import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class __snake_case       (		unittest.TestCase	):




			def 			__a		(					self    :       List[str]   ):



					"""simple docstring"""






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




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



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   1
					SCREAMING_SNAKE_CASE__    							=   3
					SCREAMING_SNAKE_CASE__    							=   (32, 32)

					SCREAMING_SNAKE_CASE__    							=   floats_tensor((batch_size, num_channels) + sizes ,			rng=random.Random(0   )   ).to(UpperCAmelCase_   )
					return image




			@property
			def 			__a		(					self    :       Any   ):



					"""simple docstring"""






					torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   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 ,			)
					return model




			@property
			def 			__a		(					self    :       Any   ):



					"""simple docstring"""






					torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   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 ,			)
					return model




			@property
			def 			__a		(					self    :       str   ):



					"""simple docstring"""






					torch.manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   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=10_00 ,			)
					return CLIPTextModel(UpperCAmelCase_   )




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



					"""simple docstring"""






					def extract(*_lowercase    :       List[Any] ,			**_lowercase    :       Dict   ):







							class __snake_case       :




										def __init__(					self    :       List[Any]   ):



												"""simple docstring"""






												SCREAMING_SNAKE_CASE__    							=   torch.ones([0]   )




										def 			__a		(					self    :       Dict ,			_lowercase    :       int   ):



												"""simple docstring"""






												self.pixel_values.to(UpperCAmelCase_   )
												return self





							return Out()

					return extract




			def 			__a		(					self    :       str   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE__    							=   self.dummy_cond_unet
					SCREAMING_SNAKE_CASE__    							=   DDIMScheduler(
					    beta_start=0.0_00_85 ,			beta_end=0.0_12 ,			beta_schedule="""scaled_linear""" ,			clip_sample=UpperCAmelCase_ ,			set_alpha_to_one=UpperCAmelCase_ ,			)

					SCREAMING_SNAKE_CASE__    							=   self.dummy_vae
					SCREAMING_SNAKE_CASE__    							=   self.dummy_text_encoder
					SCREAMING_SNAKE_CASE__    							=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

					# make sure here that pndm scheduler skips prk
					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline(
					    unet=UpperCAmelCase_ ,			scheduler=UpperCAmelCase_ ,			vae=UpperCAmelCase_ ,			text_encoder=UpperCAmelCase_ ,			tokenizer=UpperCAmelCase_ ,			safety_checker=UpperCAmelCase_ ,			feature_extractor=self.dummy_extractor ,			)
					SCREAMING_SNAKE_CASE__    							=   sd_pipe.to(UpperCAmelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

					SCREAMING_SNAKE_CASE__    							=   'A painting of a squirrel eating a burger'

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe([prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=6.0 ,			num_inference_steps=2 ,			output_type="""np"""   )
					SCREAMING_SNAKE_CASE__    							=   output.images

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=6.0 ,			num_inference_steps=2 ,			output_type="""np""" ,			return_dict=UpperCAmelCase_ ,			)[0]

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

					assert image.shape == (1, 64, 64, 3)
					SCREAMING_SNAKE_CASE__    							=   np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]   )

					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    :       Dict   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   'cpu'  # ensure determinism for the device-dependent torch.Generator
					SCREAMING_SNAKE_CASE__    							=   self.dummy_cond_unet
					SCREAMING_SNAKE_CASE__    							=   PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   self.dummy_vae
					SCREAMING_SNAKE_CASE__    							=   self.dummy_text_encoder
					SCREAMING_SNAKE_CASE__    							=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

					# make sure here that pndm scheduler skips prk
					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline(
					    unet=UpperCAmelCase_ ,			scheduler=UpperCAmelCase_ ,			vae=UpperCAmelCase_ ,			text_encoder=UpperCAmelCase_ ,			tokenizer=UpperCAmelCase_ ,			safety_checker=UpperCAmelCase_ ,			feature_extractor=self.dummy_extractor ,			)
					SCREAMING_SNAKE_CASE__    							=   sd_pipe.to(UpperCAmelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

					SCREAMING_SNAKE_CASE__    							=   'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe([prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=6.0 ,			num_inference_steps=2 ,			output_type="""np"""   )

					SCREAMING_SNAKE_CASE__    							=   output.images

					SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=UpperCAmelCase_   ).manual_seed(0   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=6.0 ,			num_inference_steps=2 ,			output_type="""np""" ,			return_dict=UpperCAmelCase_ ,			)[0]

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

					assert image.shape == (1, 64, 64, 3)
					SCREAMING_SNAKE_CASE__    							=   np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]   )

					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    :       Dict   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline.from_pretrained(
					    """hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,			safety_checker=UpperCAmelCase_   )
					assert isinstance(UpperCAmelCase_ ,			UpperCAmelCase_   )
					assert isinstance(pipe.scheduler ,			UpperCAmelCase_   )
					assert pipe.safety_checker is None

					SCREAMING_SNAKE_CASE__    							=   pipe("""example prompt""" ,			num_inference_steps=2   ).images[0]
					assert image is not None

					# check that there's no error when saving a pipeline with one of the models being None
					with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(UpperCAmelCase_   )
							SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline.from_pretrained(UpperCAmelCase_   )

					# sanity check that the pipeline still works
					assert pipe.safety_checker is None
					SCREAMING_SNAKE_CASE__    							=   pipe("""example prompt""" ,			num_inference_steps=2   ).images[0]
					assert image is not None




			@unittest.skipIf(torch_device != """cuda""" ,			"""This test requires a GPU"""   )
			def 			__a		(					self    :       List[Any]   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   self.dummy_cond_unet
					SCREAMING_SNAKE_CASE__    							=   PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   self.dummy_vae
					SCREAMING_SNAKE_CASE__    							=   self.dummy_text_encoder
					SCREAMING_SNAKE_CASE__    							=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

					# put models in fp16
					SCREAMING_SNAKE_CASE__    							=   unet.half()
					SCREAMING_SNAKE_CASE__    							=   vae.half()
					SCREAMING_SNAKE_CASE__    							=   bert.half()

					# make sure here that pndm scheduler skips prk
					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline(
					    unet=UpperCAmelCase_ ,			scheduler=UpperCAmelCase_ ,			vae=UpperCAmelCase_ ,			text_encoder=UpperCAmelCase_ ,			tokenizer=UpperCAmelCase_ ,			safety_checker=UpperCAmelCase_ ,			feature_extractor=self.dummy_extractor ,			)
					SCREAMING_SNAKE_CASE__    							=   sd_pipe.to(UpperCAmelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

					SCREAMING_SNAKE_CASE__    							=   'A painting of a squirrel eating a burger'
					SCREAMING_SNAKE_CASE__    							=   sd_pipe([prompt] ,			num_inference_steps=2 ,			output_type="""np"""   ).images

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







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




			def 			__a		(					self    :       Optional[int]   ):



					"""simple docstring"""






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




			def 			__a		(					self    :       List[Any]   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,			safety_checker=UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe.to(UpperCAmelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

					SCREAMING_SNAKE_CASE__    							=   (
					    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
					    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
					    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
					    ' children from bahnhof zoo, detailed '
					)
					SCREAMING_SNAKE_CASE__    							=   40_03_66_03_46
					SCREAMING_SNAKE_CASE__    							=   7

					# without safety guidance (sld_guidance_scale = 0)
					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=UpperCAmelCase_ ,			num_inference_steps=50 ,			output_type="""np""" ,			width=5_12 ,			height=5_12 ,			sld_guidance_scale=0 ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images
					SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE__    							=   [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

					assert image.shape == (1, 5_12, 5_12, 3)

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

					# without safety guidance (strong configuration)
					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=UpperCAmelCase_ ,			num_inference_steps=50 ,			output_type="""np""" ,			width=5_12 ,			height=5_12 ,			sld_guidance_scale=20_00 ,			sld_warmup_steps=7 ,			sld_threshold=0.0_25 ,			sld_momentum_scale=0.5 ,			sld_mom_beta=0.7 ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images
					SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE__    							=   [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

					assert image.shape == (1, 5_12, 5_12, 3)

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




			def 			__a		(					self    :       Optional[Any]   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,			safety_checker=UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe.to(UpperCAmelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

					SCREAMING_SNAKE_CASE__    							=   'padme amidala taking a bath artwork, safe for work, no nudity'
					SCREAMING_SNAKE_CASE__    							=   27_34_97_17_55
					SCREAMING_SNAKE_CASE__    							=   7

					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=UpperCAmelCase_ ,			num_inference_steps=50 ,			output_type="""np""" ,			width=5_12 ,			height=5_12 ,			sld_guidance_scale=0 ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images
					SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE__    							=   [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

					assert image.shape == (1, 5_12, 5_12, 3)

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

					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=UpperCAmelCase_ ,			num_inference_steps=50 ,			output_type="""np""" ,			width=5_12 ,			height=5_12 ,			sld_guidance_scale=20_00 ,			sld_warmup_steps=7 ,			sld_threshold=0.0_25 ,			sld_momentum_scale=0.5 ,			sld_mom_beta=0.7 ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images
					SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE__    							=   [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

					assert image.shape == (1, 5_12, 5_12, 3)

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




			def 			__a		(					self    :       Any   ):



					"""simple docstring"""






					SCREAMING_SNAKE_CASE__    							=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe.to(UpperCAmelCase_   )
					sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

					SCREAMING_SNAKE_CASE__    							=   (
					    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
					    ' leyendecker'
					)
					SCREAMING_SNAKE_CASE__    							=   10_44_35_52_34
					SCREAMING_SNAKE_CASE__    							=   12

					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=UpperCAmelCase_ ,			num_inference_steps=50 ,			output_type="""np""" ,			width=5_12 ,			height=5_12 ,			sld_guidance_scale=0 ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images
					SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE__    							=   np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   )

					assert image.shape == (1, 5_12, 5_12, 3)

					assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-7

					SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(UpperCAmelCase_   )
					SCREAMING_SNAKE_CASE__    							=   sd_pipe(
					    [prompt] ,			generator=UpperCAmelCase_ ,			guidance_scale=UpperCAmelCase_ ,			num_inference_steps=50 ,			output_type="""np""" ,			width=5_12 ,			height=5_12 ,			sld_guidance_scale=20_00 ,			sld_warmup_steps=7 ,			sld_threshold=0.0_25 ,			sld_momentum_scale=0.5 ,			sld_mom_beta=0.7 ,			)

					SCREAMING_SNAKE_CASE__    							=   output.images
					SCREAMING_SNAKE_CASE__    							=   image[0, -3:, -3:, -1]
					SCREAMING_SNAKE_CASE__    							=   np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]   )
					assert image.shape == (1, 5_12, 5_12, 3)

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

import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
   import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase    :				Any							     =	logging.get_logger(__name__)

__lowerCamelCase    :				Optional[Any]							     =	{'''vocab_file''': '''vocab.txt'''}

__lowerCamelCase    :				Union[str, Any]							     =	{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

__lowerCamelCase    :				Optional[Any]							     =	{
    '''openbmb/cpm-ant-10b''': 1024,
}



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict()
  with open(__UpperCamelCase				,  """r"""				,  encoding="""utf-8"""			) as reader:
    SCREAMING_SNAKE_CASE__    							=   reader.readlines()
  for index, token in enumerate(__UpperCamelCase			):
    SCREAMING_SNAKE_CASE__    							=   token.rstrip("""\n"""			)
    SCREAMING_SNAKE_CASE__    							=   index
  return vocab







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Optional[int] ,			_lowercase    :       Optional[Any] ,			_lowercase    :       int="<unk>" ,			_lowercase    :       int=2_00   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   vocab
     SCREAMING_SNAKE_CASE__    							=   unk_token
     SCREAMING_SNAKE_CASE__    							=   max_input_chars_per_word




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   list(_lowercase   )
     if len(_lowercase   ) > self.max_input_chars_per_word:
       return [self.unk_token]

     SCREAMING_SNAKE_CASE__    							=   0
     SCREAMING_SNAKE_CASE__    							=   []
     while start < len(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   len(_lowercase   )
       SCREAMING_SNAKE_CASE__    							=   None
       while start < end:
         SCREAMING_SNAKE_CASE__    							=   """""".join(chars[start:end]   )
         if substr in self.vocab:
           SCREAMING_SNAKE_CASE__    							=   substr
           break
         end -= 1
       if cur_substr is None:
         sub_tokens.append(self.unk_token   )
         start += 1
       else:
         sub_tokens.append(_lowercase   )
         SCREAMING_SNAKE_CASE__    							=   end

     return sub_tokens







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							VOCAB_FILES_NAMES
   lowerCAmelCase_														=							PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase_														=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase_														=							["input_ids", "attention_mask"]
   lowerCAmelCase_														=							False




   def __init__(					self    :       int ,			_lowercase    :       str ,			_lowercase    :       List[Any]="<d>" ,			_lowercase    :       List[Any]="</d>" ,			_lowercase    :       Union[str, Any]="<s>" ,			_lowercase    :       List[str]="</s>" ,			_lowercase    :       str="<pad>" ,			_lowercase    :       int="<unk>" ,			_lowercase    :       List[str]="</n>" ,			_lowercase    :       Tuple="</_>" ,			_lowercase    :       Any="left" ,			**_lowercase    :       Any ,			):



     """simple docstring"""






     requires_backends(self ,			["""jieba"""]   )
     super().__init__(
         bod_token=_lowercase ,			eod_token=_lowercase ,			bos_token=_lowercase ,			eos_token=_lowercase ,			pad_token=_lowercase ,			unk_token=_lowercase ,			line_token=_lowercase ,			space_token=_lowercase ,			padding_side=_lowercase ,			**_lowercase ,			)
     SCREAMING_SNAKE_CASE__    							=   bod_token
     SCREAMING_SNAKE_CASE__    							=   eod_token
     SCREAMING_SNAKE_CASE__    							=   load_vocab(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   self.encoder[space_token]
     SCREAMING_SNAKE_CASE__    							=   self.encoder[line_token]

     del self.encoder[space_token]
     del self.encoder[line_token]

     SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict(sorted(self.encoder.items() ,			key=lambda _lowercase   : x[1]   )   )
     SCREAMING_SNAKE_CASE__    							=   {v: k for k, v in self.encoder.items()}

     SCREAMING_SNAKE_CASE__    							=   WordpieceTokenizer(vocab=self.encoder ,			unk_token=self.unk_token   )




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



     """simple docstring"""






     return self.encoder[self.bod_token]




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



     """simple docstring"""






     return self.encoder[self.eod_token]




   @property
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return self.encoder["\n"]




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



     """simple docstring"""






     return len(self.encoder   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   []
     for x in jieba.cut(_lowercase ,			cut_all=_lowercase   ):
       output_tokens.extend(self.wordpiece_tokenizer.tokenize(_lowercase   )   )
     return output_tokens




   def 			__a		(					self    :       int ,			_lowercase    :       Any ,			**_lowercase    :       Any   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   [i for i in token_ids if i >= 0]
     SCREAMING_SNAKE_CASE__    							=   [
         x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
     ]
     return super()._decode(_lowercase ,			**_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return token in self.encoder




   def 			__a		(					self    :       List[str] ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     return "".join(_lowercase   )




   def 			__a		(					self    :       Optional[int] ,			_lowercase    :       Any   ):



     """simple docstring"""






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




   def 			__a		(					self    :       Tuple ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     return self.decoder.get(_lowercase ,			self.unk_token   )




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       str ,			_lowercase    :       Optional[str] = None   ):



     """simple docstring"""






     if os.path.isdir(_lowercase   ):
       SCREAMING_SNAKE_CASE__    							=   os.path.join(
           _lowercase ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )
     else:
       SCREAMING_SNAKE_CASE__    							=   (filename_prefix + """-""" if filename_prefix else """""") + save_directory
     SCREAMING_SNAKE_CASE__    							=   0
     if " " in self.encoder:
       SCREAMING_SNAKE_CASE__    							=   self.encoder[""" """]
       del self.encoder[" "]
     if "\n" in self.encoder:
       SCREAMING_SNAKE_CASE__    							=   self.encoder["""\n"""]
       del self.encoder["\n"]
     SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict(sorted(self.encoder.items() ,			key=lambda _lowercase   : x[1]   )   )
     with open(_lowercase ,			"""w""" ,			encoding="""utf-8"""   ) as writer:
       for token, token_index in self.encoder.items():
         if index != token_index:
           logger.warning(
               f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
               """ Please check that the vocabulary is not corrupted!"""   )
           SCREAMING_SNAKE_CASE__    							=   token_index
         writer.write(token + """\n"""   )
         index += 1
     return (vocab_file,)




   def 			__a		(					self    :       int ,			_lowercase    :       List[int] ,			_lowercase    :       List[int] = None   ):



     """simple docstring"""






     if token_ids_a is None:
       return [self.bos_token_id] + token_ids_a
     return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




   def 			__a		(					self    :       Union[str, Any] ,			_lowercase    :       List[int] ,			_lowercase    :       Optional[List[int]] = None ,			_lowercase    :       bool = False   ):



     """simple docstring"""






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

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