






"""simple docstring"""


import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


_UpperCamelCase    :   Any   			=						'scheduler_config.json'






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					1
    __magic_name__   					=					2
    __magic_name__   					=					3
    __magic_name__   					=					4
    __magic_name__   					=					5






@dataclass
class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					42






class 						snake_case  :
    __magic_name__   					=					SCHEDULER_CONFIG_NAME
    __magic_name__   					=					['''dtype''']
    __magic_name__   					=					[]
    __magic_name__   					=					True





    @classmethod
    def     lowerCamelCase__				(							cls     : Tuple    ,		A     : Dict[str, Any] = None    ,		A     : Optional[str] = None    ,		A     : Dict=False    ,		**A     : Union[str, Any]    ,		):


      '''simple docstring'''




      a,					a					:      int    =       cls.load_config(
          pretrained_model_name_or_path=A    ,		subfolder=A    ,		return_unused_kwargs=A    ,		**A    ,		)
      a,					a					:      List[Any]    =       cls.from_config(A    ,		return_unused_kwargs=A    ,		**A      )

      if hasattr(A    ,		'create_state'      ) and getattr(A    ,		'has_state'    ,		A      ):
        a					:      List[str]    =       scheduler.create_state()

      if return_unused_kwargs:
        return scheduler, state, unused_kwargs

      return scheduler, state





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Union[str, os.PathLike]    ,		A     : bool = False    ,		**A     : Any      ):


      '''simple docstring'''




      self.save_config(save_directory=A    ,		push_to_hub=A    ,		**A      )





    @property
    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      return self._get_compatibles()





    @classmethod
    def     lowerCamelCase__				(							cls     : List[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       list(set([cls.__name__] + cls._compatibles      )      )
      a					:      Tuple    =       importlib.import_module(__name__.split('.'      )[0]      )
      a					:      Tuple    =       [
          getattr(A    ,		A      ) for c in compatible_classes_str if hasattr(A    ,		A      )
      ]
      return compatible_classes




def       snake_case  (A_				:jnp.ndarray     ,      A_				:Tuple[int]				):







  '''simple docstring'''

  assert len(A_				) >= x.ndim
  return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(A_				) - x.ndim)				)     ,      A_				)




def       snake_case  (A_				:int     ,      A_				:str=0.999     ,      A_				:int=jnp.floataa				):







  '''simple docstring'''

  def alpha_bar(A_				:int				):
    return math.cos((time_step + 0.008) / 1.008 * math.pi / 2				) ** 2

  a					:      Optional[int]    =       []
  for i in range(A_				):
    a					:      Any    =       i / num_diffusion_timesteps
    a					:      str    =       (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar(A_				) / alpha_bar(A_				)     ,      A_				)				)
  return jnp.array(A_     ,      dtype=A_				)






@flax.struct.dataclass
class 						snake_case  :
    __magic_name__   					=					42
    __magic_name__   					=					42
    __magic_name__   					=					42





    @classmethod
    def     lowerCamelCase__				(							cls     : List[Any]    ,		A     : Optional[int]      ):


      '''simple docstring'''




      a					:      int    =       scheduler.config

      if config.trained_betas is not None:
        a					:      Tuple    =       jnp.asarray(config.trained_betas    ,		dtype=scheduler.dtype      )
      elif config.beta_schedule == "linear":
        a					:      Tuple    =       jnp.linspace(config.beta_start    ,		config.beta_end    ,		config.num_train_timesteps    ,		dtype=scheduler.dtype      )
      elif config.beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        a					:      Optional[Any]    =       (
            jnp.linspace(
                config.beta_start**0.5    ,		config.beta_end**0.5    ,		config.num_train_timesteps    ,		dtype=scheduler.dtype      )
            ** 2
        )
      elif config.beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        a					:      List[str]    =       betas_for_alpha_bar(config.num_train_timesteps    ,		dtype=scheduler.dtype      )
      else:
        raise NotImplementedError(
            F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''      )

      a					:      Optional[int]    =       1.0 - betas

      a					:      Any    =       jnp.cumprod(A    ,		axis=0      )

      return cls(
          alphas=A    ,		betas=A    ,		alphas_cumprod=A    ,		)




def       snake_case  (A_				:CommonSchedulerState     ,      A_				:jnp.ndarray     ,      A_				:jnp.ndarray     ,      A_				:jnp.ndarray				):







  '''simple docstring'''

  a					:      List[Any]    =       state.alphas_cumprod

  a					:      Union[str, Any]    =       alphas_cumprod[timesteps] ** 0.5
  a					:      int    =       sqrt_alpha_prod.flatten()
  a					:      str    =       broadcast_to_shape_from_left(A_     ,      original_samples.shape				)

  a					:      Union[str, Any]    =       (1 - alphas_cumprod[timesteps]) ** 0.5
  a					:      Any    =       sqrt_one_minus_alpha_prod.flatten()
  a					:      Union[str, Any]    =       broadcast_to_shape_from_left(A_     ,      original_samples.shape				)

  return sqrt_alpha_prod, sqrt_one_minus_alpha_prod




def       snake_case  (A_				:CommonSchedulerState     ,      A_				:jnp.ndarray     ,      A_				:jnp.ndarray     ,      A_				:jnp.ndarray				):







  '''simple docstring'''

  a,					a					:      List[str]    =       get_sqrt_alpha_prod(A_     ,      A_     ,      A_     ,      A_				)
  a					:      Union[str, Any]    =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
  return noisy_samples






def       snake_case  (A_				:CommonSchedulerState     ,      A_				:jnp.ndarray     ,      A_				:jnp.ndarray     ,      A_				:jnp.ndarray				):







  '''simple docstring'''

  a,					a					:      Optional[int]    =       get_sqrt_alpha_prod(A_     ,      A_     ,      A_     ,      A_				)
  a					:      Optional[Any]    =       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
  return velocity












"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCamelCase    :   int   			=						get_tests_dir('fixtures/test_sentencepiece_bpe.model')






class 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase	):
    __magic_name__   					=					BartphoTokenizer
    __magic_name__   					=					False
    __magic_name__   					=					True





    def     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      super().setUp()

      a					:      Any    =       ['▁This', '▁is', '▁a', '▁t', 'est']
      a					:      List[Any]    =       dict(zip(A    ,		range(len(A      )      )      )      )
      a					:      int    =       {'unk_token': '<unk>'}

      a					:      Tuple    =       os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['monolingual_vocab_file']      )
      with open(self.monolingual_vocab_file    ,		'w'    ,		encoding='utf-8'      ) as fp:
        for token in vocab_tokens:
          fp.write(F'''{token} {vocab_tokens[token]}\n'''      )

      a					:      Optional[int]    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      tokenizer.save_pretrained(self.tmpdirname      )





    def     lowerCamelCase__				(							self     : Dict    ,		**A     : str      ):


      '''simple docstring'''




      kwargs.update(self.special_tokens_map      )
      return BartphoTokenizer.from_pretrained(self.tmpdirname    ,		**A      )





    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Dict      ):


      '''simple docstring'''




      a					:      Tuple    =       'This is a là test'
      a					:      List[Any]    =       'This is a<unk><unk> test'
      return input_text, output_text





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       BartphoTokenizer(A    ,		self.monolingual_vocab_file    ,		**self.special_tokens_map      )
      a					:      int    =       'This is a là test'
      a					:      int    =       '▁This ▁is ▁a ▁l à ▁t est'.split()
      a					:      str    =       tokenizer.tokenize(A      )
      self.assertListEqual(A    ,		A      )

      a					:      Union[str, Any]    =       tokens + [tokenizer.unk_token]
      a					:      Dict    =       [4, 5, 6, 3, 3, 7, 8, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A      )    ,		A      )




