





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     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        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,**__lowerCamelCase ):
        return AutoTokenizer.from_pretrained(self.checkpoint,**_UpperCAmelCase )



 def 				UpperCamelCase			(							self ):
        shutil.rmtree(self.tmpdirname )



 def 				UpperCamelCase			(							self ):
        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 ):
        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 ):
        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 ):
        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() )












'''simple docstring'''




lowercase__    :      Dict						=    {
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}