






'''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 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''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  __UpperCamelCase							(  self ,   **lowerCamelCase      )							->Optional[Any]:



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




      def  __UpperCamelCase							(  self      )							->str:



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




      def  __UpperCamelCase							(  self      )							->List[str]:



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

         __a				     =    BarkProcessor(tokenizer=lowerCamelCase__      )

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

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




      @slow
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''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  __UpperCamelCase							(  self      )							->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(lowerCamelCase__      ),
             '''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=lowerCamelCase__      )

         __a				     =    inputs['''history_prompt''']
         for key in voice_preset:
            self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(lowerCamelCase__ ,   np.array([]      )      ).tolist()      )

         # test loading voice preset from npz file
         __a				     =    os.path.join(self.tmpdirname ,   'file.npz'      )
         np.savez(lowerCamelCase__ ,   **lowerCamelCase__      )
         __a				     =    processor(text=self.input_string ,   voice_preset=lowerCamelCase__      )
         __a				     =    inputs['''history_prompt''']

         for key in voice_preset:
            self.assertListEqual(voice_preset[key].tolist() ,   processed_voice_preset.get(lowerCamelCase__ ,   np.array([]      )      ).tolist()      )

         # test loading voice preset from the hub
         __a				     =    processor(text=self.input_string ,   voice_preset=self.voice_preset      )






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



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

         __a				     =    BarkProcessor(tokenizer=lowerCamelCase__      )

         __a				     =    processor(text=self.input_string      )

         __a				     =    tokenizer(
             self.input_string ,   padding='max_length' ,   max_length=256 ,   add_special_tokens=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   )

         for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key] ,   encoded_processor[key].squeeze().tolist()      )







'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Dict:

   """simple docstring"""
   __a     ,			__a				     =    image.size
   __a     ,			__a				     =    (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
   __a				     =    image.resize((w, h),					resample=PIL_INTERPOLATION['lanczos']    )
   __a				     =    np.array(SCREAMING_SNAKE_CASE__    ).astype(np.floataa    ) / 2_5_5.0
   __a				     =    image[None].transpose(0,					3,					1,					2    )
   __a				     =    torch.from_numpy(SCREAMING_SNAKE_CASE__    )
   return 2.0 * image - 1.0



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   )							->Dict:



         '''simple docstring'''
         super().__init__()
         self.register_modules(vqvae=lowerCamelCase ,   unet=lowerCamelCase ,   scheduler=lowerCamelCase      )






      @torch.no_grad()
      def __call__(  self ,   lowerCamelCase = None ,   lowerCamelCase = 1 ,   lowerCamelCase = 100 ,   lowerCamelCase = 0.0 ,   lowerCamelCase = None ,   lowerCamelCase = "pil" ,   lowerCamelCase = True ,   )							->Union[Tuple, ImagePipelineOutput]:



         '''simple docstring'''
         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    1
         elif isinstance(lowerCamelCase ,   torch.Tensor      ):
            __a				     =    image.shape[0]
         else:
            raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowerCamelCase      )}"""      )

         if isinstance(lowerCamelCase ,   PIL.Image.Image      ):
            __a				     =    preprocess(lowerCamelCase      )

         __a     ,			__a				     =    image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         __a				     =    (batch_size, self.unet.config.in_channels // 2, height, width)
         __a				     =    next(self.unet.parameters()      ).dtype

         __a				     =    randn_tensor(lowerCamelCase ,   generator=lowerCamelCase ,   device=self.device ,   dtype=lowerCamelCase      )

         __a				     =    image.to(device=self.device ,   dtype=lowerCamelCase      )

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(lowerCamelCase ,   device=self.device      )
         __a				     =    self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         __a				     =    latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         __a				     =    'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
         __a				     =    {}
         if accepts_eta:
            __a				     =    eta

         for t in self.progress_bar(lowerCamelCase      ):
            # concat latents and low resolution image in the channel dimension.
            __a				     =    torch.cat([latents, image] ,   dim=1      )
            __a				     =    self.scheduler.scale_model_input(lowerCamelCase ,   lowerCamelCase      )
            # predict the noise residual
            __a				     =    self.unet(lowerCamelCase ,   lowerCamelCase      ).sample
            # compute the previous noisy sample x_t -> x_t-1
            __a				     =    self.scheduler.step(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      ).prev_sample

         # decode the image latents with the VQVAE
         __a				     =    self.vqvae.decode(lowerCamelCase      ).sample
         __a				     =    torch.clamp(lowerCamelCase ,   -1.0 ,   1.0      )
         __a				     =    image / 2 + 0.5
         __a				     =    image.cpu().permute(0 ,   2 ,   3 ,   1      ).numpy()

         if output_type == "pil":
            __a				     =    self.numpy_to_pil(lowerCamelCase      )

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase      )