase			,    timesteps=_UpperCAmelCase			,    )
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		state.common.alphas_cumprod[timestep]
  													a_					=		jnp.where(
  													    prev_timestep >= 0			,    state.common.alphas_cumprod[prev_timestep]			,    state.final_alpha_cumprod		)
  													a_					=		1 - alpha_prod_t
  													a_					=		1 - alpha_prod_t_prev
  
  													a_					=		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
  
  													return variance
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase = 0.0			,    _UpperCAmelCase = True			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													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)
  													a_					=		timestep - self.config.num_train_timesteps // state.num_inference_steps
  
  													a_					=		state.common.alphas_cumprod
  													a_					=		state.final_alpha_cumprod
  
  													# 2. compute alphas, betas
  													a_					=		alphas_cumprod[timestep]
  													a_					=		jnp.where(prev_timestep >= 0			,    alphas_cumprod[prev_timestep]			,    _UpperCAmelCase		)
  
  													a_					=		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":
  																			a_					=		(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  																			a_					=		model_output
  													elif self.config.prediction_type == "sample":
  																			a_					=		model_output
  																			a_					=		(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
  													elif self.config.prediction_type == "v_prediction":
  																			a_					=		(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  																			a_					=		(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)
  													a_					=		self._get_variance(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		eta * variance ** 0.5
  
  													# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  													a_					=		(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
  													a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return add_noise_common(state.common			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return get_velocity_common(state.common			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  
  
  
  
  							def __len__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.config.num_train_timesteps
style_context: 
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_tokenizers, require_torch
  from transformers.utils import cached_property
  
  from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		BartTokenizer
  							snake_case_        =		BartTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		filter_roberta_detectors
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  													a_					=		[
  													    """l""",
  													    """o""",
  													    """w""",
  													    """e""",
  													    """r""",
  													    """s""",
  													    """t""",
  													    """i""",
  													    """d""",
  													    """n""",
  													    """\u0120""",
  													    """\u0120l""",
  													    """\u0120n""",
  													    """\u0120lo""",
  													    """\u0120low""",
  													    """er""",
  													    """\u0120lowest""",
  													    """\u0120newer""",
  													    """\u0120wider""",
  													    """<unk>""",
  													]
  													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
  													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
  													a_					=		{"""unk_token""": """<unk>"""}
  
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""merges_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write(json.dumps(_UpperCAmelCase		) + """\n"""		)
  													with open(self.merges_file			,    """w"""			,    encoding="""utf-8"""		) as fp:
  																			fp.write("""\n""".join(_UpperCAmelCase		)		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													kwargs.update(self.special_tokens_map		)
  													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return "lower newer", "lower newer"
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    max_length=len(_UpperCAmelCase		)			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
  																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
  																			a_					=		batch.input_ids.tolist()[0]
  																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			# Test that special tokens are reset
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			# check if input_ids are returned and no labels
  																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
  																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
  																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		[
  													    """Summary of the text.""",
  													    """Another summary.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    max_length=32			,    padding="""max_length"""			,    return_tensors="""pt"""		)
  																			self.assertEqual(32			,    targets["""input_ids"""].shape[1]		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(
  																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)
  
  
  							@require_torch
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		["""A long paragraph for summarization."""]
  													a_					=		[
  													    """Summary of the text.""",
  													]
  													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
  																			a_					=		inputs["""input_ids"""]
  																			a_					=		targets["""input_ids"""]
  																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
  																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
  																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													pass
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
  																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
  																									a_					=		"""A, <mask> AllenNLP sentence."""
  																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
  
  																									# token_type_ids should put 0 everywhere
  																									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)			,    sum(tokens_p["""token_type_ids"""]		)		)
  
  																									# attention_mask should put 1 everywhere, so sum over length should be 1
  																									self.assertEqual(
  																									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)			,    sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)			,    )
  
  																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
  																									a_					=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)
  
  																									# Rust correctly handles the space before the mask while python doesnt
  																									self.assertSequenceEqual(tokens_p["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
  
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
  																									self.assertSequenceEqual(
  																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)