

'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowerCAmelCase__						:       str										=						{"""LayoutLMv2Config""", """LayoutLMv3Config"""}




@is_pipeline_test
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
						__UpperCAmelCase			     =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

						if model_mapping is not None:
												__UpperCAmelCase			     =    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
						if tf_model_mapping is not None:
												__UpperCAmelCase			     =    {
												    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
												}







						@require_torch
						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  pipeline(
								    task='''text-classification'''			,			model='''hf-internal-testing/tiny-random-distilbert'''			,			framework='''pt'''  )

								snake_case__						:       Any       						=  text_classifier('''This is great !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]  )

								snake_case__						:       Dict       						=  text_classifier('''This is great !'''			,			top_k=2  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}]  )

								snake_case__						:       int       						=  text_classifier(['''This is great !''', '''This is bad''']			,			top_k=2  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								    ]			,			)

								snake_case__						:       Tuple       						=  text_classifier('''This is great !'''			,			top_k=1  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]  )

								# Legacy behavior
								snake_case__						:       Optional[Any]       						=  text_classifier('''This is great !'''			,			return_all_scores=lowercase__  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]  )

								snake_case__						:       List[Any]       						=  text_classifier('''This is great !'''			,			return_all_scores=lowercase__  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}]]  )

								snake_case__						:       Any       						=  text_classifier(['''This is great !''', '''Something else''']			,			return_all_scores=lowercase__  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
								    ]			,			)

								snake_case__						:       Optional[int]       						=  text_classifier(['''This is great !''', '''Something else''']			,			return_all_scores=lowercase__  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[
								        {'''label''': '''LABEL_0''', '''score''': 0.5_0_4},
								        {'''label''': '''LABEL_0''', '''score''': 0.5_0_4},
								    ]			,			)







						@require_torch
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								import torch

								snake_case__						:       Optional[int]       						=  pipeline(
								    task='''text-classification'''			,			model='''hf-internal-testing/tiny-random-distilbert'''			,			framework='''pt'''			,			device=torch.device('''cpu'''  )			,			)

								snake_case__						:       Optional[Any]       						=  text_classifier('''This is great !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]  )







						@require_tf
						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       str       						=  pipeline(
								    task='''text-classification'''			,			model='''hf-internal-testing/tiny-random-distilbert'''			,			framework='''tf'''  )

								snake_case__						:       Optional[int]       						=  text_classifier('''This is great !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]  )







						@slow
						@require_torch
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  pipeline('''text-classification'''  )

								snake_case__						:       str       						=  text_classifier('''This is great !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''POSITIVE''', '''score''': 1.0}]  )
								snake_case__						:       List[Any]       						=  text_classifier('''This is bad !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''NEGATIVE''', '''score''': 1.0}]  )
								snake_case__						:       List[str]       						=  text_classifier('''Birds are a type of animal'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''POSITIVE''', '''score''': 0.9_8_8}]  )







						@slow
						@require_tf
						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       int       						=  pipeline('''text-classification'''			,			framework='''tf'''  )

								snake_case__						:       Optional[int]       						=  text_classifier('''This is great !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''POSITIVE''', '''score''': 1.0}]  )
								snake_case__						:       List[Any]       						=  text_classifier('''This is bad !'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''NEGATIVE''', '''score''': 1.0}]  )
								snake_case__						:       Optional[int]       						=  text_classifier('''Birds are a type of animal'''  )
								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': '''POSITIVE''', '''score''': 0.9_8_8}]  )







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      List[Any]			,			snake_case_					:      Any			,			snake_case_					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  TextClassificationPipeline(model=lowercase__			,			tokenizer=lowercase__  )
								return text_classifier, ["HuggingFace is in", "This is another test"]







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      Optional[Any]			,			snake_case_					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  text_classifier.model
								# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
								snake_case__						:       Union[str, Any]       						=  """HuggingFace is in"""
								snake_case__						:       str       						=  text_classifier(lowercase__  )

								self.assertEqual(nested_simplify(lowercase__  )			,			[{'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}]  )
								self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()  )

								snake_case__						:       Optional[int]       						=  ["""HuggingFace is in """, """Paris is in France"""]
								snake_case__						:       List[str]       						=  text_classifier(lowercase__  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[{'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}, {'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}]			,			)
								self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()  )
								self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()  )

								# Forcing to get all results with `top_k=None`
								# This is NOT the legacy format
								snake_case__						:       List[Any]       						=  text_classifier(lowercase__			,			top_k=lowercase__  )
								snake_case__						:       int       						=  len(model.config.idalabel.values()  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[[{'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}] * N, [{'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}] * N]			,			)

								snake_case__						:       List[str]       						=  {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
								snake_case__						:       Optional[int]       						=  text_classifier(lowercase__  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			{'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}			,			)
								self.assertTrue(outputs['''label'''] in model.config.idalabel.values()  )

								# This might be used a text pair, but tokenizer + pipe interaction
								# makes it hard to understand that it's not using the pair properly
								# https://github.com/huggingface/transformers/issues/17305
								# We disabled this usage instead as it was outputting wrong outputs.
								snake_case__						:       Optional[int]       						=  [["""HuggingFace is in """, """Paris is in France"""]]
								with self.assertRaises(lowercase__  ):
										text_classifier(lowercase__  )

								# This used to be valid for doing text pairs
								# We're keeping it working because of backward compatibility
								snake_case__						:       Union[str, Any]       						=  text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]  )
								self.assertEqual(
								    nested_simplify(lowercase__  )			,			[{'''label''': ANY(lowercase__  ), '''score''': ANY(lowercase__  )}]			,			)
								self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()  )






'''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 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       Union[str, Any]       						=  np.array([[1, item, train_mtch[i]] for i, item in enumerate(__lowerCAmelCase     )]     )
		snake_case__						:       Dict       						=  np.array(__lowerCAmelCase     )
		snake_case__						:       List[str]       						=  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 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[str]       						=  (1, 2, 1)
		snake_case__						:       Union[str, Any]       						=  (1, 1, 0, 7)
		snake_case__						:       List[str]       						=  SARIMAX(
		    __lowerCAmelCase   ,							exog=__lowerCAmelCase   ,							order=__lowerCAmelCase   ,							seasonal_order=__lowerCAmelCase     )
		snake_case__						:       str       						=  model.fit(disp=__lowerCAmelCase   ,							maxiter=6_00   ,							method='''nm'''     )
		snake_case__						:       List[str]       						=  model_fit.predict(1   ,							len(__lowerCAmelCase     )   ,							exog=[test_match]     )
		return result[0]




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		snake_case__						:       List[Any]       						=  SVR(kernel='''rbf'''   ,							C=1   ,							gamma=0.1   ,							epsilon=0.1     )
		regressor.fit(__lowerCAmelCase   ,							__lowerCAmelCase     )
		snake_case__						:       Optional[Any]       						=  regressor.predict(__lowerCAmelCase     )
		return y_pred[0]




def 	_a       (				__lowerCAmelCase		:      list     ):

		"""simple docstring"""

		train_user.sort()
		snake_case__						:       int       						=  np.percentile(__lowerCAmelCase   ,							25     )
		snake_case__						:       List[str]       						=  np.percentile(__lowerCAmelCase   ,							75     )
		snake_case__						:       Dict       						=  qa - qa
		snake_case__						:       Optional[int]       						=  qa - (iqr * 0.1)
		return low_lim




def 	_a       (				__lowerCAmelCase		:      list   ,							__lowerCAmelCase		:      float     ):

		"""simple docstring"""

		snake_case__						:       int       						=  0
		snake_case__						:       List[Any]       						=  0
		for i in list_vote:
				if i > actual_result:
						snake_case__						:       Optional[Any]       						=  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)
					lowerCAmelCase__						:       Optional[int]										=						[[18231, 0.0, 1], [22621, 1.0, 2], [15675, 0.0, 3], [23583, 1.0, 4]]
					lowerCAmelCase__						:       List[Any]										=						pd.DataFrame(
					    data_input, columns=["""total_user""", """total_even""", """days"""]
					)
					lowerCAmelCase__						:       Union[str, Any]										=						Normalizer().fit_transform(data_input_df.values)
					# split data
					lowerCAmelCase__						:       Union[str, Any]										=						normalize_df[:, 2].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 0].tolist()
					lowerCAmelCase__						:       List[str]										=						normalize_df[:, 1].tolist()

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

					# for linear regression & sarimax
					lowerCAmelCase__						:       Union[str, Any]										=						total_date[: len(total_date) - 1]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[: len(total_user) - 1]
					lowerCAmelCase__						:       List[Any]										=						total_match[: len(total_match) - 1]

					lowerCAmelCase__						:       Optional[int]										=						total_date[len(total_date) - 1 :]
					lowerCAmelCase__						:       Union[str, Any]										=						total_user[len(total_user) - 1 :]
					lowerCAmelCase__						:       List[Any]										=						total_match[len(total_match) - 1 :]

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



