code: 
  '''simple docstring'''
  
  
  
  import json
  import os
  import shutil
  import tempfile
  import unittest
  
  from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
  from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
  from transformers.utils import FEATURE_EXTRACTOR_NAME
  
  from .test_feature_extraction_wavaveca import floats_list
  
  
  
  
  
  class 			A							(       unittest.TestCase				):
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       """<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K ' X J Q Z""".split(""" """       )
  									lowercase__							 =       dict(zip(lowerCamelCase__       ,     range(len(lowerCamelCase__       )       )       )       )
  
  									lowercase__							 =       {
  									    """pad_token""": """<pad>""",
  									    """unk_token""": """<unk>""",
  									    """bos_token""": """<s>""",
  									    """eos_token""": """</s>""",
  									}
  									lowercase__							 =       {
  									    """feature_size""": 1,
  									    """padding_value""": 0.0,
  									    """sampling_rate""": 16_000,
  									    """return_attention_mask""": False,
  									    """do_normalize""": True,
  									}
  
  									lowercase__							 =       tempfile.mkdtemp()
  									lowercase__							 =       os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES["""vocab_file"""]       )
  									lowercase__							 =       os.path.join(self.tmpdirname       ,     lowerCamelCase__       )
  									with open(self.vocab_file       ,     """w"""       ,     encoding="""utf-8"""       ) as fp:
  															fp.write(json.dumps(lowerCamelCase__       ) + """\n"""       )
  
  									with open(self.feature_extraction_file       ,     """w"""       ,     encoding="""utf-8"""       ) as fp:
  															fp.write(json.dumps(lowerCamelCase__       ) + """\n"""       )
  
  
  
  			def A__ (					self       ,     **lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.add_kwargs_tokens_map.copy()
  									kwargs.update(lowerCamelCase__       )
  									return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       ,     **lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     **lowerCamelCase__       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       ,     **lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									shutil.rmtree(self.tmpdirname       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_tokenizer()
  									lowercase__							 =       self.get_feature_extractor()
  
  									lowercase__							 =       WavaVecaProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									processor.save_pretrained(self.tmpdirname       )
  									lowercase__							 =       WavaVecaProcessor.from_pretrained(self.tmpdirname       )
  
  									self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer.get_vocab()       )
  									self.assertIsInstance(processor.tokenizer       ,     lowerCamelCase__       )
  
  									self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor.to_json_string()       )
  									self.assertIsInstance(processor.feature_extractor       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       WavaVecaProcessor(tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()       )
  									processor.save_pretrained(self.tmpdirname       )
  
  									lowercase__							 =       self.get_tokenizer(bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""       )
  									lowercase__							 =       self.get_feature_extractor(do_normalize=lowerCamelCase__       ,     padding_value=1.0       )
  
  									lowercase__							 =       WavaVecaProcessor.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.feature_extractor.to_json_string()       ,     feature_extractor_add_kwargs.to_json_string()       )
  									self.assertIsInstance(processor.feature_extractor       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       WavaVecaProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       floats_list((3, 1_000)       )
  
  									lowercase__							 =       feature_extractor(lowerCamelCase__       ,     return_tensors="""np"""       )
  									lowercase__							 =       processor(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 A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       WavaVecaProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       """This is a test string"""
  
  									lowercase__							 =       processor(text=lowerCamelCase__       )
  
  									lowercase__							 =       tokenizer(lowerCamelCase__       )
  
  									for key in encoded_tok.keys():
  															self.assertListEqual(encoded_tok[key]       ,     encoded_processor[key]       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       WavaVecaProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									lowercase__							 =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  									lowercase__							 =       processor.batch_decode(lowerCamelCase__       )
  									lowercase__							 =       tokenizer.batch_decode(lowerCamelCase__       )
  
  									self.assertListEqual(lowerCamelCase__       ,     lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.get_feature_extractor()
  									lowercase__							 =       self.get_tokenizer()
  
  									lowercase__							 =       WavaVecaProcessor(tokenizer=lowerCamelCase__       ,     feature_extractor=lowerCamelCase__       )
  
  									self.assertListEqual(
  									    processor.model_input_names       ,     feature_extractor.model_input_names       ,     msg="""`processor` and `feature_extractor` model input names do not match"""       ,     )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
  
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  
  import flax
  import jax.numpy as jnp
  from jax import random
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
  
  
  
  
  
  @flax.struct.dataclass
  class 			A							:
  			# setable values
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  			@classmethod
  			def A__ (					cls       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return cls()
  
  
  
  
  
  @dataclass
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							ScoreSdeVeSchedulerState
  			lowerCamelCase							:							jnp.ndarray
  			lowerCamelCase							:							Optional[jnp.ndarray]       		=  None
  
  
  
  
  
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return True
  
  
  
  			@register_to_config
  			def __init__(					self       ,     lowerCamelCase__ = 2_000       ,     lowerCamelCase__ = 0.15       ,     lowerCamelCase__ = 0.01       ,     lowerCamelCase__ = 13_48.0       ,     lowerCamelCase__ = 1e-5       ,     lowerCamelCase__ = 1       ,     )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									pass
  
  
  
  			def A__ (					self       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       ScoreSdeVeSchedulerState.create()
  									return self.set_sigmas(
  									    lowerCamelCase__       ,     self.config.num_train_timesteps       ,     self.config.sigma_min       ,     self.config.sigma_max       ,     self.config.sampling_eps       ,     )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = ()       ,     lowerCamelCase__ = None       )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
  									lowercase__							 =       jnp.linspace(1       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return state.replace(timesteps=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     lowerCamelCase__ = None       ,     )			->		ScoreSdeVeSchedulerState:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       sigma_min if sigma_min is not None else self.config.sigma_min
  									lowercase__							 =       sigma_max if sigma_max is not None else self.config.sigma_max
  									lowercase__							 =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
  									if state.timesteps is None:
  															lowercase__							 =       self.set_timesteps(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  
  									lowercase__							 =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__       )       ,     jnp.log(lowerCamelCase__       )       ,     lowerCamelCase__       )       )
  									lowercase__							 =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )
  
  									return state.replace(discrete_sigmas=lowerCamelCase__       ,     sigmas=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return jnp.where(timesteps == 0       ,     jnp.zeros_like(lowerCamelCase__       )       ,     state.discrete_sigmas[timesteps - 1]       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									lowercase__							 =       timestep * jnp.ones(
  									    sample.shape[0]       ,     )
  									lowercase__							 =       (timestep * (len(state.timesteps       ) - 1)).long()
  
  									lowercase__							 =       state.discrete_sigmas[timesteps]
  									lowercase__							 =       self.get_adjacent_sigma(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									lowercase__							 =       jnp.zeros_like(lowerCamelCase__       )
  									lowercase__							 =       (sigma**2 - adjacent_sigma**2) ** 0.5
  
  									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
  									# also equation 47 shows the analog from SDE models to ancestral sampling methods
  									lowercase__							 =       diffusion.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       drift - diffusion**2 * model_output
  
  									#  equation 6: sample noise for the diffusion term of
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  									lowercase__							 =       sample - drift  # subtract because `dt` is a small negative timestep
  									# TODO is the variable diffusion the correct scaling term for the noise?
  									lowercase__							 =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g
  
  									if not return_dict:
  															return (prev_sample, prev_sample_mean, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     prev_sample_mean=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__ = True       ,     )			->		Union[FlaxSdeVeOutput, Tuple]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if state.timesteps is None:
  															raise ValueError(
  															    """`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""       )
  
  									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
  									# sample noise for correction
  									lowercase__							 =       random.split(lowerCamelCase__       ,     num=1       )
  									lowercase__							 =       random.normal(key=lowerCamelCase__       ,     shape=sample.shape       )
  
  									# compute step size from the model_output, the noise, and the snr
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       jnp.linalg.norm(lowerCamelCase__       )
  									lowercase__							 =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
  									lowercase__							 =       step_size * jnp.ones(sample.shape[0]       )
  
  									# compute corrected sample: model_output term and noise term
  									lowercase__							 =       step_size.flatten()
  									lowercase__							 =       broadcast_to_shape_from_left(lowerCamelCase__       ,     sample.shape       )
  									lowercase__							 =       sample + step_size * model_output
  									lowercase__							 =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise
  
  									if not return_dict:
  															return (prev_sample, state)
  
  									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__       ,     state=lowerCamelCase__       )
  
  
  
  			def __len__(					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return self.config.num_train_timesteps
  
  
  
  
  
  
  