






import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
					from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

					from .test_feature_extraction_speechta import floats_list


__snake_case			:			Any			   =   get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")


@require_torch
class 			__SCREAMING_SNAKE_CASE    (      unittest.TestCase):






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  tempfile.mkdtemp()

												lowerCAmelCase__			  =  SpeechTaTokenizer(_UpperCamelCase      )
												tokenizer.save_pretrained(self.tmpdirname      )

												lowerCAmelCase__			  =  {
												    'feature_size': 1,
												    'padding_value': 0.0,
												    'sampling_rate': 1_60_00,
												    'do_normalize': False,
												    'num_mel_bins': 80,
												    'hop_length': 16,
												    'win_length': 64,
												    'win_function': 'hann_window',
												    'fmin': 80,
												    'fmax': 76_00,
												    'mel_floor': 1E-10,
												    'reduction_factor': 2,
												    'return_attention_mask': True,
												}

												lowerCAmelCase__			  =  os.path.join(self.tmpdirname   ,  _UpperCamelCase      )
												with open(self.feature_extraction_file   ,  'w'   ,  encoding='utf-8'      ) as fp:
																		fp.write(json.dumps(_UpperCamelCase      ) + '\n'      )






						def 					UpperCamelCase__					(		self   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,  **_UpperCamelCase      )






						def 					UpperCamelCase__					(		self   ,  **_UpperCamelCase      ):






												"""simple docstring"""
												return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,  **_UpperCamelCase      )






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												shutil.rmtree(self.tmpdirname      )






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_tokenizer()
												lowerCAmelCase__			  =  self.get_feature_extractor()

												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=_UpperCamelCase   ,  feature_extractor=_UpperCamelCase      )

												processor.save_pretrained(self.tmpdirname      )
												lowerCAmelCase__			  =  SpeechTaProcessor.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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,  feature_extractor=self.get_feature_extractor()      )
												processor.save_pretrained(self.tmpdirname      )

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

												lowerCAmelCase__			  =  SpeechTaProcessor.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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_feature_extractor()
												lowerCAmelCase__			  =  self.get_tokenizer()

												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=_UpperCamelCase   ,  feature_extractor=_UpperCamelCase      )

												lowerCAmelCase__			  =  floats_list((3, 10_00)      )

												lowerCAmelCase__			  =  feature_extractor(audio=_UpperCamelCase   ,  return_tensors='np'      )
												lowerCAmelCase__			  =  processor(audio=_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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_feature_extractor()
												lowerCAmelCase__			  =  self.get_tokenizer()

												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=_UpperCamelCase   ,  feature_extractor=_UpperCamelCase      )

												lowerCAmelCase__			  =  floats_list((3, 10_00)      )

												lowerCAmelCase__			  =  feature_extractor(audio_target=_UpperCamelCase   ,  return_tensors='np'      )
												lowerCAmelCase__			  =  processor(audio_target=_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 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_feature_extractor()
												lowerCAmelCase__			  =  self.get_tokenizer()

												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=_UpperCamelCase   ,  feature_extractor=_UpperCamelCase      )

												lowerCAmelCase__			  =  'This is a test string'

												lowerCAmelCase__			  =  processor(text=_UpperCamelCase      )
												lowerCAmelCase__			  =  tokenizer(_UpperCamelCase      )

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






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_feature_extractor()
												lowerCAmelCase__			  =  self.get_tokenizer()

												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=_UpperCamelCase   ,  feature_extractor=_UpperCamelCase      )

												lowerCAmelCase__			  =  'This is a test string'

												lowerCAmelCase__			  =  processor(text_target=_UpperCamelCase      )
												lowerCAmelCase__			  =  tokenizer(_UpperCamelCase      )

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






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_feature_extractor()
												lowerCAmelCase__			  =  self.get_tokenizer()

												lowerCAmelCase__			  =  SpeechTaProcessor(tokenizer=_UpperCamelCase   ,  feature_extractor=_UpperCamelCase      )

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

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

												self.assertListEqual(_UpperCamelCase   ,  _UpperCamelCase      )







						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.get_feature_extractor()
												lowerCAmelCase__			  =  self.get_tokenizer()

												lowerCAmelCase__			  =  SpeechTaProcessor(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 numpy as np
import requests







def 		_UpperCamelCase      (			)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  requests.get(
						    'https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/'
						    'master/Week1/ADRvsRating.csv'						)
						lowerCAmelCase__			  =  response.text.splitlines()
						lowerCAmelCase__			  =  []
						for item in lines:
												lowerCAmelCase__			  =  item.split(','						)
												data.append(UpperCamelCase_						)
						data.pop(0						)  # This is for removing the labels from the list
						lowerCAmelCase__			  =  np.matrix(UpperCamelCase_						)
						return dataset







def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    int						)     ->      int:

						"""simple docstring"""



						lowerCAmelCase__			  =  len_data

						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta - (alpha / n) * sum_grad
						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    str			,				UpperCamelCase_				:    Any			,				UpperCamelCase_				:    str			,				UpperCamelCase_				:    List[Any]						)     ->      Dict:

						"""simple docstring"""



						lowerCAmelCase__			  =  np.dot(UpperCamelCase_			,				data_x.transpose()						)
						prod -= data_y.transpose()
						lowerCAmelCase__			  =  np.sum(np.square(UpperCamelCase_						)						)
						lowerCAmelCase__			  =  sum_elem / (2 * len_data)
						return error







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    List[Any]						)     ->      str:

						"""simple docstring"""



						lowerCAmelCase__			  =  10_0000
						lowerCAmelCase__			  =  0.000_1550

						lowerCAmelCase__			  =  data_x.shape[1]
						lowerCAmelCase__			  =  data_x.shape[0] - 1

						lowerCAmelCase__			  =  np.zeros((1, no_features)						)

						for i in range(0			,				UpperCamelCase_						):
												lowerCAmelCase__			  =  run_steep_gradient_descent(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												lowerCAmelCase__			  =  sum_of_square_error(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
												print(F"At Iteration {i + 1} - Error is {error:.5f}"						)

						return theta







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    Any						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  sum(abs(y - predicted_y[i]						) for i, y in enumerate(UpperCamelCase_						)						)
						return total / len(UpperCamelCase_						)







def 		_UpperCamelCase      (			)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  collect_dataset()

						lowerCAmelCase__			  =  data.shape[0]
						lowerCAmelCase__			  =  np.c_[np.ones(UpperCamelCase_						), data[:, :-1]].astype(UpperCamelCase_						)
						lowerCAmelCase__			  =  data[:, -1].astype(UpperCamelCase_						)

						lowerCAmelCase__			  =  run_linear_regression(UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  theta.shape[1]
						print('Resultant Feature vector : '						)
						for i in range(0			,				UpperCamelCase_						):
												print(F"{theta[0, i]:.5f}"						)


if __name__ == "__main__":
					main()





