import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							KandinskyVaaControlnetImgaImgPipeline
					A	       =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
					A	       =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
					A	       =							[
					    'generator',
					    'height',
					    'width',
					    'strength',
					    'guidance_scale',
					    'num_inference_steps',
					    'return_dict',
					    'guidance_scale',
					    'num_images_per_prompt',
					    'output_type',
					    'return_dict',
					]
					A	       =							False

					@property
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return 32

					@property
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return 32

					@property
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							return self.time_input_dim

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return self.time_input_dim * 4

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return 100

					@property
					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							torch.manual_seed(0    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {
							    "in_channels": 8,
							    # Out channels is double in channels because predicts mean and variance
							    "out_channels": 8,
							    "addition_embed_type": "image_hint",
							    "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,
							    "encoder_hid_dim": self.text_embedder_hidden_size,
							    "encoder_hid_dim_type": "image_proj",
							    "cross_attention_dim": self.cross_attention_dim,
							    "attention_head_dim": 4,
							    "resnet_time_scale_shift": "scale_shift",
							    "class_embed_type": None,
							}

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    UNetaDConditionModel(**_A    )
							return model

					@property
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							return {
							    "block_out_channels": [32, 32, 64, 64],
							    "down_block_types": [
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "AttnDownEncoderBlock2D",
							    ],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "vq_embed_dim": 4,
							}

					@property
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    VQModel(**self.dummy_movq_kwargs    )
							return model

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.dummy_unet
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.dummy_movq

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    {
							    "num_train_timesteps": 1000,
							    "beta_schedule": "linear",
							    "beta_start": 0.00085,
							    "beta_end": 0.012,
							    "clip_sample": False,
							    "set_alpha_to_one": False,
							    "steps_offset": 0,
							    "prediction_type": "epsilon",
							    "thresholding": False,
							}

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    DDIMScheduler(**_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {
							    "unet": unet,
							    "scheduler": scheduler,
							    "movq": movq,
							}

							return components

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any,_A      :       str=0    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(_A    )    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    floats_tensor((1, self.text_embedder_hidden_size),rng=random.Random(seed + 1    )    ).to(
							    _A    )
							# create init_image
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    floats_tensor((1, 3, 64, 64),rng=random.Random(_A    )    ).to(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    image.cpu().permute(0,2,3,1    )[0]
							SCREAMING_SNAKE_CASE_  :		Any				      =    Image.fromarray(np.uinta(_A    )    ).convert("RGB"    ).resize((256, 256)    )
							# create hint
							SCREAMING_SNAKE_CASE_  :		str				      =    floats_tensor((1, 3, 64, 64),rng=random.Random(_A    )    ).to(_A    )

							if str(_A    ).startswith("mps"    ):
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.manual_seed(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		str				      =    torch.Generator(device=_A    ).manual_seed(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
							    "image": init_image,
							    "image_embeds": image_embeds,
							    "negative_image_embeds": negative_image_embeds,
							    "hint": hint,
							    "generator": generator,
							    "height": 64,
							    "width": 64,
							    "num_inference_steps": 10,
							    "guidance_scale": 7.0,
							    "strength": 0.2,
							    "output_type": "np",
							}
							return inputs






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "cpu"

							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.get_dummy_components()

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.pipeline_class(**_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe.to(_A    )

							pipe.set_progress_bar_config(disable=_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(**self.get_dummy_inputs(_A    )    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    output.images

							SCREAMING_SNAKE_CASE_  :		int				      =    pipe(
							    **self.get_dummy_inputs(_A    ),return_dict=_A,)[0]

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    image[0, -3:, -3:, -1]
							SCREAMING_SNAKE_CASE_  :		str				      =    image_from_tuple[0, -3:, -3:, -1]

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

							SCREAMING_SNAKE_CASE_  :		Dict				      =    np.array(
							    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]    )
							assert (
							    np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
							), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2
							), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







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

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






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






					def 				__UpperCamelCase   (    self      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"    )
							SCREAMING_SNAKE_CASE_  :		int				      =    init_image.resize((512, 512)    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinskyv22/hint_image_cat.png"    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.from_numpy(np.array(_A    )    ).float() / 255.0
							SCREAMING_SNAKE_CASE_  :		str				      =    hint.permute(2,0,1    ).unsqueeze(0    )

							SCREAMING_SNAKE_CASE_  :		int				      =    "A robot, 4k photo"

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-2-prior",torch_dtype=torch.floataa    )
							pipe_prior.to(_A    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-2-controlnet-depth",torch_dtype=torch.floataa    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    pipeline.to(_A    )

							pipeline.set_progress_bar_config(disable=_A    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.Generator(device="cpu"    ).manual_seed(0    )

							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		int				      =    pipe_prior(
							    _A,image=_A,strength=0.85,generator=_A,negative_prompt="",).to_tuple()

							SCREAMING_SNAKE_CASE_  :		str				      =    pipeline(
							    image=_A,image_embeds=_A,negative_image_embeds=_A,hint=_A,generator=_A,num_inference_steps=100,height=512,width=512,strength=0.5,output_type="np",)

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(_A,_A    )

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


__lowerCamelCase					:	List[str]       =  logging.get_logger(__name__)


__lowerCamelCase					:	Tuple       =  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

__lowerCamelCase					:	List[Any]       =  {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

__lowerCamelCase					:	int       =  {
    '''allenai/longformer-base-4096''': 40_96,
    '''allenai/longformer-large-4096''': 40_96,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 40_96,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 40_96,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 40_96,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def       _snake_case	(   ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
		    list(range(ord("!"	)   , ord("~"	) + 1	)	) + list(range(ord("¡"	)   , ord("¬"	) + 1	)	) + list(range(ord("®"	)   , ord("ÿ"	) + 1	)	)
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    bs[:]
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    0
		for b in range(2**8	):
				if b not in bs:
						bs.append(lowerCAmelCase	)
						cs.append(2**8 + n	)
						n += 1
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    [chr(lowerCAmelCase	) for n in cs]
		return dict(zip(lowerCAmelCase   , lowerCAmelCase	)	)




def       _snake_case	(   lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    set()
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)	)
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    char
		return pairs







class 		a__						(					A__					):
					A	       =							VOCAB_FILES_NAMES
					A	       =							PRETRAINED_VOCAB_FILES_MAP
					A	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A	       =							['input_ids', 'attention_mask']

					def __init__(    self      :       Union[str, Any],_A      :       List[Any],_A      :       Tuple,_A      :       str="replace",_A      :       Optional[int]="<s>",_A      :       Dict="</s>",_A      :       Any="</s>",_A      :       Optional[Any]="<s>",_A      :       Union[str, Any]="<unk>",_A      :       int="<pad>",_A      :       Dict="<mask>",_A      :       int=False,**_A      :       Dict,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else bos_token
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else eos_token
							SCREAMING_SNAKE_CASE_  :		str				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else sep_token
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else cls_token
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else unk_token
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							SCREAMING_SNAKE_CASE_  :		Dict				      =    AddedToken(_A,lstrip=_A,rstrip=_A    ) if isinstance(_A,_A    ) else mask_token

							super().__init__(
							    errors=_A,bos_token=_A,eos_token=_A,unk_token=_A,sep_token=_A,cls_token=_A,pad_token=_A,mask_token=_A,add_prefix_space=_A,**_A,)

							with open(_A,encoding="utf-8"    ) as vocab_handle:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    json.load(_A    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {v: k for k, v in self.encoder.items()}
							SCREAMING_SNAKE_CASE_  :		Any				      =    errors  # how to handle errors in decoding
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    bytes_to_unicode()
							SCREAMING_SNAKE_CASE_  :		str				      =    {v: k for k, v in self.byte_encoder.items()}
							with open(_A,encoding="utf-8"    ) as merges_handle:
									SCREAMING_SNAKE_CASE_  :		int				      =    merges_handle.read().split("\n"    )[1:-1]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(merge.split()    ) for merge in bpe_merges]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    dict(zip(_A,range(len(_A    )    )    )    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    {}
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"    )

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							return len(self.encoder    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							return dict(self.encoder,**self.added_tokens_encoder    )

					def 				__UpperCamelCase   (    self      :       Any,_A      :       int    ):







							"""simple docstring"""






							if token in self.cache:
									return self.cache[token]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tuple(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    get_pairs(_A    )

							if not pairs:
									return token

							while True:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    min(_A,key=lambda _A    : self.bpe_ranks.get(_A,float("inf"    )    )    )
									if bigram not in self.bpe_ranks:
											break
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Tuple				      =    bigram
									SCREAMING_SNAKE_CASE_  :		int				      =    []
									SCREAMING_SNAKE_CASE_  :		Dict				      =    0
									while i < len(_A    ):
											try:
													SCREAMING_SNAKE_CASE_  :		Tuple				      =    word.index(_A,_A    )
											except ValueError:
													new_word.extend(word[i:]    )
													break
											else:
													new_word.extend(word[i:j]    )
													SCREAMING_SNAKE_CASE_  :		str				      =    j

											if word[i] == first and i < len(_A    ) - 1 and word[i + 1] == second:
													new_word.append(first + second    )
													i += 2
											else:
													new_word.append(word[i]    )
													i += 1
									SCREAMING_SNAKE_CASE_  :		Dict				      =    tuple(_A    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    new_word
									if len(_A    ) == 1:
											break
									else:
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    get_pairs(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    " ".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    word
							return word

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for token in re.findall(self.pat,_A    ):
									SCREAMING_SNAKE_CASE_  :		Any				      =    "".join(
									    self.byte_encoder[b] for b in token.encode("utf-8"    )    )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
									bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_A    ).split(" "    )    )
							return bpe_tokens

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       str    ):







							"""simple docstring"""






							return self.encoder.get(_A,self.encoder.get(self.unk_token    )    )

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       str    ):







							"""simple docstring"""






							return self.decoder.get(_A    )

					def 				__UpperCamelCase   (    self      :       List[str],_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "".join(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    bytearray([self.byte_decoder[c] for c in text]    ).decode("utf-8",errors=self.errors    )
							return text

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       str,_A      :       Optional[str] = None    ):







							"""simple docstring"""






							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"]    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    os.path.join(
							    _A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]    )

							with open(_A,"w",encoding="utf-8"    ) as f:
									f.write(json.dumps(self.encoder,indent=2,sort_keys=_A,ensure_ascii=_A    ) + "\n"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
							with open(_A,"w",encoding="utf-8"    ) as writer:
									writer.write("#version: 0.2\n"    )
									for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),key=lambda _A    : kv[1]    ):
											if index != token_index:
													logger.warning(
													    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
													    " Please check that the tokenizer is not corrupted!"    )
													SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    token_index
											writer.write(" ".join(_A    ) + "\n"    )
											index += 1

							return vocab_file, merge_file

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[int],_A      :       Optional[List[int]] = None,_A      :       bool = False    ):







							"""simple docstring"""






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

							if token_ids_a is None:
									return [1] + ([0] * len(_A    )) + [1]
							return [1] + ([0] * len(_A    )) + [1, 1] + ([0] * len(_A    )) + [1]

					def 				__UpperCamelCase   (    self      :       Any,_A      :       List[int],_A      :       Optional[List[int]] = None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    [self.sep_token_id]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    [self.cls_token_id]

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






					def 				__UpperCamelCase   (    self      :       Any,_A      :       Union[str, Any],_A      :       Any=False,**_A      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    kwargs.pop("add_prefix_space",self.add_prefix_space    )
							if (is_split_into_words or add_prefix_space) and (len(_A    ) > 0 and not text[0].isspace()):
									SCREAMING_SNAKE_CASE_  :		str				      =    " " + text
							return (text, kwargs)
