

'''simple docstring'''


import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY




@is_pipeline_test
class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
						__UpperCAmelCase			     =    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING







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



								'''simple docstring'''



								snake_case__						:       Dict       						=  AudioClassificationPipeline(model=lowercase_			,			feature_extractor=lowercase_  )

								# test with a raw waveform
								snake_case__						:       int       						=  np.zeros((3_4_0_0_0,)  )
								snake_case__						:       Union[str, Any]       						=  np.zeros((1_4_0_0_0,)  )
								return audio_classifier, [audioa, audio]







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



								'''simple docstring'''



								snake_case__						:       Tuple       						=  examples
								snake_case__						:       List[str]       						=  audio_classifier(lowercase_  )
								# by default a model is initialized with num_labels=2
								self.assertEqual(
								    lowercase_			,			[
								        {'''score''': ANY(lowercase_  ), '''label''': ANY(lowercase_  )},
								        {'''score''': ANY(lowercase_  ), '''label''': ANY(lowercase_  )},
								    ]			,			)
								snake_case__						:       Tuple       						=  audio_classifier(lowercase_			,			top_k=1  )
								self.assertEqual(
								    lowercase_			,			[
								        {'''score''': ANY(lowercase_  ), '''label''': ANY(lowercase_  )},
								    ]			,			)

								self.run_torchaudio(lowercase_  )







						@require_torchaudio
						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								import datasets

								# test with a local file
								snake_case__						:       List[Any]       						=  datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,			'''clean'''			,			split='''validation'''  )
								snake_case__						:       int       						=  dataset[0]["audio"]["array"]
								snake_case__						:       Optional[int]       						=  audio_classifier(lowercase_  )
								self.assertEqual(
								    lowercase_			,			[
								        {'''score''': ANY(lowercase_  ), '''label''': ANY(lowercase_  )},
								        {'''score''': ANY(lowercase_  ), '''label''': ANY(lowercase_  )},
								    ]			,			)







						@require_torch
						def       __magic_name__							(							self					:      Any  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  "anton-l/wav2vec2-random-tiny-classifier"

								snake_case__						:       str       						=  pipeline('''audio-classification'''			,			model=lowercase_  )

								snake_case__						:       Any       						=  np.ones((8_0_0_0,)  )
								snake_case__						:       Dict       						=  audio_classifier(lowercase_			,			top_k=4  )

								snake_case__						:       str       						=  [
								    {"score": 0.0_8_4_2, "label": "no"},
								    {"score": 0.0_8_3_8, "label": "up"},
								    {"score": 0.0_8_3_7, "label": "go"},
								    {"score": 0.0_8_3_4, "label": "right"},
								]
								snake_case__						:       Optional[Any]       						=  [
								    {"score": 0.0_8_4_5, "label": "stop"},
								    {"score": 0.0_8_4_4, "label": "on"},
								    {"score": 0.0_8_4_1, "label": "right"},
								    {"score": 0.0_8_3_4, "label": "left"},
								]
								self.assertIn(nested_simplify(lowercase_			,			decimals=4  )			,			[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]  )

								snake_case__						:       List[Any]       						=  {"array": np.ones((8_0_0_0,)  ), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
								snake_case__						:       Optional[Any]       						=  audio_classifier(lowercase_			,			top_k=4  )
								self.assertIn(nested_simplify(lowercase_			,			decimals=4  )			,			[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]  )







						@require_torch
						@slow
						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								import datasets

								snake_case__						:       Optional[Any]       						=  "superb/wav2vec2-base-superb-ks"

								snake_case__						:       Tuple       						=  pipeline('''audio-classification'''			,			model=lowercase_  )
								snake_case__						:       List[str]       						=  datasets.load_dataset('''anton-l/superb_dummy'''			,			'''ks'''			,			split='''test'''  )

								snake_case__						:       List[Any]       						=  np.array(dataset[3]['''speech''']			,			dtype=np.floataa  )
								snake_case__						:       str       						=  audio_classifier(lowercase_			,			top_k=4  )
								self.assertEqual(
								    nested_simplify(lowercase_			,			decimals=3  )			,			[
								        {'''score''': 0.9_8_1, '''label''': '''go'''},
								        {'''score''': 0.0_0_7, '''label''': '''up'''},
								        {'''score''': 0.0_0_6, '''label''': '''_unknown_'''},
								        {'''score''': 0.0_0_1, '''label''': '''down'''},
								    ]			,			)







						@require_tf
						@unittest.skip('''Audio classification is not implemented for TF'''  )
						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								pass






'''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.""")



