



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class    _A	(     unittest.TestCase   ):



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    tempfile.mkdtemp()

           # fmt: off
           __a							=    ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
           # fmt: on
           __a							=    dict(zip(__SCREAMING_SNAKE_CASE					,		range(len(__SCREAMING_SNAKE_CASE))))
           __a							=    ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
           __a							=    {'''unk_token''': '''<unk>'''}

           __a							=    os.path.join(self.tmpdirname					,		VOCAB_FILES_NAMES['''vocab_file'''])
           __a							=    os.path.join(self.tmpdirname					,		VOCAB_FILES_NAMES['''merges_file'''])
           with open(self.vocab_file					,		'''w'''					,		encoding='''utf-8''') as fp:
               fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + '''\n''')
           with open(self.merges_file					,		'''w'''					,		encoding='''utf-8''') as fp:
               fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE))

           __a							=    {
               '''do_resize''': True,
               '''size''': 20,
               '''do_center_crop''': True,
               '''crop_size''': 18,
               '''do_normalize''': True,
               '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
               '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
           }
           __a							=    os.path.join(self.tmpdirname					,		__SCREAMING_SNAKE_CASE)
           with open(self.image_processor_file					,		'''w'''					,		encoding='''utf-8''') as fp:
               json.dump(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Dict					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]):


           '''simple docstring'''

           return CLIPTokenizer.from_pretrained(self.tmpdirname					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					str					,		**__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           return CLIPImageProcessor.from_pretrained(self.tmpdirname					,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)



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


           '''simple docstring'''

           __a							=    [np.random.randint(255					,		size=(3, 30, 400)					,		dtype=np.uinta)]

           __a							=    [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE					,		0					,		-1)) for x in image_inputs]

           return image_inputs



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    self.get_tokenizer()
           __a							=    self.get_rust_tokenizer()
           __a							=    self.get_image_processor()

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)
           processor_slow.save_pretrained(self.tmpdirname)
           __a							=    CLIPProcessor.from_pretrained(self.tmpdirname					,		use_fast=__SCREAMING_SNAKE_CASE)

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)
           processor_fast.save_pretrained(self.tmpdirname)
           __a							=    CLIPProcessor.from_pretrained(self.tmpdirname)

           self.assertEqual(processor_slow.tokenizer.get_vocab()					,		tokenizer_slow.get_vocab())
           self.assertEqual(processor_fast.tokenizer.get_vocab()					,		tokenizer_fast.get_vocab())
           self.assertEqual(tokenizer_slow.get_vocab()					,		tokenizer_fast.get_vocab())
           self.assertIsInstance(processor_slow.tokenizer					,		__SCREAMING_SNAKE_CASE)
           self.assertIsInstance(processor_fast.tokenizer					,		__SCREAMING_SNAKE_CASE)

           self.assertEqual(processor_slow.image_processor.to_json_string()					,		image_processor.to_json_string())
           self.assertEqual(processor_fast.image_processor.to_json_string()					,		image_processor.to_json_string())
           self.assertIsInstance(processor_slow.image_processor					,		__SCREAMING_SNAKE_CASE)
           self.assertIsInstance(processor_fast.image_processor					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Dict):


           '''simple docstring'''

           __a							=    CLIPProcessor(tokenizer=self.get_tokenizer()					,		image_processor=self.get_image_processor())
           processor.save_pretrained(self.tmpdirname)

           __a							=    self.get_tokenizer(bos_token='''(BOS)'''					,		eos_token='''(EOS)''')
           __a							=    self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE					,		padding_value=1.0)

           __a							=    CLIPProcessor.from_pretrained(
               self.tmpdirname					,		bos_token='''(BOS)'''					,		eos_token='''(EOS)'''					,		do_normalize=__SCREAMING_SNAKE_CASE					,		padding_value=1.0)

           self.assertEqual(processor.tokenizer.get_vocab()					,		tokenizer_add_kwargs.get_vocab())
           self.assertIsInstance(processor.tokenizer					,		__SCREAMING_SNAKE_CASE)

           self.assertEqual(processor.image_processor.to_json_string()					,		image_processor_add_kwargs.to_json_string())
           self.assertIsInstance(processor.image_processor					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    self.prepare_image_inputs()

           __a							=    image_processor(__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')
           __a							=    processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors='''np''')

           for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()					,		input_processor[key].sum()					,		delta=1E-2)



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


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    '''lower newer'''

           __a							=    processor(text=__SCREAMING_SNAKE_CASE)

           __a							=    tokenizer(__SCREAMING_SNAKE_CASE)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]					,		encoded_processor[key])



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    '''lower newer'''
           __a							=    self.prepare_image_inputs()

           __a							=    processor(text=__SCREAMING_SNAKE_CASE					,		images=__SCREAMING_SNAKE_CASE)

           self.assertListEqual(list(inputs.keys())					,		['''input_ids''', '''attention_mask''', '''pixel_values'''])

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE):
               processor()



       def 		_lowerCamelCase      (		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __a							=    processor.batch_decode(__SCREAMING_SNAKE_CASE)
           __a							=    tokenizer.batch_decode(__SCREAMING_SNAKE_CASE)

           self.assertListEqual(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           __a							=    self.get_image_processor()
           __a							=    self.get_tokenizer()

           __a							=    CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,		image_processor=__SCREAMING_SNAKE_CASE)

           __a							=    '''lower newer'''
           __a							=    self.prepare_image_inputs()

           __a							=    processor(text=__SCREAMING_SNAKE_CASE					,		images=__SCREAMING_SNAKE_CASE)

           self.assertListEqual(list(inputs.keys())					,		processor.model_input_names)









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.''')





