






"""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  lowerCAmelCase__ :
   # setable values
   SCREAMING_SNAKE_CASE_         =None
   SCREAMING_SNAKE_CASE_         =None
   SCREAMING_SNAKE_CASE_         =None
   @classmethod
   def 			__a       (		cls			:  List[str]       ):


        '''simple docstring'''
        return cls()




@dataclass
class  lowerCAmelCase__ (						__magic_name__      ):
   SCREAMING_SNAKE_CASE_         =42
   SCREAMING_SNAKE_CASE_         =42
   SCREAMING_SNAKE_CASE_         =None




class  lowerCAmelCase__ (						__magic_name__       ,	__magic_name__      ):
   @property
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        return True
   @register_to_config
   def __init__(		self			:  Tuple       , snake_case__			:  int = 2_0_0_0       , snake_case__			:  float = 0.15       , snake_case__			:  float = 0.01       , snake_case__			:  float = 1348.0       , snake_case__			:  float = 1e-5       , snake_case__			:  int = 1       , ):


        '''simple docstring'''
        pass
   def 			__a       (		self			:  Any       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Any       =       ScoreSdeVeSchedulerState.create()
        return self.set_sigmas(
            snake_case__       , self.config.num_train_timesteps       , self.config.sigma_min       , self.config.sigma_max       , self.config.sampling_eps       , )
   def 			__a       (		self			:  List[Any]       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  int       , snake_case__			:  Tuple = ()       , snake_case__			:  float = None       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

        UpperCAmelCase__	:						Optional[int]       =       jnp.linspace(1       , snake_case__       , snake_case__       )
        return state.replace(timesteps=snake_case__       )
   def 			__a       (		self			:  int       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  int       , snake_case__			:  float = None       , snake_case__			:  float = None       , snake_case__			:  float = None       , ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       sigma_min if sigma_min is not None else self.config.sigma_min
        UpperCAmelCase__	:						List[str]       =       sigma_max if sigma_max is not None else self.config.sigma_max
        UpperCAmelCase__	:						str       =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             UpperCAmelCase__	:						Union[str, Any]       =       self.set_timesteps(snake_case__       , snake_case__       , snake_case__       )

        UpperCAmelCase__	:						Union[str, Any]       =       jnp.exp(jnp.linspace(jnp.log(snake_case__       )       , jnp.log(snake_case__       )       , snake_case__       )       )
        UpperCAmelCase__	:						Any       =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]       )

        return state.replace(discrete_sigmas=snake_case__       , sigmas=snake_case__       )
   def 			__a       (		self			:  int       , snake_case__			:  Union[str, Any]       , snake_case__			:  List[Any]       , snake_case__			:  List[Any]       ):


        '''simple docstring'''
        return jnp.where(timesteps == 0       , jnp.zeros_like(snake_case__       )       , state.discrete_sigmas[timesteps - 1]       )
   def 			__a       (		self			:  Dict       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  jnp.ndarray       , snake_case__			:  int       , snake_case__			:  jnp.ndarray       , snake_case__			:  random.KeyArray       , snake_case__			:  bool = True       , ):


        '''simple docstring'''
        if state.timesteps is None:
             raise ValueError(
                 "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"       )

        UpperCAmelCase__	:						Tuple       =       timestep * jnp.ones(
            sample.shape[0]       , )
        UpperCAmelCase__	:						Any       =       (timestep * (len(state.timesteps       ) - 1)).long()

        UpperCAmelCase__	:						int       =       state.discrete_sigmas[timesteps]
        UpperCAmelCase__	:						Union[str, Any]       =       self.get_adjacent_sigma(snake_case__       , snake_case__       , snake_case__       )
        UpperCAmelCase__	:						Dict       =       jnp.zeros_like(snake_case__       )
        UpperCAmelCase__	:						Dict       =       (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
        UpperCAmelCase__	:						Dict       =       diffusion.flatten()
        UpperCAmelCase__	:						int       =       broadcast_to_shape_from_left(snake_case__       , sample.shape       )
        UpperCAmelCase__	:						str       =       drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        UpperCAmelCase__	:						Dict       =       random.split(snake_case__       , num=1       )
        UpperCAmelCase__	:						str       =       random.normal(key=snake_case__       , shape=sample.shape       )
        UpperCAmelCase__	:						List[Any]       =       sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        UpperCAmelCase__	:						Optional[int]       =       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=snake_case__       , prev_sample_mean=snake_case__       , state=snake_case__       )
   def 			__a       (		self			:  Union[str, Any]       , snake_case__			:  ScoreSdeVeSchedulerState       , snake_case__			:  jnp.ndarray       , snake_case__			:  jnp.ndarray       , snake_case__			:  random.KeyArray       , snake_case__			:  bool = True       , ):


        '''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
        UpperCAmelCase__	:						Dict       =       random.split(snake_case__       , num=1       )
        UpperCAmelCase__	:						List[Any]       =       random.normal(key=snake_case__       , shape=sample.shape       )

        # compute step size from the model_output, the noise, and the snr
        UpperCAmelCase__	:						Optional[int]       =       jnp.linalg.norm(snake_case__       )
        UpperCAmelCase__	:						Union[str, Any]       =       jnp.linalg.norm(snake_case__       )
        UpperCAmelCase__	:						int       =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
        UpperCAmelCase__	:						str       =       step_size * jnp.ones(sample.shape[0]       )

        # compute corrected sample: model_output term and noise term
        UpperCAmelCase__	:						Dict       =       step_size.flatten()
        UpperCAmelCase__	:						Union[str, Any]       =       broadcast_to_shape_from_left(snake_case__       , sample.shape       )
        UpperCAmelCase__	:						Optional[Any]       =       sample + step_size * model_output
        UpperCAmelCase__	:						Tuple       =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

        if not return_dict:
             return (prev_sample, state)

        return FlaxSdeVeOutput(prev_sample=snake_case__       , state=snake_case__       )




   def __len__(		self			:  str       ):


        '''simple docstring'''
        return self.config.num_train_timesteps










"""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  lowerCAmelCase__ (						unittest.TestCase      ):
   def 			__a       (		self			:  str       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Tuple       =       "<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(" "       )
        UpperCAmelCase__	:						Dict       =       dict(zip(snake_case__       , range(len(snake_case__       )       )       )       )

        UpperCAmelCase__	:						Dict       =       {
            "pad_token": "<pad>",
            "unk_token": "<unk>",
            "bos_token": "<s>",
            "eos_token": "</s>",
        }
        UpperCAmelCase__	:						str       =       {
            "feature_size": 1,
            "padding_value": 0.0,
            "sampling_rate": 1_6_0_0_0,
            "return_attention_mask": False,
            "do_normalize": True,
        }

        UpperCAmelCase__	:						List[str]       =       tempfile.mkdtemp()
        UpperCAmelCase__	:						int       =       os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["vocab_file"]       )
        UpperCAmelCase__	:						List[str]       =       os.path.join(self.tmpdirname       , snake_case__       )
        with open(self.vocab_file       , "w"       , encoding="utf-8"       ) as fp:
             fp.write(json.dumps(snake_case__       ) + "\n"       )

        with open(self.feature_extraction_file       , "w"       , encoding="utf-8"       ) as fp:
             fp.write(json.dumps(snake_case__       ) + "\n"       )
   def 			__a       (		self			:  List[str]       , **snake_case__			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.add_kwargs_tokens_map.copy()
        kwargs.update(snake_case__       )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname       , **snake_case__       )
   def 			__a       (		self			:  List[Any]       , **snake_case__			:  str       ):


        '''simple docstring'''
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname       , **snake_case__       )
   def 			__a       (		self			:  List[str]       ):


        '''simple docstring'''
        shutil.rmtree(self.tmpdirname       )
   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						int       =       self.get_tokenizer()
        UpperCAmelCase__	:						List[Any]       =       self.get_feature_extractor()

        UpperCAmelCase__	:						Optional[Any]       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        processor.save_pretrained(self.tmpdirname       )
        UpperCAmelCase__	:						List[Any]       =       WavaVecaProcessor.from_pretrained(self.tmpdirname       )

        self.assertEqual(processor.tokenizer.get_vocab()       , tokenizer.get_vocab()       )
        self.assertIsInstance(processor.tokenizer       , snake_case__       )

        self.assertEqual(processor.feature_extractor.to_json_string()       , feature_extractor.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor       , snake_case__       )
   def 			__a       (		self			:  Dict       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[Any]       =       WavaVecaProcessor(tokenizer=self.get_tokenizer()       , feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

        UpperCAmelCase__	:						str       =       self.get_tokenizer(bos_token="(BOS)"       , eos_token="(EOS)"       )
        UpperCAmelCase__	:						Optional[Any]       =       self.get_feature_extractor(do_normalize=snake_case__       , padding_value=1.0       )

        UpperCAmelCase__	:						Optional[Any]       =       WavaVecaProcessor.from_pretrained(
            self.tmpdirname       , bos_token="(BOS)"       , eos_token="(EOS)"       , do_normalize=snake_case__       , padding_value=1.0       )

        self.assertEqual(processor.tokenizer.get_vocab()       , tokenizer_add_kwargs.get_vocab()       )
        self.assertIsInstance(processor.tokenizer       , snake_case__       )

        self.assertEqual(processor.feature_extractor.to_json_string()       , feature_extractor_add_kwargs.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor       , snake_case__       )
   def 			__a       (		self			:  Tuple       ):


        '''simple docstring'''
        UpperCAmelCase__	:						List[str]       =       self.get_feature_extractor()
        UpperCAmelCase__	:						str       =       self.get_tokenizer()

        UpperCAmelCase__	:						str       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        UpperCAmelCase__	:						Optional[int]       =       floats_list((3, 1_0_0_0)       )

        UpperCAmelCase__	:						int       =       feature_extractor(snake_case__       , return_tensors="np"       )
        UpperCAmelCase__	:						Optional[Any]       =       processor(snake_case__       , 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			:  Optional[Any]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.get_feature_extractor()
        UpperCAmelCase__	:						int       =       self.get_tokenizer()

        UpperCAmelCase__	:						Optional[int]       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        UpperCAmelCase__	:						Any       =       "This is a test string"

        UpperCAmelCase__	:						Optional[int]       =       processor(text=snake_case__       )

        UpperCAmelCase__	:						Optional[int]       =       tokenizer(snake_case__       )

        for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key]       , encoded_processor[key]       )
   def 			__a       (		self			:  Optional[int]       ):


        '''simple docstring'''
        UpperCAmelCase__	:						str       =       self.get_feature_extractor()
        UpperCAmelCase__	:						str       =       self.get_tokenizer()

        UpperCAmelCase__	:						Optional[int]       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        UpperCAmelCase__	:						Optional[int]       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        UpperCAmelCase__	:						Dict       =       processor.batch_decode(snake_case__       )
        UpperCAmelCase__	:						Tuple       =       tokenizer.batch_decode(snake_case__       )

        self.assertListEqual(snake_case__       , snake_case__       )




   def 			__a       (		self			:  int       ):


        '''simple docstring'''
        UpperCAmelCase__	:						Dict       =       self.get_feature_extractor()
        UpperCAmelCase__	:						Any       =       self.get_tokenizer()

        UpperCAmelCase__	:						Dict       =       WavaVecaProcessor(tokenizer=snake_case__       , feature_extractor=snake_case__       )

        self.assertListEqual(
            processor.model_input_names       , feature_extractor.model_input_names       , msg="`processor` and `feature_extractor` model input names do not match"       , )


