



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 __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											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 __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  Union[str, Any]       )		->     Optional[int]:
											return AutoTokenizer.from_pretrained(self.checkpoint						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     Union[str, Any]:
											shutil.rmtree(self.tmpdirname       )


				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											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 __UpperCamelCase						(							self      :  Tuple       )		->     int:
											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 __UpperCamelCase						(							self      :  int       )		->     List[str]:
											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 __UpperCamelCase						(							self      :  Optional[int]       )		->     Union[str, Any]:
											A      						=    self.get_tokenizer()

											A      						=    BarkProcessor(tokenizer=__UpperCamelCase       )

											A      						=    processor(text=self.input_string       )

											A      						=    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()       )




import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =sess.run(
				    None,
				    {
				        sess.get_inputs()[0].name: input_ids,
				        sess.get_inputs()[1].name: attention_mask,
				        sess.get_inputs()[2].name: token_type_ids,
				    },
				    run_options=run_opt,
				)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))