
'''simple docstring'''







import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class 			lowerCAmelCase (				unittest.TestCase			):




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= "ylacombe/bark-small"
								_SCREAMING_SNAKE_CASE				= tempfile.mkdtemp()
								_SCREAMING_SNAKE_CASE				= "en_speaker_1"
								_SCREAMING_SNAKE_CASE				= "This is a test string"
								_SCREAMING_SNAKE_CASE				= "speaker_embeddings_path.json"
								_SCREAMING_SNAKE_CASE				= "speaker_embeddings"




		def     lowercase       (				self					,						**UpperCamelCase      ):
								return AutoTokenizer.from_pretrained(self.checkpoint					,						**UpperCamelCase      )




		def     lowercase       (				self      ):
								shutil.rmtree(self.tmpdirname      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()

								_SCREAMING_SNAKE_CASE				= BarkProcessor(tokenizer=UpperCamelCase      )

								processor.save_pretrained(self.tmpdirname      )
								_SCREAMING_SNAKE_CASE				= BarkProcessor.from_pretrained(self.tmpdirname      )

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




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint					,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,						)
								processor.save_pretrained(
								    self.tmpdirname					,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,						speaker_embeddings_directory=self.speaker_embeddings_directory					,						)

								_SCREAMING_SNAKE_CASE				= self.get_tokenizer(bos_token="(BOS)"					,						eos_token="(EOS)"      )

								_SCREAMING_SNAKE_CASE				= BarkProcessor.from_pretrained(
								    self.tmpdirname					,						self.speaker_embeddings_dict_path					,						bos_token="(BOS)"					,						eos_token="(EOS)"					,						)

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint					,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,						)

								_SCREAMING_SNAKE_CASE				= 35
								_SCREAMING_SNAKE_CASE				= 2
								_SCREAMING_SNAKE_CASE				= 8

								_SCREAMING_SNAKE_CASE				= {
								    "semantic_prompt": np.ones(UpperCamelCase      ),
								    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)      ),
								    "fine_prompt": np.ones((nb_codebooks_total, seq_len)      ),
								}

								# test providing already loaded voice_preset
								_SCREAMING_SNAKE_CASE				= processor(text=self.input_string					,						voice_preset=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= inputs["history_prompt"]
								for key in voice_preset:
														self.assertListEqual(voice_preset[key].tolist()					,						processed_voice_preset.get(UpperCamelCase					,						np.array([]      )      ).tolist()      )

								# test loading voice preset from npz file
								_SCREAMING_SNAKE_CASE				= os.path.join(self.tmpdirname					,						"file.npz"      )
								np.savez(UpperCamelCase					,						**UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processor(text=self.input_string					,						voice_preset=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= inputs["history_prompt"]

								for key in voice_preset:
														self.assertListEqual(voice_preset[key].tolist()					,						processed_voice_preset.get(UpperCamelCase					,						np.array([]      )      ).tolist()      )

								# test loading voice preset from the hub
								_SCREAMING_SNAKE_CASE				= processor(text=self.input_string					,						voice_preset=self.voice_preset      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.get_tokenizer()

								_SCREAMING_SNAKE_CASE				= BarkProcessor(tokenizer=UpperCamelCase      )

								_SCREAMING_SNAKE_CASE				= processor(text=self.input_string      )

								_SCREAMING_SNAKE_CASE				= tokenizer(
								    self.input_string					,						padding="max_length"					,						max_length=256					,						add_special_tokens=UpperCamelCase					,						return_attention_mask=UpperCamelCase					,						return_token_type_ids=UpperCamelCase					,						)

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

'''simple docstring'''







import numpy

# List of input, output pairs
_snake_case				:   Optional[Any]     =				(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
_snake_case				:   Any     =				(((515, 22, 13), 555), ((61, 35, 49), 150))
_snake_case				:   Any     =				[2, 4, 1, 5]
_snake_case				:   str     =				len(train_data)
_snake_case				:   int     =				0.009



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]	,			_SCREAMING_SNAKE_CASE		:   List[Any]="train"  ):
						return calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) - output(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   List[Any]  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(len(_SCREAMING_SNAKE_CASE  ) - 1  ):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str	,			_SCREAMING_SNAKE_CASE		:   Union[str, Any]  ):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Any	,			_SCREAMING_SNAKE_CASE		:   str  ):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]  )
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]  )
						return None



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Union[str, Any]	,			_SCREAMING_SNAKE_CASE		:   Optional[int]=m  ):
						_SCREAMING_SNAKE_CASE				= 0
						for i in range(_SCREAMING_SNAKE_CASE  ):
												if index == -1:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  )
												else:
																		summation_value += _error(_SCREAMING_SNAKE_CASE  ) * train_data[i][0][index]
						return summation_value



def 			_a		(	_SCREAMING_SNAKE_CASE		:   Optional[Any]  ):
						_SCREAMING_SNAKE_CASE				= summation_of_cost_derivative(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  ) / m
						return cost_derivative_value



def 			_a		(	):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						_SCREAMING_SNAKE_CASE				= 0.000_002
						_SCREAMING_SNAKE_CASE				= 0
						_SCREAMING_SNAKE_CASE				= 0
						while True:
												j += 1
												_SCREAMING_SNAKE_CASE				= [0, 0, 0, 0]
												for i in range(0	,			len(_SCREAMING_SNAKE_CASE  )  ):
																		_SCREAMING_SNAKE_CASE				= get_cost_derivative(i - 1  )
																		_SCREAMING_SNAKE_CASE				= (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			atol=_SCREAMING_SNAKE_CASE	,			rtol=_SCREAMING_SNAKE_CASE	,			):
																		break
												_SCREAMING_SNAKE_CASE				= temp_parameter_vector
						print(("Number of iterations:", j)  )



def 			_a		(	):
						for i in range(len(_SCREAMING_SNAKE_CASE  )  ):
												print(("Actual output value:", output(_SCREAMING_SNAKE_CASE	,			"test"  ))  )
												print(("Hypothesis output:", calculate_hypothesis_value(_SCREAMING_SNAKE_CASE	,			"test"  ))  )


if __name__ == "__main__":
						run_gradient_descent()
						print("""\nTesting gradient descent for a linear hypothesis function.\n""")
						test_gradient_descent()