



import os

import requests

UpperCAmelCase				:							Optional[int]								="""https://www.amdoren.com/api/currency.php"""


# Currency and their description
UpperCAmelCase				:							Any								="""
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 				_lowerCAmelCase      (_lowerCAmelCase = "USD"				,		_lowerCAmelCase = "INR"				,		_lowerCAmelCase = 1.0				,		_lowerCAmelCase = ""):
					UpperCamelCase_											=				locals()
					# from is a reserved keyword
					UpperCamelCase_											=				params.pop("from_")
					UpperCamelCase_											=				requests.get(_lowerCamelCase				,		params=_lowerCamelCase).json()
					return str(res["amount"]) if res["error"] == 0 else res["error_message"]


if __name__ == "__main__":
	UpperCAmelCase				:							Dict								=os.getenv("""CI""", """""")
	UpperCAmelCase				:							Optional[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,
	    )
	)





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
	import torch


class  _lowercase				:
				'''simple docstring'''







				def __init__(	self       ,	snake_case__       ,	snake_case__=14       ,	snake_case__=7       ,	snake_case__=True       ,	snake_case__=True       ,	snake_case__=False       ,	snake_case__=True       ,	snake_case__=99       ,	snake_case__=32       ,	snake_case__=5       ,	snake_case__=4       ,	snake_case__=37       ,	snake_case__="gelu"       ,	snake_case__=0.1       ,	snake_case__=0.1       ,	snake_case__=512       ,	snake_case__=0.02       ,	):

									'''simple docstring'''


									UpperCamelCase_											=				parent
									UpperCamelCase_											=				batch_size
									UpperCamelCase_											=				seq_length
									UpperCamelCase_											=				is_training
									UpperCamelCase_											=				use_input_mask
									UpperCamelCase_											=				use_token_type_ids
									UpperCamelCase_											=				use_labels
									UpperCamelCase_											=				vocab_size
									UpperCamelCase_											=				hidden_size
									UpperCamelCase_											=				num_hidden_layers
									UpperCamelCase_											=				num_attention_heads
									UpperCamelCase_											=				intermediate_size
									UpperCamelCase_											=				hidden_act
									UpperCamelCase_											=				hidden_dropout_prob
									UpperCamelCase_											=				attention_probs_dropout_prob
									UpperCamelCase_											=				max_position_embeddings
									UpperCamelCase_											=				initializer_range
									UpperCamelCase_											=				None
									UpperCamelCase_											=				vocab_size - 1
									UpperCamelCase_											=				vocab_size - 1
									UpperCamelCase_											=				vocab_size - 1

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size      )

									UpperCamelCase_											=				None
									if self.use_input_mask:
														UpperCamelCase_											=				random_attention_mask([self.batch_size, self.seq_length]      )

									UpperCamelCase_											=				GPTaConfig(
									    vocab_size=self.vocab_size       ,	n_embd=self.hidden_size       ,	n_layer=self.num_hidden_layers       ,	n_head=self.num_attention_heads       ,	n_positions=self.max_position_embeddings       ,	use_cache=snake_case__       ,	bos_token_id=self.bos_token_id       ,	eos_token_id=self.eos_token_id       ,	pad_token_id=self.pad_token_id       ,	)

									return (config, input_ids, input_mask)

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				self.prepare_config_and_inputs()
									UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				config_and_inputs
									UpperCamelCase_											=				{"input_ids": input_ids, "attention_mask": attention_mask}
									return config, inputs_dict

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				self.prepare_config_and_inputs()

									UpperCamelCase_											=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
									UpperCamelCase_											=				ids_tensor([self.batch_size, self.seq_length]       ,	vocab_size=2      )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				20
									UpperCamelCase_											=				model_class_name(snake_case__      )

									UpperCamelCase_											=				model.init_cache(input_ids.shape[0]       ,	snake_case__      )
									UpperCamelCase_											=				jnp.ones((input_ids.shape[0], max_decoder_length)       ,	dtype="i4"      )

									UpperCamelCase_											=				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1      )[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )
									UpperCamelCase_											=				model(
									    input_ids[:, :-1]       ,	attention_mask=snake_case__       ,	past_key_values=snake_case__       ,	position_ids=snake_case__       ,	)

									UpperCamelCase_											=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="i4"      )
									UpperCamelCase_											=				model(
									    input_ids[:, -1:]       ,	attention_mask=snake_case__       ,	past_key_values=outputs_cache.past_key_values       ,	position_ids=snake_case__       ,	)

									UpperCamelCase_											=				model(snake_case__      )

									UpperCamelCase_											=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
									self.parent.assertTrue(diff < 1e-3       ,	msg=F"""Max diff is {diff}"""      )

				def  _lowerCamelCase   (	self       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      ):

									'''simple docstring'''


									UpperCamelCase_											=				20
									UpperCamelCase_											=				model_class_name(snake_case__      )

									UpperCamelCase_											=				jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])      )]       ,	axis=-1       ,	)

									UpperCamelCase_											=				model.init_cache(input_ids.shape[0]       ,	snake_case__      )
									UpperCamelCase_											=				jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1      )[None, :]       ,	(input_ids.shape[0], input_ids.shape[-1] - 1)      )

									UpperCamelCase_											=				model(
									    input_ids[:, :-1]       ,	attention_mask=snake_case__       ,	past_key_values=snake_case__       ,	position_ids=snake_case__       ,	)
									UpperCamelCase_											=				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,	dtype="i4"      )
									UpperCamelCase_											=				model(
									    input_ids[:, -1:]       ,	past_key_values=outputs_cache.past_key_values       ,	attention_mask=snake_case__       ,	position_ids=snake_case__       ,	)

									UpperCamelCase_											=				model(snake_case__       ,	attention_mask=snake_case__      )

									UpperCamelCase_											=				np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])      )      )
									self.parent.assertTrue(diff < 1e-3       ,	msg=F"""Max diff is {diff}"""      )




@require_flax
class  _lowercase				(a_ ,				a_ ,				unittest.TestCase ):
				'''simple docstring'''







				lowercase__				  =  (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
				lowercase__				  =  (FlaxGPTaLMHeadModel,) if is_flax_available() else ()

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				FlaxGPTaModelTester(self      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      )

				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_					,      UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs()
														self.model_tester.check_use_cache_forward_with_attn_mask(
														    snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__      )

				@slow
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_											=				GPTaTokenizer.from_pretrained("gpt2"       ,	pad_token="</s>"       ,	padding_side="left"      )
									UpperCamelCase_											=				tokenizer(["Hello this is a long string", "Hey"]       ,	return_tensors="np"       ,	padding=snake_case__       ,	truncation=snake_case__      )

									UpperCamelCase_											=				FlaxGPTaLMHeadModel.from_pretrained("gpt2"      )
									UpperCamelCase_											=				False
									UpperCamelCase_											=				model.config.eos_token_id

									UpperCamelCase_											=				jax.jit(model.generate      )

									UpperCamelCase_											=				jit_generate(inputs["input_ids"]       ,	attention_mask=inputs["attention_mask"]      ).sequences

									UpperCamelCase_											=				tokenizer.batch_decode(snake_case__       ,	skip_special_tokens=snake_case__      )

									UpperCamelCase_											=				[
									    "Hello this is a long string of words. I'm going to start with the first one.\n",
									    "Hey, I'm not sure if I'm going to be able to do",
									]

									self.assertListEqual(snake_case__       ,	snake_case__      )

				@is_pt_flax_cross_test
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																			# prepare inputs
																			UpperCamelCase_											=				self._prepare_for_class(snake_case__       ,	snake_case__      )
																			UpperCamelCase_											=				{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCamelCase_											=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCamelCase_											=				getattr(snake_case__       ,	snake_case__      )

																			UpperCamelCase_					,      UpperCamelCase_											=				pt_inputs["input_ids"].shape
																			UpperCamelCase_											=				np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,)      )
																			for batch_idx, start_index in enumerate(snake_case__      ):
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1
																			UpperCamelCase_											=				pt_model_class(snake_case__      ).eval()
																			UpperCamelCase_											=				model_class(snake_case__       ,	dtype=jnp.floataa      )

																			UpperCamelCase_											=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,	snake_case__      )
																			UpperCamelCase_											=				fx_state

																			with torch.no_grad():
																								UpperCamelCase_											=				pt_model(**snake_case__      ).to_tuple()

																			UpperCamelCase_											=				fx_model(**snake_case__      ).to_tuple()
																			self.assertEqual(len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								pt_model.save_pretrained(snake_case__      )
																								UpperCamelCase_											=				model_class.from_pretrained(snake_case__       ,	from_pt=snake_case__      )

																			UpperCamelCase_											=				fx_model_loaded(**snake_case__      ).to_tuple()
																			self.assertEqual(
																			    len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output_loaded, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output_loaded[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

				@is_pt_flax_cross_test
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									UpperCamelCase_					,      UpperCamelCase_											=				self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
														with self.subTest(model_class.__name__      ):
																			# prepare inputs
																			UpperCamelCase_											=				self._prepare_for_class(snake_case__       ,	snake_case__      )
																			UpperCamelCase_											=				{k: torch.tensor(v.tolist()      ) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			UpperCamelCase_											=				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			UpperCamelCase_											=				getattr(snake_case__       ,	snake_case__      )

																			UpperCamelCase_											=				pt_model_class(snake_case__      ).eval()
																			UpperCamelCase_											=				model_class(snake_case__       ,	dtype=jnp.floataa      )

																			UpperCamelCase_											=				load_flax_weights_in_pytorch_model(snake_case__       ,	fx_model.params      )
																			UpperCamelCase_					,      UpperCamelCase_											=				pt_inputs["input_ids"].shape
																			UpperCamelCase_											=				np.random.randint(0       ,	seq_length - 1       ,	size=(batch_size,)      )
																			for batch_idx, start_index in enumerate(snake_case__      ):
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1
																								UpperCamelCase_											=				0
																								UpperCamelCase_											=				1

																			# make sure weights are tied in PyTorch
																			pt_model.tie_weights()

																			with torch.no_grad():
																								UpperCamelCase_											=				pt_model(**snake_case__      ).to_tuple()

																			UpperCamelCase_											=				fx_model(**snake_case__      ).to_tuple()
																			self.assertEqual(len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

																			with tempfile.TemporaryDirectory() as tmpdirname:
																								fx_model.save_pretrained(snake_case__      )
																								UpperCamelCase_											=				pt_model_class.from_pretrained(snake_case__       ,	from_flax=snake_case__      )

																			with torch.no_grad():
																								UpperCamelCase_											=				pt_model_loaded(**snake_case__      ).to_tuple()

																			self.assertEqual(
																			    len(snake_case__      )       ,	len(snake_case__      )       ,	"Output lengths differ between Flax and PyTorch"      )
																			for fx_output, pt_output in zip(snake_case__       ,	snake_case__      ):
																								self.assert_almost_equals(fx_output[:, -1]       ,	pt_output[:, -1].numpy()       ,	4e-2      )

				@slow
				def  _lowerCamelCase   (	self      ):

									'''simple docstring'''


									for model_class_name in self.all_model_classes:
														UpperCamelCase_											=				model_class_name.from_pretrained("gpt2"       ,	from_pt=snake_case__      )
														UpperCamelCase_											=				model(np.ones((1, 1)      )      )
														self.assertIsNotNone(snake_case__      )
