





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 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     Union[str, Any]   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  '''ylacombe/bark-small'''
													UpperCAmelCase_			:      Any     						=  tempfile.mkdtemp()
													UpperCAmelCase_			:      Dict     						=  '''en_speaker_1'''
													UpperCAmelCase_			:      Dict     						=  '''This is a test string'''
													UpperCAmelCase_			:      List[Any]     						=  '''speaker_embeddings_path.json'''
													UpperCAmelCase_			:      Union[str, Any]     						=  '''speaker_embeddings'''

							def     UpperCAmelCase__	(							self							:     int       ,     **__magic_name__							:     List[Any]   )					->       Any:







													"""simple docstring"""
													return AutoTokenizer.from_pretrained(self.checkpoint       ,     **__magic_name__   )

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







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

							def     UpperCAmelCase__	(							self							:     int   )					->       Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  self.get_tokenizer()

													UpperCAmelCase_			:      Tuple     						=  BarkProcessor(tokenizer=__magic_name__   )

													processor.save_pretrained(self.tmpdirname   )
													UpperCAmelCase_			:      str     						=  BarkProcessor.from_pretrained(self.tmpdirname   )

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

							@slow
							def     UpperCAmelCase__	(							self							:     Dict   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  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       ,     )

													UpperCAmelCase_			:      Dict     						=  self.get_tokenizer(bos_token='''(BOS)'''       ,     eos_token='''(EOS)'''   )

													UpperCAmelCase_			:      Tuple     						=  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							:     List[Any]   )					->       int:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BarkProcessor.from_pretrained(
													    pretrained_processor_name_or_path=self.checkpoint       ,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,     )

													UpperCAmelCase_			:      Union[str, Any]     						=  35
													UpperCAmelCase_			:      Optional[Any]     						=  2
													UpperCAmelCase_			:      Tuple     						=  8

													UpperCAmelCase_			:      str     						=  {
													    '''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
													UpperCAmelCase_			:      Optional[int]     						=  processor(text=self.input_string       ,     voice_preset=__magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  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
													UpperCAmelCase_			:      List[str]     						=  os.path.join(self.tmpdirname       ,     '''file.npz'''   )
													np.savez(__magic_name__       ,     **__magic_name__   )
													UpperCAmelCase_			:      Tuple     						=  processor(text=self.input_string       ,     voice_preset=__magic_name__   )
													UpperCAmelCase_			:      List[str]     						=  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
													UpperCAmelCase_			:      int     						=  processor(text=self.input_string       ,     voice_preset=self.voice_preset   )



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







													"""simple docstring"""
													UpperCAmelCase_			:      Any     						=  self.get_tokenizer()

													UpperCAmelCase_			:      str     						=  BarkProcessor(tokenizer=__magic_name__   )

													UpperCAmelCase_			:      Dict     						=  processor(text=self.input_string   )

													UpperCAmelCase_			:      Optional[int]     						=  tokenizer(
													    self.input_string       ,     padding='''max_length'''       ,     max_length=2_56       ,     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()   )












'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


snake_case_     :       List[str]						=  False


class 					__a      (unittest.TestCase     ):
							pass





@nightly
@require_torch_gpu
class 					__a      (unittest.TestCase     ):

							def     UpperCAmelCase__	(							self							:     int   )					->       str:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      List[Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )

													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Union[str, Any]     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     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__   )
																			UpperCAmelCase_			:      Optional[int]     						=  VersatileDiffusionPipeline.from_pretrained(__magic_name__       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Any     						=  generator.manual_seed(0   )
													UpperCAmelCase_			:      Dict     						=  pipe.dual_guided(
													    prompt='''first prompt'''       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     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     UpperCAmelCase__	(							self							:     str   )					->       Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,     torch_dtype=torch.floataa   )
													pipe.to(__magic_name__   )
													pipe.set_progress_bar_config(disable=__magic_name__   )

													UpperCAmelCase_			:      Union[str, Any]     						=  '''cyberpunk 2077'''
													UpperCAmelCase_			:      Union[str, Any]     						=  load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''   )
													UpperCAmelCase_			:      Tuple     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      Optional[Any]     						=  pipe.dual_guided(
													    prompt=__magic_name__       ,     image=__magic_name__       ,     text_to_image_strength=0.7_5       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''       ,     ).images

													UpperCAmelCase_			:      List[str]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Union[str, Any]     						=  np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]   )

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

													UpperCAmelCase_			:      Tuple     						=  '''A painting of a squirrel eating a burger '''
													UpperCAmelCase_			:      Optional[int]     						=  torch.manual_seed(0   )
													UpperCAmelCase_			:      List[Any]     						=  pipe.text_to_image(
													    prompt=__magic_name__       ,     generator=__magic_name__       ,     guidance_scale=7.5       ,     num_inference_steps=50       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Tuple     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      Any     						=  np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]   )

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

													UpperCAmelCase_			:      Tuple     						=  pipe.image_variation(__magic_name__       ,     generator=__magic_name__       ,     output_type='''numpy'''   ).images

													UpperCAmelCase_			:      Optional[Any]     						=  image[0, 2_53:2_56, 2_53:2_56, -1]

													assert image.shape == (1, 5_12, 5_12, 3)
													UpperCAmelCase_			:      List[str]     						=  np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]   )

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





