






"""simple docstring"""

import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
			from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

			from .test_feature_extraction_speechta import floats_list


lowercase           =							get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')

@require_torch
class     A_				(   unittest.TestCase	):







				def      _snake_case       (				self				:    Dict						)					->       Tuple:
										__magic_name__      =  tempfile.mkdtemp()

										__magic_name__      =  SpeechTaTokenizer(lowerCamelCase_						)
										tokenizer.save_pretrained(self.tmpdirname						)

										__magic_name__      =  {
										    "feature_size": 1,
										    "padding_value": 0.0,
										    "sampling_rate": 1_6_0_0_0,
										    "do_normalize": False,
										    "num_mel_bins": 8_0,
										    "hop_length": 1_6,
										    "win_length": 6_4,
										    "win_function": "hann_window",
										    "fmin": 8_0,
										    "fmax": 7_6_0_0,
										    "mel_floor": 1e-10,
										    "reduction_factor": 2,
										    "return_attention_mask": True,
										}

										__magic_name__      =  os.path.join(self.tmpdirname    ,     lowerCamelCase_						)
										with open(self.feature_extraction_file    ,     "w"    ,     encoding="utf-8"						) as fp:
																fp.write(json.dumps(lowerCamelCase_						) + "\n"						)







				def      _snake_case       (				self				:    Union[str, Any]    ,     **__lowerCamelCase				:    List[str]						)					->       List[Any]:
										return SpeechTaTokenizer.from_pretrained(self.tmpdirname    ,     **lowerCamelCase_						)







				def      _snake_case       (				self				:    Dict    ,     **__lowerCamelCase				:    Optional[Any]						)					->       Optional[int]:
										return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname    ,     **lowerCamelCase_						)







				def      _snake_case       (				self				:    Dict						)					->       Tuple:
										shutil.rmtree(self.tmpdirname						)







				def      _snake_case       (				self				:    Optional[int]						)					->       Dict:
										__magic_name__      =  self.get_tokenizer()
										__magic_name__      =  self.get_feature_extractor()

										__magic_name__      =  SpeechTaProcessor(tokenizer=lowerCamelCase_    ,     feature_extractor=lowerCamelCase_						)

										processor.save_pretrained(self.tmpdirname						)
										__magic_name__      =  SpeechTaProcessor.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      _snake_case       (				self				:    Dict						)					->       Union[str, Any]:
										__magic_name__      =  SpeechTaProcessor(tokenizer=self.get_tokenizer()    ,     feature_extractor=self.get_feature_extractor()						)
										processor.save_pretrained(self.tmpdirname						)

										__magic_name__      =  self.get_tokenizer(bos_token="(BOS)"    ,     eos_token="(EOS)"						)
										__magic_name__      =  self.get_feature_extractor(do_normalize=lowerCamelCase_    ,     padding_value=1.0						)

										__magic_name__      =  SpeechTaProcessor.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      _snake_case       (				self				:    List[str]						)					->       Dict:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechTaProcessor(tokenizer=lowerCamelCase_    ,     feature_extractor=lowerCamelCase_						)

										__magic_name__      =  floats_list((3, 1_0_0_0)						)

										__magic_name__      =  feature_extractor(audio=lowerCamelCase_    ,     return_tensors="np"						)
										__magic_name__      =  processor(audio=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      _snake_case       (				self				:    List[Any]						)					->       int:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechTaProcessor(tokenizer=lowerCamelCase_    ,     feature_extractor=lowerCamelCase_						)

										__magic_name__      =  floats_list((3, 1_0_0_0)						)

										__magic_name__      =  feature_extractor(audio_target=lowerCamelCase_    ,     return_tensors="np"						)
										__magic_name__      =  processor(audio_target=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      _snake_case       (				self				:    str						)					->       str:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechTaProcessor(tokenizer=lowerCamelCase_    ,     feature_extractor=lowerCamelCase_						)

										__magic_name__      =  "This is a test string"

										__magic_name__      =  processor(text=lowerCamelCase_						)
										__magic_name__      =  tokenizer(lowerCamelCase_						)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]    ,     encoded_processor[key]						)







				def      _snake_case       (				self				:    Tuple						)					->       Optional[Any]:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechTaProcessor(tokenizer=lowerCamelCase_    ,     feature_extractor=lowerCamelCase_						)

										__magic_name__      =  "This is a test string"

										__magic_name__      =  processor(text_target=lowerCamelCase_						)
										__magic_name__      =  tokenizer(lowerCamelCase_						)

										for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]    ,     encoded_processor[key]						)







				def      _snake_case       (				self				:    Tuple						)					->       Any:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechTaProcessor(tokenizer=lowerCamelCase_    ,     feature_extractor=lowerCamelCase_						)

										__magic_name__      =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

										__magic_name__      =  processor.batch_decode(lowerCamelCase_						)
										__magic_name__      =  tokenizer.batch_decode(lowerCamelCase_						)

										self.assertListEqual(lowerCamelCase_    ,     lowerCamelCase_						)







				def      _snake_case       (				self				:    str						)					->       Any:
										__magic_name__      =  self.get_feature_extractor()
										__magic_name__      =  self.get_tokenizer()

										__magic_name__      =  SpeechTaProcessor(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"    ,     )




"""simple docstring"""


# 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 					a					:
  A_ :				CommonSchedulerState
  A_ :				jnp.ndarray

  # setable values
  A_ :				jnp.ndarray
  A_ :				jnp.ndarray
  A_ :				Optional[int] 			=							None



  @classmethod
  def        lowerCAmelCase_				(		cls   :    Optional[int]						,  lowerCamelCase_   :    CommonSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> Optional[Any]:
      return cls(
          common=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )






@dataclass
class 					a					(				A_							):
  A_ :				DDIMSchedulerState






class 					a					(				A_   ,		A_							):
  A_ :				Optional[int] 			=							[e.name for e in FlaxKarrasDiffusionSchedulers]

  A_ :				jnp.dtype



  @property
  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      return True



  @register_to_config
  def __init__(		self   :    Optional[int]						,  lowerCamelCase_   :    int = 10_00						,  lowerCamelCase_   :    float = 0.00_01						,  lowerCamelCase_   :    float = 0.02						,  lowerCamelCase_   :    str = "linear"						,  lowerCamelCase_   :    Optional[jnp.ndarray] = None						,  lowerCamelCase_   :    bool = True						,  lowerCamelCase_   :    int = 0						,  lowerCamelCase_   :    str = "epsilon"						,  lowerCamelCase_   :    jnp.dtype = jnp.floataa						,  )      -> Any:
      __a			      =						dtype



  def        lowerCAmelCase_				(		self   :    Dict						,  lowerCamelCase_   :    Optional[CommonSchedulerState] = None     )      -> DDIMSchedulerState:
      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=lowerCamelCase_						,  final_alpha_cumprod=lowerCamelCase_						,  init_noise_sigma=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    Optional[int] = None     )      -> jnp.ndarray:
      return sample



  def        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Tuple = ()     )      -> DDIMSchedulerState:
      __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						,  lowerCamelCase_     ) * step_ratio).round()[::-1] + self.config.steps_offset

      return state.replace(
          num_inference_steps=lowerCamelCase_						,  timesteps=lowerCamelCase_						,  )



  def        lowerCAmelCase_				(		self   :    int						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    Any     )      -> str:
      __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        lowerCAmelCase_				(		self   :    List[str]						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    int						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    float = 0.0						,  lowerCamelCase_   :    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)
      __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]						,  lowerCamelCase_     )

      __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(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )
      __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=lowerCamelCase_						,  state=lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    str						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return add_noise_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def        lowerCAmelCase_				(		self   :    Any						,  lowerCamelCase_   :    DDIMSchedulerState						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  lowerCamelCase_   :    jnp.ndarray						,  )      -> jnp.ndarray:
      return get_velocity_common(state.common						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_     )



  def __len__(		self   :    Tuple     )      -> Union[str, Any]:
      return self.config.num_train_timesteps
