



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 						lowerCAmelCase__	(  _lowerCamelCase       ,       _lowerCamelCase       ,       _lowerCamelCase       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Any     	=   StableDiffusionImageVariationPipeline
	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   IMAGE_VARIATION_PARAMS
	_SCREAMING_SNAKE_CASE  :		Any     	=   IMAGE_VARIATION_BATCH_PARAMS
	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   frozenset([]   )
	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   frozenset([]   )





	def  _lowerCAmelCase    (			self							:					str						)	->  Tuple:





					"""simple docstring"""


					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Any 							=    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    ,					)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE						)
					torch.manual_seed(0						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    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						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    CLIPVisionConfig(
					    hidden_size=32    ,					projection_dim=32    ,					intermediate_size=37    ,					layer_norm_eps=1E-0_5    ,					num_attention_heads=4    ,					num_hidden_layers=5    ,					image_size=32    ,					patch_size=4    ,					)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    CLIPImageProcessor(crop_size=32    ,					size=32						)

					SCREAMING_SNAKE_CASE	:       Any 							=    {
					    'unet': unet,
					    'scheduler': scheduler,
					    'vae': vae,
					    'image_encoder': image_encoder,
					    'feature_extractor': feature_extractor,
					    'safety_checker': None,
					}
					return components





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=0						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    floats_tensor((1, 3, 32, 32)    ,					rng=random.Random(_SCREAMING_SNAKE_CASE						)						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    image.cpu().permute(0    ,					2    ,					3    ,					1						)[0]
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE						)						).convert('RGB'						).resize((32, 32)						)
					if str(_SCREAMING_SNAKE_CASE						).startswith('mps'						):
									SCREAMING_SNAKE_CASE	:       Dict 							=    torch.manual_seed(_SCREAMING_SNAKE_CASE						)
					else:
									SCREAMING_SNAKE_CASE	:       Dict 							=    torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    {
					    'image': image,
					    'generator': generator,
					    'num_inference_steps': 2,
					    'guidance_scale': 6.0,
					    'output_type': 'numpy',
					}
					return inputs





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_dummy_components()
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    StableDiffusionImageVariationPipeline(**_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       int 							=    sd_pipe.to(_SCREAMING_SNAKE_CASE						)
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    sd_pipe(**_SCREAMING_SNAKE_CASE						).images
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					SCREAMING_SNAKE_CASE	:       Any 							=    np.array([0.5_2_3_9, 0.5_7_2_3, 0.4_7_9_6, 0.5_0_4_9, 0.5_5_5_0, 0.4_6_8_5, 0.5_3_2_9, 0.4_8_9_1, 0.4_9_2_1]						)

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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.get_dummy_components()
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    StableDiffusionImageVariationPipeline(**_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Any 							=    sd_pipe.to(_SCREAMING_SNAKE_CASE						)
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Any 							=    2 * [inputs['image']]
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    sd_pipe(**_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    output.images

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    image[-1, -3:, -3:, -1]

					assert image.shape == (2, 64, 64, 3)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    np.array([0.6_8_9_2, 0.5_6_3_7, 0.5_8_3_6, 0.5_7_7_1, 0.6_2_5_4, 0.6_4_0_9, 0.5_5_8_0, 0.5_5_6_9, 0.5_2_8_9]						)

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







	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  List[Any]:





					"""simple docstring"""


					super().test_inference_batch_single_identical(expected_max_diff=3E-3						)




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


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  List[str]:





					"""simple docstring"""


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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       int 							=    load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    np.random.RandomState(_SCREAMING_SNAKE_CASE						).standard_normal((1, 4, 64, 64)						)
					SCREAMING_SNAKE_CASE	:       str 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(device=_SCREAMING_SNAKE_CASE    ,					dtype=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    {
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 3,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    StableDiffusionImageVariationPipeline.from_pretrained(
					    'lambdalabs/sd-image-variations-diffusers'    ,					safety_checker=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    sd_pipe.to(_SCREAMING_SNAKE_CASE						)
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.get_inputs(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    sd_pipe(**_SCREAMING_SNAKE_CASE						).images
					SCREAMING_SNAKE_CASE	:       Any 							=    image[0, -3:, -3:, -1].flatten()

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    np.array([0.8_4_4_9_1, 0.9_0_7_8_9, 0.7_5_7_0_8, 0.7_8_7_3_4, 0.8_3_4_8_5, 0.7_0_0_9_9, 0.6_6_9_3_8, 0.6_8_7_2_7, 0.6_1_3_7_9]						)
					assert np.abs(image_slice - expected_slice						).max() < 6E-3





	def  _lowerCAmelCase    (			self							:					List[Any]						)	->  List[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    0

					def callback_fn(_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					torch.FloatTensor						) -> None:
									SCREAMING_SNAKE_CASE	:       List[str] 							=    True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
													SCREAMING_SNAKE_CASE	:       List[Any] 							=    latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 64)
													SCREAMING_SNAKE_CASE	:       str 							=    latents[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE	:       str 							=    np.array(
													    [-0.1_6_2_1, 0.2_8_3_7, -0.7_9_7_9, -0.1_2_2_1, -1.3_0_5_7, 0.7_6_8_1, -2.1_1_9_1, 0.0_4_6_4, 1.6_3_0_9]						)

													assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
									elif step == 2:
													SCREAMING_SNAKE_CASE	:       Tuple 							=    latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 64, 64)
													SCREAMING_SNAKE_CASE	:       str 							=    latents[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE	:       str 							=    np.array([0.6_2_9_9, 1.7_5_0_0, 1.1_9_9_2, -2.1_5_8_2, -1.8_9_9_4, 0.7_3_3_4, -0.7_0_9_0, 1.0_1_3_7, 1.5_2_7_3]						)

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

					SCREAMING_SNAKE_CASE	:       Tuple 							=    False

					SCREAMING_SNAKE_CASE	:       int 							=    StableDiffusionImageVariationPipeline.from_pretrained(
					    'fusing/sd-image-variations-diffusers'    ,					safety_checker=_SCREAMING_SNAKE_CASE    ,					torch_dtype=torch.floataa    ,					)
					pipe.to(_SCREAMING_SNAKE_CASE						)
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
					pipe.enable_attention_slicing()

					SCREAMING_SNAKE_CASE	:       Any 							=    self.get_inputs(_SCREAMING_SNAKE_CASE    ,					dtype=torch.floataa						)
					pipe(**_SCREAMING_SNAKE_CASE    ,					callback=_SCREAMING_SNAKE_CASE    ,					callback_steps=1						)
					assert callback_fn.has_been_called
					assert number_of_steps == inputs["num_inference_steps"]







	def  _lowerCAmelCase    (			self							:					int						)	->  Tuple:





					"""simple docstring"""


					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    'fusing/sd-image-variations-diffusers'
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    StableDiffusionImageVariationPipeline.from_pretrained(
					    _SCREAMING_SNAKE_CASE    ,					safety_checker=_SCREAMING_SNAKE_CASE    ,					torch_dtype=torch.floataa						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    pipe.to(_SCREAMING_SNAKE_CASE						)
					pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)
					pipe.enable_attention_slicing(1						)
					pipe.enable_sequential_cpu_offload()

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.get_inputs(_SCREAMING_SNAKE_CASE    ,					dtype=torch.floataa						)
					SCREAMING_SNAKE_CASE	:       Any 							=    pipe(**_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.cuda.max_memory_allocated()
					# make sure that less than 2.6 GB is allocated
					assert mem_bytes < 2.6 * 10**9




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


	'''simple docstring'''







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





					"""simple docstring"""


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





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    torch.Generator(device=_SCREAMING_SNAKE_CASE						).manual_seed(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    load_image(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/input_image_vermeer.png'						)
					SCREAMING_SNAKE_CASE	:       int 							=    np.random.RandomState(_SCREAMING_SNAKE_CASE						).standard_normal((1, 4, 64, 64)						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.from_numpy(_SCREAMING_SNAKE_CASE						).to(device=_SCREAMING_SNAKE_CASE    ,					dtype=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    {
					    'image': init_image,
					    'latents': latents,
					    'generator': generator,
					    'num_inference_steps': 50,
					    'guidance_scale': 7.5,
					    'output_type': 'numpy',
					}
					return inputs





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'						)
					sd_pipe.to(_SCREAMING_SNAKE_CASE						)
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.get_inputs(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    sd_pipe(**_SCREAMING_SNAKE_CASE						).images[0]

					SCREAMING_SNAKE_CASE	:       str 							=    load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    np.abs(expected_image - image						).max()
					assert max_diff < 1E-3







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'						)
					SCREAMING_SNAKE_CASE	:       Any 							=    DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config						)
					sd_pipe.to(_SCREAMING_SNAKE_CASE						)
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       int 							=    self.get_inputs(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Dict 							=    25
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    sd_pipe(**_SCREAMING_SNAKE_CASE						).images[0]

					SCREAMING_SNAKE_CASE	:       str 							=    load_numpy(
					    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
					    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    np.abs(expected_image - image						).max()
					assert max_diff < 1E-3










"""simple docstring"""

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


A_	:						Tuple				=			logging.get_logger(__name__)

A_	:						Optional[Any]				=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

A_	:						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'
        ),
    },
    'tokenizer_file': {
        'allenai/longformer-base-4096': (
            'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
        ),
    },
}

A_	:						int				=			{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE  :		List[Any]     	=   PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE  :		int     	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   ['''input_ids''', '''attention_mask''']
	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   LongformerTokenizer





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





					"""simple docstring"""


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

					SCREAMING_SNAKE_CASE	:       List[str] 							=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
					if pre_tok_state.get('add_prefix_space'    ,					_SCREAMING_SNAKE_CASE						) != add_prefix_space:
									SCREAMING_SNAKE_CASE	:       Dict 							=    getattr(_SCREAMING_SNAKE_CASE    ,					pre_tok_state.pop('type'						)						)
									SCREAMING_SNAKE_CASE	:       Any 							=    add_prefix_space
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    pre_tok_class(**_SCREAMING_SNAKE_CASE						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    add_prefix_space

					SCREAMING_SNAKE_CASE	:       List[str] 							=    'post_processor'
					SCREAMING_SNAKE_CASE	:       str 							=    getattr(self.backend_tokenizer    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
					if tokenizer_component_instance:
									SCREAMING_SNAKE_CASE	:       Any 							=    json.loads(tokenizer_component_instance.__getstate__()						)

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
													SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tuple(state['sep']						)
									if "cls" in state:
													SCREAMING_SNAKE_CASE	:       Tuple 							=    tuple(state['cls']						)

									SCREAMING_SNAKE_CASE	:       List[str] 							=    False

									if state.get('add_prefix_space'    ,					_SCREAMING_SNAKE_CASE						) != add_prefix_space:
													SCREAMING_SNAKE_CASE	:       Dict 							=    add_prefix_space
													SCREAMING_SNAKE_CASE	:       Dict 							=    True

									if state.get('trim_offsets'    ,					_SCREAMING_SNAKE_CASE						) != trim_offsets:
													SCREAMING_SNAKE_CASE	:       str 							=    trim_offsets
													SCREAMING_SNAKE_CASE	:       int 							=    True

									if changes_to_apply:
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    getattr(_SCREAMING_SNAKE_CASE    ,					state.pop('type'						)						)
													SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    component_class(**_SCREAMING_SNAKE_CASE						)
													setattr(self.backend_tokenizer    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)





	@property
	def  _lowerCAmelCase    (			self							:					Dict						)	->  str:





					"""simple docstring"""


					if self._mask_token is None:
									if self.verbose:
													logger.error('Using mask_token, but it is not set yet.'						)
									return None
					return str(self._mask_token						)





	@mask_token.setter
	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else value
					SCREAMING_SNAKE_CASE	:       Any 							=    value





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					*_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					**_SCREAMING_SNAKE_CASE							:					int						)	->  BatchEncoding:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    kwargs.get('is_split_into_words'    ,					_SCREAMING_SNAKE_CASE						)
					assert self.add_prefix_space or not is_split_into_words, (
					    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    "to use it with pretokenized inputs."
					)

					return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					List[Any]    ,					*_SCREAMING_SNAKE_CASE							:					Optional[Any]    ,					**_SCREAMING_SNAKE_CASE							:					str						)	->  BatchEncoding:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[Any] 							=    kwargs.get('is_split_into_words'    ,					_SCREAMING_SNAKE_CASE						)

					assert self.add_prefix_space or not is_split_into_words, (
					    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
					    "to use it with pretokenized inputs."
					)

					return super()._encode_plus(*_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self._tokenizer.model.save(_SCREAMING_SNAKE_CASE    ,					name=_SCREAMING_SNAKE_CASE						)
					return tuple(_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Dict=None						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
									return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.sep_token_id]
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [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]





