code: # 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 				_lowercase						:
  
  
  
  
  
  
      '''simple docstring'''
      # setable values
      SCREAMING_SNAKE_CASE:							Optional[jnp.ndarray]        =						None
      SCREAMING_SNAKE_CASE:							Optional[jnp.ndarray]        =						None
      SCREAMING_SNAKE_CASE:							Optional[jnp.ndarray]        =						None
  
  
  
  
      @classmethod
      def     _a						(						cls   ):
         return cls()
  
  
  
  
  
  @dataclass
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							ScoreSdeVeSchedulerState
      SCREAMING_SNAKE_CASE:							jnp.ndarray
      SCREAMING_SNAKE_CASE:							Optional[jnp.ndarray]        =						None
  
  
  
  
  
  class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      @property
      def     _a						(						self   ):
         return True
  
  
  
  
      @register_to_config
      def __init__(						self					,	lowerCamelCase__ = 2_000					,	lowerCamelCase__ = 0.1_5					,	lowerCamelCase__ = 0.0_1					,	lowerCamelCase__ = 1_3_4_8.0					,	lowerCamelCase__ = 1E-5					,	lowerCamelCase__ = 1					,	):
         pass
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Tuple         =  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   ):
         lowerCAmelCase_:       str         =  sampling_eps if sampling_eps is not None else self.config.sampling_eps
  
         lowerCAmelCase_:       List[str]         =  jnp.linspace(1					,	lowerCamelCase__					,	lowerCamelCase__   )
         return state.replace(timesteps=lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = None					,	lowerCamelCase__ = None					,	lowerCamelCase__ = None					,	):
         lowerCAmelCase_:       Dict         =  sigma_min if sigma_min is not None else self.config.sigma_min
         lowerCAmelCase_:       Dict         =  sigma_max if sigma_max is not None else self.config.sigma_max
         lowerCAmelCase_:       int         =  sampling_eps if sampling_eps is not None else self.config.sampling_eps
         if state.timesteps is None:
            lowerCAmelCase_:       Dict         =  self.set_timesteps(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
  
         lowerCAmelCase_:       Optional[int]         =  jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__   )					,	jnp.log(lowerCamelCase__   )					,	lowerCamelCase__   )   )
         lowerCAmelCase_:       Union[str, Any]         =  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__   ):
         return jnp.where(timesteps == 0					,	jnp.zeros_like(lowerCamelCase__   )					,	state.discrete_sigmas[timesteps - 1]   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = True					,	):
         if state.timesteps is None:
            raise ValueError(
                "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )
  
         lowerCAmelCase_:       Optional[int]         =  timestep * jnp.ones(
             sample.shape[0]					,	)
         lowerCAmelCase_:       Dict         =  (timestep * (len(state.timesteps   ) - 1)).long()
  
         lowerCAmelCase_:       Optional[int]         =  state.discrete_sigmas[timesteps]
         lowerCAmelCase_:       int         =  self.get_adjacent_sigma(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )
         lowerCAmelCase_:       Optional[Any]         =  jnp.zeros_like(lowerCamelCase__   )
         lowerCAmelCase_:       int         =  (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
         lowerCAmelCase_:       Any         =  diffusion.flatten()
         lowerCAmelCase_:       Union[str, Any]         =  broadcast_to_shape_from_left(lowerCamelCase__					,	sample.shape   )
         lowerCAmelCase_:       Union[str, Any]         =  drift - diffusion**2 * model_output
  
         #  equation 6: sample noise for the diffusion term of
         lowerCAmelCase_:       Optional[Any]         =  random.split(lowerCamelCase__					,	num=1   )
         lowerCAmelCase_:       Union[str, Any]         =  random.normal(key=lowerCamelCase__					,	shape=sample.shape   )
         lowerCAmelCase_:       Dict         =  sample - drift  # subtract because `dt` is a small negative timestep
         # TODO is the variable diffusion the correct scaling term for the noise?
         lowerCAmelCase_:       Any         =  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					,	):
         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
         lowerCAmelCase_:       Tuple         =  random.split(lowerCamelCase__					,	num=1   )
         lowerCAmelCase_:       Any         =  random.normal(key=lowerCamelCase__					,	shape=sample.shape   )
  
         # compute step size from the model_output, the noise, and the snr
         lowerCAmelCase_:       Tuple         =  jnp.linalg.norm(lowerCamelCase__   )
         lowerCAmelCase_:       Any         =  jnp.linalg.norm(lowerCamelCase__   )
         lowerCAmelCase_:       Dict         =  (self.config.snr * noise_norm / grad_norm) ** 2 * 2
         lowerCAmelCase_:       Dict         =  step_size * jnp.ones(sample.shape[0]   )
  
         # compute corrected sample: model_output term and noise term
         lowerCAmelCase_:       List[Any]         =  step_size.flatten()
         lowerCAmelCase_:       str         =  broadcast_to_shape_from_left(lowerCamelCase__					,	sample.shape   )
         lowerCAmelCase_:       str         =  sample + step_size * model_output
         lowerCAmelCase_:       str         =  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   ):
         return self.config.num_train_timesteps
style_context: import os
  
  import requests
  
  a     :			Dict					    =						"""https://www.amdoren.com/api/currency.php"""
  
  
  # Currency and their description
  a     :			List[str]					    =						"""
  AED	United Arab Emirates Dirham
  AFN	Afghan Afghani
  ALL	Albanian Lek
  AMD	Armenian Dram
  ANG	Netherlands Antillean Guilder
  AOA	Angolan Kwanza
  ARS	Argentine Peso
  AUD	Australian Dollar
  AWG	Aruban Florin
  AZN	Azerbaijani Manat
  BAM	Bosnia & Herzegovina Convertible Mark
  BBD	Barbadian Dollar
  BDT	Bangladeshi Taka
  BGN	Bulgarian Lev
  BHD	Bahraini Dinar
  BIF	Burundian Franc
  BMD	Bermudian Dollar
  BND	Brunei Dollar
  BOB	Bolivian Boliviano
  BRL	Brazilian Real
  BSD	Bahamian Dollar
  BTN	Bhutanese Ngultrum
  BWP	Botswana Pula
  BYN	Belarus Ruble
  BZD	Belize Dollar
  CAD	Canadian Dollar
  CDF	Congolese Franc
  CHF	Swiss Franc
  CLP	Chilean Peso
  CNY	Chinese Yuan
  COP	Colombian Peso
  CRC	Costa Rican Colon
  CUC	Cuban Convertible Peso
  CVE	Cape Verdean Escudo
  CZK	Czech Republic Koruna
  DJF	Djiboutian Franc
  DKK	Danish Krone
  DOP	Dominican Peso
  DZD	Algerian Dinar
  EGP	Egyptian Pound
  ERN	Eritrean Nakfa
  ETB	Ethiopian Birr
  EUR	Euro
  FJD	Fiji Dollar
  GBP	British Pound Sterling
  GEL	Georgian Lari
  GHS	Ghanaian Cedi
  GIP	Gibraltar Pound
  GMD	Gambian Dalasi
  GNF	Guinea Franc
  GTQ	Guatemalan Quetzal
  GYD	Guyanaese Dollar
  HKD	Hong Kong Dollar
  HNL	Honduran Lempira
  HRK	Croatian Kuna
  HTG	Haiti Gourde
  HUF	Hungarian Forint
  IDR	Indonesian Rupiah
  ILS	Israeli Shekel
  INR	Indian Rupee
  IQD	Iraqi Dinar
  IRR	Iranian Rial
  ISK	Icelandic Krona
  JMD	Jamaican Dollar
  JOD	Jordanian Dinar
  JPY	Japanese Yen
  KES	Kenyan Shilling
  KGS	Kyrgystani Som
  KHR	Cambodian Riel
  KMF	Comorian Franc
  KPW	North Korean Won
  KRW	South Korean Won
  KWD	Kuwaiti Dinar
  KYD	Cayman Islands Dollar
  KZT	Kazakhstan Tenge
  LAK	Laotian Kip
  LBP	Lebanese Pound
  LKR	Sri Lankan Rupee
  LRD	Liberian Dollar
  LSL	Lesotho Loti
  LYD	Libyan Dinar
  MAD	Moroccan Dirham
  MDL	Moldovan Leu
  MGA	Malagasy Ariary
  MKD	Macedonian Denar
  MMK	Myanma Kyat
  MNT	Mongolian Tugrik
  MOP	Macau Pataca
  MRO	Mauritanian Ouguiya
  MUR	Mauritian Rupee
  MVR	Maldivian Rufiyaa
  MWK	Malawi Kwacha
  MXN	Mexican Peso
  MYR	Malaysian Ringgit
  MZN	Mozambican Metical
  NAD	Namibian Dollar
  NGN	Nigerian Naira
  NIO	Nicaragua Cordoba
  NOK	Norwegian Krone
  NPR	Nepalese Rupee
  NZD	New Zealand Dollar
  OMR	Omani Rial
  PAB	Panamanian Balboa
  PEN	Peruvian Nuevo Sol
  PGK	Papua New Guinean Kina
  PHP	Philippine Peso
  PKR	Pakistani Rupee
  PLN	Polish Zloty
  PYG	Paraguayan Guarani
  QAR	Qatari Riyal
  RON	Romanian Leu
  RSD	Serbian Dinar
  RUB	Russian Ruble
  RWF	Rwanda Franc
  SAR	Saudi Riyal
  SBD	Solomon Islands Dollar
  SCR	Seychellois Rupee
  SDG	Sudanese Pound
  SEK	Swedish Krona
  SGD	Singapore Dollar
  SHP	Saint Helena Pound
  SLL	Sierra Leonean Leone
  SOS	Somali Shilling
  SRD	Surinamese Dollar
  SSP	South Sudanese Pound
  STD	Sao Tome and Principe Dobra
  SYP	Syrian Pound
  SZL	Swazi Lilangeni
  THB	Thai Baht
  TJS	Tajikistan Somoni
  TMT	Turkmenistani Manat
  TND	Tunisian Dinar
  TOP	Tonga Paanga
  TRY	Turkish Lira
  TTD	Trinidad and Tobago Dollar
  TWD	New Taiwan Dollar
  TZS	Tanzanian Shilling
  UAH	Ukrainian Hryvnia
  UGX	Ugandan Shilling
  USD	United States Dollar
  UYU	Uruguayan Peso
  UZS	Uzbekistan Som
  VEF	Venezuelan Bolivar
  VND	Vietnamese Dong
  VUV	Vanuatu Vatu
  WST	Samoan Tala
  XAF	Central African CFA franc
  XCD	East Caribbean Dollar
  XOF	West African CFA franc
  XPF	CFP Franc
  YER	Yemeni Rial
  ZAR	South African Rand
  ZMW	Zambian Kwacha
  """
  def 		snake_case__  (		lowercase = "USD"       ,							lowercase = "INR"       ,							lowercase = 1.0       ,							lowercase = ""				):
     lowerCAmelCase_:       List[str]         =  locals()
     # from is a reserved keyword
     lowerCAmelCase_:       Any         =  params.pop("from_"				)
     lowerCAmelCase_:       List[str]         =  requests.get(lowercase       ,							params=lowercase				).json()
     return str(res["amount"]				) if res["error"] == 0 else res["error_message"]
  
  
  if __name__ == "__main__":
   a     :			List[Any]					    =						os.getenv("""CI""", """""")
   a     :			List[str]					    =						os.getenv("""AMDOREN_API_KEY""", """""")
  
   if not API_KEY and not TESTING:
    raise KeyError(
        """API key must be provided in the 'AMDOREN_API_KEY' environment variable."""
    )
  
   print(
       convert_currency(
           input("""Enter from currency: """).strip(),
           input("""Enter to currency: """).strip(),
           float(input("""Enter the amount: """).strip()),
           API_KEY,
       )
   )