


'''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 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														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     __SCREAMING_SNAKE_CASE				(						self   ,       **lowerCamelCase_  )			->				int:
														return AutoTokenizer.from_pretrained(self.checkpoint   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Tuple:
														shutil.rmtree(self.tmpdirname  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      BarkProcessor(tokenizer=lowerCamelCase_  )

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

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







							@slow
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														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     __SCREAMING_SNAKE_CASE				(						self  )			->				List[Any]:
														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(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
														lowerCAmelCase__			  =      processor(text=self.input_string   ,       voice_preset=lowerCamelCase_  )

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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Any:
														lowerCAmelCase__			  =      self.get_tokenizer()

														lowerCAmelCase__			  =      BarkProcessor(tokenizer=lowerCamelCase_  )

														lowerCAmelCase__			  =      processor(text=self.input_string  )

														lowerCAmelCase__			  =      tokenizer(
														    self.input_string   ,       padding='''max_length'''   ,       max_length=2_56   ,       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 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


__UpperCAmelCase       							=				False




class 						a__							(				unittest.TestCase    ):





							'''simple docstring'''






							pass




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





							'''simple docstring'''






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







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[int]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )

														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=2   ,       output_type='''numpy'''   ,       ).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCamelCase_  )
																					lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained(lowerCamelCase_   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      generator.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt='''first prompt'''   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       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     __SCREAMING_SNAKE_CASE				(						self  )			->				Optional[Any]:
														lowerCAmelCase__			  =      VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''   ,       torch_dtype=torch.floataa  )
														pipe.to(lowerCamelCase_  )
														pipe.set_progress_bar_config(disable=lowerCamelCase_  )

														lowerCAmelCase__			  =      '''cyberpunk 2077'''
														lowerCAmelCase__			  =      load_image(
														    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''  )
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.dual_guided(
														    prompt=lowerCamelCase_   ,       image=lowerCamelCase_   ,       text_to_image_strength=0.75   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''   ,       ).images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]  )

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

														lowerCAmelCase__			  =      '''A painting of a squirrel eating a burger '''
														lowerCAmelCase__			  =      torch.manual_seed(0  )
														lowerCAmelCase__			  =      pipe.text_to_image(
														    prompt=lowerCamelCase_   ,       generator=lowerCamelCase_   ,       guidance_scale=7.5   ,       num_inference_steps=50   ,       output_type='''numpy'''  ).images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]  )

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

														lowerCAmelCase__			  =      pipe.image_variation(lowerCamelCase_   ,       generator=lowerCamelCase_   ,       output_type='''numpy'''  ).images

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

														assert image.shape == (1, 5_12, 5_12, 3)
														lowerCAmelCase__			  =      np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]  )

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