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    __A     (							self :       Optional[int]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  "ylacombe/bark-small"
        SCREAMING_SNAKE_CASE_          =  tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE_          =  "en_speaker_1"
        SCREAMING_SNAKE_CASE_          =  "This is a test string"
        SCREAMING_SNAKE_CASE_          =  "speaker_embeddings_path.json"
        SCREAMING_SNAKE_CASE_          =  "speaker_embeddings"







  def    __A     (							self :       List[str]  ,     **__magic_name__ :       Dict   )  ->	Tuple:
        return AutoTokenizer.from_pretrained(self.checkpoint  ,     **__magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        shutil.rmtree(self.tmpdirname   )







  def    __A     (							self :       Optional[int]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  BarkProcessor(tokenizer=__magic_name__   )

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

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







  @slow
  def    __A     (							self :       int   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  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  ,     )

        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer(bos_token="(BOS)"  ,     eos_token="(EOS)"   )

        SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       Optional[Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  BarkProcessor.from_pretrained(
            pretrained_processor_name_or_path=self.checkpoint  ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path  ,     )

        SCREAMING_SNAKE_CASE_          =  35
        SCREAMING_SNAKE_CASE_          =  2
        SCREAMING_SNAKE_CASE_          =  8

        SCREAMING_SNAKE_CASE_          =  {
            "semantic_prompt": np.ones(__magic_name__   ),
            "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)   ),
            "fine_prompt": np.ones((nb_codebooks_total, seq_len)   ),
        }

        # test providing already loaded voice_preset
        SCREAMING_SNAKE_CASE_          =  processor(text=self.input_string  ,     voice_preset=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  inputs["history_prompt"]
        for key in voice_preset:
              self.assertListEqual(voice_preset[key].tolist()  ,     processed_voice_preset.get(__magic_name__  ,     np.array([]   )   ).tolist()   )

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

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

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







  def    __A     (							self :       Tuple   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  self.get_tokenizer()

        SCREAMING_SNAKE_CASE_          =  BarkProcessor(tokenizer=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  processor(text=self.input_string   )

        SCREAMING_SNAKE_CASE_          =  tokenizer(
            self.input_string  ,     padding="max_length"  ,     max_length=256  ,     add_special_tokens=__magic_name__  ,     return_attention_mask=__magic_name__  ,     return_token_type_ids=__magic_name__  ,     )

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



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


A     :  List[str]					     =       False


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  pass



@nightly
@require_torch_gpu
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"   )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  generator.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=2  ,     output_type="numpy"   ).images

        assert np.abs(image - new_image   ).sum() < 1e-5, "Models don't have the same forward pass"







  def    __A     (							self :       int   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  VersatileDiffusionTextToImagePipeline.from_pretrained(
            "shi-labs/versatile-diffusion"  ,     torch_dtype=torch.floataa   )
        pipe.to(__magic_name__   )
        pipe.set_progress_bar_config(disable=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  "A painting of a squirrel eating a burger "
        SCREAMING_SNAKE_CASE_          =  torch.manual_seed(0   )
        SCREAMING_SNAKE_CASE_          =  pipe(
            prompt=__magic_name__  ,     generator=__magic_name__  ,     guidance_scale=7.5  ,     num_inference_steps=50  ,     output_type="numpy"   ).images

        SCREAMING_SNAKE_CASE_          =  image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        SCREAMING_SNAKE_CASE_          =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

        assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2


