





'''simple docstring'''







import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


snake_case_     :       int						=  get_tests_dir("fixtures/test_sentencepiece_bpe.model")


class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   str									=       BartphoTokenizer
							__a					:   Any									=       False
							__a					:   Dict									=       True

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







													"""simple docstring"""
													super().setUp()

													UpperCAmelCase_			:      Tuple     						=  ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
													UpperCAmelCase_			:      Optional[int]     						=  dict(zip(__magic_name__       ,     range(len(__magic_name__   )   )   )   )
													UpperCAmelCase_			:      Optional[int]     						=  {'''unk_token''': '''<unk>'''}

													UpperCAmelCase_			:      Dict     						=  os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['''monolingual_vocab_file''']   )
													with open(self.monolingual_vocab_file       ,     '''w'''       ,     encoding='''utf-8'''   ) as fp:
																			for token in vocab_tokens:
																									fp.write(F"""{token} {vocab_tokens[token]}\n"""   )

													UpperCAmelCase_			:      int     						=  BartphoTokenizer(__magic_name__       ,     self.monolingual_vocab_file       ,     **self.special_tokens_map   )
													tokenizer.save_pretrained(self.tmpdirname   )

							def     UpperCAmelCase__	(							self							:     str       ,     **__magic_name__							:     Dict   )					->       Optional[Any]:







													"""simple docstring"""
													kwargs.update(self.special_tokens_map   )
													return BartphoTokenizer.from_pretrained(self.tmpdirname       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Dict       ,     __magic_name__							:     Tuple   )					->       List[Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  '''This is a là test'''
													UpperCAmelCase_			:      List[Any]     						=  '''This is a<unk><unk> test'''
													return input_text, output_text



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







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BartphoTokenizer(__magic_name__       ,     self.monolingual_vocab_file       ,     **self.special_tokens_map   )
													UpperCAmelCase_			:      str     						=  '''This is a là test'''
													UpperCAmelCase_			:      int     						=  '''▁This ▁is ▁a ▁l à ▁t est'''.split()
													UpperCAmelCase_			:      str     						=  tokenizer.tokenize(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Dict     						=  tokens + [tokenizer.unk_token]
													UpperCAmelCase_			:      Dict     						=  [4, 5, 6, 3, 3, 7, 8, 3]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__   )       ,     __magic_name__   )












'''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





