




'''simple docstring'''





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   torch.FloatTensor
			lowerCAmelCase_  :   torch.FloatTensor


def   UpperCamelCase_(			snake_case							: Optional[int]							,       snake_case							: List[str]=0.999							,       snake_case							: int="cosine"							,       ):

							'''simple docstring'''






							if alpha_transform_type == "cosine":

														def alpha_bar_fn(snake_case							: Dict  ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(snake_case							: Union[str, Any]  ):
																					return math.exp(t * -1_2.0  )

							else:
														raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'  )

							snake_case_			      =   []
							for i in range(snake_case  ):
														snake_case_			      =   i / num_diffusion_timesteps
														snake_case_			      =   (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(snake_case  ) / alpha_bar_fn(snake_case  )							,       snake_case  )  )
							return torch.tensor(snake_case							,       dtype=torch.floataa  )




class 				_snake_case	(			lowercase_		,		lowercase_							):
			lowerCAmelCase_  :   Tuple		 =					1




			@register_to_config
			def __init__(      self       ,							a__ = 1_000       ,							a__ = 0.0_0_0_1       ,							a__ = 0.0_2       ,							a__ = "linear"       ,							a__ = 0.0       ,							a__ = None       ,							a__ = True       ,							)       ->       Optional[Any]:

										'''simple docstring'''




										if trained_betas is not None:
																	snake_case_			      =   torch.from_numpy(a__							)
										elif beta_schedule == "linear":
																	snake_case_			      =   torch.linspace(a__       ,							a__       ,							a__       ,							dtype=torch.floataa							)
										elif beta_schedule == "scaled_linear":
																	# this schedule is very specific to the latent diffusion model.
																	snake_case_			      =   (
																	    torch.linspace(beta_start**0.5       ,							beta_end**0.5       ,							a__       ,							dtype=torch.floataa							) ** 2
																	)
										elif beta_schedule == "squaredcos_cap_v2":
																	# Glide cosine schedule
																	snake_case_			      =   betas_for_alpha_bar(a__							)
										elif beta_schedule == "sigmoid":
																	# GeoDiff sigmoid schedule
																	snake_case_			      =   torch.linspace(-6       ,							6       ,							a__							)
																	snake_case_			      =   torch.sigmoid(a__							) * (beta_end - beta_start) + beta_start
										else:
																	raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'							)

										snake_case_			      =   1.0 - self.betas
										snake_case_			      =   torch.cumprod(self.alphas       ,							dim=0							)
										snake_case_			      =   torch.tensor(1.0							)

										snake_case_			      =   torch.tensor(1.0							)

										# standard deviation of the initial noise distribution
										snake_case_			      =   1.0

										# setable values
										snake_case_			      =   None
										snake_case_			      =   torch.from_numpy(np.arange(0       ,							a__							)[::-1].copy()							)

										snake_case_			      =   eta




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = None							)       ->       torch.FloatTensor:

										'''simple docstring'''




										return sample




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__ = 10       ,							a__ = 10       ,							a__ = None       ,							)       ->       Optional[int]:

										'''simple docstring'''




										snake_case_			      =   min(self.config.num_train_timesteps       ,							a__							)
										snake_case_			      =   num_inference_steps

										snake_case_			      =   []

										snake_case_			      =   {}
										for j in range(0       ,							num_inference_steps - jump_length       ,							a__							):
																	snake_case_			      =   jump_n_sample - 1

										snake_case_			      =   num_inference_steps
										while t >= 1:
																	snake_case_			      =   t - 1
																	timesteps.append(a__							)

																	if jumps.get(a__       ,							0							) > 0:
																								snake_case_			      =   jumps[t] - 1
																								for _ in range(a__							):
																															snake_case_			      =   t + 1
																															timesteps.append(a__							)

										snake_case_			      =   np.array(a__							) * (self.config.num_train_timesteps // self.num_inference_steps)
										snake_case_			      =   torch.from_numpy(a__							).to(a__							)




			def      lowerCAmelCase__       (      self       ,							a__							)       ->       Union[str, Any]:

										'''simple docstring'''




										snake_case_			      =   t - self.config.num_train_timesteps // self.num_inference_steps

										snake_case_			      =   self.alphas_cumprod[t]
										snake_case_			      =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										snake_case_			      =   1 - alpha_prod_t
										snake_case_			      =   1 - alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from
										# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
										# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
										# variance to pred_sample
										# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
										# without eta.
										# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
										snake_case_			      =   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

										return variance




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__       ,							a__       ,							a__       ,							a__ = None       ,							a__ = True       ,							)       ->       Union[RePaintSchedulerOutput, Tuple]:

										'''simple docstring'''




										snake_case_			      =   timestep
										snake_case_			      =   timestep - self.config.num_train_timesteps // self.num_inference_steps

										# 1. compute alphas, betas
										snake_case_			      =   self.alphas_cumprod[t]
										snake_case_			      =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
										snake_case_			      =   1 - alpha_prod_t

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										snake_case_			      =   (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	snake_case_			      =   torch.clamp(a__       ,							-1       ,							1							)

										# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
										# substitute formula (7) in the algorithm coming from DDPM paper
										# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
										# DDIM schedule gives the same results as DDPM with eta = 1.0
										# Noise is being reused in 7. and 8., but no impact on quality has
										# been observed.

										# 5. Add noise
										snake_case_			      =   model_output.device
										snake_case_			      =   randn_tensor(model_output.shape       ,							generator=a__       ,							device=a__       ,							dtype=model_output.dtype							)
										snake_case_			      =   self.eta * self._get_variance(a__							) ** 0.5

										snake_case_			      =   0
										if t > 0 and self.eta > 0:
																	snake_case_			      =   std_dev_t * noise

										# 6. compute "direction pointing to x_t" of formula (12)
										# from https://arxiv.org/pdf/2010.02502.pdf
										snake_case_			      =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

										# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
										snake_case_			      =   alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

										# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
										snake_case_			      =   (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

										# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
										snake_case_			      =   mask * prev_known_part + (1.0 - mask) * prev_unknown_part

										if not return_dict:
																	return (
																	    pred_prev_sample,
																	    pred_original_sample,
																	)

										return RePaintSchedulerOutput(prev_sample=a__       ,							pred_original_sample=a__							)




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__=None							)       ->       str:

										'''simple docstring'''




										snake_case_			      =   self.config.num_train_timesteps // self.num_inference_steps

										for i in range(a__							):
																	snake_case_			      =   self.betas[timestep + i]
																	if sample.device.type == "mps":
																								# randn does not work reproducibly on mps
																								snake_case_			      =   randn_tensor(sample.shape       ,							dtype=sample.dtype       ,							generator=a__							)
																								snake_case_			      =   noise.to(sample.device							)
																	else:
																								snake_case_			      =   randn_tensor(sample.shape       ,							generator=a__       ,							device=sample.device       ,							dtype=sample.dtype							)

																	# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																	snake_case_			      =   (1 - beta) ** 0.5 * sample + beta**0.5 * noise

										return sample




			def      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							a__       ,							)       ->       torch.FloatTensor:

										'''simple docstring'''




										raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."							)




			def __len__(      self							)       ->       Optional[Any]:

										'''simple docstring'''




										return self.config.num_train_timesteps








'''simple docstring'''





import os

import requests

_SCREAMING_SNAKE_CASE							:     int   	=				"https://www.amdoren.com/api/currency.php"


# Currency and their description
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				"\nAED	United Arab Emirates Dirham\nAFN	Afghan Afghani\nALL	Albanian Lek\nAMD	Armenian Dram\nANG	Netherlands Antillean Guilder\nAOA	Angolan Kwanza\nARS	Argentine Peso\nAUD	Australian Dollar\nAWG	Aruban Florin\nAZN	Azerbaijani Manat\nBAM	Bosnia & Herzegovina Convertible Mark\nBBD	Barbadian Dollar\nBDT	Bangladeshi Taka\nBGN	Bulgarian Lev\nBHD	Bahraini Dinar\nBIF	Burundian Franc\nBMD	Bermudian Dollar\nBND	Brunei Dollar\nBOB	Bolivian Boliviano\nBRL	Brazilian Real\nBSD	Bahamian Dollar\nBTN	Bhutanese Ngultrum\nBWP	Botswana Pula\nBYN	Belarus Ruble\nBZD	Belize Dollar\nCAD	Canadian Dollar\nCDF	Congolese Franc\nCHF	Swiss Franc\nCLP	Chilean Peso\nCNY	Chinese Yuan\nCOP	Colombian Peso\nCRC	Costa Rican Colon\nCUC	Cuban Convertible Peso\nCVE	Cape Verdean Escudo\nCZK	Czech Republic Koruna\nDJF	Djiboutian Franc\nDKK	Danish Krone\nDOP	Dominican Peso\nDZD	Algerian Dinar\nEGP	Egyptian Pound\nERN	Eritrean Nakfa\nETB	Ethiopian Birr\nEUR	Euro\nFJD	Fiji Dollar\nGBP	British Pound Sterling\nGEL	Georgian Lari\nGHS	Ghanaian Cedi\nGIP	Gibraltar Pound\nGMD	Gambian Dalasi\nGNF	Guinea Franc\nGTQ	Guatemalan Quetzal\nGYD	Guyanaese Dollar\nHKD	Hong Kong Dollar\nHNL	Honduran Lempira\nHRK	Croatian Kuna\nHTG	Haiti Gourde\nHUF	Hungarian Forint\nIDR	Indonesian Rupiah\nILS	Israeli Shekel\nINR	Indian Rupee\nIQD	Iraqi Dinar\nIRR	Iranian Rial\nISK	Icelandic Krona\nJMD	Jamaican Dollar\nJOD	Jordanian Dinar\nJPY	Japanese Yen\nKES	Kenyan Shilling\nKGS	Kyrgystani Som\nKHR	Cambodian Riel\nKMF	Comorian Franc\nKPW	North Korean Won\nKRW	South Korean Won\nKWD	Kuwaiti Dinar\nKYD	Cayman Islands Dollar\nKZT	Kazakhstan Tenge\nLAK	Laotian Kip\nLBP	Lebanese Pound\nLKR	Sri Lankan Rupee\nLRD	Liberian Dollar\nLSL	Lesotho Loti\nLYD	Libyan Dinar\nMAD	Moroccan Dirham\nMDL	Moldovan Leu\nMGA	Malagasy Ariary\nMKD	Macedonian Denar\nMMK	Myanma Kyat\nMNT	Mongolian Tugrik\nMOP	Macau Pataca\nMRO	Mauritanian Ouguiya\nMUR	Mauritian Rupee\nMVR	Maldivian Rufiyaa\nMWK	Malawi Kwacha\nMXN	Mexican Peso\nMYR	Malaysian Ringgit\nMZN	Mozambican Metical\nNAD	Namibian Dollar\nNGN	Nigerian Naira\nNIO	Nicaragua Cordoba\nNOK	Norwegian Krone\nNPR	Nepalese Rupee\nNZD	New Zealand Dollar\nOMR	Omani Rial\nPAB	Panamanian Balboa\nPEN	Peruvian Nuevo Sol\nPGK	Papua New Guinean Kina\nPHP	Philippine Peso\nPKR	Pakistani Rupee\nPLN	Polish Zloty\nPYG	Paraguayan Guarani\nQAR	Qatari Riyal\nRON	Romanian Leu\nRSD	Serbian Dinar\nRUB	Russian Ruble\nRWF	Rwanda Franc\nSAR	Saudi Riyal\nSBD	Solomon Islands Dollar\nSCR	Seychellois Rupee\nSDG	Sudanese Pound\nSEK	Swedish Krona\nSGD	Singapore Dollar\nSHP	Saint Helena Pound\nSLL	Sierra Leonean Leone\nSOS	Somali Shilling\nSRD	Surinamese Dollar\nSSP	South Sudanese Pound\nSTD	Sao Tome and Principe Dobra\nSYP	Syrian Pound\nSZL	Swazi Lilangeni\nTHB	Thai Baht\nTJS	Tajikistan Somoni\nTMT	Turkmenistani Manat\nTND	Tunisian Dinar\nTOP	Tonga Paanga\nTRY	Turkish Lira\nTTD	Trinidad and Tobago Dollar\nTWD	New Taiwan Dollar\nTZS	Tanzanian Shilling\nUAH	Ukrainian Hryvnia\nUGX	Ugandan Shilling\nUSD	United States Dollar\nUYU	Uruguayan Peso\nUZS	Uzbekistan Som\nVEF	Venezuelan Bolivar\nVND	Vietnamese Dong\nVUV	Vanuatu Vatu\nWST	Samoan Tala\nXAF	Central African CFA franc\nXCD	East Caribbean Dollar\nXOF	West African CFA franc\nXPF	CFP Franc\nYER	Yemeni Rial\nZAR	South African Rand\nZMW	Zambian Kwacha\n"


def   UpperCamelCase_(			snake_case							: str = "USD"							,       snake_case							: str = "INR"							,       snake_case							: float = 1.0							,       snake_case							: str = ""  ):

							'''simple docstring'''






							snake_case_			      =   locals()
							# from is a reserved keyword
							snake_case_			      =   params.pop("from_"  )
							snake_case_			      =   requests.get(snake_case							,       params=snake_case  ).json()
							return str(res["amount"]  ) if res["error"] == 0 else res["error_message"]


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:     Any   	=				os.getenv("CI", "")
		_SCREAMING_SNAKE_CASE							:     List[Any]   	=				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,
		    )
		)




