



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        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return AutoTokenizer.from_pretrained(self.checkpoint	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    shutil.rmtree(self.tmpdirname				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_tokenizer()

    __lowerCamelCase													=    BarkProcessor(tokenizer=__UpperCAmelCase				)

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

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




 @slow
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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	,				)

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

    __lowerCamelCase													=    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				):



    '''simple docstring'''
    __lowerCamelCase													=    BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint	,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,				)

    __lowerCamelCase													=    35
    __lowerCamelCase													=    2
    __lowerCamelCase													=    8

    __lowerCamelCase													=    {
        '''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
    __lowerCamelCase													=    processor(text=self.input_string	,				voice_preset=__UpperCAmelCase				)

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_tokenizer()

    __lowerCamelCase													=    BarkProcessor(tokenizer=__UpperCAmelCase				)

    __lowerCamelCase													=    processor(text=self.input_string				)

    __lowerCamelCase													=    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 torch

from diffusers import StableDiffusionPipeline


a_			=					"""path-to-your-trained-model"""
a_			=					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")

a_			=					"""A photo of sks dog in a bucket"""
a_			=					pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("""dog-bucket.png""")






