





import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()



class 							_UpperCamelCase					(	unittest.TestCase     ):
       _UpperCamelCase				:					Any         =   StableDiffusionLDMaDPipeline
       _UpperCamelCase				:					Any         =   TEXT_TO_IMAGE_PARAMS
       _UpperCamelCase				:					Union[str, Any]         =   TEXT_TO_IMAGE_BATCH_PARAMS
       _UpperCamelCase				:					Any         =   TEXT_TO_IMAGE_IMAGE_PARAMS

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




              """simple docstring"""


              torch.manual_seed(0		)
              UpperCamelCase_          =  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					,   )
              UpperCamelCase_          =  DDIMScheduler(
                  beta_start=0.0_00_85					,   beta_end=0.0_12					,   beta_schedule="scaled_linear"					,   clip_sample=_SCREAMING_SNAKE_CASE					,   set_alpha_to_one=_SCREAMING_SNAKE_CASE					,   )
              torch.manual_seed(0		)
              UpperCamelCase_          =  AutoencoderKL(
                  block_out_channels=[32, 64]					,   in_channels=6					,   out_channels=6					,   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(_SCREAMING_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 lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: Optional[int]=0		)       -> Union[str, Any]:




              """simple docstring"""


              if str(_SCREAMING_SNAKE_CASE		).startswith("mps"		):
                     UpperCamelCase_          =  torch.manual_seed(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  {
                  "prompt": "A painting of a squirrel eating a burger",
                  "generator": generator,
                  "num_inference_steps": 2,
                  "guidance_scale": 6.0,
                  "output_type": "numpy",
              }
              return inputs

       def lowercase (		self: int		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator

              UpperCamelCase_          =  self.get_dummy_components()
              UpperCamelCase_          =  StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
              ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth

              UpperCamelCase_          =  rgb[0, -3:, -3:, -1]
              UpperCamelCase_          =  depth[0, -3:, -1]

              assert rgb.shape == (1, 64, 64, 3)
              assert depth.shape == (1, 64, 64)

              UpperCamelCase_          =  np.array(
                  [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]		)
              UpperCamelCase_          =  np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]		)

              assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb		).max() < 1e-2
              assert np.abs(image_slice_depth.flatten() - expected_slice_depth		).max() < 1e-2

       def lowercase (		self: Tuple		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  self.get_dummy_components()
              UpperCamelCase_          =  StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
              ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  3 * [inputs["prompt"]]

              # forward
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth
              UpperCamelCase_          =  rgb_slice_a[0, -3:, -3:, -1]
              UpperCamelCase_          =  depth_slice_a[0, -3:, -1]

              UpperCamelCase_          =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  3 * [inputs.pop("prompt"		)]

              UpperCamelCase_          =  ldmad_pipe.tokenizer(
                  _SCREAMING_SNAKE_CASE					,   padding="max_length"					,   max_length=ldmad_pipe.tokenizer.model_max_length					,   truncation=_SCREAMING_SNAKE_CASE					,   return_tensors="pt"					,   )
              UpperCamelCase_          =  text_inputs["input_ids"].to(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  ldmad_pipe.text_encoder(_SCREAMING_SNAKE_CASE		)[0]

              UpperCamelCase_          =  prompt_embeds

              # forward
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth
              UpperCamelCase_          =  rgb_slice_a[0, -3:, -3:, -1]
              UpperCamelCase_          =  depth_slice_a[0, -3:, -1]

              assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()		).max() < 1e-4
              assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()		).max() < 1e-4



       def lowercase (		self: Dict		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "cpu"  # ensure determinism for the device-dependent torch.Generator
              UpperCamelCase_          =  self.get_dummy_components()
              UpperCamelCase_          =  PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  StableDiffusionLDMaDPipeline(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
              ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  "french fries"
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE					,   negative_prompt=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth
              UpperCamelCase_          =  rgb[0, -3:, -3:, -1]
              UpperCamelCase_          =  depth[0, -3:, -1]

              assert rgb.shape == (1, 64, 64, 3)
              assert depth.shape == (1, 64, 64)

              UpperCamelCase_          =  np.array(
                  [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]		)
              UpperCamelCase_          =  np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]		)
              assert np.abs(rgb_slice.flatten() - expected_slice_rgb		).max() < 1e-2
              assert np.abs(depth_slice.flatten() - expected_slice_depth		).max() < 1e-2



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

       def lowercase (		self: int		)       -> int:




              """simple docstring"""


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

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Dict="cpu"					,   _SCREAMING_SNAKE_CASE: Optional[Any]=torch.floataa					,   _SCREAMING_SNAKE_CASE: Tuple=0		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 4, 64, 64)		)
              UpperCamelCase_          =  torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE					,   dtype=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  {
                  "prompt": "a photograph of an astronaut riding a horse",
                  "latents": latents,
                  "generator": generator,
                  "num_inference_steps": 3,
                  "guidance_scale": 7.5,
                  "output_type": "numpy",
              }
              return inputs



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




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		)
              UpperCamelCase_          =  ldmad_pipe.to(_SCREAMING_SNAKE_CASE		)
              ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth
              UpperCamelCase_          =  rgb[0, -3:, -3:, -1].flatten()
              UpperCamelCase_          =  rgb[0, -3:, -1].flatten()

              assert rgb.shape == (1, 512, 512, 3)
              assert depth.shape == (1, 512, 512)

              UpperCamelCase_          =  np.array(
                  [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]		)
              UpperCamelCase_          =  np.array(
                  [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]		)
              assert np.abs(rgb_slice - expected_slice_rgb		).max() < 3e-3
              assert np.abs(depth_slice - expected_slice_depth		).max() < 3e-3



@nightly
@require_torch_gpu
class 							_UpperCamelCase					(	unittest.TestCase     ):

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




              """simple docstring"""


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

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: int="cpu"					,   _SCREAMING_SNAKE_CASE: Dict=torch.floataa					,   _SCREAMING_SNAKE_CASE: Any=0		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  np.random.RandomState(_SCREAMING_SNAKE_CASE		).standard_normal((1, 4, 64, 64)		)
              UpperCamelCase_          =  torch.from_numpy(_SCREAMING_SNAKE_CASE		).to(device=_SCREAMING_SNAKE_CASE					,   dtype=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  {
                  "prompt": "a photograph of an astronaut riding a horse",
                  "latents": latents,
                  "generator": generator,
                  "num_inference_steps": 50,
                  "guidance_scale": 7.5,
                  "output_type": "numpy",
              }
              return inputs

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




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		).to(_SCREAMING_SNAKE_CASE		)
              ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth

              UpperCamelCase_          =  0.49_55_86
              UpperCamelCase_          =  0.33_79_55_15
              UpperCamelCase_          =  1_12.4_85_18
              UpperCamelCase_          =  98.48_97_46
              assert np.abs(expected_rgb_mean - rgb.mean()		) < 1e-3
              assert np.abs(expected_rgb_std - rgb.std()		) < 1e-3
              assert np.abs(expected_depth_mean - depth.mean()		) < 1e-3
              assert np.abs(expected_depth_std - depth.std()		) < 1e-3



       def lowercase (		self: int		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"		).to(_SCREAMING_SNAKE_CASE		)
              ldmad_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_inputs(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  ldmad_pipe(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_       , UpperCamelCase_          =  output.rgb, output.depth

              UpperCamelCase_          =  0.4_19_41_27
              UpperCamelCase_          =  0.35_37_55_86
              UpperCamelCase_          =  0.5_63_85_02
              UpperCamelCase_          =  0.34_68_61_03

              assert rgb.shape == (1, 512, 512, 3)
              assert depth.shape == (1, 512, 512, 1)
              assert np.abs(expected_rgb_mean - rgb.mean()		) < 1e-3
              assert np.abs(expected_rgb_std - rgb.std()		) < 1e-3
              assert np.abs(expected_depth_mean - depth.mean()		) < 1e-3
              assert np.abs(expected_depth_std - depth.std()		) < 1e-3








import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				{'vocab_file': 'spiece.model'}

_UpperCAmelCase        =				{
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    }
}

_UpperCAmelCase        =				{
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

# Segments (not really needed)
_UpperCAmelCase        =				0
_UpperCAmelCase        =				1
_UpperCAmelCase        =				2
_UpperCAmelCase        =				3
_UpperCAmelCase        =				4



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[str]         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					int         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					Any         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					Dict         =   '''left'''

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




              """simple docstring"""


              UpperCamelCase_          =  AddedToken(_SCREAMING_SNAKE_CASE					,   lstrip=_SCREAMING_SNAKE_CASE					,   rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) else mask_token

              UpperCamelCase_          =  {} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  do_lower_case=_SCREAMING_SNAKE_CASE					,   remove_space=_SCREAMING_SNAKE_CASE					,   keep_accents=_SCREAMING_SNAKE_CASE					,   bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   sep_token=_SCREAMING_SNAKE_CASE					,   pad_token=_SCREAMING_SNAKE_CASE					,   cls_token=_SCREAMING_SNAKE_CASE					,   mask_token=_SCREAMING_SNAKE_CASE					,   additional_special_tokens=_SCREAMING_SNAKE_CASE					,   sp_model_kwargs=self.sp_model_kwargs					,   **_SCREAMING_SNAKE_CASE					,   )

              UpperCamelCase_          =  3

              UpperCamelCase_          =  do_lower_case
              UpperCamelCase_          =  remove_space
              UpperCamelCase_          =  keep_accents
              UpperCamelCase_          =  vocab_file

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              return len(self.sp_model		)

       def lowercase (		self: List[Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
              vocab.update(self.added_tokens_encoder		)
              return vocab

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




              """simple docstring"""


              UpperCamelCase_          =  self.__dict__.copy()
              UpperCamelCase_          =  None
              return state

       def __setstate__(		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              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 lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> int:




              """simple docstring"""


              if self.remove_space:
                     UpperCamelCase_          =  " ".join(inputs.strip().split()		)
              else:
                     UpperCamelCase_          =  inputs
              UpperCamelCase_          =  outputs.replace("``"					,   "\""		).replace("''"					,   "\""		)

              if not self.keep_accents:
                     UpperCamelCase_          =  unicodedata.normalize("NFKD"					,   _SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_          =  "".join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE		)]		)
              if self.do_lower_case:
                     UpperCamelCase_          =  outputs.lower()

              return outputs

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  self.preprocess_text(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.sp_model.encode(_SCREAMING_SNAKE_CASE					,   out_type=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  []
              for piece in pieces:
                     if len(_SCREAMING_SNAKE_CASE		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
                            UpperCamelCase_          =  self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE					,   ""		)		)
                            if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                                   if len(cur_pieces[0]		) == 1:
                                          UpperCamelCase_          =  cur_pieces[1:]
                                   else:
                                          UpperCamelCase_          =  cur_pieces[0][1:]
                            cur_pieces.append(piece[-1]		)
                            new_pieces.extend(_SCREAMING_SNAKE_CASE		)
                     else:
                            new_pieces.append(_SCREAMING_SNAKE_CASE		)

              return new_pieces

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str		)       -> Any:




              """simple docstring"""


              return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> str:




              """simple docstring"""


              return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE					,   " "		).strip()
              return out_string

       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: bool = False					,   _SCREAMING_SNAKE_CASE: bool = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   **_SCREAMING_SNAKE_CASE: Dict					,   )       -> str:




              """simple docstring"""


              UpperCamelCase_          =  kwargs.pop("use_source_tokenizer"					,   _SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE					,   skip_special_tokens=_SCREAMING_SNAKE_CASE		)

              # To avoid mixing byte-level and unicode for byte-level BPT
              # we need to build string separately for added tokens and byte-level tokens
              # cf. https://github.com/huggingface/transformers/issues/1133
              UpperCamelCase_          =  []
              UpperCamelCase_          =  []
              for token in filtered_tokens:
                     if skip_special_tokens and token in self.all_special_ids:
                            continue
                     if token in self.added_tokens_encoder:
                            if current_sub_text:
                                   sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)
                                   UpperCamelCase_          =  []
                            sub_texts.append(_SCREAMING_SNAKE_CASE		)
                     else:
                            current_sub_text.append(_SCREAMING_SNAKE_CASE		)
              if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)		)

              # Mimic the behavior of the Rust tokenizer:
              # By default, there are no spaces between special tokens
              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  (
                  clean_up_tokenization_spaces
                  if clean_up_tokenization_spaces is not None
                  else self.clean_up_tokenization_spaces
              )
              if clean_up_tokenization_spaces:
                     UpperCamelCase_          =  self.clean_up_tokenization(_SCREAMING_SNAKE_CASE		)
                     return clean_text
              else:
                     return text

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




              """simple docstring"""


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

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




              """simple docstring"""


              if already_has_special_tokens:
                     return super().get_special_tokens_mask(
                         token_ids_a=_SCREAMING_SNAKE_CASE					,   token_ids_a=_SCREAMING_SNAKE_CASE					,   already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

              if token_ids_a is not None:
                     return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1]
              return ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1, 1]

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




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [2]

              if token_ids_a is None:
                     return len(token_ids_a + sep		) * [0] + cls_segment_id
              return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id



       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Tuple[str]:




              """simple docstring"""


              if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
                     return
              UpperCamelCase_          =  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		) and os.path.isfile(self.vocab_file		):
                     copyfile(self.vocab_file					,   _SCREAMING_SNAKE_CASE		)
              elif not os.path.isfile(self.vocab_file		):
                     with open(_SCREAMING_SNAKE_CASE					,   "wb"		) as fi:
                            UpperCamelCase_          =  self.sp_model.serialized_model_proto()
                            fi.write(_SCREAMING_SNAKE_CASE		)

              return (out_vocab_file,)

