

"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class      __a							(UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[Any]       = UnCLIPImageVariationPipeline
     _SCREAMING_SNAKE_CASE						:int       = IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
     _SCREAMING_SNAKE_CASE						:Tuple       = IMAGE_VARIATION_BATCH_PARAMS

     _SCREAMING_SNAKE_CASE						:Optional[Any]       = [
         """generator""",
         """return_dict""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     _SCREAMING_SNAKE_CASE						:Tuple       = False

     @property
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return 32

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


           """simple docstring"""


           return 32

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


           """simple docstring"""


           return self.time_input_dim

     @property
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.time_input_dim * 4

     @property
     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           return 100

     @property
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )
           return tokenizer

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


           """simple docstring"""


           torch.manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	CLIPTextConfig(
               bos_token_id=0	,    eos_token_id=2	,    hidden_size=self.text_embedder_hidden_size	,    projection_dim=self.text_embedder_hidden_size	,    intermediate_size=37	,    layer_norm_eps=1E-0_5	,    num_attention_heads=4	,    num_hidden_layers=5	,    pad_token_id=1	,    vocab_size=1_000	,    )
           return CLIPTextModelWithProjection(_a  )

     @property
     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           torch.manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      str      		=	CLIPVisionConfig(
               hidden_size=self.text_embedder_hidden_size	,    projection_dim=self.text_embedder_hidden_size	,    num_hidden_layers=5	,    num_attention_heads=4	,    image_size=32	,    intermediate_size=37	,    patch_size=1	,    )
           return CLIPVisionModelWithProjection(_a  )

     @property
     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{
               """clip_embeddings_dim""": self.text_embedder_hidden_size,
               """time_embed_dim""": self.time_embed_dim,
               """cross_attention_dim""": self.cross_attention_dim,
           }

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	UnCLIPTextProjModel(**_a  )
           return model

     @property
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	{
               """sample_size""": 32,
               # RGB in channels
               """in_channels""": 3,
               # Out channels is double in channels because predicts mean and variance
               """out_channels""": 6,
               """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
               """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
               """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
               """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
               """layers_per_block""": 1,
               """cross_attention_dim""": self.cross_attention_dim,
               """attention_head_dim""": 4,
               """resnet_time_scale_shift""": """scale_shift""",
               """class_embed_type""": """identity""",
           }

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	UNetaDConditionModel(**_a  )
           return model

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


           """simple docstring"""


           return {
               "sample_size": 64,
               "layers_per_block": 1,
               "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
               "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
               "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
               "in_channels": 6,
               "out_channels": 3,
           }

     @property
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           torch.manual_seed(0  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	UNetaDModel(**self.dummy_super_res_kwargs  )
           return model

     @property
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           torch.manual_seed(1  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	UNetaDModel(**self.dummy_super_res_kwargs  )
           return model

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.dummy_decoder
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_text_proj
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.dummy_text_encoder
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.dummy_tokenizer
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.dummy_super_res_first
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.dummy_super_res_last

           SCREAMING_SNAKE_CASE__   :      int      		=	UnCLIPScheduler(
               variance_type="""learned_range"""	,    prediction_type="""epsilon"""	,    num_train_timesteps=1_000	,    )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	UnCLIPScheduler(
               variance_type="""fixed_small_log"""	,    prediction_type="""epsilon"""	,    num_train_timesteps=1_000	,    )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	CLIPImageProcessor(crop_size=32	,    size=32  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.dummy_image_encoder

           return {
               "decoder": decoder,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "text_proj": text_proj,
               "feature_extractor": feature_extractor,
               "image_encoder": image_encoder,
               "super_res_first": super_res_first,
               "super_res_last": super_res_last,
               "decoder_scheduler": decoder_scheduler,
               "super_res_scheduler": super_res_scheduler,
           }

     def 					_a			(						self	,    _a	,    _a=0	,    _a=True  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	floats_tensor((1, 3, 32, 32)	,    rng=random.Random(_a  )  ).to(_a  )
           if str(_a  ).startswith("""mps"""  ):
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	torch.manual_seed(_a  )
           else:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	torch.Generator(device=_a  ).manual_seed(_a  )

           if pil_image:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	input_image * 0.5 + 0.5
                 SCREAMING_SNAKE_CASE__   :      int      		=	input_image.clamp(0	,    1  )
                 SCREAMING_SNAKE_CASE__   :      int      		=	input_image.cpu().permute(0	,    2	,    3	,    1  ).float().numpy()
                 SCREAMING_SNAKE_CASE__   :      str      		=	DiffusionPipeline.numpy_to_pil(_a  )[0]

           return {
               "image": input_image,
               "generator": generator,
               "decoder_num_inference_steps": 2,
               "super_res_num_inference_steps": 2,
               "output_type": "np",
           }

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_dummy_components()

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.pipeline_class(**_a  )
           SCREAMING_SNAKE_CASE__   :      str      		=	pipe.to(_a  )

           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe(**_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	output.images

           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	pipe(
               **_a	,    return_dict=_a	,    )[0]

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE__   :      str      		=	np.array(
               [
                   0.9_997,
                   0.0_002,
                   0.9_997,
                   0.9_997,
                   0.9_969,
                   0.0_023,
                   0.9_997,
                   0.9_969,
                   0.9_970,
               ]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.get_dummy_components()

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.pipeline_class(**_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	pipe.to(_a  )

           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )

           SCREAMING_SNAKE_CASE__   :      str      		=	pipe(**_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	output.images

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	pipe(
               **_a	,    return_dict=_a	,    )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)

           SCREAMING_SNAKE_CASE__   :      Any      		=	np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	"""cpu"""

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_dummy_components()

           SCREAMING_SNAKE_CASE__   :      str      		=	self.pipeline_class(**_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipe.to(_a  )

           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	[
               pipeline_inputs["""image"""],
               pipeline_inputs["""image"""],
           ]

           SCREAMING_SNAKE_CASE__   :      int      		=	pipe(**_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	output.images

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[
               tuple_pipeline_inputs["""image"""],
               tuple_pipeline_inputs["""image"""],
           ]

           SCREAMING_SNAKE_CASE__   :      int      		=	pipe(
               **_a	,    return_dict=_a	,    )[0]

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (2, 64, 64, 3)

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	np.array(
               [
                   0.9_997,
                   0.9_989,
                   0.0_008,
                   0.0_021,
                   0.9_960,
                   0.0_018,
                   0.0_014,
                   0.0_002,
                   0.9_933,
               ]  )

           assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.device("""cpu"""  )

           class      __a							:






                '''simple docstring'''
                _SCREAMING_SNAKE_CASE						:str       = 1





           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_dummy_components()

           SCREAMING_SNAKE_CASE__   :      str      		=	self.pipeline_class(**_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe.to(_a  )

           pipe.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	torch.Generator(device=_a  ).manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	pipe.decoder.dtype
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	1

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	(
               batch_size,
               pipe.decoder.config.in_channels,
               pipe.decoder.config.sample_size,
               pipe.decoder.config.sample_size,
           )
           SCREAMING_SNAKE_CASE__   :      Any      		=	pipe.prepare_latents(
               _a	,    dtype=_a	,    device=_a	,    generator=_a	,    latents=_a	,    scheduler=DummyScheduler()  )

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	(
               batch_size,
               pipe.super_res_first.config.in_channels // 2,
               pipe.super_res_first.config.sample_size,
               pipe.super_res_first.config.sample_size,
           )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	pipe.prepare_latents(
               _a	,    dtype=_a	,    device=_a	,    generator=_a	,    latents=_a	,    scheduler=DummyScheduler()  )

           SCREAMING_SNAKE_CASE__   :      int      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	pipe(
               **_a	,    decoder_latents=_a	,    super_res_latents=_a  ).images

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.get_dummy_inputs(_a	,    pil_image=_a  )
           # Don't pass image, instead pass embedding
           SCREAMING_SNAKE_CASE__   :      Dict      		=	pipeline_inputs.pop("""image"""  )
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	pipe.image_encoder(_a  ).image_embeds

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	pipe(
               **_a	,    decoder_latents=_a	,    super_res_latents=_a	,    image_embeddings=_a	,    ).images

           # make sure passing text embeddings manually is identical
           assert np.abs(img_out_a - img_out_a  ).max() < 1E-4

     @skip_mps
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch_device == """cpu"""

           # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	1E-2

           self._test_attention_slicing_forward_pass(
               test_max_difference=_a	,    expected_max_diff=_a  )

     @skip_mps
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	torch_device == """cpu"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	True
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	[
               """decoder_num_inference_steps""",
               """super_res_num_inference_steps""",
           ]

           self._test_inference_batch_single_identical(
               test_max_difference=_a	,    relax_max_difference=_a	,    additional_params_copy_to_batched_inputs=_a	,    )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[
               """decoder_num_inference_steps""",
               """super_res_num_inference_steps""",
           ]

           if torch_device == "mps":
                 # TODO: MPS errors with larger batch sizes
                 SCREAMING_SNAKE_CASE__   :      List[Any]      		=	[2, 3]
                 self._test_inference_batch_consistent(
                     batch_sizes=_a	,    additional_params_copy_to_batched_inputs=_a	,    )
           else:
                 self._test_inference_batch_consistent(
                     additional_params_copy_to_batched_inputs=_a  )

     @skip_mps
     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return super().test_dict_tuple_outputs_equivalent()

     @skip_mps
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return super().test_save_load_local()





     @skip_mps
     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           return super().test_save_load_optional_components()





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






     '''simple docstring'''

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


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





     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	load_image(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""  )
           SCREAMING_SNAKE_CASE__   :      str      		=	load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	UnCLIPImageVariationPipeline.from_pretrained(
               """kakaobrain/karlo-v1-alpha-image-variations"""	,    torch_dtype=torch.floataa  )
           SCREAMING_SNAKE_CASE__   :      int      		=	pipeline.to(_a  )
           pipeline.set_progress_bar_config(disable=_a  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	torch.Generator(device="""cpu"""  ).manual_seed(0  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	pipeline(
               _a	,    generator=_a	,    output_type="""np"""	,    )

           SCREAMING_SNAKE_CASE__   :      Any      		=	output.images[0]

           assert image.shape == (256, 256, 3)

           assert_mean_pixel_difference(_a	,    _a	,    15  )







"""simple docstring"""


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

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


if is_sentencepiece_available():
  from .tokenization_xlnet import XLNetTokenizer
else:
  a					:List[Any]    = None


a					:Optional[int]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a					:Optional[int]    = {
    "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",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a					:Dict    = {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a					:int    = "▁"

# Segments (not really needed)
a					:Dict    = 0
a					:Optional[int]    = 1
a					:Tuple    = 2
a					:List[str]    = 3
a					:Optional[Any]    = 4

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = """left"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = XLNetTokenizer

     def __init__(						self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=False	,    _a="<s>"	,    _a="</s>"	,    _a="<unk>"	,    _a="<sep>"	,    _a="<pad>"	,    _a="<cls>"	,    _a="<mask>"	,    _a=["<eop>", "<eod>"]	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token

           super().__init__(
               vocab_file=_a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    bos_token=_a	,    eos_token=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    additional_special_tokens=_a	,    **_a	,    )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	3
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_lower_case
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	remove_space
           SCREAMING_SNAKE_CASE__   :      int      		=	keep_accents
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_file
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	False if not self.vocab_file else True

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[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 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[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 					_a			(						self	,    _a	,    _a = 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(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(
               _a	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

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

           return (out_vocab_file,)




