




'''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      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_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      __lowerCAmelCase    (						self :						Tuple							,    **lowerCAmelCase_ :						List[Any]      )    ->							Dict:


														"""simple docstring"""


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







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Union[str, Any]:


														"""simple docstring"""


														shutil.rmtree(self.tmpdirname      )







							def      __lowerCAmelCase    (						self :						Any      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       self.get_tokenizer()

														_a			  =       BarkProcessor(tokenizer=UpperCAmelCase_      )

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

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







							@slow
							def      __lowerCAmelCase    (						self :						str      )    ->							int:


														"""simple docstring"""


														_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      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Optional[int]:


														"""simple docstring"""


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

														_a			  =       35
														_a			  =       2
														_a			  =       8

														_a			  =       {
														    '''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
														_a			  =       processor(text=self.input_string							,    voice_preset=UpperCAmelCase_      )

														_a			  =       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
														_a			  =       os.path.join(self.tmpdirname							,    '''file.npz'''      )
														np.savez(UpperCAmelCase_							,    **UpperCAmelCase_      )
														_a			  =       processor(text=self.input_string							,    voice_preset=UpperCAmelCase_      )
														_a			  =       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
														_a			  =       processor(text=self.input_string							,    voice_preset=self.voice_preset      )







							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														_a			  =       self.get_tokenizer()

														_a			  =       BarkProcessor(tokenizer=UpperCAmelCase_      )

														_a			  =       processor(text=self.input_string      )

														_a			  =       tokenizer(
														    self.input_string							,    padding='''max_length'''							,    max_length=2_56							,    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'''


_snake_case   :   Any     							=	tuple[float, float, float]
_snake_case   :   Optional[int]     							=	tuple[float, float, float]
def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad   ):





							'''simple docstring'''


							_a			  =       end_pointa[0] - end_pointa[0]
							_a			  =       end_pointa[1] - end_pointa[1]
							_a			  =       end_pointa[2] - end_pointa[2]
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						Vectorad   ):





							'''simple docstring'''


							_a			  =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							_a			  =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							_a			  =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							return tuple(round(UpperCamelCase				,		UpperCamelCase   ) for x in vector   ) == (0, 0, 0)

def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						int = 10   ):





							'''simple docstring'''


							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							return is_zero_vector(get_ad_vectors_cross(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase   )

