code: 
  
  
  '''simple docstring'''
  
  
  
  
  import os
  import tempfile
  import unittest
  
  import numpy as np
  import torch
  
  from diffusers.models import ModelMixin, UNetaDConditionModel
  from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
  from diffusers.utils import (
      floats_tensor,
      logging,
      skip_mps,
      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()
  
  __lowercase  :      List[Any]      		=				logging.get_logger(__name__)
  
  def 							a_       (						lowercase							,						lowercase = True    )			->List[str]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      List[Any]											=					{}
  				for name in model.attn_processors.keys():
  								__lowerCamelCase		:      List[Any]											=					name.endswith("attn2.processor"    ) and not (
  								    name.startswith("transformer_in"    ) or "temp_attentions" in name.split("."    )
  								)
  								__lowerCamelCase		:      Dict											=					model.config.cross_attention_dim if has_cross_attention else None
  								if name.startswith("mid_block"    ):
  												__lowerCamelCase		:      Union[str, Any]											=					model.config.block_out_channels[-1]
  								elif name.startswith("up_blocks"    ):
  												__lowerCamelCase		:      List[str]											=					int(name[len("up_blocks."    )]    )
  												__lowerCamelCase		:      List[Any]											=					list(reversed(model.config.block_out_channels    )    )[block_id]
  								elif name.startswith("down_blocks"    ):
  												__lowerCamelCase		:      int											=					int(name[len("down_blocks."    )]    )
  												__lowerCamelCase		:      int											=					model.config.block_out_channels[block_id]
  								elif name.startswith("transformer_in"    ):
  												# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
  												__lowerCamelCase		:      Dict											=					8 * model.config.attention_head_dim
  
  								__lowerCamelCase		:      Optional[int]											=					LoRAAttnProcessor(hidden_size=lowercase							,						cross_attention_dim=lowercase    )
  								__lowerCamelCase		:      str											=					lora_attn_procs[name].to(model.device    )
  
  								if mock_weights:
  												# add 1 to weights to mock trained weights
  												with torch.no_grad():
  																lora_attn_procs[name].to_q_lora.up.weight += 1
  																lora_attn_procs[name].to_k_lora.up.weight += 1
  																lora_attn_procs[name].to_v_lora.up.weight += 1
  																lora_attn_procs[name].to_out_lora.up.weight += 1
  
  				return lora_attn_procs
  
  
  
  
  
  @skip_mps
  class _snake_case   ( a__    ,   a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     UNetaDConditionModel
  							__a												=     '''sample'''
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Dict											=					4
  											__lowerCamelCase		:      Any											=					4
  											__lowerCamelCase		:      List[str]											=					4
  											__lowerCamelCase		:      List[str]											=					(32, 32)
  
  											__lowerCamelCase		:      Dict											=					floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(__A	)
  											__lowerCamelCase		:      List[str]											=					torch.tensor([10]	).to(__A	)
  											__lowerCamelCase		:      str											=					floats_tensor((batch_size, 4, 32)	).to(__A	)
  
  											return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return (4, 4, 32, 32)
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											return (4, 4, 32, 32)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					{
  											    "block_out_channels": (32, 64),
  											    "down_block_types": (
  											        "CrossAttnDownBlock3D",
  											        "DownBlock3D",
  											    ),
  											    "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"),
  											    "cross_attention_dim": 32,
  											    "attention_head_dim": 8,
  											    "out_channels": 4,
  											    "in_channels": 4,
  											    "layers_per_block": 1,
  											    "sample_size": 32,
  											}
  											__lowerCamelCase		:      Optional[int]											=					self.dummy_input
  											return init_dict, inputs_dict
  
  
  
  
  
  
  
  							@unittest.skipIf(
  							    torch_device != "cuda" or not is_xformers_available()			,				reason="XFormers attention is only available with CUDA and `xformers` installed"			,				)
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					self.prepare_init_args_and_inputs_for_common()
  											__lowerCamelCase		:      int											=					self.model_class(**__A	)
  
  											model.enable_xformers_memory_efficient_attention()
  
  											assert (
  											    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
  											    == "XFormersAttnProcessor"
  											), "xformers is not enabled"
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[Any]											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Optional[int]											=					32
  
  											__lowerCamelCase		:      str											=					self.model_class(**__A	)
  											model.to(__A	)
  											model.eval()
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Optional[Any]											=					model(**__A	)
  
  															if isinstance(__A			,				__A	):
  																			__lowerCamelCase		:      Union[str, Any]											=					output.sample
  
  											self.assertIsNotNone(__A	)
  											__lowerCamelCase		:      Optional[Any]											=					inputs_dict["sample"].shape
  											self.assertEqual(output.shape			,				__A			,				"Input and output shapes do not match"	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					self.prepare_init_args_and_inputs_for_common()
  											__lowerCamelCase		:      str											=					self.model_class(**__A	)
  											model.to(__A	)
  											model.eval()
  
  											with torch.no_grad():
  															# Warmup pass when using mps (see #372)
  															if torch_device == "mps" and isinstance(__A			,				__A	):
  																			model(**self.dummy_input	)
  
  															__lowerCamelCase		:      Tuple											=					model(**__A	)
  															if isinstance(__A			,				__A	):
  																			__lowerCamelCase		:      List[Any]											=					first.sample
  
  															__lowerCamelCase		:      List[str]											=					model(**__A	)
  															if isinstance(__A			,				__A	):
  																			__lowerCamelCase		:      Union[str, Any]											=					second.sample
  
  											__lowerCamelCase		:      Any											=					first.cpu().numpy()
  											__lowerCamelCase		:      Tuple											=					second.cpu().numpy()
  											__lowerCamelCase		:      Tuple											=					out_a[~np.isnan(__A	)]
  											__lowerCamelCase		:      Union[str, Any]											=					out_a[~np.isnan(__A	)]
  											__lowerCamelCase		:      int											=					np.amax(np.abs(out_a - out_a	)	)
  											self.assertLessEqual(__A			,				1E-5	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Tuple											=					8
  
  											__lowerCamelCase		:      Dict											=					self.model_class(**__A	)
  											model.to(__A	)
  											model.eval()
  
  											model.set_attention_slice("auto"	)
  											with torch.no_grad():
  															__lowerCamelCase		:      int											=					model(**__A	)
  											assert output is not None
  
  											model.set_attention_slice("max"	)
  											with torch.no_grad():
  															__lowerCamelCase		:      Any											=					model(**__A	)
  											assert output is not None
  
  											model.set_attention_slice(2	)
  											with torch.no_grad():
  															__lowerCamelCase		:      Union[str, Any]											=					model(**__A	)
  											assert output is not None
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Tuple											=					8
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.model_class(**__A	)
  											model.to(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Optional[Any]											=					model(**__A	).sample
  
  											__lowerCamelCase		:      List[str]											=					create_lora_layers(__A	)
  
  											# make sure we can set a list of attention processors
  											model.set_attn_processor(__A	)
  											model.to(__A	)
  
  											# test that attn processors can be set to itself
  											model.set_attn_processor(model.attn_processors	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      str											=					model(**__A			,				cross_attention_kwargs={"scale": 0.0}	).sample
  															__lowerCamelCase		:      List[str]											=					model(**__A			,				cross_attention_kwargs={"scale": 0.5}	).sample
  															__lowerCamelCase		:      str											=					model(**__A			,				cross_attention_kwargs={"scale": 0.5}	).sample
  
  											assert (samplea - samplea).abs().max() < 3E-3
  											assert (samplea - samplea).abs().max() < 3E-3
  
  											# sample 2 and sample 3 should be different
  											assert (samplea - samplea).abs().max() > 3E-3
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[Any]											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Tuple											=					8
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Dict											=					self.model_class(**__A	)
  											model.to(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      List[Any]											=					model(**__A	).sample
  
  											__lowerCamelCase		:      List[Any]											=					create_lora_layers(__A	)
  											model.set_attn_processor(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Dict											=					model(**__A			,				cross_attention_kwargs={"scale": 0.5}	).sample
  
  											with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(__A	)
  															self.assertTrue(os.path.isfile(os.path.join(__A			,				"pytorch_lora_weights.bin"	)	)	)
  															torch.manual_seed(0	)
  															__lowerCamelCase		:      List[str]											=					self.model_class(**__A	)
  															new_model.to(__A	)
  															new_model.load_attn_procs(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Tuple											=					new_model(**__A			,				cross_attention_kwargs={"scale": 0.5}	).sample
  
  											assert (sample - new_sample).abs().max() < 1E-3
  
  											# LoRA and no LoRA should NOT be the same
  											assert (sample - old_sample).abs().max() > 1E-4
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Tuple											=					8
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Any											=					self.model_class(**__A	)
  											model.to(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Optional[int]											=					model(**__A	).sample
  
  											__lowerCamelCase		:      Optional[Any]											=					create_lora_layers(__A	)
  											model.set_attn_processor(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Optional[int]											=					model(**__A			,				cross_attention_kwargs={"scale": 0.5}	).sample
  
  											with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(__A			,				safe_serialization=__A	)
  															self.assertTrue(os.path.isfile(os.path.join(__A			,				"pytorch_lora_weights.safetensors"	)	)	)
  															torch.manual_seed(0	)
  															__lowerCamelCase		:      Any											=					self.model_class(**__A	)
  															new_model.to(__A	)
  															new_model.load_attn_procs(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      List[str]											=					new_model(**__A			,				cross_attention_kwargs={"scale": 0.5}	).sample
  
  											assert (sample - new_sample).abs().max() < 3E-3
  
  											# LoRA and no LoRA should NOT be the same
  											assert (sample - old_sample).abs().max() > 1E-4
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      List[Any]											=					8
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Union[str, Any]											=					self.model_class(**__A	)
  											model.to(__A	)
  
  											__lowerCamelCase		:      List[str]											=					create_lora_layers(__A			,				mock_weights=__A	)
  											model.set_attn_processor(__A	)
  											# Saving as torch, properly reloads with directly filename
  											with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(__A	)
  															self.assertTrue(os.path.isfile(os.path.join(__A			,				"pytorch_lora_weights.bin"	)	)	)
  															torch.manual_seed(0	)
  															__lowerCamelCase		:      Optional[Any]											=					self.model_class(**__A	)
  															new_model.to(__A	)
  															new_model.load_attn_procs(__A			,				weight_name="pytorch_lora_weights.bin"	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      List[str]											=					8
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Optional[Any]											=					self.model_class(**__A	)
  											model.to(__A	)
  
  											__lowerCamelCase		:      Optional[Any]											=					create_lora_layers(__A			,				mock_weights=__A	)
  											model.set_attn_processor(__A	)
  											# Saving as torch, properly reloads with directly filename
  											with tempfile.TemporaryDirectory() as tmpdirname:
  															model.save_attn_procs(__A	)
  															self.assertTrue(os.path.isfile(os.path.join(__A			,				"pytorch_lora_weights.bin"	)	)	)
  															torch.manual_seed(0	)
  															__lowerCamelCase		:      str											=					self.model_class(**__A	)
  															new_model.to(__A	)
  															with self.assertRaises(__A	) as e:
  																			new_model.load_attn_procs(__A			,				use_safetensors=__A	)
  															self.assertIn("Error no file named pytorch_lora_weights.safetensors"			,				str(e.exception	)	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Dict											=					8
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Tuple											=					self.model_class(**__A	)
  											model.to(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Optional[Any]											=					model(**__A	).sample
  
  											__lowerCamelCase		:      Optional[Any]											=					create_lora_layers(__A	)
  											model.set_attn_processor(__A	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      int											=					model(**__A			,				cross_attention_kwargs={"scale": 0.0}	).sample
  
  											model.set_attn_processor(AttnProcessor()	)
  
  											with torch.no_grad():
  															__lowerCamelCase		:      List[str]											=					model(**__A	).sample
  
  											assert (sample - new_sample).abs().max() < 1E-4
  											assert (sample - old_sample).abs().max() < 3E-3
  
  
  
  
  
  
  
  							@unittest.skipIf(
  							    torch_device != "cuda" or not is_xformers_available()			,				reason="XFormers attention is only available with CUDA and `xformers` installed"			,				)
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					self.prepare_init_args_and_inputs_for_common()
  
  											__lowerCamelCase		:      Union[str, Any]											=					4
  
  											torch.manual_seed(0	)
  											__lowerCamelCase		:      Union[str, Any]											=					self.model_class(**__A	)
  											model.to(__A	)
  											__lowerCamelCase		:      Optional[Any]											=					create_lora_layers(__A	)
  											model.set_attn_processor(__A	)
  
  											# default
  											with torch.no_grad():
  															__lowerCamelCase		:      List[Any]											=					model(**__A	).sample
  
  															model.enable_xformers_memory_efficient_attention()
  															__lowerCamelCase		:      int											=					model(**__A	).sample
  
  															model.disable_xformers_memory_efficient_attention()
  															__lowerCamelCase		:      int											=					model(**__A	).sample
  
  											assert (sample - on_sample).abs().max() < 1E-4
  											assert (sample - off_sample).abs().max() < 1E-4
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      int											=					self.prepare_init_args_and_inputs_for_common()
  											__lowerCamelCase		:      Tuple											=					32
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.model_class(**__A	)
  											model.to(__A	)
  											model.eval()
  
  											with torch.no_grad():
  															__lowerCamelCase		:      Any											=					model(**__A	)[0]
  
  											model.enable_forward_chunking()
  											with torch.no_grad():
  															__lowerCamelCase		:      int											=					model(**__A	)[0]
  
  											self.assertEqual(output.shape			,				output_a.shape			,				"Shape doesn't match"	)
  											assert np.abs(output.cpu() - output_a.cpu()	).max() < 1E-2
  
  
  # (todo: sayakpaul) implement SLOW tests.
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers, processors
  
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_longformer import LongformerTokenizer
  
  
  __lowercase  :      str      		=				logging.get_logger(__name__)
  
  __lowercase  :      Any      		=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  __lowercase  :      Optional[Any]      		=				{
      """vocab_file""": {
          """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
          """allenai/longformer-large-4096""": (
              """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
          ),
          """allenai/longformer-large-4096-finetuned-triviaqa""": (
              """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
          ),
          """allenai/longformer-base-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
          ),
          """allenai/longformer-large-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
          ),
      },
      """merges_file""": {
          """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
          """allenai/longformer-large-4096""": (
              """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
          ),
          """allenai/longformer-large-4096-finetuned-triviaqa""": (
              """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
          ),
          """allenai/longformer-base-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
          ),
          """allenai/longformer-large-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
          ),
      },
      """tokenizer_file""": {
          """allenai/longformer-base-4096""": (
              """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-large-4096""": (
              """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-large-4096-finetuned-triviaqa""": (
              """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-base-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
          ),
          """allenai/longformer-large-4096-extra.pos.embd.only""": (
              """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  __lowercase  :      Tuple      		=				{
      """allenai/longformer-base-4096""": 40_96,
      """allenai/longformer-large-4096""": 40_96,
      """allenai/longformer-large-4096-finetuned-triviaqa""": 40_96,
      """allenai/longformer-base-4096-extra.pos.embd.only""": 40_96,
      """allenai/longformer-large-4096-extra.pos.embd.only""": 40_96,
  }
  
  
  
  
  
  class _snake_case   ( a__   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     VOCAB_FILES_NAMES
  							__a												=     PRETRAINED_VOCAB_FILES_MAP
  							__a												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  							__a												=     ['''input_ids''', '''attention_mask''']
  							__a												=     LongformerTokenizer
  
  
  
  
  
  
  
  							def __init__(   self			,				__A=None			,				__A=None			,				__A=None			,				__A="replace"			,				__A="<s>"			,				__A="</s>"			,				__A="</s>"			,				__A="<s>"			,				__A="<unk>"			,				__A="<pad>"			,				__A="<mask>"			,				__A=False			,				__A=True			,				**__A			,				)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().__init__(
  											    __A			,				__A			,				tokenizer_file=__A			,				errors=__A			,				bos_token=__A			,				eos_token=__A			,				sep_token=__A			,				cls_token=__A			,				unk_token=__A			,				pad_token=__A			,				mask_token=__A			,				add_prefix_space=__A			,				trim_offsets=__A			,				**__A			,				)
  
  											__lowerCamelCase		:      int											=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
  											if pre_tok_state.get("add_prefix_space"			,				__A	) != add_prefix_space:
  															__lowerCamelCase		:      Any											=					getattr(__A			,				pre_tok_state.pop("type"	)	)
  															__lowerCamelCase		:      Any											=					add_prefix_space
  															__lowerCamelCase		:      Union[str, Any]											=					pre_tok_class(**__A	)
  
  											__lowerCamelCase		:      Any											=					add_prefix_space
  
  											__lowerCamelCase		:      int											=					"post_processor"
  											__lowerCamelCase		:      Optional[int]											=					getattr(self.backend_tokenizer			,				__A			,				__A	)
  											if tokenizer_component_instance:
  															__lowerCamelCase		:      Tuple											=					json.loads(tokenizer_component_instance.__getstate__()	)
  
  															# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
  															if "sep" in state:
  																			__lowerCamelCase		:      Dict											=					tuple(state["sep"]	)
  															if "cls" in state:
  																			__lowerCamelCase		:      Union[str, Any]											=					tuple(state["cls"]	)
  
  															__lowerCamelCase		:      Union[str, Any]											=					False
  
  															if state.get("add_prefix_space"			,				__A	) != add_prefix_space:
  																			__lowerCamelCase		:      Dict											=					add_prefix_space
  																			__lowerCamelCase		:      str											=					True
  
  															if state.get("trim_offsets"			,				__A	) != trim_offsets:
  																			__lowerCamelCase		:      Tuple											=					trim_offsets
  																			__lowerCamelCase		:      Optional[int]											=					True
  
  															if changes_to_apply:
  																			__lowerCamelCase		:      List[Any]											=					getattr(__A			,				state.pop("type"	)	)
  																			__lowerCamelCase		:      int											=					component_class(**__A	)
  																			setattr(self.backend_tokenizer			,				__A			,				__A	)
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if self._mask_token is None:
  															if self.verbose:
  																			logger.error("Using mask_token, but it is not set yet."	)
  															return None
  											return str(self._mask_token	)
  
  
  
  
  
  
  
  							@mask_token.setter
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[Any]											=					AddedToken(__A			,				lstrip=__A			,				rstrip=__A	) if isinstance(__A			,				__A	) else value
  											__lowerCamelCase		:      Optional[int]											=					value
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				*__A			,				**__A	)     ->BatchEncoding:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					kwargs.get("is_split_into_words"			,				__A	)
  											assert self.add_prefix_space or not is_split_into_words, (
  											    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
  											    "to use it with pretokenized inputs."
  											)
  
  											return super()._batch_encode_plus(*__A			,				**__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				*__A			,				**__A	)     ->BatchEncoding:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      str											=					kwargs.get("is_split_into_words"			,				__A	)
  
  											assert self.add_prefix_space or not is_split_into_words, (
  											    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
  											    "to use it with pretokenized inputs."
  											)
  
  											return super()._encode_plus(*__A			,				**__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->Tuple[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					self._tokenizer.model.save(__A			,				name=__A	)
  											return tuple(__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A=None	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
  											if token_ids_a is None:
  															return output
  
  											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A = None	)     ->List[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      List[str]											=					[self.sep_token_id]
  											__lowerCamelCase		:      Tuple											=					[self.cls_token_id]
  
  											if token_ids_a is None:
  															return len(cls + token_ids_a + sep	) * [0]
  											return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
  