



import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	lowerCamelCase__      (							lowerCamelCase__				,    lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =AutoencoderKL
		snake_case_     ="""sample"""
		snake_case_     =1e-2



		@property
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					4
						lowerCAmelCase__					:    List[str]												=					3
						lowerCAmelCase__					:    Any												=					(32, 32)

						lowerCAmelCase__					:    str												=					floats_tensor((batch_size, num_channels) + sizes			).to(__lowerCamelCase			)

						return {"sample": image}



		@property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return (3, 32, 32)



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						return (3, 32, 32)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					{
						    '''block_out_channels''': [32, 64],
						    '''in_channels''': 3,
						    '''out_channels''': 3,
						    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
						    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
						    '''latent_channels''': 4,
						}
						lowerCAmelCase__					:    Optional[Any]												=					self.dummy_input
						return init_dict, inputs_dict



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						pass



		@unittest.skipIf(torch_device == '''mps'''		,'''Gradient checkpointing skipped on MPS'''			)
		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__					:    Tuple												=					self.prepare_init_args_and_inputs_for_common()
						lowerCAmelCase__					:    List[Any]												=					self.model_class(**__lowerCamelCase			)
						model.to(__lowerCamelCase			)

						assert not model.is_gradient_checkpointing and model.training

						lowerCAmelCase__					:    List[Any]												=					model(**__lowerCamelCase			).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model.zero_grad()

						lowerCAmelCase__					:    Dict												=					torch.randn_like(__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					(out - labels).mean()
						loss.backward()

						# re-instantiate the model now enabling gradient checkpointing
						lowerCAmelCase__					:    str												=					self.model_class(**__lowerCamelCase			)
						# clone model
						model_a.load_state_dict(model.state_dict()			)
						model_a.to(__lowerCamelCase			)
						model_a.enable_gradient_checkpointing()

						assert model_a.is_gradient_checkpointing and model_a.training

						lowerCAmelCase__					:    Any												=					model_a(**__lowerCamelCase			).sample
						# run the backwards pass on the model. For backwards pass, for simplicity purpose,
						# we won't calculate the loss and rather backprop on out.sum()
						model_a.zero_grad()
						lowerCAmelCase__					:    Optional[int]												=					(out_a - labels).mean()
						loss_a.backward()

						# compare the output and parameters gradients
						self.assertTrue((loss - loss_a).abs() < 1e-5			)
						lowerCAmelCase__					:    Any												=					dict(model.named_parameters()			)
						lowerCAmelCase__					:    List[Any]												=					dict(model_a.named_parameters()			)
						for name, param in named_params.items():
										self.assertTrue(torch_all_close(param.grad.data		,named_params_a[name].grad.data		,atol=5e-5			)			)



		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''		,output_loading_info=__lowerCamelCase			)
						self.assertIsNotNone(__lowerCamelCase			)
						self.assertEqual(len(loading_info['''missing_keys''']			)		,0			)

						model.to(__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					model(**self.dummy_input			)

						assert image is not None, "Make sure output is not None"



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

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''			)
						lowerCAmelCase__					:    Optional[int]												=					model.to(__lowerCamelCase			)
						model.eval()

						if torch_device == "mps":
										lowerCAmelCase__					:    Union[str, Any]												=					torch.manual_seed(0			)
						else:
										lowerCAmelCase__					:    Dict												=					torch.Generator(device=__lowerCamelCase			).manual_seed(0			)

						lowerCAmelCase__					:    Dict												=					torch.randn(
						    1		,model.config.in_channels		,model.config.sample_size		,model.config.sample_size		,generator=torch.manual_seed(0			)		,)
						lowerCAmelCase__					:    Optional[int]												=					image.to(__lowerCamelCase			)
						with torch.no_grad():
										lowerCAmelCase__					:    Optional[int]												=					model(__lowerCamelCase		,sample_posterior=__lowerCamelCase		,generator=__lowerCamelCase			).sample

						lowerCAmelCase__					:    Optional[int]												=					output[0, -1, -3:, -3:].flatten().cpu()

						# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
						# the expected output slices are not the same for CPU and GPU.
						if torch_device == "mps":
										lowerCAmelCase__					:    Tuple												=					torch.tensor(
										    [
										        -4.0078e-01,
										        -3.8323e-04,
										        -1.2681e-01,
										        -1.1462e-01,
										        2.0095e-01,
										        1.0893e-01,
										        -8.8247e-02,
										        -3.0361e-01,
										        -9.8644e-03,
										    ]			)
						elif torch_device == "cpu":
										lowerCAmelCase__					:    str												=					torch.tensor(
										    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]			)
						else:
										lowerCAmelCase__					:    Tuple												=					torch.tensor(
										    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]			)

						self.assertTrue(torch_all_close(__lowerCamelCase		,__lowerCamelCase		,rtol=1e-2			)			)



@slow
class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						return f"""gaussian_noise_s={seed}_shape={'_'.join([str(__lowerCamelCase			) for s in shape]			)}.npy"""



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=0		,__lowerCamelCase=(4, 3, 5_12, 5_12)		,__lowerCamelCase=False			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					torch.floataa if fpaa else torch.floataa
						lowerCAmelCase__					:    Optional[Any]												=					torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCamelCase		,__lowerCamelCase			)			)			).to(__lowerCamelCase			).to(__lowerCamelCase			)
						return image



		def 		lowerCAmelCase__				(self		,__lowerCamelCase="CompVis/stable-diffusion-v1-4"		,__lowerCamelCase=False			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Tuple												=					'''fp16''' if fpaa else None
						lowerCAmelCase__					:    Union[str, Any]												=					torch.floataa if fpaa else torch.floataa

						lowerCAmelCase__					:    Dict												=					AutoencoderKL.from_pretrained(
						    __lowerCamelCase		,subfolder='''vae'''		,torch_dtype=__lowerCamelCase		,revision=__lowerCamelCase		,)
						model.to(__lowerCamelCase			).eval()

						return model



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






						if torch_device == "mps":
										return torch.manual_seed(__lowerCamelCase			)
						return torch.Generator(device=__lowerCamelCase			).manual_seed(__lowerCamelCase			)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ]			)
		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.get_sd_vae_model()
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_sd_image(__lowerCamelCase			)
						lowerCAmelCase__					:    Tuple												=					self.get_generator(__lowerCamelCase			)

						with torch.no_grad():
										lowerCAmelCase__					:    str												=					model(__lowerCamelCase		,generator=__lowerCamelCase		,sample_posterior=__lowerCamelCase			).sample

						assert sample.shape == image.shape

						lowerCAmelCase__					:    Any												=					sample[-1, -2:, -2:, :2].flatten().float().cpu()
						lowerCAmelCase__					:    Optional[Any]												=					torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice			)

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=3e-3			)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
		        [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
		        # fmt: on
		    ]			)
		@require_torch_gpu
		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.get_sd_vae_model(fpaa=__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					self.get_sd_image(__lowerCamelCase		,fpaa=__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					self.get_generator(__lowerCamelCase			)

						with torch.no_grad():
										lowerCAmelCase__					:    Optional[int]												=					model(__lowerCamelCase		,generator=__lowerCamelCase		,sample_posterior=__lowerCamelCase			).sample

						assert sample.shape == image.shape

						lowerCAmelCase__					:    Tuple												=					sample[-1, -2:, :2, -2:].flatten().float().cpu()
						lowerCAmelCase__					:    Union[str, Any]												=					torch.tensor(__lowerCamelCase			)

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=1e-2			)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
		        [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
		        # fmt: on
		    ]			)
		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.get_sd_vae_model()
						lowerCAmelCase__					:    Union[str, Any]												=					self.get_sd_image(__lowerCamelCase			)

						with torch.no_grad():
										lowerCAmelCase__					:    Optional[int]												=					model(__lowerCamelCase			).sample

						assert sample.shape == image.shape

						lowerCAmelCase__					:    int												=					sample[-1, -2:, -2:, :2].flatten().float().cpu()
						lowerCAmelCase__					:    Any												=					torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice			)

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=3e-3			)



		@parameterized.expand(
		    [
		        # fmt: off
		        [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
		        [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
		        # fmt: on
		    ]			)
		@require_torch_gpu
		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.get_sd_vae_model()
						lowerCAmelCase__					:    Optional[int]												=					self.get_sd_image(__lowerCamelCase		,shape=(3, 4, 64, 64)			)

						with torch.no_grad():
										lowerCAmelCase__					:    Tuple												=					model.decode(__lowerCamelCase			).sample

						assert list(sample.shape			) == [3, 3, 5_12, 5_12]

						lowerCAmelCase__					:    Dict												=					sample[-1, -2:, :2, -2:].flatten().cpu()
						lowerCAmelCase__					:    int												=					torch.tensor(__lowerCamelCase			)

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=1e-3			)



		@parameterized.expand(
		    [
		        # fmt: off
		        [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
		        [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
		        # fmt: on
		    ]			)
		@require_torch_gpu
		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					self.get_sd_vae_model(fpaa=__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					self.get_sd_image(__lowerCamelCase		,shape=(3, 4, 64, 64)		,fpaa=__lowerCamelCase			)

						with torch.no_grad():
										lowerCAmelCase__					:    Dict												=					model.decode(__lowerCamelCase			).sample

						assert list(sample.shape			) == [3, 3, 5_12, 5_12]

						lowerCAmelCase__					:    Optional[Any]												=					sample[-1, -2:, :2, -2:].flatten().float().cpu()
						lowerCAmelCase__					:    Union[str, Any]												=					torch.tensor(__lowerCamelCase			)

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=5e-3			)



		@parameterized.expand([(13,), (16,), (27,)]			)
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()		,reason='''xformers is not required when using PyTorch 2.0.'''			)
		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					self.get_sd_vae_model(fpaa=__lowerCamelCase			)
						lowerCAmelCase__					:    Any												=					self.get_sd_image(__lowerCamelCase		,shape=(3, 4, 64, 64)		,fpaa=__lowerCamelCase			)

						with torch.no_grad():
										lowerCAmelCase__					:    List[str]												=					model.decode(__lowerCamelCase			).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										lowerCAmelCase__					:    List[Any]												=					model.decode(__lowerCamelCase			).sample

						assert list(sample.shape			) == [3, 3, 5_12, 5_12]

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=1e-1			)



		@parameterized.expand([(13,), (16,), (37,)]			)
		@require_torch_gpu
		@unittest.skipIf(not is_xformers_available()		,reason='''xformers is not required when using PyTorch 2.0.'''			)
		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.get_sd_vae_model()
						lowerCAmelCase__					:    int												=					self.get_sd_image(__lowerCamelCase		,shape=(3, 4, 64, 64)			)

						with torch.no_grad():
										lowerCAmelCase__					:    Any												=					model.decode(__lowerCamelCase			).sample

						model.enable_xformers_memory_efficient_attention()
						with torch.no_grad():
										lowerCAmelCase__					:    Dict												=					model.decode(__lowerCamelCase			).sample

						assert list(sample.shape			) == [3, 3, 5_12, 5_12]

						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=1e-2			)



		@parameterized.expand(
		    [
		        # fmt: off
		        [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
		        [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
		        # fmt: on
		    ]			)
		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.get_sd_vae_model()
						lowerCAmelCase__					:    Tuple												=					self.get_sd_image(__lowerCamelCase			)
						lowerCAmelCase__					:    List[Any]												=					self.get_generator(__lowerCamelCase			)

						with torch.no_grad():
										lowerCAmelCase__					:    Tuple												=					model.encode(__lowerCamelCase			).latent_dist
										lowerCAmelCase__					:    int												=					dist.sample(generator=__lowerCamelCase			)

						assert list(sample.shape			) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

						lowerCAmelCase__					:    List[Any]												=					sample[0, -1, -3:, -3:].flatten().cpu()
						lowerCAmelCase__					:    List[str]												=					torch.tensor(__lowerCamelCase			)

						lowerCAmelCase__					:    int												=					3e-3 if torch_device != '''mps''' else 1e-2
						assert torch_all_close(__lowerCamelCase		,__lowerCamelCase		,atol=__lowerCamelCase			)






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case    :		int  =get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =XLMProphetNetTokenizer
		snake_case_     =False
		snake_case_     =True



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__					:    int												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)
						tokenizer.save_pretrained(self.tmpdirname			)



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

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					'''[PAD]'''
						lowerCAmelCase__					:    Tuple												=					0

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



		def 		lowerCAmelCase__				(self			)					->      Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					list(self.get_tokenizer().get_vocab().keys()			)

						self.assertEqual(vocab_keys[0]		,'''[PAD]'''			)
						self.assertEqual(vocab_keys[1]		,'''[CLS]'''			)
						self.assertEqual(vocab_keys[-1]		,'''j'''			)
						self.assertEqual(len(__lowerCamelCase			)		,10_12			)



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

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size		,10_12			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					XLMProphetNetTokenizer(__lowerCamelCase		,keep_accents=__lowerCamelCase			)

						lowerCAmelCase__					:    Tuple												=					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]]		,)

						lowerCAmelCase__					:    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''',
						        '''.''',
						    ]		,)
						lowerCAmelCase__					:    Optional[Any]												=					tokenizer.convert_tokens_to_ids(__lowerCamelCase			)
						self.assertListEqual(
						    __lowerCamelCase		,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]		,)

						lowerCAmelCase__					:    Optional[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]''',
						        '''.''',
						    ]		,)



		@cached_property
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						return XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					'''Hello World!'''
						lowerCAmelCase__					:    str												=					[3_53_89, 66_72, 49, 2]
						self.assertListEqual(__lowerCamelCase		,self.big_tokenizer.encode(__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					{'''input_ids''': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__lowerCamelCase		,model_name='''microsoft/xprophetnet-large-wiki100-cased'''		,revision='''1acad1643ddd54a44df6a1b797ada8373685d90e'''		,)

