


'''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     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''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       UpperCamelCase__		(self    :    List[Any]   ,						**UpperCamelCase    :    Dict ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             shutil.rmtree(self.tmpdirname )



      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= self.get_tokenizer()

             lowercase__      			= BarkProcessor(tokenizer=UpperCamelCase )

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

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



      @slow
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''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       UpperCamelCase__		(self    :    Optional[int] ):

             '''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(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
             lowercase__      			= processor(text=self.input_string   ,						voice_preset=UpperCamelCase )

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






      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= self.get_tokenizer()

             lowercase__      			= BarkProcessor(tokenizer=UpperCamelCase )

             lowercase__      			= processor(text=self.input_string )

             lowercase__      			= 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() )








'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase							:   Dict  						=	logging.get_logger(__name__)

lowerCamelCase							:   Union[str, Any]  						=	{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							"""megatron-bert"""



      def __init__(self    :    Tuple   ,						UpperCamelCase    :    Optional[int]=29056   ,						UpperCamelCase    :    Optional[Any]=1024   ,						UpperCamelCase    :    Any=24   ,						UpperCamelCase    :    int=16   ,						UpperCamelCase    :    Optional[int]=4096   ,						UpperCamelCase    :    int="gelu"   ,						UpperCamelCase    :    int=0.1   ,						UpperCamelCase    :    Tuple=0.1   ,						UpperCamelCase    :    Any=512   ,						UpperCamelCase    :    int=2   ,						UpperCamelCase    :    Dict=0.02   ,						UpperCamelCase    :    Dict=1E-12   ,						UpperCamelCase    :    List[Any]=0   ,						UpperCamelCase    :    Optional[int]="absolute"   ,						UpperCamelCase    :    List[Any]=True   ,						**UpperCamelCase    :    str   ,						):

             '''simple docstring'''







             super().__init__(pad_token_id=UpperCamelCase   ,						**UpperCamelCase )

             lowercase__      			= vocab_size
             lowercase__      			= hidden_size
             lowercase__      			= num_hidden_layers
             lowercase__      			= num_attention_heads
             lowercase__      			= hidden_act
             lowercase__      			= intermediate_size
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= max_position_embeddings
             lowercase__      			= type_vocab_size
             lowercase__      			= initializer_range
             lowercase__      			= layer_norm_eps
             lowercase__      			= position_embedding_type
             lowercase__      			= use_cache




