

import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


lowerCAmelCase_ 			=			get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_torch
@require_torchaudio
@require_sentencepiece
class 			snake_case_     (	unittest.TestCase      ):







			'''simple docstring'''

			def        snake_case__(  self				:       List[Any]							)   ->str:
									snake_case_     =			tempfile.mkdtemp()

									snake_case_     =			['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
									snake_case_     =			dict(zip(_UpperCamelCase							,     range(len(_UpperCamelCase							)							)							)							)
									snake_case_     =			Path(self.tmpdirname							)
									save_json(_UpperCamelCase							,     save_dir / VOCAB_FILES_NAMES['''vocab_file''']							)
									if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
															copyfile(_UpperCamelCase							,     save_dir / VOCAB_FILES_NAMES['''spm_file''']							)

									snake_case_     =			SpeechaTextTokenizer.from_pretrained(self.tmpdirname							)
									tokenizer.save_pretrained(self.tmpdirname							)

									snake_case_     =			{
									    '''feature_size''': 2_4,
									    '''num_mel_bins''': 2_4,
									    '''padding_value''': 0.0,
									    '''sampling_rate''': 1_6_0_0_0,
									    '''return_attention_mask''': False,
									    '''do_normalize''': True,
									}
									save_json(_UpperCamelCase							,     save_dir / FEATURE_EXTRACTOR_NAME							)







			def        snake_case__(  self				:       str							,     **_UpperCamelCase				:       List[Any]							)   ->List[Any]:
									return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							,     **_UpperCamelCase							)







			def        snake_case__(  self				:       Union[str, Any]							,     **_UpperCamelCase				:       Union[str, Any]							)   ->Any:
									return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							,     **_UpperCamelCase							)







			def        snake_case__(  self				:       int							)   ->Any:
									shutil.rmtree(self.tmpdirname							)







			def        snake_case__(  self				:       List[str]							)   ->str:
									snake_case_     =			self.get_tokenizer()
									snake_case_     =			self.get_feature_extractor()

									snake_case_     =			SpeechaTextProcessor(tokenizer=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							)

									processor.save_pretrained(self.tmpdirname							)
									snake_case_     =			SpeechaTextProcessor.from_pretrained(self.tmpdirname							)

									self.assertEqual(processor.tokenizer.get_vocab()							,     tokenizer.get_vocab()							)
									self.assertIsInstance(processor.tokenizer							,     _UpperCamelCase							)

									self.assertEqual(processor.feature_extractor.to_json_string()							,     feature_extractor.to_json_string()							)
									self.assertIsInstance(processor.feature_extractor							,     _UpperCamelCase							)







			def        snake_case__(  self				:       int							)   ->Dict:
									snake_case_     =			SpeechaTextProcessor(
									    tokenizer=self.get_tokenizer()							,     feature_extractor=self.get_feature_extractor()							)
									processor.save_pretrained(self.tmpdirname							)

									snake_case_     =			self.get_tokenizer(bos_token='''(BOS)'''							,     eos_token='''(EOS)'''							)
									snake_case_     =			self.get_feature_extractor(do_normalize=_UpperCamelCase							,     padding_value=1.0							)

									snake_case_     =			SpeechaTextProcessor.from_pretrained(
									    self.tmpdirname							,     bos_token='''(BOS)'''							,     eos_token='''(EOS)'''							,     do_normalize=_UpperCamelCase							,     padding_value=1.0							)

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

									self.assertEqual(processor.feature_extractor.to_json_string()							,     feature_extractor_add_kwargs.to_json_string()							)
									self.assertIsInstance(processor.feature_extractor							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Dict							)   ->Tuple:
									snake_case_     =			self.get_feature_extractor()
									snake_case_     =			self.get_tokenizer()

									snake_case_     =			SpeechaTextProcessor(tokenizer=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							)

									snake_case_     =			floats_list((3, 1_0_0_0)							)

									snake_case_     =			feature_extractor(_UpperCamelCase							,     return_tensors='''np'''							)
									snake_case_     =			processor(_UpperCamelCase							,     return_tensors='''np'''							)

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







			def        snake_case__(  self				:       Union[str, Any]							)   ->Any:
									snake_case_     =			self.get_feature_extractor()
									snake_case_     =			self.get_tokenizer()

									snake_case_     =			SpeechaTextProcessor(tokenizer=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							)

									snake_case_     =			'''This is a test string'''

									snake_case_     =			processor(text=_UpperCamelCase							)

									snake_case_     =			tokenizer(_UpperCamelCase							)

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







			def        snake_case__(  self				:       Any							)   ->List[str]:
									snake_case_     =			self.get_feature_extractor()
									snake_case_     =			self.get_tokenizer()

									snake_case_     =			SpeechaTextProcessor(tokenizer=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							)

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

									snake_case_     =			processor.batch_decode(_UpperCamelCase							)
									snake_case_     =			tokenizer.batch_decode(_UpperCamelCase							)

									self.assertListEqual(_UpperCamelCase							,     _UpperCamelCase							)







			def        snake_case__(  self				:       Tuple							)   ->Tuple:
									snake_case_     =			self.get_feature_extractor()
									snake_case_     =			self.get_tokenizer()

									snake_case_     =			SpeechaTextProcessor(tokenizer=_UpperCamelCase							,     feature_extractor=_UpperCamelCase							)

									self.assertListEqual(
									    processor.model_input_names							,     feature_extractor.model_input_names							,     msg='''`processor` and `feature_extractor` model input names do not match'''							,     )


import random





class 			snake_case_     :







			'''simple docstring'''

			def __init__(  self				:       List[str]							,     _UpperCamelCase				:       list[list[float]]							,     _UpperCamelCase				:       list[int]							,     _UpperCamelCase				:       float = 0.01							,     _UpperCamelCase				:       int = 1_0_0_0							,     _UpperCamelCase				:       float = -1							,     )   ->None:
									snake_case_     =			sample
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									snake_case_     =			target
									if len(self.target							) == 0:
															raise ValueError('''Target data can not be empty'''							)
									if len(self.sample							) != len(self.target							):
															raise ValueError('''Sample data and Target data do not have matching lengths'''							)
									snake_case_     =			learning_rate
									snake_case_     =			epoch_number
									snake_case_     =			bias
									snake_case_     =			len(_UpperCamelCase							)
									snake_case_     =			len(sample[0]							)  # number of columns in dataset
									snake_case_     =			[]







			def        snake_case__(  self				:       List[str]							)   ->None:
									for sample in self.sample:
															sample.insert(0							,     self.bias							)

									for _ in range(self.col_sample							):
															self.weight.append(random.random()							)

									self.weight.insert(0							,     self.bias							)

									snake_case_     =			0

									while True:
															snake_case_     =			False
															for i in range(self.number_sample							):
																					snake_case_     =			0
																					for j in range(self.col_sample + 1							):
																											snake_case_     =			u + self.weight[j] * self.sample[i][j]
																					snake_case_     =			self.sign(_UpperCamelCase							)
																					if y != self.target[i]:
																											for j in range(self.col_sample + 1							):
																																	snake_case_     =			(
																																	    self.weight[j]
																																	    + self.learning_rate
																																	    * (self.target[i] - y)
																																	    * self.sample[i][j]
																																	)
																											snake_case_     =			True
            # print('Epoch: \n',epoch_count)
															snake_case_     =			epoch_count + 1
															# if you want control the epoch or just by error
															if not has_misclassified:
																					print(('''\nEpoch:\n''', epoch_count)							)
																					print('''------------------------\n'''							)
																					# if epoch_count > self.epoch_number or not error:
																					break







			def        snake_case__(  self				:       Union[str, Any]							,     _UpperCamelCase				:       list[float]							)   ->None:
									if len(self.sample							) == 0:
															raise ValueError('''Sample data can not be empty'''							)
									sample.insert(0							,     self.bias							)
									snake_case_     =			0
									for i in range(self.col_sample + 1							):
															snake_case_     =			u + self.weight[i] * sample[i]

									snake_case_     =			self.sign(_UpperCamelCase							)

									if y == -1:
															print(('''Sample: ''', sample)							)
															print('''classification: P1'''							)
									else:
															print(('''Sample: ''', sample)							)
															print('''classification: P2'''							)







			def        snake_case__(  self				:       str							,     _UpperCamelCase				:       float							)   ->int:
									return 1 if u >= 0 else -1






lowerCAmelCase_ 			=			[
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

lowerCAmelCase_ 			=			[
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase_ 			=			Perceptron(
					    sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
					)
					network.training()
					print('''Finished training perceptron''')
					print('''Enter values to predict or q to exit''')
					while True:
										lowerCAmelCase_ 			=			[]
										for i in range(len(samples[0])):
															lowerCAmelCase_ 			=			input('''value: ''').strip()
															if user_input == "q":
																				break
															lowerCAmelCase_ 			=			float(user_input)
															sample.insert(i, observation)
										network.sort(sample)