


import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase_	(							a__,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					PhobertTokenizer
       __UpperCamelCase      :     Union[str, Any]							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase    :	str   	=		['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
              UpperCamelCase    :	Union[str, Any]   	=		dict(zip(_A ,					range(len(_A						)						)						)						)
              UpperCamelCase    :	Optional[int]   	=		['#version: 0.2', 'l à</w>']
              UpperCamelCase    :	Tuple   	=		{'unk_token': '<unk>'}

              UpperCamelCase    :	List[Any]   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']						)
              UpperCamelCase    :	int   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']						)

              with open(self.vocab_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     for token in vocab_tokens:
                            fp.write(f"""{token} {vocab_tokens[token]}\n"""						)
              with open(self.merges_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     fp.write('''\n'''.join(_A						)						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              kwargs.update(self.special_tokens_map						)
              return PhobertTokenizer.from_pretrained(self.tmpdirname ,					**_A						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		'Tôi là VinAI Research'
              UpperCamelCase    :	Any   	=		'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
              return input_text, output_text






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		PhobertTokenizer(self.vocab_file ,					self.merges_file ,					**self.special_tokens_map						)
              UpperCamelCase    :	List[Any]   	=		'Tôi là VinAI Research'
              UpperCamelCase    :	Dict   	=		'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
              UpperCamelCase    :	Optional[int]   	=		tokenizer.tokenize(_A						)
              print(_A						)
              self.assertListEqual(_A ,					_A						)

              UpperCamelCase    :	List[Any]   	=		tokens + [tokenizer.unk_token]

              UpperCamelCase    :	Dict   	=		[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(_A						) ,					_A						)





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

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

