


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  UpperCamelCase					(	__magic_name__     :							list    ,    __magic_name__     :							list    ,    __magic_name__     :							list    ,    __magic_name__     :							list    ,    __magic_name__     :							list     )   ->  float:
		"""simple docstring"""




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






def  UpperCamelCase					(	__magic_name__     :							list    ,    __magic_name__     :							list    ,    __magic_name__     :							list     )   ->  float:
		"""simple docstring"""




		lowercase__     			=		(1, 2, 1)
		lowercase__     			=		(1, 1, 0, 7)
		lowercase__     			=		SARIMAX(
		    snake_case_    ,    exog=snake_case_    ,    order=snake_case_    ,    seasonal_order=snake_case_     )
		lowercase__     			=		model.fit(disp=snake_case_    ,    maxiter=600    ,    method="""nm"""     )
		lowercase__     			=		model_fit.predict(1    ,    len(snake_case_     )    ,    exog=[test_match]     )
		return result[0]






def  UpperCamelCase					(	__magic_name__     :							list    ,    __magic_name__     :							list    ,    __magic_name__     :							list     )   ->  float:
		"""simple docstring"""




		lowercase__     			=		SVR(kernel="""rbf"""    ,    C=1    ,    gamma=0.1    ,    epsilon=0.1     )
		regressor.fit(snake_case_    ,    snake_case_     )
		lowercase__     			=		regressor.predict(snake_case_     )
		return y_pred[0]






def  UpperCamelCase					(	__magic_name__     :							list     )   ->  float:
		"""simple docstring"""




		train_user.sort()
		lowercase__     			=		np.percentile(snake_case_    ,    25     )
		lowercase__     			=		np.percentile(snake_case_    ,    75     )
		lowercase__     			=		qa - qa
		lowercase__     			=		qa - (iqr * 0.1)
		return low_lim






def  UpperCamelCase					(	__magic_name__     :							list    ,    __magic_name__     :							float     )   ->  bool:
		"""simple docstring"""




		lowercase__     			=		0
		lowercase__     			=		0
		for i in list_vote:
				if i > actual_result:
						lowercase__     			=		not_safe + 1
				else:
						if abs(abs(snake_case_     ) - abs(snake_case_     )     ) <= 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)
	A       :		List[Any]    =   [[1_8_2_3_1, 0.0, 1], [2_2_6_2_1, 1.0, 2], [1_5_6_7_5, 0.0, 3], [2_3_5_8_3, 1.0, 4]]
	A       :		Dict    =   pd.DataFrame(
	    data_input, columns=['total_user', 'total_even', 'days']
	)
	A       :		Any    =   Normalizer().fit_transform(data_input_df.values)
	# split data
	A       :		List[str]    =   normalize_df[:, 2].tolist()
	A       :		str    =   normalize_df[:, 0].tolist()
	A       :		str    =   normalize_df[:, 1].tolist()

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

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

	A       :		Optional[int]    =   total_date[len(total_date) - 1 :]
	A       :		str    =   total_user[len(total_user) - 1 :]
	A       :		int    =   total_match[len(total_match) - 1 :]

	# voting system with forecasting
	A       :		Optional[int]    =   [
	    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
	A       :		Optional[int]    =   '' if data_safety_checker(res_vote, tst_user) else 'not '
	print('Today\'s data is {not_str}safe.')


'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
    import jax

    from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    Optional[Any]   ,			__a    :    Optional[Any]=13   ,			__a    :    int=30   ,			__a    :    Union[str, Any]=2   ,			__a    :    Dict=3   ,			__a    :    List[Any]=True   ,			__a    :    Optional[Any]=True   ,			__a    :    List[Any]=32   ,			__a    :    Any=5   ,			__a    :    str=4   ,			__a    :    Optional[int]=37   ,			__a    :    Optional[int]="gelu"   ,			__a    :    List[str]=0.1   ,			__a    :    Tuple=0.1   ,			__a    :    List[str]=10   ,			__a    :    Optional[int]=0.02   ,			):
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   patch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   is_training
         UpperCAmelCase_           =   use_labels
         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_           =   type_sequence_label_size
         UpperCAmelCase_           =   initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (image_size // patch_size) ** 2
         UpperCAmelCase_           =   num_patches + 1



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase_           =   ViTConfig(
             image_size=self.image_size   ,			patch_size=self.patch_size   ,			num_channels=self.num_channels   ,			hidden_size=self.hidden_size   ,			num_hidden_layers=self.num_hidden_layers   ,			num_attention_heads=self.num_attention_heads   ,			intermediate_size=self.intermediate_size   ,			hidden_act=self.hidden_act   ,			hidden_dropout_prob=self.hidden_dropout_prob   ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,			is_decoder=__a   ,			initializer_range=self.initializer_range   ,			)

         return config, pixel_values



    def 					_lowercase						(self    :    Dict   ,			__a    :    Any   ,			__a    :    List[Any] ):
         UpperCAmelCase_           =   FlaxViTModel(config=__a )
         UpperCAmelCase_           =   model(__a )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         UpperCAmelCase_           =   (self.image_size, self.image_size)
         UpperCAmelCase_           =   (self.patch_size, self.patch_size)
         UpperCAmelCase_           =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, num_patches + 1, self.hidden_size) )



    def 					_lowercase						(self    :    Tuple   ,			__a    :    str   ,			__a    :    Any ):
         UpperCAmelCase_           =   self.type_sequence_label_size
         UpperCAmelCase_           =   FlaxViTForImageClassification(config=__a )
         UpperCAmelCase_           =   model(__a )
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.type_sequence_label_size) )

         # test greyscale images
         UpperCAmelCase_           =   1
         UpperCAmelCase_           =   FlaxViTForImageClassification(__a )

         UpperCAmelCase_           =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
         UpperCAmelCase_           =   model(__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )    , (
             UpperCAmelCase_
         )    , 
         )           =   config_and_inputs
         UpperCAmelCase_           =   {"pixel_values": pixel_values}
         return config, inputs_dict




@require_flax
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Tuple          =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()



    def 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   FlaxViTModelTester(self )
         UpperCAmelCase_           =   ConfigTester(self   ,			config_class=__a   ,			has_text_modality=__a   ,			hidden_size=37 )



    def 					_lowercase						(self    :    Tuple ):
         self.config_tester.run_common_tests()



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__a )



    def 					_lowercase						(self    :    str ):
         UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__a )



    def 					_lowercase						(self    :    Tuple ):
         UpperCAmelCase_    , UpperCAmelCase_           =   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_           =   model_class(__a )
              UpperCAmelCase_           =   inspect.signature(model.__call__ )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_           =   [*signature.parameters.keys()]

              UpperCAmelCase_           =   ["pixel_values"]
              self.assertListEqual(arg_names[:1]   ,			__a )



    def 					_lowercase						(self    :    Optional[Any] ):
         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__ ):
                   UpperCAmelCase_           =   self._prepare_for_class(__a   ,			__a )
                   UpperCAmelCase_           =   model_class(__a )

                   @jax.jit
                   def model_jitted(__a    :    Tuple   ,			**__a    :    List[Any] ):
                        return model(pixel_values=__a   ,			**__a )

                   with self.subTest("JIT Enabled" ):
                        UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   with self.subTest("JIT Disabled" ):
                        with jax.disable_jit():
                             UpperCAmelCase_           =   model_jitted(**__a ).to_tuple()

                   self.assertEqual(len(__a )   ,			len(__a ) )
                   for jitted_output, output in zip(__a   ,			__a ):
                        self.assertEqual(jitted_output.shape   ,			output.shape )






    @slow
    def 					_lowercase						(self    :    Tuple ):
         for model_class_name in self.all_model_classes:
              UpperCAmelCase_           =   model_class_name.from_pretrained("google/vit-base-patch16-224" )
              UpperCAmelCase_           =   model(np.ones((1, 3, 224, 224) ) )
              self.assertIsNotNone(__a )




