



'''simple docstring'''



import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def 					UpperCAmelCase   (						):
							SCREAMING_SNAKE_CASE					:      Optional[int]     					=			argparse.ArgumentParser()
							parser.add_argument(
							    '''-m'''					,		'''--pretrained_model_name_or_path'''					,		type=A					,		default=A					,		required=A					,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,		)
							parser.add_argument(
							    '''-c'''					,		'''--caption'''					,		type=A					,		default='''robotic cat with wings'''					,		help='''Text used to generate images.'''					,		)
							parser.add_argument(
							    '''-n'''					,		'''--images_num'''					,		type=A					,		default=4					,		help='''How much images to generate.'''					,		)
							parser.add_argument(
							    '''-s'''					,		'''--seed'''					,		type=A					,		default=42					,		help='''Seed for random process.'''					,		)
							parser.add_argument(
							    '''-ci'''					,		'''--cuda_id'''					,		type=A					,		default=0					,		help='''cuda_id.'''					,		)
							SCREAMING_SNAKE_CASE					:      Dict     					=			parser.parse_args()
							return args
def 					UpperCAmelCase   (						A  :				Tuple					,		A  :				Dict					,		A  :				Any				):
							if not len(A				) == rows * cols:
														raise ValueError('''The specified number of rows and columns are not correct.'''				)

							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			imgs[0].size
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Image.new('''RGB'''					,		size=(cols * w, rows * h)				)
							SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			grid.size

							for i, img in enumerate(A				):
														grid.paste(A					,		box=(i % cols * w, i // cols * h)				)
							return grid
def 					UpperCAmelCase   (						A  :				List[str]					,		A  :				Optional[int]="robotic cat with wings"					,		A  :				Tuple=7.5					,		A  :				int=50					,		A  :				Tuple=1					,		A  :				Union[str, Any]=42					,		):
							SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.Generator(pipeline.device				).manual_seed(A				)
							SCREAMING_SNAKE_CASE					:      Tuple     					=			pipeline(
							    A					,		guidance_scale=A					,		num_inference_steps=A					,		generator=A					,		num_images_per_prompt=A					,		).images
							SCREAMING_SNAKE_CASE					:      Tuple     					=			int(math.sqrt(A				)				)
							SCREAMING_SNAKE_CASE					:      str     					=			image_grid(A					,		rows=_rows					,		cols=num_images_per_prompt // _rows				)
							return grid, images


lowerCAmelCase_     :       Any								=						parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase_     :       Any								=						CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase_     :       int								=						CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase_     :       Union[str, Any]								=						AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase_     :       List[str]								=						UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')

lowerCAmelCase_     :       str								=						StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase_     :       Dict								=						lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
						lowerCAmelCase_     :       Dict								=						load(args.pretrained_model_name_or_path, model=unet)
						unet.eval()
						setattr(pipeline, 'unet', unet)
else:
						lowerCAmelCase_     :       List[Any]								=						unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase_     :       Dict								=						pipeline.to(unet.device)
lowerCAmelCase_     :       Any								=						generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase_     :       Optional[Any]								=						os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
						image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))









'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_     :       int								=						get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				XLMRobertaTokenizer
						_lowerCAmelCase     :   Optional[Any]	     =				XLMRobertaTokenizerFast
						_lowerCAmelCase     :   List[str]	     =				True
						_lowerCAmelCase     :   Union[str, Any]	     =				True







						def 					__lowercase    (  self   :      int				):

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

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)
													tokenizer.save_pretrained(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''<pad>'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__				)				,    lowerCAmelCase__				)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__				)				,    lowerCAmelCase__				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,    '''<s>'''				)
													self.assertEqual(vocab_keys[1]				,    '''<pad>'''				)
													self.assertEqual(vocab_keys[-1]				,    '''<mask>'''				)
													self.assertEqual(len(lowerCAmelCase__				)				,    10_02				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.assertEqual(self.get_tokenizer().vocab_size				,    10_02				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCAmelCase__				,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(
													    tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)				,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,    )

													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        SPIECE_UNDERLINE + '''I''',
													        SPIECE_UNDERLINE + '''was''',
													        SPIECE_UNDERLINE + '''b''',
													        '''or''',
													        '''n''',
													        SPIECE_UNDERLINE + '''in''',
													        SPIECE_UNDERLINE + '''''',
													        '''9''',
													        '''2''',
													        '''0''',
													        '''0''',
													        '''0''',
													        ''',''',
													        SPIECE_UNDERLINE + '''and''',
													        SPIECE_UNDERLINE + '''this''',
													        SPIECE_UNDERLINE + '''is''',
													        SPIECE_UNDERLINE + '''f''',
													        '''al''',
													        '''s''',
													        '''é''',
													        '''.''',
													    ]				,    )
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        value + tokenizer.fairseq_offset
													        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]				,    )

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        SPIECE_UNDERLINE + '''I''',
													        SPIECE_UNDERLINE + '''was''',
													        SPIECE_UNDERLINE + '''b''',
													        '''or''',
													        '''n''',
													        SPIECE_UNDERLINE + '''in''',
													        SPIECE_UNDERLINE + '''''',
													        '''<unk>''',
													        '''2''',
													        '''0''',
													        '''0''',
													        '''0''',
													        ''',''',
													        SPIECE_UNDERLINE + '''and''',
													        SPIECE_UNDERLINE + '''this''',
													        SPIECE_UNDERLINE + '''is''',
													        SPIECE_UNDERLINE + '''f''',
													        '''al''',
													        '''s''',
													        '''<unk>''',
													        '''.''',
													    ]				,    )







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													if not self.test_slow_tokenizer:
																				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																				return

													SCREAMING_SNAKE_CASE					:      Tuple     					=			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																											SCREAMING_SNAKE_CASE					:      int     					=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self.tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      int     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files + the tokenizer.json file for the fast one
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=True
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      str     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=False
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it saved the tokenizer.json file
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)







						@cached_property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													with tempfile.NamedTemporaryFile() as f:
																				shutil.copyfile(lowerCAmelCase__				,    f.name				)
																				SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(f.name				,    keep_accents=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			pickle.dumps(lowerCAmelCase__				)
													pickle.loads(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''I was born in 92000, and this is falsé.'''

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.tokenize(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			rust_tokenizer.tokenize(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.encode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''Hello World!'''
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[0, 3_53_78, 66_61, 38, 2]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(lowerCAmelCase__				,    self.big_tokenizer.encode(lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			(
													    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
													    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
													)
													SCREAMING_SNAKE_CASE					:      Any     					=			[
													    0,
													    32_93,
													    83,
													    10,
													    45_52,
													    49_89,
													    79_86,
													    6_78,
													    10,
													    59_15,
													    1_11,
													    17_94_59,
													    12_48_50,
													    4,
													    60_44,
													    2_37,
													    12,
													    6,
													    5,
													    6,
													    4,
													    67_80,
													    7_05,
													    15,
													    13_88,
													    44,
													    3_78,
													    1_01_14,
													    7_11,
													    1_52,
													    20,
													    6,
													    5,
													    2_23_76,
													    6_42,
													    12_21,
													    1_51_90,
													    3_41_53,
													    4_50,
													    56_08,
													    9_59,
													    11_19,
													    5_77_02,
													    1_36,
													    1_86,
													    47,
													    10_98,
													    2_93_67,
													    47,
													    # 4426, # What fairseq tokenizes from "<unk>": "_<"
													    # 3678, # What fairseq tokenizes from "<unk>": "unk"
													    # 2740, # What fairseq tokenizes from "<unk>": ">"
													    3,  # What we tokenize from "<unk>": "<unk>"
													    6,  # Residue from the tokenization: an extra sentencepiece underline
													    4,
													    60_44,
													    2_37,
													    62_84,
													    5_09_01,
													    5_28,
													    31,
													    90,
													    34,
													    9_27,
													    2,
													]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

													self.assertListEqual(lowerCAmelCase__				,    self.big_tokenizer.encode(lowerCAmelCase__				)				)







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													# fmt: off
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
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													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCAmelCase__				,    model_name='''xlm-roberta-base'''				,    revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,    )




