



import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class    _A	(     lowerCAmelCase_				,unittest.TestCase   ):
       # TODO: is there an appropriate internal test set?
       UpperCamelCase__  :   Tuple						 =						'ssube/stable-diffusion-x4-upscaler-onnx'



       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Any=0):


           '''simple docstring'''

           __a							=    floats_tensor((1, 3, 128, 128)					,		rng=random.Random(__lowerCAmelCase))
           __a							=    torch.manual_seed(__lowerCAmelCase)
           __a							=    {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''generator''': generator,
               '''num_inference_steps''': 3,
               '''guidance_scale''': 7.5,
               '''output_type''': '''numpy''',
           }
           return inputs



       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,		provider='''CPUExecutionProvider''')
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    self.get_dummy_inputs()
           __a							=    pipe(**__lowerCAmelCase).images
           __a							=    image[0, -3:, -3:, -1].flatten()

           # started as 128, should now be 512
           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array(
               [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23])
           assert np.abs(image_slice - expected_slice).max() < 1E-1



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,		provider='''CPUExecutionProvider''')
           __a							=    PNDMScheduler.from_config(pipe.scheduler.config					,		skip_prk_steps=__lowerCAmelCase)
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    self.get_dummy_inputs()
           __a							=    pipe(**__lowerCAmelCase).images
           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array(
               [0.6_89_88_92, 0.59_24_05_56, 0.52_49_95_27, 0.58_86_62_15, 0.52_25_82_35, 0.52_57_27_15, 0.62_41_44_73, 0.6_17_43_87, 0.6_21_49_64])
           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,		provider='''CPUExecutionProvider''')
           __a							=    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    self.get_dummy_inputs()
           __a							=    pipe(**__lowerCAmelCase).images
           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array(
               [0.7_65_92_78, 0.76_43_76_64, 0.75_57_91_07, 0.7_69_11_16, 0.77_66_69_86, 0.7_72_76_72, 0.7_75_86_64, 0.7_81_22_26, 0.76_94_25_15])

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



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,		provider='''CPUExecutionProvider''')
           __a							=    EulerDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    self.get_dummy_inputs()
           __a							=    pipe(**__lowerCAmelCase).images
           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array(
               [0.6_97_47_82, 0.68_90_20_93, 0.70_13_58_85, 0.7_58_36_18, 0.7_80_45_45, 0.7_85_49_12, 0.78_66_74_26, 0.78_74_38_63, 0.78_07_02_23])
           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1





       def 		_lowerCamelCase      (		self							:					List[str]):


           '''simple docstring'''

           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint					,		provider='''CPUExecutionProvider''')
           __a							=    EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    self.get_dummy_inputs()
           __a							=    pipe(**__lowerCAmelCase).images
           __a							=    image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           __a							=    np.array(
               [0.77_42_44_96, 0.77_36_01, 0.7_64_52_88, 0.7_76_95_98, 0.7_77_27_39, 0.7_73_86_88, 0.78_18_72_33, 0.77_87_95_84, 0.76_70_43])

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


@nightly
@require_onnxruntime
@require_torch_gpu
class    _A	(     unittest.TestCase   ):



       @property
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           return (
               "CUDAExecutionProvider",
               {
                   "gpu_mem_limit": "15000000000",  # 15GB
                   "arena_extend_strategy": "kSameAsRequested",
               },
           )



       @property
       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    ort.SessionOptions()
           __a							=    False
           return options



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/img2img/sketch-mountains-input.jpg''')
           __a							=    init_image.resize((128, 128))
           # using the PNDM scheduler by default
           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(
               '''ssube/stable-diffusion-x4-upscaler-onnx'''					,		provider=self.gpu_provider					,		sess_options=self.gpu_options					,		)
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    '''A fantasy landscape, trending on artstation'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__lowerCAmelCase					,		image=__lowerCAmelCase					,		guidance_scale=7.5					,		num_inference_steps=10					,		generator=__lowerCAmelCase					,		output_type='''np'''					,		)
           __a							=    output.images
           __a							=    images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           __a							=    np.array([0.48_83, 0.49_47, 0.49_80, 0.49_75, 0.49_82, 0.49_80, 0.50_00, 0.50_06, 0.49_72])
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2





       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/img2img/sketch-mountains-input.jpg''')
           __a							=    init_image.resize((128, 128))
           __a							=    LMSDiscreteScheduler.from_pretrained(
               '''ssube/stable-diffusion-x4-upscaler-onnx'''					,		subfolder='''scheduler''')
           __a							=    OnnxStableDiffusionUpscalePipeline.from_pretrained(
               '''ssube/stable-diffusion-x4-upscaler-onnx'''					,		scheduler=__lowerCAmelCase					,		provider=self.gpu_provider					,		sess_options=self.gpu_options					,		)
           pipe.set_progress_bar_config(disable=__lowerCAmelCase)

           __a							=    '''A fantasy landscape, trending on artstation'''

           __a							=    torch.manual_seed(0)
           __a							=    pipe(
               prompt=__lowerCAmelCase					,		image=__lowerCAmelCase					,		guidance_scale=7.5					,		num_inference_steps=20					,		generator=__lowerCAmelCase					,		output_type='''np'''					,		)
           __a							=    output.images
           __a							=    images[0, 255:258, 383:386, -1]

           assert images.shape == (1, 512, 512, 3)
           __a							=    np.array(
               [0.50_17_37_53, 0.50_22_33_56, 0.50_20_39, 0.50_23_30_36, 0.5_02_37_25, 0.5_02_26_01, 0.5_01_87_58, 0.50_23_40_85, 0.50_24_15_66])
           # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

           assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2









import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    np.array([[1, item, train_mtch[i]] for i, item in enumerate(_UpperCAmelCase    )]    )
    __a							=    np.array(_UpperCAmelCase    )
    __a							=    np.dot(np.dot(np.linalg.inv(np.dot(x.transpose()       ,			_UpperCAmelCase    )    )       ,			x.transpose()    )       ,			_UpperCAmelCase    )
    return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2]    )





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    (1, 2, 1)
    __a							=    (1, 1, 0, 7)
    __a							=    SARIMAX(
        _UpperCAmelCase       ,			exog=_UpperCAmelCase       ,			order=_UpperCAmelCase       ,			seasonal_order=_UpperCAmelCase    )
    __a							=    model.fit(disp=_UpperCAmelCase       ,			maxiter=600       ,			method='''nm'''    )
    __a							=    model_fit.predict(1       ,			len(_UpperCAmelCase    )       ,			exog=[test_match]    )
    return result[0]





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    SVR(kernel='''rbf'''       ,			C=1       ,			gamma=0.1       ,			epsilon=0.1    )
    regressor.fit(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    regressor.predict(_UpperCAmelCase    )
    return y_pred[0]





def 					__snake_case     (      _UpperCAmelCase    ):
    train_user.sort()
    __a							=    np.percentile(_UpperCAmelCase       ,			25    )
    __a							=    np.percentile(_UpperCAmelCase       ,			75    )
    __a							=    qa - qa
    __a							=    qa - (iqr * 0.1)
    return low_lim





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):
    __a							=    0
    __a							=    0
    for i in list_vote:
        if i > actual_result:
            __a							=    not_safe + 1
        else:
            if abs(abs(_UpperCAmelCase    ) - abs(_UpperCAmelCase    )    ) <= 0.1:
                safe += 1
            else:
                not_safe += 1
    return safe > not_safe


if __name__ == "__main__":
 # data_input_df = pd.read_csv("ex_data.csv", header=None)
 __snake_case						:Any						=					[[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
 __snake_case						:List[Any]						=					pd.DataFrame(
     data_input, columns=['''total_user''', '''total_even''', '''days''']
 )
 __snake_case						:Dict						=					Normalizer().fit_transform(data_input_df.values)
 # split data
 __snake_case						:Any						=					normalize_df[:, 2].tolist()
 __snake_case						:Tuple						=					normalize_df[:, 0].tolist()
 __snake_case						:Union[str, Any]						=					normalize_df[:, 1].tolist()

 # for svr (input variable = total date and total match)
 __snake_case						:Tuple						=					normalize_df[:, [1, 2]].tolist()
 __snake_case						:List[str]						=					x[: len(x) - 1]
 __snake_case						:int						=					x[len(x) - 1 :]

 # for linear regression & sarimax
 __snake_case						:int						=					total_date[: len(total_date) - 1]
 __snake_case						:Dict						=					total_user[: len(total_user) - 1]
 __snake_case						:int						=					total_match[: len(total_match) - 1]

 __snake_case						:List[Any]						=					total_date[len(total_date) - 1 :]
 __snake_case						:Tuple						=					total_user[len(total_user) - 1 :]
 __snake_case						:List[str]						=					total_match[len(total_match) - 1 :]

 # voting system with forecasting
 __snake_case						:Optional[Any]						=					[
     linear_regression_prediction(
         trn_date, trn_user, trn_match, tst_date, tst_match
     ),
     sarimax_predictor(trn_user, trn_match, tst_match),
     support_vector_regressor(x_train, x_test, trn_user),
 ]

 # check the safety of today's data
 __snake_case						:Union[str, Any]						=					'''''' if data_safety_checker(res_vote, tst_user) else '''not '''
 print('''Today\'s data is {not_str}safe.''')





