

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 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Union[str, Any]   =						'''ylacombe/bark-small'''
												_a      :       Dict   =						tempfile.mkdtemp()
												_a      :       int   =						'''en_speaker_1'''
												_a      :       Dict   =						'''This is a test string'''
												_a      :       int   =						'''speaker_embeddings_path.json'''
												_a      :       List[Any]   =						'''speaker_embeddings'''


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return AutoTokenizer.from_pretrained(self.checkpoint			,				**_a			)


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


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       int   =						self.get_tokenizer()

												_a      :       Optional[Any]   =						BarkProcessor(tokenizer=_a			)

												processor.save_pretrained(self.tmpdirname			)
												_a      :       Optional[Any]   =						BarkProcessor.from_pretrained(self.tmpdirname			)

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


							@slow
							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       str   =						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      :       int   =						self.get_tokenizer(bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			)

												_a      :       int   =						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			)       ->       Optional[int]:
												_a      :       List[Any]   =						BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint			,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,				)

												_a      :       Optional[int]   =						3_5
												_a      :       str   =						2
												_a      :       Dict   =						8

												_a      :       Any   =						{
												    '''semantic_prompt''': np.ones(_a			),
												    '''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      :       int   =						processor(text=self.input_string			,				voice_preset=_a			)

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

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

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

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




							def 							__lowercase							(     self			)       ->       str:
												_a      :       int   =						self.get_tokenizer()

												_a      :       Any   =						BarkProcessor(tokenizer=_a			)

												_a      :       int   =						processor(text=self.input_string			)

												_a      :       Optional[Any]   =						tokenizer(
												    self.input_string			,				padding='''max_length'''			,				max_length=2_5_6			,				add_special_tokens=_a			,				return_attention_mask=_a			,				return_token_type_ids=_a			,				)

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





"""simple docstring"""




import math







class     lowercase:



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			0.0
         a__		=			0.0
         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             da += math.pow((sample[i] - weights[0][i])     ,				2   )
             da += math.pow((sample[i] - weights[1][i])     ,				2   )
             return 0 if da > da else 1
         return 0




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     list[list[int | float]]:







         """simple docstring"""



         for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
             weights[j][i] += alpha * (sample[i] - weights[j][i])
         return weights




def     __magic_name__		(					)     ->						None:
    # Training Examples ( m, n )
    a__		=			[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

    # weight initialization ( n, C )
    a__		=			[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

    # training
    a__		=			SelfOrganizingMap()
    a__		=			3
    a__		=			0.5

    for _ in range(UpperCamelCase     ):
        for j in range(len(UpperCamelCase     )     ):
            # training sample
            a__		=			training_samples[j]

            # Compute the winning vector
            a__		=			self_organizing_map.get_winner(UpperCamelCase							,		UpperCamelCase     )

            # Update the winning vector
            a__		=			self_organizing_map.update(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )

    # classify test sample
    a__		=			[0, 0, 0, 1]
    a__		=			self_organizing_map.get_winner(UpperCamelCase							,		UpperCamelCase     )

    # results
    print(f'Clusters that the test sample belongs to : {winner}'     )
    print(f'Weights that have been trained : {weights}'     )


# running the main() function
if __name__ == "__main__":
    main()


