

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 SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
		"""simple docstring"""






		lowerCamelCase   :      Tuple										=AutoencoderKL
		lowerCamelCase   :      Tuple										="sample"
		lowerCamelCase   :      Dict										=1e-2







		@property
		def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Dict:






					"""simple docstring"""







					__lowerCAmelCase  :      str    =						4
					__lowerCAmelCase  :      Dict    =						3
					__lowerCAmelCase  :      Optional[Any]    =						(32, 32)

					__lowerCAmelCase  :      Union[str, Any]    =						floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase   )

					return {"sample": image}







		@property
		def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     int:






					"""simple docstring"""







					return (3, 32, 32)







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






					"""simple docstring"""







					return (3, 32, 32)







		def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






					"""simple docstring"""







					__lowerCAmelCase  :      List[Any]    =						{
					    """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[int]    =						self.dummy_input
					return init_dict, inputs_dict







		def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     str:






					"""simple docstring"""







					pass







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






					"""simple docstring"""







					pass







		@unittest.skipIf(torch_device == """mps"""	,							"""Gradient checkpointing skipped on MPS"""   )
		def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      str    =						self.prepare_init_args_and_inputs_for_common()
					__lowerCAmelCase  :      Dict    =						self.model_class(**lowerCAmelCase   )
					model.to(lowerCAmelCase   )

					assert not model.is_gradient_checkpointing and model.training

					__lowerCAmelCase  :      str    =						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  :      Any    =						torch.randn_like(lowerCAmelCase   )
					__lowerCAmelCase  :      str    =						(out - labels).mean()
					loss.backward()

					# re-instantiate the model now enabling gradient checkpointing
					__lowerCAmelCase  :      List[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  :      Dict    =						(out_a - labels).mean()
					loss_a.backward()

					# compare the output and parameters gradients
					self.assertTrue((loss - loss_a).abs() < 1e-5   )
					__lowerCAmelCase  :      int    =						dict(model.named_parameters()   )
					__lowerCAmelCase  :      Union[str, 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 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[str]:






					"""simple docstring"""







					__lowerCAmelCase  :      List[Any]    =						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  :      int    =						model(**self.dummy_input   )

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







		def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Any:






					"""simple docstring"""







					__lowerCAmelCase  :      Dict    =						AutoencoderKL.from_pretrained("""fusing/autoencoder-kl-dummy"""   )
					__lowerCAmelCase  :      Optional[Any]    =						model.to(lowerCAmelCase   )
					model.eval()

					if torch_device == "mps":
								__lowerCAmelCase  :      List[Any]    =						torch.manual_seed(0   )
					else:
								__lowerCAmelCase  :      Any    =						torch.Generator(device=lowerCAmelCase   ).manual_seed(0   )

					__lowerCAmelCase  :      Optional[int]    =						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  :      Union[str, Any]    =						model(lowerCAmelCase	,							sample_posterior=lowerCAmelCase	,							generator=lowerCAmelCase   ).sample

					__lowerCAmelCase  :      Dict    =						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  :      List[str]    =						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  :      Union[str, Any]    =						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 SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
		"""simple docstring"""






		def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   str   )							->     int:






					"""simple docstring"""







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







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






					"""simple docstring"""







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







		def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Union[str, Any]=0	,							lowerCAmelCase			:   Any=(4, 3, 5_12, 5_12)	,							lowerCAmelCase			:   Any=False   )							->     Union[str, Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Union[str, Any]    =						torch.floataa if fpaa else torch.floataa
					__lowerCAmelCase  :      Optional[int]    =						torch.from_numpy(load_hf_numpy(self.get_file_format(lowerCAmelCase	,							lowerCAmelCase   )   )   ).to(lowerCAmelCase   ).to(lowerCAmelCase   )
					return image







		def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]="CompVis/stable-diffusion-v1-4"	,							lowerCAmelCase			:   int=False   )							->     Union[str, Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[Any]    =						"""fp16""" if fpaa else None
					__lowerCAmelCase  :      List[str]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Tuple=0   )							->     Tuple:






					"""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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   Dict   )							->     List[Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Dict    =						self.get_sd_vae_model()
					__lowerCAmelCase  :      Optional[int]    =						self.get_sd_image(lowerCAmelCase   )
					__lowerCAmelCase  :      List[str]    =						self.get_generator(lowerCAmelCase   )

					with torch.no_grad():
								__lowerCAmelCase  :      Optional[Any]    =						model(lowerCAmelCase	,							generator=lowerCAmelCase	,							sample_posterior=lowerCAmelCase   ).sample

					assert sample.shape == image.shape

					__lowerCAmelCase  :      Any    =						sample[-1, -2:, -2:, :2].flatten().float().cpu()
					__lowerCAmelCase  :      List[str]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]   )							->     Any:






					"""simple docstring"""







					__lowerCAmelCase  :      List[Any]    =						self.get_sd_vae_model(fpaa=lowerCAmelCase   )
					__lowerCAmelCase  :      Tuple    =						self.get_sd_image(lowerCAmelCase	,							fpaa=lowerCAmelCase   )
					__lowerCAmelCase  :      Optional[int]    =						self.get_generator(lowerCAmelCase   )

					with torch.no_grad():
								__lowerCAmelCase  :      Dict    =						model(lowerCAmelCase	,							generator=lowerCAmelCase	,							sample_posterior=lowerCAmelCase   ).sample

					assert sample.shape == image.shape

					__lowerCAmelCase  :      List[str]    =						sample[-1, -2:, :2, -2:].flatten().float().cpu()
					__lowerCAmelCase  :      Optional[int]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Any   )							->     str:






					"""simple docstring"""







					__lowerCAmelCase  :      Union[str, Any]    =						self.get_sd_vae_model()
					__lowerCAmelCase  :      Optional[int]    =						self.get_sd_image(lowerCAmelCase   )

					with torch.no_grad():
								__lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase   ).sample

					assert sample.shape == image.shape

					__lowerCAmelCase  :      Union[str, Any]    =						sample[-1, -2:, -2:, :2].flatten().float().cpu()
					__lowerCAmelCase  :      str    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   int   )							->     str:






					"""simple docstring"""







					__lowerCAmelCase  :      Dict    =						self.get_sd_vae_model()
					__lowerCAmelCase  :      Optional[Any]    =						self.get_sd_image(lowerCAmelCase	,							shape=(3, 4, 64, 64)   )

					with torch.no_grad():
								__lowerCAmelCase  :      Optional[Any]    =						model.decode(lowerCAmelCase   ).sample

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

					__lowerCAmelCase  :      Union[str, Any]    =						sample[-1, -2:, :2, -2:].flatten().cpu()
					__lowerCAmelCase  :      Tuple    =						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 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Any   )							->     List[Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Tuple    =						self.get_sd_vae_model(fpaa=lowerCAmelCase   )
					__lowerCAmelCase  :      str    =						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  :      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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Any   )							->     Any:






					"""simple docstring"""







					__lowerCAmelCase  :      List[Any]    =						self.get_sd_vae_model(fpaa=lowerCAmelCase   )
					__lowerCAmelCase  :      Union[str, Any]    =						self.get_sd_image(lowerCAmelCase	,							shape=(3, 4, 64, 64)	,							fpaa=lowerCAmelCase   )

					with torch.no_grad():
								__lowerCAmelCase  :      Union[str, Any]    =						model.decode(lowerCAmelCase   ).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
								__lowerCAmelCase  :      int    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[Any]   )							->     Dict:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[int]    =						self.get_sd_vae_model()
					__lowerCAmelCase  :      Optional[Any]    =						self.get_sd_image(lowerCAmelCase	,							shape=(3, 4, 64, 64)   )

					with torch.no_grad():
								__lowerCAmelCase  :      Optional[Any]    =						model.decode(lowerCAmelCase   ).sample

					model.enable_xformers_memory_efficient_attention()
					with torch.no_grad():
								__lowerCAmelCase  :      Tuple    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   List[Any]   )							->     Any:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[Any]    =						self.get_sd_vae_model()
					__lowerCAmelCase  :      List[str]    =						self.get_sd_image(lowerCAmelCase   )
					__lowerCAmelCase  :      Any    =						self.get_generator(lowerCAmelCase   )

					with torch.no_grad():
								__lowerCAmelCase  :      Optional[int]    =						model.encode(lowerCAmelCase   ).latent_dist
								__lowerCAmelCase  :      Union[str, Any]    =						dist.sample(generator=lowerCAmelCase   )

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

					__lowerCAmelCase  :      Any    =						sample[0, -1, -3:, -3:].flatten().cpu()
					__lowerCAmelCase  :      int    =						torch.tensor(lowerCAmelCase   )

					__lowerCAmelCase  :      str    =						3e-3 if torch_device != """mps""" else 1e-2
					assert torch_all_close(lowerCAmelCase	,							lowerCAmelCase	,							atol=lowerCAmelCase   )





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=DebertaTokenizer
  lowerCamelCase   :      Dict										=True
  lowerCamelCase   :      List[str]										=DebertaTokenizerFast







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






     """simple docstring"""







     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __lowerCAmelCase  :      Optional[int]    =						[
         """l""",
         """o""",
         """w""",
         """e""",
         """r""",
         """s""",
         """t""",
         """i""",
         """d""",
         """n""",
         """\u0120""",
         """\u0120l""",
         """\u0120n""",
         """\u0120lo""",
         """\u0120low""",
         """er""",
         """\u0120lowest""",
         """\u0120newer""",
         """\u0120wider""",
         """[UNK]""",
     ]
     __lowerCAmelCase  :      Optional[int]    =						dict(zip(lowerCAmelCase	,							range(len(lowerCAmelCase   )   )   )   )
     __lowerCAmelCase  :      List[str]    =						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
     __lowerCAmelCase  :      Any    =						{"""unk_token""": """[UNK]"""}

     __lowerCAmelCase  :      Tuple    =						os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""vocab_file"""]   )
     __lowerCAmelCase  :      Optional[int]    =						os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""merges_file"""]   )
     with open(self.vocab_file	,							"""w"""	,							encoding="""utf-8"""   ) as fp:
        fp.write(json.dumps(lowerCAmelCase   ) + """\n"""   )
     with open(self.merges_file	,							"""w"""	,							encoding="""utf-8"""   ) as fp:
        fp.write("""\n""".join(lowerCAmelCase   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Tuple   )							->     Dict:






     """simple docstring"""







     kwargs.update(self.special_tokens_map   )
     return self.tokenizer_class.from_pretrained(self.tmpdirname	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						"""lower newer"""
     __lowerCAmelCase  :      Union[str, Any]    =						"""lower newer"""
     return input_text, output_text







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.get_tokenizer()
     __lowerCAmelCase  :      int    =						"""lower newer"""
     __lowerCAmelCase  :      Any    =						["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.tokenize(lowerCAmelCase   )
     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						tokens + [tokenizer.unk_token]
     __lowerCAmelCase  :      Union[str, Any]    =						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase   )	,							lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.get_tokenizer()
     __lowerCAmelCase  :      Any    =						tokenizer("""Hello"""	,							"""World"""   )
     __lowerCAmelCase  :      Tuple    =						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
     self.assertListEqual(tokd["""token_type_ids"""]	,							lowerCAmelCase   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

     __lowerCAmelCase  :      Optional[Any]    =						tokenizer.encode("""sequence builders"""	,							add_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						tokenizer.encode("""multi-sequence build"""	,							add_special_tokens=lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						tokenizer.encode(
         """sequence builders"""	,							add_special_tokens=lowerCAmelCase	,							add_prefix_space=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						tokenizer.encode(
         """sequence builders"""	,							"""multi-sequence build"""	,							add_special_tokens=lowerCAmelCase	,							add_prefix_space=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase	,							lowerCAmelCase   )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[self.tokenizer_class]
     if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

     for tokenizer_class in tokenizer_classes:
        __lowerCAmelCase  :      int    =						tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

        __lowerCAmelCase  :      Optional[Any]    =						[
            """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
            """ALBERT incorporates two parameter reduction techniques""",
            """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
            """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
            """ vocabulary embedding.""",
        ]

        __lowerCAmelCase  :      List[str]    =						tokenizer(lowerCAmelCase	,							padding=lowerCAmelCase   )
        __lowerCAmelCase  :      List[str]    =						[tokenizer.decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   ) for seq in encoding["""input_ids"""]]

        # fmt: off
        __lowerCAmelCase  :      Any    =						{
            """input_ids""": [
                [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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],
                [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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],
                [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
            ],
            """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, 0, 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, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        __lowerCAmelCase  :      int    =						[
            """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
            """ALBERT incorporates two parameter reduction techniques""",
            """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
            """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
            """ vocabulary embedding.""",
        ]

        self.assertDictEqual(encoding.data	,							lowerCAmelCase   )

        for expected, decoded in zip(lowerCAmelCase	,							lowerCAmelCase   ):
           self.assertEqual(lowerCAmelCase	,							lowerCAmelCase   )


