




import os

import requests

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


# Currency and their description
lowerCamelCase_  						=   "\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_    (	__UpperCamelCase = "USD",				__UpperCamelCase = "INR",				__UpperCamelCase = 1.0,				__UpperCamelCase = ""	):
    SCREAMING_SNAKE_CASE__								=locals()
    # from is a reserved keyword
    SCREAMING_SNAKE_CASE__								=params.pop("""from_"""	)
    SCREAMING_SNAKE_CASE__								=requests.get(__UpperCamelCase,				params=__UpperCamelCase	).json()
    return str(res["""amount"""]	) if res["error"] == 0 else res["error_message"]


if __name__ == "__main__":
    lowerCamelCase_  						=   os.getenv("CI", "")
    lowerCamelCase_  						=   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 gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class       __a					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    int									=  StableDiffusionImageVariationPipeline
     _A				:    List[str]									=  IMAGE_VARIATION_PARAMS
     _A				:    List[str]									=  IMAGE_VARIATION_BATCH_PARAMS
     _A				:    Any									=  frozenset([]     )
     # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _A				:    Tuple									=  frozenset([]     )




     def   __A				(	self			: List[str]						)		->   Optional[int]:


         '''simple docstring'''





         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__								=UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)				,layers_per_block=2				,sample_size=3_2				,in_channels=4				,out_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,cross_attention_dim=3_2				,)
         SCREAMING_SNAKE_CASE__								=PNDMScheduler(skip_prk_steps=_UpperCamelCase						)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__								=AutoencoderKL(
             block_out_channels=[3_2, 6_4]				,in_channels=3				,out_channels=3				,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,latent_channels=4				,)
         torch.manual_seed(0						)
         SCREAMING_SNAKE_CASE__								=CLIPVisionConfig(
             hidden_size=3_2				,projection_dim=3_2				,intermediate_size=3_7				,layer_norm_eps=1e-05				,num_attention_heads=4				,num_hidden_layers=5				,image_size=3_2				,patch_size=4				,)
         SCREAMING_SNAKE_CASE__								=CLIPVisionModelWithProjection(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=CLIPImageProcessor(crop_size=3_2				,size=3_2						)

         SCREAMING_SNAKE_CASE__								={
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """image_encoder""": image_encoder,
             """feature_extractor""": feature_extractor,
             """safety_checker""": None,
         }
         return components




     def   __A				(	self			: str				,_UpperCamelCase			: Optional[int]				,_UpperCamelCase			: Union[str, Any]=0						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=floats_tensor((1, 3, 3_2, 3_2)				,rng=random.Random(_UpperCamelCase						)						)
         SCREAMING_SNAKE_CASE__								=image.cpu().permute(0				,2				,3				,1						)[0]
         SCREAMING_SNAKE_CASE__								=Image.fromarray(np.uinta(_UpperCamelCase						)						).convert("""RGB"""						).resize((3_2, 3_2)						)
         if str(_UpperCamelCase						).startswith("""mps"""						):
             SCREAMING_SNAKE_CASE__								=torch.manual_seed(_UpperCamelCase						)
         else:
             SCREAMING_SNAKE_CASE__								=torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								={
             """image""": image,
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A				(	self			: int						)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								="""cpu"""  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE__								=self.get_dummy_components()
         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_dummy_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						).images
         SCREAMING_SNAKE_CASE__								=image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         SCREAMING_SNAKE_CASE__								=np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]						)

         assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




     def   __A				(	self			: Tuple						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								="""cpu"""  # ensure determinism for the device-dependent torch.Generator
         SCREAMING_SNAKE_CASE__								=self.get_dummy_components()
         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline(**_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_dummy_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=2 * [inputs["""image"""]]
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=output.images

         SCREAMING_SNAKE_CASE__								=image[-1, -3:, -3:, -1]

         assert image.shape == (2, 6_4, 6_4, 3)
         SCREAMING_SNAKE_CASE__								=np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]						)

         assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-3




     def   __A				(	self			: Dict						)		->   Dict:


         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3						)






@slow
@require_torch_gpu
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def   __A				(	self			: str						)		->   List[str]:


         '''simple docstring'''





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   __A				(	self			: Optional[int]				,_UpperCamelCase			: Dict				,_UpperCamelCase			: Union[str, Any]="cpu"				,_UpperCamelCase			: Tuple=torch.floataa				,_UpperCamelCase			: List[Any]=0						)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/input_image_vermeer.png"""						)
         SCREAMING_SNAKE_CASE__								=np.random.RandomState(_UpperCamelCase						).standard_normal((1, 4, 6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__								=torch.from_numpy(_UpperCamelCase						).to(device=_UpperCamelCase				,dtype=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								={
             """image""": init_image,
             """latents""": latents,
             """generator""": generator,
             """num_inference_steps""": 3,
             """guidance_scale""": 7.5,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A				(	self			: List[Any]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained(
             """lambdalabs/sd-image-variations-diffusers"""				,safety_checker=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						).images
         SCREAMING_SNAKE_CASE__								=image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         SCREAMING_SNAKE_CASE__								=np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379]						)
         assert np.abs(image_slice - expected_slice						).max() < 6e-3




     def   __A				(	self			: Optional[int]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=0

         def callback_fn(_UpperCamelCase			: int				,_UpperCamelCase			: int				,_UpperCamelCase			: torch.FloatTensor						) -> None:
             SCREAMING_SNAKE_CASE__								=True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                 SCREAMING_SNAKE_CASE__								=latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 6_4, 6_4)
                 SCREAMING_SNAKE_CASE__								=latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__								=np.array(
                     [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]						)

                 assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2
             elif step == 2:
                 SCREAMING_SNAKE_CASE__								=latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 6_4, 6_4)
                 SCREAMING_SNAKE_CASE__								=latents[0, -3:, -3:, -1]
                 SCREAMING_SNAKE_CASE__								=np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]						)

                 assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5e-2

         SCREAMING_SNAKE_CASE__								=False

         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained(
             """fusing/sd-image-variations-diffusers"""				,safety_checker=_UpperCamelCase				,torch_dtype=torch.floataa				,)
         pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)
         pipe.enable_attention_slicing()

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase				,dtype=torch.floataa						)
         pipe(**_UpperCamelCase				,callback=_UpperCamelCase				,callback_steps=1						)
         assert callback_fn.has_been_called
         assert number_of_steps == inputs["num_inference_steps"]




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


         '''simple docstring'''





         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         SCREAMING_SNAKE_CASE__								="""fusing/sd-image-variations-diffusers"""
         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained(
             _UpperCamelCase				,safety_checker=_UpperCamelCase				,torch_dtype=torch.floataa						)
         SCREAMING_SNAKE_CASE__								=pipe.to(_UpperCamelCase						)
         pipe.set_progress_bar_config(disable=_UpperCamelCase						)
         pipe.enable_attention_slicing(1						)
         pipe.enable_sequential_cpu_offload()

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase				,dtype=torch.floataa						)
         SCREAMING_SNAKE_CASE__								=pipe(**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=torch.cuda.max_memory_allocated()
         # make sure that less than 2.6 GB is allocated
         assert mem_bytes < 2.6 * 1_0**9






@nightly
@require_torch_gpu
class       __a					( unittest.TestCase     ):


     """simple docstring"""


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


         '''simple docstring'''





         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   __A				(	self			: Dict				,_UpperCamelCase			: List[str]				,_UpperCamelCase			: Union[str, Any]="cpu"				,_UpperCamelCase			: Any=torch.floataa				,_UpperCamelCase			: Tuple=0						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=torch.Generator(device=_UpperCamelCase						).manual_seed(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/input_image_vermeer.png"""						)
         SCREAMING_SNAKE_CASE__								=np.random.RandomState(_UpperCamelCase						).standard_normal((1, 4, 6_4, 6_4)						)
         SCREAMING_SNAKE_CASE__								=torch.from_numpy(_UpperCamelCase						).to(device=_UpperCamelCase				,dtype=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								={
             """image""": init_image,
             """latents""": latents,
             """generator""": generator,
             """num_inference_steps""": 5_0,
             """guidance_scale""": 7.5,
             """output_type""": """numpy""",
         }
         return inputs




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""						)
         sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						).images[0]

         SCREAMING_SNAKE_CASE__								=load_numpy(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""						)
         SCREAMING_SNAKE_CASE__								=np.abs(expected_image - image						).max()
         assert max_diff < 1e-3




     def   __A				(	self			: List[str]						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""						)
         SCREAMING_SNAKE_CASE__								=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config						)
         sd_pipe.to(_UpperCamelCase						)
         sd_pipe.set_progress_bar_config(disable=_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.get_inputs(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=2_5
         SCREAMING_SNAKE_CASE__								=sd_pipe(**_UpperCamelCase						).images[0]

         SCREAMING_SNAKE_CASE__								=load_numpy(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""						)
         SCREAMING_SNAKE_CASE__								=np.abs(expected_image - image						).max()
         assert max_diff < 1e-3






