





'''simple docstring'''
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin

class      snake_case     (    __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					int					=PhobertTokenizer
 SCREAMING_SNAKE_CASE_	:					Union[str, Any]					=False






 def        _lowerCamelCase					(       self       :							Any	):
        super().setUp()

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

        __UpperCamelCase       =  os.path.join(self.tmpdirname							,	VOCAB_FILES_NAMES['vocab_file']	)
        __UpperCamelCase       =  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        _lowerCamelCase					(       self       :							Dict							,	**__A       :							Tuple	):
        kwargs.update(self.special_tokens_map	)
        return PhobertTokenizer.from_pretrained(self.tmpdirname							,	**__A	)






 def        _lowerCamelCase					(       self       :							str							,	__A       :							List[Any]	):
        __UpperCamelCase       =  'Tôi là VinAI Research'
        __UpperCamelCase       =  'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
        return input_text, output_text






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

        __UpperCamelCase       =  tokens + [tokenizer.unk_token]

        __UpperCamelCase       =  [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

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
   import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class        lowerCamelCase_							(      unittest.TestCase					):


 '''simple docstring'''







 @property
 def       lowerCAmelCase_  (							self  :		Optional[Any]						):
       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )



 @property
 def       lowerCAmelCase_  (							self  :		str						):
       SCREAMING_SNAKE_CASE_							=			ort.SessionOptions()
       SCREAMING_SNAKE_CASE_							=			False
       return options




 def       lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo.png'						)
       SCREAMING_SNAKE_CASE_							=			load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'						)
       SCREAMING_SNAKE_CASE_							=			load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'						)

       # using the PNDM scheduler by default
       SCREAMING_SNAKE_CASE_							=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           'CompVis/stable-diffusion-v1-4'				,				revision='onnx'				,				safety_checker=_lowerCAmelCase				,				feature_extractor=_lowerCAmelCase				,				provider=self.gpu_provider				,				sess_options=self.gpu_options				,				)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_							=			'A red cat sitting on a park bench'

       SCREAMING_SNAKE_CASE_							=			np.random.RandomState(0						)
       SCREAMING_SNAKE_CASE_							=			pipe(
           prompt=_lowerCAmelCase				,				image=_lowerCAmelCase				,				mask_image=_lowerCAmelCase				,				strength=0.75				,				guidance_scale=7.5				,				num_inference_steps=15				,				generator=_lowerCAmelCase				,				output_type='np'				,				)

       SCREAMING_SNAKE_CASE_							=			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image						).max() < 1E-2