






"""simple docstring"""


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 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







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



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= (1, 2, 1)
  __A									= (1, 1, 0, 7)
  __A									= SARIMAX(
      lowerCamelCase__		,       exog=lowerCamelCase__		,       order=lowerCamelCase__		,       seasonal_order=lowerCamelCase__ )
  __A									= model.fit(disp=lowerCamelCase__		,       maxiter=6_0_0		,       method='''nm''' )
  __A									= model_fit.predict(1		,       len(lowerCamelCase__ )		,       exog=[test_match] )
  return result[0]



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= SVR(kernel='''rbf'''		,       C=1		,       gamma=0.1		,       epsilon=0.1 )
  regressor.fit(lowerCamelCase__		,       lowerCamelCase__ )
  __A									= regressor.predict(lowerCamelCase__ )
  return y_pred[0]



def 					lowerCAmelCase				(    __UpperCamelCase ):




  """simple docstring"""







  train_user.sort()
  __A									= np.percentile(lowerCamelCase__		,       2_5 )
  __A									= np.percentile(lowerCamelCase__		,       7_5 )
  __A									= qa - qa
  __A									= qa - (iqr * 0.1)
  return low_lim



def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase ):




  """simple docstring"""







  __A									= 0
  __A									= 0
  for i in list_vote:
    if i > actual_result:
      __A									= not_safe + 1
    else:
      if abs(abs(lowerCamelCase__ ) - abs(lowerCamelCase__ ) ) <= 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)
       lowercase_				     =     [[1_8231, 0.0, 1], [2_2621, 1.0, 2], [1_5675, 0.0, 3], [2_3583, 1.0, 4]]
       lowercase_				     =     pd.DataFrame(
           data_input, columns=['total_user', 'total_even', 'days']
       )
       lowercase_				     =     Normalizer().fit_transform(data_input_df.values)
       # split data
       lowercase_				     =     normalize_df[:, 2].tolist()
       lowercase_				     =     normalize_df[:, 0].tolist()
       lowercase_				     =     normalize_df[:, 1].tolist()

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

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

       lowercase_				     =     total_date[len(total_date) - 1 :]
       lowercase_				     =     total_user[len(total_user) - 1 :]
       lowercase_				     =     total_match[len(total_match) - 1 :]

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









'''simple docstring'''




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=13       ,      lowercase=3       ,      lowercase=True       ,      lowercase=True       ,      lowercase=0.1       ,      lowercase=0.1       ,      lowercase=224       ,      lowercase=1000       ,      lowercase=[3, 3, 6, 4]       ,      lowercase=[48, 56, 112, 220]       ,      ):
				A_	:						Dict	=     parent
				A_	:						List[Any]	=     batch_size
				A_	:						Dict	=     num_channels
				A_	:						Optional[Any]	=     is_training
				A_	:						List[str]	=     use_labels
				A_	:						List[Any]	=     hidden_dropout_prob
				A_	:						Optional[int]	=     attention_probs_dropout_prob
				A_	:						Tuple	=     num_labels
				A_	:						List[str]	=     image_size
				A_	:						str	=     layer_depths
				A_	:						Optional[int]	=     embed_dims


	def     _a	(self							):
				A_	:						Union[str, Any]	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				A_	:						int	=     None
				if self.use_labels:
							A_	:						Tuple	=     ids_tensor([self.batch_size]       ,      self.num_labels							)

				A_	:						int	=     self.get_config()

				return config, pixel_values, labels


	def     _a	(self							):
				return SwiftFormerConfig(
				    depths=self.layer_depths       ,      embed_dims=self.embed_dims       ,      mlp_ratio=4       ,      downsamples=[True, True, True, True]       ,      hidden_act="""gelu"""       ,      num_labels=self.num_labels       ,      down_patch_size=3       ,      down_stride=2       ,      down_pad=1       ,      drop_rate=0.0       ,      drop_path_rate=0.0       ,      use_layer_scale=lowercase       ,      layer_scale_init_value=1E-5       ,      )


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						List[Any]	=     SwiftFormerModel(config=lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Union[str, Any]	=     model(lowercase							)
				self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.embed_dims[-1], 7, 7)							)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase							):
				A_	:						Any	=     self.num_labels
				A_	:						Any	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()
				A_	:						Optional[int]	=     model(lowercase       ,      labels=lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)

				A_	:						int	=     SwiftFormerForImageClassification(lowercase							)
				model.to(lowercase							)
				model.eval()

				A_	:						int	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
				A_	:						Dict	=     model(lowercase							)
				self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)


	def     _a	(self							):
				((A_),    (A_),    (A_))	:						int	=     self.prepare_config_and_inputs()
				A_	:						Any	=     {"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(
	    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__SCREAMING_SNAKE_CASE   :		int					=					False
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					False
	__SCREAMING_SNAKE_CASE   :		List[str]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False


	def     _a	(self							):
				A_	:						Optional[int]	=     SwiftFormerModelTester(self							)
				A_	:						Any	=     ConfigTester(
				    self       ,      config_class=lowercase       ,      has_text_modality=lowercase       ,      hidden_size=37       ,      num_attention_heads=12       ,      num_hidden_layers=12       ,      )


	def     _a	(self							):
				self.config_tester.run_common_tests()


	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				A_,    A_	:						Tuple	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						Optional[Any]	=     model_class(lowercase							)
							A_	:						Optional[int]	=     model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowercase       ,      nn.Linear							)							)


	def     _a	(self							):
				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     model_class(lowercase							)
							A_	:						Optional[int]	=     inspect.signature(model.forward							)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							A_	:						int	=     [*signature.parameters.keys()]

							A_	:						Tuple	=     ["""pixel_values"""]
							self.assertListEqual(arg_names[:1]       ,      lowercase							)


	def     _a	(self							):
				A_	:						str	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase							)


	def     _a	(self							):
				A_	:						List[Any]	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase							)


	@slow
	def     _a	(self							):
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							A_	:						Optional[Any]	=     SwiftFormerModel.from_pretrained(lowercase							)
							self.assertIsNotNone(lowercase							)


	@unittest.skip(reason="""SwiftFormer does not output attentions"""							)
	def     _a	(self							):
				pass


	def     _a	(self							):
				def check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							):
							A_	:						str	=     model_class(lowercase							)
							model.to(lowercase							)
							model.eval()

							with torch.no_grad():
										A_	:						Optional[int]	=     model(**self._prepare_for_class(lowercase       ,      lowercase							)							)

							A_	:						Any	=     outputs.hidden_states

							A_	:						Any	=     8
							self.assertEqual(len(lowercase							)       ,      lowercase							)  # TODO

							# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
							# with the width and height being successively divided by 2, after every 2 blocks
							for i in range(len(lowercase							)							):
										self.assertEqual(
										    hidden_states[i].shape       ,      torch.Size(
										        [
										            self.model_tester.batch_size,
										            self.model_tester.embed_dims[i // 2],
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
										        ]							)       ,      )

				A_,    A_	:						str	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							A_	:						str	=     True
							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							A_	:						str	=     True

							check_hidden_states_output(lowercase       ,      lowercase       ,      lowercase							)


	def     _a	(self							):
				def _config_zero_init(lowercase							):
							A_	:						Optional[Any]	=     copy.deepcopy(lowercase							)
							for key in configs_no_init.__dict__.keys():
										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
													setattr(lowercase       ,      lowercase       ,      1E-10							)
										if isinstance(getattr(lowercase       ,      lowercase       ,      lowercase							)       ,      lowercase							):
													A_	:						Any	=     _config_zero_init(getattr(lowercase       ,      lowercase							)							)
													setattr(lowercase       ,      lowercase       ,      lowercase							)
							return configs_no_init

				A_,    A_	:						Optional[int]	=     self.model_tester.prepare_config_and_inputs_for_common()

				A_	:						Any	=     _config_zero_init(lowercase							)
				for model_class in self.all_model_classes:
							A_	:						List[str]	=     model_class(config=lowercase							)
							for name, param in model.named_parameters():
										if param.requires_grad:
													self.assertIn(
													    ((param.data.mean() * 1E9) / 1E9).round().item()       ,      [0.0, 1.0]       ,      msg=F'Parameter {name} of model {model_class} seems not properly initialized'       ,      )


	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
	def     _a	(self							):
				pass






def 	a			( ):
			'''simple docstring'''





			A_	:						List[str]	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
			return image







@require_torch
@require_vision
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@cached_property
	def     _a	(self							):
				return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None


	@slow
	def     _a	(self							):
				A_	:						Any	=     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(lowercase							)

				A_	:						Dict	=     self.default_image_processor
				A_	:						Dict	=     prepare_img()
				A_	:						int	=     image_processor(images=lowercase       ,      return_tensors="""pt"""							).to(lowercase							)

				# forward pass
				with torch.no_grad():
							A_	:						int	=     model(**lowercase							)

				# verify the logits
				A_	:						int	=     torch.Size((1, 1000)							)
				self.assertEqual(outputs.logits.shape       ,      lowercase							)

				A_	:						List[str]	=     torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]							).to(lowercase							)
				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      lowercase       ,      atol=1E-4							)							)