code: 
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from scipy import integrate
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import (
      CommonSchedulerState,
      FlaxKarrasDiffusionSchedulers,
      FlaxSchedulerMixin,
      FlaxSchedulerOutput,
      broadcast_to_shape_from_left,
  )
  
  @flax.struct.dataclass
  class __UpperCamelCase    :
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      42
  
  					# setable values
  					__magic_name__     			=      42
  					__magic_name__     			=      42
  					__magic_name__     			=      42
  					__magic_name__     			=      None
  
  					# running values
  					__magic_name__     			=      None
  
  
  
  
  
  					@classmethod
  					def       _UpperCAmelCase						(							cls							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						return cls(common=lowerCamelCase__							,			init_noise_sigma=lowerCamelCase__							,			timesteps=lowerCamelCase__							,			sigmas=lowerCamelCase__	)
  
  @dataclass
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      42
  
  class __UpperCamelCase    (							_a				,_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					__magic_name__     			=      [e.name for e in FlaxKarrasDiffusionSchedulers]
  
  					__magic_name__     			=      42
  
  
  
  
  
  					@property
  					def       _UpperCAmelCase						(							self	):
  						return True
  
  
  
  
  
  					@register_to_config
  					def __init__(							self							,			lowerCamelCase__ = 1_0_0_0							,			lowerCamelCase__ = 0.0_001							,			lowerCamelCase__ = 0.02							,			lowerCamelCase__ = "linear"							,			lowerCamelCase__ = None							,			lowerCamelCase__ = "epsilon"							,			lowerCamelCase__ = jnp.floataa							,			):
  						UpperCAmelCase__:  Optional[Any]		       =   dtype
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__ = None	):
  						if common is None:
  							UpperCAmelCase__:  Union[str, Any]		       =   CommonSchedulerState.create(self	)
  
  						UpperCAmelCase__:  int		       =   jnp.arange(0							,			self.config.num_train_timesteps	).round()[::-1]
  						UpperCAmelCase__:  Dict		       =   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5
  
  						# standard deviation of the initial noise distribution
  						UpperCAmelCase__:  Optional[Any]		       =   sigmas.max()
  
  						return LMSDiscreteSchedulerState.create(
  						    common=lowerCamelCase__							,			init_noise_sigma=lowerCamelCase__							,			timesteps=lowerCamelCase__							,			sigmas=lowerCamelCase__							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  						((UpperCAmelCase__)       ,	):  Optional[Any]		       =   jnp.where(state.timesteps == timestep							,			size=1	)
  						UpperCAmelCase__:  Optional[Any]		       =   step_index[0]
  
  						UpperCAmelCase__:  Dict		       =   state.sigmas[step_index]
  						UpperCAmelCase__:  Optional[Any]		       =   sample / ((sigma**2 + 1) ** 0.5)
  						return sample
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	):
  
  						def lms_derivative(lowerCamelCase__	):
  							UpperCAmelCase__:  Optional[Any]		       =   1.0
  							for k in range(lowerCamelCase__	):
  								if current_order == k:
  									continue
  								prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
  							return prod
  
  						UpperCAmelCase__:  Tuple		       =   integrate.quad(lowerCamelCase__							,			state.sigmas[t]							,			state.sigmas[t + 1]							,			epsrel=1e-4	)[0]
  
  						return integrated_coeff
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__ = ()	):
  						UpperCAmelCase__:  int		       =   jnp.linspace(self.config.num_train_timesteps - 1							,			0							,			lowerCamelCase__							,			dtype=self.dtype	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   jnp.floor(lowerCamelCase__	).astype(jnp.intaa	)
  						UpperCAmelCase__:  Union[str, Any]		       =   jnp.ceil(lowerCamelCase__	).astype(jnp.intaa	)
  
  						UpperCAmelCase__:  List[str]		       =   jnp.mod(lowerCamelCase__							,			1.0	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
  						UpperCAmelCase__:  Union[str, Any]		       =   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
  						UpperCAmelCase__:  Optional[Any]		       =   jnp.concatenate([sigmas, jnp.array([0.0]							,			dtype=self.dtype	)]	)
  
  						UpperCAmelCase__:  str		       =   timesteps.astype(jnp.intaa	)
  
  						# initial running values
  						UpperCAmelCase__:  Dict		       =   jnp.zeros((0,) + shape							,			dtype=self.dtype	)
  
  						return state.replace(
  						    timesteps=lowerCamelCase__							,			sigmas=lowerCamelCase__							,			num_inference_steps=lowerCamelCase__							,			derivatives=lowerCamelCase__							,			)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__ = 4							,			lowerCamelCase__ = True							,			):
  						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"	)
  
  						UpperCAmelCase__:  Optional[int]		       =   state.sigmas[timestep]
  
  						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
  						if self.config.prediction_type == "epsilon":
  							UpperCAmelCase__:  Union[str, Any]		       =   sample - sigma * model_output
  						elif self.config.prediction_type == "v_prediction":
  							# * c_out + input * c_skip
  							UpperCAmelCase__:  Dict		       =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
  						else:
  							raise ValueError(
  							    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"	)
  
  						# 2. Convert to an ODE derivative
  						UpperCAmelCase__:  Optional[int]		       =   (sample - pred_original_sample) / sigma
  						UpperCAmelCase__:  List[Any]		       =   state.replace(derivatives=jnp.append(state.derivatives							,			lowerCamelCase__	)	)
  						if len(state.derivatives	) > order:
  							UpperCAmelCase__:  Tuple		       =   state.replace(derivatives=jnp.delete(state.derivatives							,			0	)	)
  
  						# 3. Compute linear multistep coefficients
  						UpperCAmelCase__:  Union[str, Any]		       =   min(timestep + 1							,			lowerCamelCase__	)
  						UpperCAmelCase__:  Any		       =   [self.get_lms_coefficient(lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__	) for curr_order in range(lowerCamelCase__	)]
  
  						# 4. Compute previous sample based on the derivatives path
  						UpperCAmelCase__:  Optional[int]		       =   sample + sum(
  						    coeff * derivative for coeff, derivative in zip(lowerCamelCase__							,			reversed(state.derivatives	)	)	)
  
  						if not return_dict:
  							return (prev_sample, state)
  
  						return FlaxLMSSchedulerOutput(prev_sample=lowerCamelCase__							,			state=lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__							,			):
  						UpperCAmelCase__:  Tuple		       =   state.sigmas[timesteps].flatten()
  						UpperCAmelCase__:  int		       =   broadcast_to_shape_from_left(lowerCamelCase__							,			noise.shape	)
  
  						UpperCAmelCase__:  str		       =   original_samples + noise * sigma
  
  						return noisy_samples
  
  
  
  
  
  					def __len__(							self	):
  						return self.config.num_train_timesteps
style_context: 
  
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers import BertTokenizer, BertTokenizerFast
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_vision
  from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
  
  
  if is_vision_available():
  					from PIL import Image
  
  					from transformers import FlavaImageProcessor, FlavaProcessor
  					from transformers.models.flava.image_processing_flava import (
  					    FLAVA_CODEBOOK_MEAN,
  					    FLAVA_CODEBOOK_STD,
  					    FLAVA_IMAGE_MEAN,
  					    FLAVA_IMAGE_STD,
  					)
  
  @require_vision
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[int]		       =   tempfile.mkdtemp()
  
  						# fmt: off
  						UpperCAmelCase__:  Optional[Any]		       =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
  						# fmt: on
  						UpperCAmelCase__:  str		       =   os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]	)
  
  						with open(self.vocab_file							,			"w"							,			encoding="utf-8"	) as fp:
  							fp.write("".join([x + "\n" for x in vocab_tokens]	)	)
  
  						UpperCAmelCase__:  Tuple		       =   {
  						    "image_mean": FLAVA_IMAGE_MEAN,
  						    "image_std": FLAVA_IMAGE_STD,
  						    "do_normalize": True,
  						    "do_resize": True,
  						    "size": 2_2_4,
  						    "do_center_crop": True,
  						    "crop_size": 2_2_4,
  						    "input_size_patches": 1_4,
  						    "total_mask_patches": 7_5,
  						    "mask_group_max_patches": None,
  						    "mask_group_min_patches": 1_6,
  						    "mask_group_min_aspect_ratio": 0.3,
  						    "mask_group_max_aspect_ratio": None,
  						    "codebook_do_resize": True,
  						    "codebook_size": 1_1_2,
  						    "codebook_do_center_crop": True,
  						    "codebook_crop_size": 1_1_2,
  						    "codebook_do_map_pixels": True,
  						    "codebook_do_normalize": True,
  						    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
  						    "codebook_image_std": FLAVA_CODEBOOK_STD,
  						}
  
  						UpperCAmelCase__:  Tuple		       =   os.path.join(self.tmpdirname							,			lowerCamelCase__	)
  						with open(self.image_processor_file							,			"w"							,			encoding="utf-8"	) as fp:
  							json.dump(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return BertTokenizer.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return BertTokenizerFast.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return FlavaImageProcessor.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						shutil.rmtree(self.tmpdirname	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   [np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta	)]
  
  						UpperCAmelCase__:  Any		       =   [Image.fromarray(np.moveaxis(lowerCamelCase__							,			0							,			-1	)	) for x in image_inputs]
  
  						return image_inputs
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  						UpperCAmelCase__:  Any		       =   self.get_rust_tokenizer()
  						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
  
  						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  						processor_slow.save_pretrained(self.tmpdirname	)
  						UpperCAmelCase__:  Dict		       =   FlavaProcessor.from_pretrained(self.tmpdirname							,			use_fast=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  						processor_fast.save_pretrained(self.tmpdirname	)
  						UpperCAmelCase__:  int		       =   FlavaProcessor.from_pretrained(self.tmpdirname	)
  
  						self.assertEqual(processor_slow.tokenizer.get_vocab()							,			tokenizer_slow.get_vocab()	)
  						self.assertEqual(processor_fast.tokenizer.get_vocab()							,			tokenizer_fast.get_vocab()	)
  						self.assertEqual(tokenizer_slow.get_vocab()							,			tokenizer_fast.get_vocab()	)
  						self.assertIsInstance(processor_slow.tokenizer							,			lowerCamelCase__	)
  						self.assertIsInstance(processor_fast.tokenizer							,			lowerCamelCase__	)
  
  						self.assertEqual(processor_slow.image_processor.to_json_string()							,			image_processor.to_json_string()	)
  						self.assertEqual(processor_fast.image_processor.to_json_string()							,			image_processor.to_json_string()	)
  						self.assertIsInstance(processor_slow.image_processor							,			lowerCamelCase__	)
  						self.assertIsInstance(processor_fast.image_processor							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()	)
  						processor.save_pretrained(self.tmpdirname	)
  
  						UpperCAmelCase__:  Any		       =   self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"	)
  						UpperCAmelCase__:  str		       =   self.get_image_processor(do_normalize=lowerCamelCase__							,			padding_value=1.0	)
  
  						UpperCAmelCase__:  str		       =   FlavaProcessor.from_pretrained(
  						    self.tmpdirname							,			bos_token="(BOS)"							,			eos_token="(EOS)"							,			do_normalize=lowerCamelCase__							,			padding_value=1.0	)
  
  						self.assertEqual(processor.tokenizer.get_vocab()							,			tokenizer_add_kwargs.get_vocab()	)
  						self.assertIsInstance(processor.tokenizer							,			lowerCamelCase__	)
  
  						self.assertEqual(processor.image_processor.to_json_string()							,			image_processor_add_kwargs.to_json_string()	)
  						self.assertIsInstance(processor.image_processor							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.get_image_processor()
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Optional[Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[int]		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  Tuple		       =   image_processor(lowerCamelCase__							,			return_tensors="np"	)
  						UpperCAmelCase__:  str		       =   processor(images=lowerCamelCase__							,			return_tensors="np"	)
  
  						for key in input_feat_extract.keys():
  							self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1e-2	)
  
  						# With rest of the args
  						random.seed(1_2_3_4	)
  						UpperCAmelCase__:  Optional[int]		       =   image_processor(
  						    lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
  						random.seed(1_2_3_4	)
  						UpperCAmelCase__:  Tuple		       =   processor(
  						    images=lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
  
  						for key in input_feat_extract.keys():
  							self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1e-2	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   self.get_image_processor()
  						UpperCAmelCase__:  str		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  List[str]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   "lower newer"
  
  						UpperCAmelCase__:  Optional[Any]		       =   processor(text=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__	)
  
  						for key in encoded_tok.keys():
  							self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  str		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Any		       =   "lower newer"
  						UpperCAmelCase__:  Any		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  Optional[int]		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)
  
  						self.assertListEqual(list(inputs.keys()	)							,			["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)
  
  						# add extra args
  						UpperCAmelCase__:  str		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_image_mask=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    list(inputs.keys()	)							,			[
  						        "input_ids",
  						        "token_type_ids",
  						        "attention_mask",
  						        "pixel_values",
  						        "codebook_pixel_values",
  						        "bool_masked_pos",
  						    ]							,			)
  
  						# test if it raises when no input is passed
  						with pytest.raises(lowerCamelCase__	):
  							processor()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_image_processor()
  						UpperCAmelCase__:  List[Any]		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  						UpperCAmelCase__:  str		       =   processor.batch_decode(lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__	)
  
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.get_image_processor()
  						UpperCAmelCase__:  Dict		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   "lower newer"
  						UpperCAmelCase__:  Dict		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  int		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)
  
  						self.assertListEqual(list(inputs.keys()	)							,			processor.model_input_names	)