
"""simple docstring"""



import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case          =						get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							int							=					XLNetTokenizer
				SCREAMING_SNAKE_CASE_:							Dict							=					XLNetTokenizerFast
				SCREAMING_SNAKE_CASE_:							Tuple							=					True
				SCREAMING_SNAKE_CASE_:							int							=					True


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







											"""simple docstring"""




											super().setUp()

											# We have a SentencePiece fixture for testing
											_lowerCAmelCase  	=			XLNetTokenizer(UpperCAmelCase_				,     keep_accents=UpperCAmelCase_  )
											tokenizer.sanitize_special_tokens()
											tokenizer.save_pretrained(self.tmpdirname  )


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			'<s>'
											_lowerCAmelCase  	=			1

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


				def 				__lowerCamelCase	(					self	:			str  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			list(self.get_tokenizer().get_vocab().keys()  )

											self.assertEqual(vocab_keys[0]				,     '<unk>'  )
											self.assertEqual(vocab_keys[1]				,     '<s>'  )
											self.assertEqual(vocab_keys[-1]				,     '<eod>'  )
											self.assertEqual(len(UpperCAmelCase_  )				,     1_006  )


				def 				__lowerCamelCase	(					self	:			int  )		->  Optional[Any]:







											"""simple docstring"""




											self.assertEqual(self.get_tokenizer().vocab_size				,     1_000  )


				def 				__lowerCamelCase	(					self	:			int  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLNetTokenizer(UpperCAmelCase_				,     keep_accents=UpperCAmelCase_  )

											_lowerCAmelCase  	=			tokenizer.tokenize('This is a test'  )
											self.assertListEqual(UpperCAmelCase_				,     ['▁This', '▁is', '▁a', '▁t', 'est']  )

											self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )				,     [285, 46, 10, 170, 382]  )

											_lowerCAmelCase  	=			tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
											self.assertListEqual(
											    UpperCAmelCase_				,     [
											        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',
											        '.',
											    ]				,     )
											_lowerCAmelCase  	=			tokenizer.convert_tokens_to_ids(UpperCAmelCase_  )
											self.assertListEqual(UpperCAmelCase_				,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]  )

											_lowerCAmelCase  	=			tokenizer.convert_ids_to_tokens(UpperCAmelCase_  )
											self.assertListEqual(
											    UpperCAmelCase_				,     [
											        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 				__lowerCamelCase	(					self	:			str  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLNetTokenizer(UpperCAmelCase_				,     do_lower_case=UpperCAmelCase_  )
											_lowerCAmelCase  	=			tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
											self.assertListEqual(
											    UpperCAmelCase_				,     [
											        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',
											        'se',
											        '.',
											    ]				,     )
											self.assertListEqual(tokenizer.tokenize('H\u00E9llo'  )				,     ['▁he', 'll', 'o']  )


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLNetTokenizer(UpperCAmelCase_				,     do_lower_case=UpperCAmelCase_  )
											_lowerCAmelCase  	=			tokenizer.tokenize('I was born in 92000, and this is falsé.'  )
											self.assertListEqual(
											    UpperCAmelCase_				,     [
											        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',
											        'se',
											        '.',
											    ]				,     )


				@slow
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			XLNetTokenizer.from_pretrained('xlnet-base-cased'  )

											_lowerCAmelCase  	=			tokenizer.encode('sequence builders'				,     add_special_tokens=UpperCAmelCase_  )
											_lowerCAmelCase  	=			tokenizer.encode('multi-sequence build'				,     add_special_tokens=UpperCAmelCase_  )

											_lowerCAmelCase  	=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_  )
											_lowerCAmelCase  	=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_				,     UpperCAmelCase_  )

											assert encoded_sentence == text + [4, 3]
											assert encoded_pair == text + [4] + text_a + [4, 3]







				@slow
				def 				__lowerCamelCase	(					self	:			int  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			{'input_ids': [[17, 21_442, 270, 17, 10, 14_645, 318, 34, 17, 4_546, 3_145, 787, 13, 7_752, 22_018, 23, 21, 17, 4_546, 3_145, 787, 13, 3_352, 14_431, 13, 5_500, 11, 1_176, 580, 13, 16_819, 4_797, 23, 17, 10, 17_135, 658, 19, 457, 7_932, 13, 184, 19, 3_154, 17_135, 6_468, 19, 1_404, 12_269, 19, 4_229, 5_356, 16_264, 46, 19, 17, 20_545, 10_395, 9, 9, 9, 11, 28, 6_421, 9_531, 20_729, 17, 10, 353, 17_022, 11, 21, 6_421, 9_531, 16_949, 17, 10, 11_509, 753, 11, 33, 95, 2_421, 7_385, 956, 14_431, 2_626, 25, 842, 7_385, 4_836, 21, 1_429, 2_272, 9_855, 3_120, 161, 24_738, 19, 13_203, 658, 218, 787, 21, 430, 18_482, 847, 2_637, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 322, 22_178, 27, 1_064, 22, 956, 13, 11_101, 1_429, 5_854, 24_313, 18_953, 40, 422, 24_366, 68, 1_758, 37, 10_483, 14_257, 31, 207, 263, 21, 203, 3_773, 25, 71, 9_735, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 32, 2_049, 3_442, 17, 13_894, 3_380, 23, 95, 18, 17_634, 2_288, 9, 4, 3]], 'token_type_ids': [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '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], [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, 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, 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]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCAmelCase_				,     model_name='xlnet-base-cased'				,     revision='c841166438c31ec7ca9a106dee7bb312b73ae511'				,     )








"""simple docstring"""



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_snake_case          =						argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
_snake_case          =						parser.parse_args()


_snake_case          =						'''cpu'''
_snake_case          =						'''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

_snake_case          =						'''path-to-your-trained-model'''
_snake_case          =						StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
	_snake_case          =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_snake_case          =						pipe.to(device)

# to channels last
_snake_case          =						pipe.unet.to(memory_format=torch.channels_last)
_snake_case          =						pipe.vae.to(memory_format=torch.channels_last)
_snake_case          =						pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
	_snake_case          =						pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_snake_case          =						torch.randn(2, 4, 6_4, 6_4)
_snake_case          =						torch.rand(1) * 9_9_9
_snake_case          =						torch.randn(2, 7_7, 7_6_8)
_snake_case          =						(sample, timestep, encoder_hidden_status)
try:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
	_snake_case          =						ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_snake_case          =						ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
	_snake_case          =						ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_snake_case          =						6_6_6
_snake_case          =						torch.Generator(device).manual_seed(seed)
_snake_case          =						{'''generator''': generator}
if args.steps is not None:
	_snake_case          =						args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
	_snake_case          =						pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')






