






"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class    __lowerCamelCase    (  _a							,	_a							,	_a							,	unittest.TestCase  ):
   a      :				Tuple 						=StableDiffusionInstructPixaPixPipeline
   a      :				Optional[Any] 						=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
   a      :				Any 						=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   a      :				Dict 						=IMAGE_TO_IMAGE_IMAGE_PARAMS
   a      :				List[str] 						=IMAGE_TO_IMAGE_IMAGE_PARAMS

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       torch.manual_seed(0      )
       UpperCamelCase__         =    UNetaDConditionModel(
           block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=8 ,				out_channels=4 ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,				cross_attention_dim=32 ,				)
       UpperCamelCase__         =    PNDMScheduler(skip_prk_steps=snake_case_      )
       torch.manual_seed(0      )
       UpperCamelCase__         =    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 ,				)
       torch.manual_seed(0      )
       UpperCamelCase__         =    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 ,				)
       UpperCamelCase__         =    CLIPTextModel(snake_case_      )
       UpperCamelCase__         =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

       UpperCamelCase__         =    {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=0      )		->       Union[str, Any]:
       UpperCamelCase__         =    floats_tensor((1, 3, 32, 32) ,				rng=random.Random(snake_case_      )      ).to(snake_case_      )
       UpperCamelCase__         =    image.cpu().permute(0 ,				2 ,				3 ,				1      )[0]
       UpperCamelCase__         =    Image.fromarray(np.uinta(snake_case_      )      ).convert('RGB'      )
       if str(snake_case_      ).startswith('mps'      ):
           UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       else:
           UpperCamelCase__         =    torch.Generator(device=snake_case_      ).manual_seed(snake_case_      )
       UpperCamelCase__         =    {
           'prompt': 'A painting of a squirrel eating a burger',
           'image': image,
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'image_guidance_scale': 1,
           'output_type': 'numpy',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    sd_pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    'french fries'
       UpperCamelCase__         =    sd_pipe(**snake_case_ ,				negative_prompt=snake_case_      )
       UpperCamelCase__         =    output.images
       UpperCamelCase__         =    image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    [inputs['prompt']] * 2

       UpperCamelCase__         =    np.array(inputs['image']      ).astype(np.floataa      ) / 255.0
       UpperCamelCase__         =    torch.from_numpy(snake_case_      ).unsqueeze(0      ).to(snake_case_      )
       UpperCamelCase__         =    image / 2 + 0.5
       UpperCamelCase__         =    image.permute(0 ,				3 ,				1 ,				2      )
       UpperCamelCase__         =    image.repeat(2 ,				1 ,				1 ,				1      )

       UpperCamelCase__         =    sd_pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    'cpu'  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    EulerAncestralDiscreteScheduler(
           beta_start=0.00_085 ,				beta_end=0.012 ,				beta_schedule='scaled_linear'      )
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    sd_pipe.to(snake_case_      )
       sd_pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    self.get_dummy_inputs(snake_case_      )
       UpperCamelCase__         =    sd_pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1]

       UpperCamelCase__         =    [round(snake_case_ ,				4      ) for x in image_slice.flatten().tolist()]
       print(','.join([str(snake_case_      ) for x in slice]      )      )

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase__         =    np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    self.get_dummy_components()
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline(**snake_case_      )
       UpperCamelCase__         =    VaeImageProcessor(do_resize=snake_case_ ,				do_normalize=snake_case_      )
       UpperCamelCase__         =    pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )

       UpperCamelCase__         =    pipe(**self.get_dummy_inputs_by_type(snake_case_ ,				input_image_type='pt'      )      )[0]

       UpperCamelCase__         =    components['vae']
       UpperCamelCase__         =    self.get_dummy_inputs_by_type(snake_case_ ,				input_image_type='pt'      )

       for image_param in self.image_latents_params:
           if image_param in inputs.keys():
               UpperCamelCase__         =    vae.encode(inputs[image_param]      ).latent_dist.mode()

       UpperCamelCase__         =    pipe(**snake_case_      )[0]

       UpperCamelCase__         =    np.abs(out - out_latents_inputs      ).max()
       self.assertLess(snake_case_ ,				1E-4 ,				'passing latents as image input generate different result from passing image'      )







@slow
@require_torch_gpu
class    __lowerCamelCase    (  unittest.TestCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_=0      )		->       Tuple:
       UpperCamelCase__         =    torch.manual_seed(snake_case_      )
       UpperCamelCase__         =    load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'      )
       UpperCamelCase__         =    {
           'prompt': 'turn him into a cyborg',
           'image': image,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'image_guidance_scale': 1.0,
           'output_type': 'numpy',
       }
       return inputs

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_      )
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__         =    np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_      )
       UpperCamelCase__         =    LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__         =    np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_      )
       UpperCamelCase__         =    DDIMScheduler.from_config(pipe.scheduler.config      )
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      ).images
       UpperCamelCase__         =    image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       UpperCamelCase__         =    np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]      )

       assert np.abs(expected_slice - image_slice      ).max() < 1E-3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       UpperCamelCase__         =    0

       def callback_fn(snake_case_ ,				snake_case_ ,				snake_case_      ) -> None:
           UpperCamelCase__         =    True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
               UpperCamelCase__         =    latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCamelCase__         =    latents[0, -3:, -3:, -1]
               UpperCamelCase__         =    np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]      )

               assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
           elif step == 2:
               UpperCamelCase__         =    latents.detach().cpu().numpy()
               assert latents.shape == (1, 4, 64, 64)
               UpperCamelCase__         =    latents[0, -3:, -3:, -1]
               UpperCamelCase__         =    np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]      )

               assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2

       UpperCamelCase__         =    False

       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_ ,				torch_dtype=torch.floataa      )
       UpperCamelCase__         =    pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    self.get_inputs()
       pipe(**snake_case_ ,				callback=snake_case_ ,				callback_steps=1      )
       assert callback_fn.has_been_called
       assert number_of_steps == 3

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           'timbrooks/instruct-pix2pix' ,				safety_checker=snake_case_ ,				torch_dtype=torch.floataa      )
       UpperCamelCase__         =    pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing(1      )
       pipe.enable_sequential_cpu_offload()

       UpperCamelCase__         =    self.get_inputs()
       UpperCamelCase__         =    pipe(**snake_case_      )

       UpperCamelCase__         =    torch.cuda.max_memory_allocated()
       # make sure that less than 2.2 GB is allocated
       assert mem_bytes < 2.2 * 10**9







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.get_inputs()
       # resize to resolution that is divisible by 8 but not 16 or 32
       UpperCamelCase__         =    inputs['image'].resize((504, 504)      )

       UpperCamelCase__         =    'timbrooks/instruct-pix2pix'
       UpperCamelCase__         =    StableDiffusionInstructPixaPixPipeline.from_pretrained(
           snake_case_ ,				safety_checker=snake_case_ ,				)
       pipe.to(snake_case_      )
       pipe.set_progress_bar_config(disable=snake_case_      )
       pipe.enable_attention_slicing()

       UpperCamelCase__         =    pipe(**snake_case_      )
       UpperCamelCase__         =    output.images[0]

       UpperCamelCase__         =    image[255:258, 383:386, -1]

       assert image.shape == (504, 504, 3)
       UpperCamelCase__         =    np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]      )

       assert np.abs(image_slice.flatten() - expected_slice      ).max() < 5E-3












"""simple docstring"""





import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


A__					: Dict=	logging.get_logger(__name__)

A__					: str=	{"""vocab_file""": """spiece.model"""}

A__					: Union[str, Any]=	{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A__					: Union[str, Any]=	{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

A__					: Optional[Any]=	"""▁"""
class    __lowerCamelCase    (  _a  ):
   a      :				Dict 						=VOCAB_FILES_NAMES
   a      :				str 						=PRETRAINED_VOCAB_FILES_MAP
   a      :				Union[str, Any] 						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a      :				List[str] 						=["""input_ids""", """attention_mask"""]

   def __init__(					self ,				snake_case_ ,				snake_case_="</s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_=100 ,				snake_case_=None ,				snake_case_ = None ,				snake_case_=True ,				**snake_case_ ,				)		->       None:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__         =    [F'<extra_id_{i}>' for i in range(snake_case_      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase__         =    len(set(filter(lambda snake_case_      : bool('extra_id' in str(snake_case_      )      ) ,				snake_case_      )      )      )
           if extra_tokens != extra_ids:
               raise ValueError(
                   F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                   ' tokens'      )
       if legacy:
           logger.warning_once(
               F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'      )

       UpperCamelCase__         =    legacy
       UpperCamelCase__         =    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=snake_case_ ,				unk_token=snake_case_ ,				pad_token=snake_case_ ,				extra_ids=snake_case_ ,				additional_special_tokens=snake_case_ ,				sp_model_kwargs=self.sp_model_kwargs ,				legacy=snake_case_ ,				**snake_case_ ,				)

       UpperCamelCase__         =    vocab_file
       UpperCamelCase__         =    extra_ids

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__         =    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
           elif init_max_model_length is None:
               warnings.warn(
                   'This tokenizer was incorrectly instantiated with a model max length of'
                   F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                   ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                   F' {pretrained_model_name_or_path} automatically truncating your input to'
                   F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                   ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,				snake_case_ ,				)

       return max_model_length

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return self.sp_model.get_piece_size() + self._extra_ids

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = False      )		->       List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_ ,				token_ids_a=snake_case_ ,				already_has_special_tokens=snake_case_      )

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(snake_case_      )) + [1]
       return ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return list(
           set(filter(lambda snake_case_      : bool(re.search(r'<extra_id_\d+>' ,				snake_case_      )      ) is not None ,				self.additional_special_tokens      )      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[int]:
       if len(snake_case_      ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'      )
           return token_ids
       else:
           return token_ids + [self.eos_token_id]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    [self.eos_token_id]

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
       if token_ids_a is None:
           return token_ids_a
       else:
           UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
           return token_ids_a + token_ids_a

   def __getstate__(					self      )		->       str:
       UpperCamelCase__         =    self.__dict__.copy()
       UpperCamelCase__         =    None
       return state

   def __setstate__(					self ,				snake_case_      )		->       Any:
       UpperCamelCase__         =    d

       # for backward compatibility
       if not hasattr(self ,				'sp_model_kwargs'      ):
           UpperCamelCase__         =    {}

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(self.vocab_file      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[str]:
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
           UpperCamelCase__         =    SPIECE_UNDERLINE + text.replace(snake_case_ ,				' '      )
       return super().tokenize(snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       if not self.legacy:
           UpperCamelCase__         =    text.startswith(snake_case_      )
           if is_first:
               UpperCamelCase__         =    text[1:]

       UpperCamelCase__         =    self.sp_model.encode(snake_case_ ,				out_type=snake_case_      )

       if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(snake_case_      ):
           UpperCamelCase__         =    ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
       return tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       if token.startswith('<extra_id_'      ):
           UpperCamelCase__         =    re.match(r'<extra_id_(\d+)>' ,				snake_case_      )
           UpperCamelCase__         =    int(match.group(1      )      )
           return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       if index < self.sp_model.get_piece_size():
           UpperCamelCase__         =    self.sp_model.IdToPiece(snake_case_      )
       else:
           UpperCamelCase__         =    F'<extra_id_{self.vocab_size - 1 - index}>'
       return token

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    ''
       UpperCamelCase__         =    False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(snake_case_      ) + token
               UpperCamelCase__         =    True
               UpperCamelCase__         =    []
           else:
               current_sub_tokens.append(snake_case_      )
               UpperCamelCase__         =    False
       out_string += self.sp_model.decode(snake_case_      )
       return out_string.strip()







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       Tuple[str]:
       if not os.path.isdir(snake_case_      ):
           logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
           return
       UpperCamelCase__         =    os.path.join(
           snake_case_ ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

       if os.path.abspath(self.vocab_file      ) != os.path.abspath(snake_case_      ) and os.path.isfile(self.vocab_file      ):
           copyfile(self.vocab_file ,				snake_case_      )
       elif not os.path.isfile(self.vocab_file      ):
           with open(snake_case_ ,				'wb'      ) as fi:
               UpperCamelCase__         =    self.sp_model.serialized_model_proto()
               fi.write(snake_case_      )

       return (out_vocab_file,)




