"""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 						a__							(       unittest.TestCase		):



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       'ylacombe/bark-small'
								__a		 =       tempfile.mkdtemp()
								__a		 =       'en_speaker_1'
								__a		 =       'This is a test string'
								__a		 =       'speaker_embeddings_path.json'
								__a		 =       'speaker_embeddings'



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return AutoTokenizer.from_pretrained(self.checkpoint				,						**__lowerCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								shutil.rmtree(self.tmpdirname   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.get_tokenizer()

								__a		 =       BarkProcessor(tokenizer=__lowerCAmelCase   )

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

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



			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       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				,						)

								__a		 =       self.get_tokenizer(bos_token='(BOS)'				,						eos_token='(EOS)'   )

								__a		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       BarkProcessor.from_pretrained(
								    pretrained_processor_name_or_path=self.checkpoint				,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,						)

								__a		 =       3_5
								__a		 =       2
								__a		 =       8

								__a		 =       {
								    'semantic_prompt': np.ones(__lowerCAmelCase   ),
								    'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)   ),
								    'fine_prompt': np.ones((nb_codebooks_total, seq_len)   ),
								}

								# test providing already loaded voice_preset
								__a		 =       processor(text=self.input_string				,						voice_preset=__lowerCAmelCase   )

								__a		 =       inputs['history_prompt']
								for key in voice_preset:
													self.assertListEqual(voice_preset[key].tolist()				,						processed_voice_preset.get(__lowerCAmelCase				,						np.array([]   )   ).tolist()   )

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

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

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







			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       self.get_tokenizer()

								__a		 =       BarkProcessor(tokenizer=__lowerCAmelCase   )

								__a		 =       processor(text=self.input_string   )

								__a		 =       tokenizer(
								    self.input_string				,						padding='max_length'				,						max_length=2_5_6				,						add_special_tokens=__lowerCAmelCase				,						return_attention_mask=__lowerCAmelCase				,						return_token_type_ids=__lowerCAmelCase				,						)

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





import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 							lowerCAmelCase(     __lowerCamelCase						):
					return (data["data"], data["target"])
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       XGBRegressor(verbosity=0				,    random_state=42						)
					xgb.fit(__lowerCamelCase				,    __lowerCamelCase						)
					# Predict target for test data
					__a		 =       xgb.predict(__lowerCamelCase						)
					__a		 =       predictions.reshape(len(__lowerCamelCase						)				,    1						)
					return predictions
def 							lowerCAmelCase(     ):
					__a		 =       fetch_california_housing()
					__a ,				__a		 =       data_handling(__lowerCamelCase						)
					__a ,				__a ,				__a ,				__a		 =       train_test_split(
					    __lowerCamelCase				,    __lowerCamelCase				,    test_size=0.25				,    random_state=1						)
					__a		 =       xgboost(__lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						)
					# Error printing
					print(f'''Mean Absolute Error : {mean_absolute_error(__lowerCamelCase				,    __lowerCamelCase						)}'''						)
					print(f'''Mean Square Error  : {mean_squared_error(__lowerCamelCase				,    __lowerCamelCase						)}'''						)


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()




