

'''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       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		='ylacombe/bark-small'
            __lowercase      		=tempfile.mkdtemp()
            __lowercase      		='en_speaker_1'
            __lowercase      		='This is a test string'
            __lowercase      		='speaker_embeddings_path.json'
            __lowercase      		='speaker_embeddings'







       def       snake_case (				self      :			int    ,   **__lowercase      :			List[str]   ):







            """simple docstring"""







            return AutoTokenizer.from_pretrained(self.checkpoint    ,   **__lowercase   )







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            shutil.rmtree(self.tmpdirname   )







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=self.get_tokenizer()

            __lowercase      		=BarkProcessor(tokenizer=__lowercase   )

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

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







       @slow
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=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    ,   )

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

            __lowercase      		=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       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=BarkProcessor.from_pretrained(
                pretrained_processor_name_or_path=self.checkpoint    ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,   )

            __lowercase      		=35
            __lowercase      		=2
            __lowercase      		=8

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

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

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

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

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

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







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=self.get_tokenizer()

            __lowercase      		=BarkProcessor(tokenizer=__lowercase   )

            __lowercase      		=processor(text=self.input_string   )

            __lowercase      		=tokenizer(
                self.input_string    ,   padding='max_length'    ,   max_length=256    ,   add_special_tokens=__lowercase    ,   return_attention_mask=__lowercase    ,   return_token_type_ids=__lowercase    ,   )

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





'''simple docstring'''

from __future__ import annotations

def 					__UpperCamelCase				(						lowercase__    :	float,       lowercase__    :	float,       lowercase__    :	float			):






     '''simple docstring'''





     if (voltage, current, resistance).count(0			) != 1:
          raise ValueError('One and only one argument must be 0'			)
     if resistance < 0:
          raise ValueError('Resistance cannot be negative'			)
     if voltage == 0:
          return {"voltage": float(current * resistance			)}
     elif current == 0:
          return {"current": voltage / resistance}
     elif resistance == 0:
          return {"resistance": voltage / current}
     else:
          raise ValueError('Exactly one argument must be 0'			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


