





import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device



def _a							(      lowerCamelCase:  nn.Module					)      ->		Dict:







      '''simple docstring'''






      __A			  =							{}
      for name in unet.attn_processors.keys():
            __A			  =							None if name.endswith('''attn1.processor'''					) else unet.config.cross_attention_dim
            if name.startswith('''mid_block'''					):
                  __A			  =							unet.config.block_out_channels[-1]
            elif name.startswith('''up_blocks'''					):
                  __A			  =							int(name[len('''up_blocks.'''					)]					)
                  __A			  =							list(reversed(unet.config.block_out_channels					)					)[block_id]
            elif name.startswith('''down_blocks'''					):
                  __A			  =							int(name[len('''down_blocks.'''					)]					)
                  __A			  =							unet.config.block_out_channels[block_id]
            __A			  =							(
                LoRAAttnProcessor2_0 if hasattr(UpperCAmelCase__   ,							'''scaled_dot_product_attention'''					) else LoRAAttnProcessor
            )
            __A			  =							lora_attn_processor_class(
                hidden_size=UpperCAmelCase__   ,							cross_attention_dim=UpperCAmelCase__					)
      __A			  =							AttnProcsLayers(UpperCAmelCase__					)
      return lora_attn_procs, unet_lora_layers



def _a							(      lowerCamelCase:  nn.Module					)      ->		str:







      '''simple docstring'''






      __A			  =							{}
      __A			  =							(
          LoRAAttnProcessor2_0 if hasattr(UpperCAmelCase__   ,							'''scaled_dot_product_attention'''					) else LoRAAttnProcessor
      )
      for name, module in text_encoder.named_modules():
            if name.endswith(UpperCAmelCase__					):
                  __A			  =							lora_attn_processor_class(
                      hidden_size=module.out_proj.out_features   ,							cross_attention_dim=UpperCAmelCase__					)
      return text_lora_attn_procs



def _a							(      lowerCamelCase:  nn.Module					)      ->		Optional[int]:







      '''simple docstring'''






      __A			  =							create_text_encoder_lora_attn_procs(UpperCAmelCase__					)
      __A			  =							AttnProcsLayers(UpperCAmelCase__					)
      return text_encoder_lora_layers



def _a							(      lowerCamelCase:  Dict   ,							lowerCamelCase:  Optional[int]=False					)      ->		List[str]:







      '''simple docstring'''






      for _, attn_proc in text_lora_attn_procs.items():
            # set up.weights
            for layer_name, layer_module in attn_proc.named_modules():
                  if layer_name.endswith('''_lora'''					):
                        __A			  =							(
                            torch.randn_like(layer_module.up.weight					)
                            if randn_weight
                            else torch.zeros_like(layer_module.up.weight					)
                        )
                        __A			  =							torch.nn.Parameter(UpperCAmelCase__					)







class A_   (     unittest.TestCase  ):




   def _lowerCAmelCase (self      :Tuple					)->   Dict:
         torch.manual_seed(0					)
         __A			  =							UNetaDConditionModel(
             block_out_channels=(32, 64)       ,  layers_per_block=2       ,  sample_size=32       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=32       ,  )
         __A			  =							DDIMScheduler(
             beta_start=0.0_0_0_8_5       ,  beta_end=0.0_1_2       ,  beta_schedule='''scaled_linear'''       ,  clip_sample=_SCREAMING_SNAKE_CASE       ,  set_alpha_to_one=_SCREAMING_SNAKE_CASE       ,  steps_offset=1       ,  )
         torch.manual_seed(0					)
         __A			  =							AutoencoderKL(
             block_out_channels=[32, 64]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
         __A			  =							CLIPTextConfig(
             bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=32       ,  intermediate_size=37       ,  layer_norm_eps=1e-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=1000       ,  )
         __A			  =							CLIPTextModel(_SCREAMING_SNAKE_CASE					)
         __A			  =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

         __A			  =							create_unet_lora_layers(_SCREAMING_SNAKE_CASE					)
         __A			  =							create_text_encoder_lora_layers(_SCREAMING_SNAKE_CASE					)

         __A			  =							{
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """safety_checker""": None,
             """feature_extractor""": None,
         }
         __A			  =							{
             """unet_lora_layers""": unet_lora_layers,
             """text_encoder_lora_layers""": text_encoder_lora_layers,
             """unet_lora_attn_procs""": unet_lora_attn_procs,
         }
         return pipeline_components, lora_components




   def _lowerCAmelCase (self      :Union[str, Any]					)->   Dict:
         __A			  =							1
         __A			  =							10
         __A			  =							4
         __A			  =							(32, 32)

         __A			  =							torch.manual_seed(0					)
         __A			  =							floats_tensor((batch_size, num_channels) + sizes					)
         __A			  =							torch.randint(1       ,  _SCREAMING_SNAKE_CASE       ,  size=(batch_size, sequence_length)       ,  generator=_SCREAMING_SNAKE_CASE					)

         __A			  =							{
             """prompt""": """A painting of a squirrel eating a burger""",
             """generator""": generator,
             """num_inference_steps""": 2,
             """guidance_scale""": 6.0,
             """output_type""": """numpy""",
         }

         return noise, input_ids, pipeline_inputs

         # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb




   def _lowerCAmelCase (self      :int					)->   Optional[Any]:
         __A			  =							77

         __A			  =							torch.randint(2       ,  56       ,  size=(1, max_seq_length)       ,  generator=torch.manual_seed(0					)					)

         __A			  =							{}
         __A			  =							inputs
         return prepared_inputs




   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Optional[Any]					)->   Any:
         __A			  =							self.get_dummy_components()
         LoraLoaderMixin.save_lora_weights(
             save_directory=_SCREAMING_SNAKE_CASE       ,  unet_lora_layers=lora_components['''unet_lora_layers''']       ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']       ,  )
         self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE       ,  '''pytorch_lora_weights.bin'''					)					)					)




   def _lowerCAmelCase (self      :Optional[int]					)->   Dict:
         __A			  =							self.get_dummy_components()
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
         __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_inputs()

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
               LoraLoaderMixin.save_lora_weights(
                   save_directory=_SCREAMING_SNAKE_CASE       ,  unet_lora_layers=lora_components['''unet_lora_layers''']       ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']       ,  )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE       ,  '''pytorch_lora_weights.bin'''					)					)					)
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_SCREAMING_SNAKE_CASE					)       ,  torch.from_numpy(_SCREAMING_SNAKE_CASE					)					)					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.get_dummy_components()
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
         __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_inputs()

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
               LoraLoaderMixin.save_lora_weights(
                   save_directory=_SCREAMING_SNAKE_CASE       ,  unet_lora_layers=lora_components['''unet_lora_layers''']       ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']       ,  safe_serialization=_SCREAMING_SNAKE_CASE       ,  )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE       ,  '''pytorch_lora_weights.safetensors'''					)					)					)
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_SCREAMING_SNAKE_CASE					)       ,  torch.from_numpy(_SCREAMING_SNAKE_CASE					)					)					)




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[int]:
         __A			  =							self.get_dummy_components()
         __A			  =							lora_components["""unet_lora_attn_procs"""]
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
         __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_inputs()

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
               __A			  =							sd_pipe.unet
               unet.set_attn_processor(_SCREAMING_SNAKE_CASE					)
               unet.save_attn_procs(_SCREAMING_SNAKE_CASE					)
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE       ,  '''pytorch_lora_weights.bin'''					)					)					)
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_SCREAMING_SNAKE_CASE					)       ,  torch.from_numpy(_SCREAMING_SNAKE_CASE					)					)					)




   def _lowerCAmelCase (self      :Dict					)->   Dict:
         __A			  =							self.get_dummy_components()
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_tokens()

         # inference without lora
         __A			  =							pipe.text_encoder(**_SCREAMING_SNAKE_CASE					)[0]
         assert outputs_without_lora.shape == (1, 77, 32)

         # create lora_attn_procs with zeroed out up.weights
         __A			  =							create_text_encoder_lora_attn_procs(pipe.text_encoder					)
         set_lora_up_weights(_SCREAMING_SNAKE_CASE       ,  randn_weight=_SCREAMING_SNAKE_CASE					)

         # monkey patch
         pipe._modify_text_encoder(_SCREAMING_SNAKE_CASE					)

         # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
         del text_attn_procs
         gc.collect()

         # inference with lora
         __A			  =							pipe.text_encoder(**_SCREAMING_SNAKE_CASE					)[0]
         assert outputs_with_lora.shape == (1, 77, 32)

         assert torch.allclose(
             _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

         # create lora_attn_procs with randn up.weights
         __A			  =							create_text_encoder_lora_attn_procs(pipe.text_encoder					)
         set_lora_up_weights(_SCREAMING_SNAKE_CASE       ,  randn_weight=_SCREAMING_SNAKE_CASE					)

         # monkey patch
         pipe._modify_text_encoder(_SCREAMING_SNAKE_CASE					)

         # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
         del text_attn_procs
         gc.collect()

         # inference with lora
         __A			  =							pipe.text_encoder(**_SCREAMING_SNAKE_CASE					)[0]
         assert outputs_with_lora.shape == (1, 77, 32)

         assert not torch.allclose(
             _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"




   def _lowerCAmelCase (self      :str					)->   List[Any]:
         __A			  =							self.get_dummy_components()
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_tokens()

         # inference without lora
         __A			  =							pipe.text_encoder(**_SCREAMING_SNAKE_CASE					)[0]
         assert outputs_without_lora.shape == (1, 77, 32)

         # create lora_attn_procs with randn up.weights
         __A			  =							create_text_encoder_lora_attn_procs(pipe.text_encoder					)
         set_lora_up_weights(_SCREAMING_SNAKE_CASE       ,  randn_weight=_SCREAMING_SNAKE_CASE					)

         # monkey patch
         pipe._modify_text_encoder(_SCREAMING_SNAKE_CASE					)

         # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
         del text_attn_procs
         gc.collect()

         # inference with lora
         __A			  =							pipe.text_encoder(**_SCREAMING_SNAKE_CASE					)[0]
         assert outputs_with_lora.shape == (1, 77, 32)

         assert not torch.allclose(
             _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					), "lora outputs should be different to without lora outputs"

         # remove monkey patch
         pipe._remove_text_encoder_monkey_patch()

         # inference with removed lora
         __A			  =							pipe.text_encoder(**_SCREAMING_SNAKE_CASE					)[0]
         assert outputs_without_lora_removed.shape == (1, 77, 32)

         assert torch.allclose(
             _SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					), "remove lora monkey patch should restore the original outputs"




   def _lowerCAmelCase (self      :int					)->   Union[str, Any]:
         __A			  =							self.get_dummy_components()
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
         __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_inputs()

         with tempfile.TemporaryDirectory() as tmpdirname:
               LoraLoaderMixin.save_lora_weights(
                   save_directory=_SCREAMING_SNAKE_CASE       ,  unet_lora_layers=lora_components['''unet_lora_layers''']       ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']       ,  )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE       ,  '''pytorch_lora_weights.bin'''					)					)					)
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							lora_images[0, -3:, -3:, -1]

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE       ,  cross_attention_kwargs={'''scale''': 0.5}					).images
         __A			  =							lora_images_with_scale[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(
             torch.allclose(torch.from_numpy(_SCREAMING_SNAKE_CASE					)       ,  torch.from_numpy(_SCREAMING_SNAKE_CASE					)					)					)




   def _lowerCAmelCase (self      :Optional[int]					)->   str:






         with tempfile.TemporaryDirectory() as tmpdirname:
               self.create_lora_weight_file(_SCREAMING_SNAKE_CASE					)

               __A			  =							self.get_dummy_components()
               __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
               __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
               sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

               # check if vanilla attention processors are used
               for _, module in sd_pipe.unet.named_modules():
                     if isinstance(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					):
                           self.assertIsInstance(module.processor       ,  (AttnProcessor, AttnProcessor2_0)					)

            # load LoRA weight file
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

               # check if lora attention processors are used
               for _, module in sd_pipe.unet.named_modules():
                     if isinstance(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					):
                           __A			  =							(
                               LoRAAttnProcessor2_0 if hasattr(_SCREAMING_SNAKE_CASE       ,  '''scaled_dot_product_attention'''					) else LoRAAttnProcessor
                           )
                           self.assertIsInstance(module.processor       ,  _SCREAMING_SNAKE_CASE					)




   @unittest.skipIf(torch_device != '''cuda'''       ,  '''This test is supposed to run on GPU'''					)
   def _lowerCAmelCase (self      :Dict					)->   Dict:






         with tempfile.TemporaryDirectory() as tmpdirname:
               self.create_lora_weight_file(_SCREAMING_SNAKE_CASE					)

               __A			  =							self.get_dummy_components()
               __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
               __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
               sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

               # enable XFormers
               sd_pipe.enable_xformers_memory_efficient_attention()

               # check if xFormers attention processors are used
               for _, module in sd_pipe.unet.named_modules():
                     if isinstance(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					):
                           self.assertIsInstance(module.processor       ,  _SCREAMING_SNAKE_CASE					)

            # load LoRA weight file
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

               # check if lora attention processors are used
               for _, module in sd_pipe.unet.named_modules():
                     if isinstance(_SCREAMING_SNAKE_CASE       ,  _SCREAMING_SNAKE_CASE					):
                           self.assertIsInstance(module.processor       ,  _SCREAMING_SNAKE_CASE					)






   @unittest.skipIf(torch_device != '''cuda'''       ,  '''This test is supposed to run on GPU'''					)
   def _lowerCAmelCase (self      :Union[str, Any]					)->   str:
         __A			  =							self.get_dummy_components()
         __A			  =							StableDiffusionPipeline(**_SCREAMING_SNAKE_CASE					)
         __A			  =							sd_pipe.to(_SCREAMING_SNAKE_CASE					)
         sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

         __A			  =							self.get_dummy_inputs()

         # enable XFormers
         sd_pipe.enable_xformers_memory_efficient_attention()

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
               LoraLoaderMixin.save_lora_weights(
                   save_directory=_SCREAMING_SNAKE_CASE       ,  unet_lora_layers=lora_components['''unet_lora_layers''']       ,  text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']       ,  )
               self.assertTrue(os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE       ,  '''pytorch_lora_weights.bin'''					)					)					)
               sd_pipe.load_lora_weights(_SCREAMING_SNAKE_CASE					)

         __A			  =							sd_pipe(**_SCREAMING_SNAKE_CASE					).images
         __A			  =							lora_images[0, -3:, -3:, -1]

         # Outputs shouldn't match.
         self.assertFalse(torch.allclose(torch.from_numpy(_SCREAMING_SNAKE_CASE					)       ,  torch.from_numpy(_SCREAMING_SNAKE_CASE					)					)					)







'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_xglm import XGLMTokenizer
else:
	__SCREAMING_SNAKE_CASE						=      None


__SCREAMING_SNAKE_CASE						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						=      {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE						=      {
    "vocab_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE						=      {
    "facebook/xglm-564M": 2048,
}




class     __snake_case			(    _a	):
		a_          =					VOCAB_FILES_NAMES
		a_          =					PRETRAINED_VOCAB_FILES_MAP
		a_          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a_          =					["input_ids", "attention_mask"]
		a_          =					XGLMTokenizer




		def __init__(  self					:str			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:List[str]=None			,    _SCREAMING_SNAKE_CASE					:Dict="<s>"			,    _SCREAMING_SNAKE_CASE					:Dict="</s>"			,    _SCREAMING_SNAKE_CASE					:List[Any]="</s>"			,    _SCREAMING_SNAKE_CASE					:Tuple="<s>"			,    _SCREAMING_SNAKE_CASE					:int="<unk>"			,    _SCREAMING_SNAKE_CASE					:List[str]="<pad>"			,    **_SCREAMING_SNAKE_CASE					:Optional[int]			,    )					->			Optional[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     7
								UpperCamelCase_     :	int					     =     [F"""<madeupword{i}>""" for i in range(self.num_madeup_words)]

								UpperCamelCase_     :	Optional[Any]					     =     kwargs.get("""additional_special_tokens"""			,    [])
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    _SCREAMING_SNAKE_CASE			,    tokenizer_file=_SCREAMING_SNAKE_CASE			,    bos_token=_SCREAMING_SNAKE_CASE			,    eos_token=_SCREAMING_SNAKE_CASE			,    sep_token=_SCREAMING_SNAKE_CASE			,    cls_token=_SCREAMING_SNAKE_CASE			,    unk_token=_SCREAMING_SNAKE_CASE			,    pad_token=_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE			,    )

								UpperCamelCase_     :	int					     =     vocab_file
								UpperCamelCase_     :	Any					     =     False if not self.vocab_file else True




		def 	_snake_case	(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[int]			,    _SCREAMING_SNAKE_CASE					:Optional[List[int]] = None)					->			List[int]:






								'''simple docstring'''






								if token_ids_a is None:
														return [self.sep_token_id] + token_ids_a
								UpperCamelCase_     :	str					     =     [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




		def 	_snake_case	(  self					:int			,    _SCREAMING_SNAKE_CASE					:List[int]			,    _SCREAMING_SNAKE_CASE					:Optional[List[int]] = None)					->			List[int]:






								'''simple docstring'''






								UpperCamelCase_     :	Optional[Any]					     =     [self.sep_token_id]

								if token_ids_a is None:
														return len(sep + token_ids_a) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]





		def 	_snake_case	(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:str			,    _SCREAMING_SNAKE_CASE					:Optional[str] = None)					->			Tuple[str]:






								'''simple docstring'''






								if not self.can_save_slow_tokenizer:
														raise ValueError(
														    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
														    """tokenizer.""")

								if not os.path.isdir(_SCREAMING_SNAKE_CASE):
														logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""")
														return
								UpperCamelCase_     :	Dict					     =     os.path.join(
								    _SCREAMING_SNAKE_CASE			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

								if os.path.abspath(self.vocab_file) != os.path.abspath(_SCREAMING_SNAKE_CASE):
														copyfile(self.vocab_file			,    _SCREAMING_SNAKE_CASE)

								return (out_vocab_file,)




