



'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()






class 				lowercase__      (				unittest.TestCase  ):





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	@property
	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  1
							_UpperCamelCase   :						Union[str, Any]												=  3
							_UpperCamelCase   :						List[str]												=  (32, 32)

							_UpperCamelCase   :						List[str]												=  floats_tensor((batch_size, num_channels) + sizes					,rng=random.Random(0					)					).to(lowerCamelCase__					)
							return image





	@property
	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Tuple												=  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       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Union[str, Any]												=  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       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						Optional[Any]												=  RobertaSeriesConfig(
							    hidden_size=32					,project_dim=32					,intermediate_size=37					,layer_norm_eps=1E-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=5006					,)
							return RobertaSeriesModelWithTransformation(lowerCamelCase__					)





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







							'''simple docstring'''
							def extract(*lowerCamelCase__   :							str					,**lowerCamelCase__   :							Any					):






													class 				lowercase__      :





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







																				'''simple docstring'''
																				_UpperCamelCase   :						Optional[Any]												=  torch.ones([0]					)





														def       UpperCamelCase_    (					self   :							Optional[int]					,lowerCamelCase__   :							Optional[int]					):







																				'''simple docstring'''
																				self.pixel_values.to(lowerCamelCase__					)
																				return self
													return Out()

							return extract





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase   :						Optional[Any]												=  self.dummy_cond_unet
							_UpperCamelCase   :						str												=  PNDMScheduler(skip_prk_steps=lowerCamelCase__					)
							_UpperCamelCase   :						int												=  self.dummy_vae
							_UpperCamelCase   :						Union[str, Any]												=  self.dummy_text_encoder
							_UpperCamelCase   :						List[str]												=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'					)
							_UpperCamelCase   :						List[Any]												=  77

							_UpperCamelCase   :						Optional[int]												=  self.dummy_image.to(lowerCamelCase__					)
							_UpperCamelCase   :						Dict												=  init_image / 2 + 0.5

							# make sure here that pndm scheduler skips prk
							_UpperCamelCase   :						Tuple												=  AltDiffusionImgaImgPipeline(
							    unet=lowerCamelCase__					,scheduler=lowerCamelCase__					,vae=lowerCamelCase__					,text_encoder=lowerCamelCase__					,tokenizer=lowerCamelCase__					,safety_checker=lowerCamelCase__					,feature_extractor=self.dummy_extractor					,)
							_UpperCamelCase   :						Optional[int]												=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,do_normalize=lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  alt_pipe.to(lowerCamelCase__					)
							alt_pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						List[Any]												=  'A painting of a squirrel eating a burger'
							_UpperCamelCase   :						int												=  torch.Generator(device=lowerCamelCase__					).manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  alt_pipe(
							    [prompt]					,generator=lowerCamelCase__					,guidance_scale=6.0					,num_inference_steps=2					,output_type='np'					,image=lowerCamelCase__					,)

							_UpperCamelCase   :						Optional[Any]												=  output.images

							_UpperCamelCase   :						Union[str, Any]												=  torch.Generator(device=lowerCamelCase__					).manual_seed(0					)
							_UpperCamelCase   :						List[Any]												=  alt_pipe(
							    [prompt]					,generator=lowerCamelCase__					,guidance_scale=6.0					,num_inference_steps=2					,output_type='np'					,image=lowerCamelCase__					,return_dict=lowerCamelCase__					,)[0]

							_UpperCamelCase   :						Tuple												=  image[0, -3:, -3:, -1]
							_UpperCamelCase   :						Optional[Any]												=  image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							_UpperCamelCase   :						Tuple												=  np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]					)

							assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-3
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 5E-3





	@unittest.skipIf(torch_device != 'cuda'					,'This test requires a GPU'					)
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  self.dummy_cond_unet
							_UpperCamelCase   :						List[str]												=  PNDMScheduler(skip_prk_steps=lowerCamelCase__					)
							_UpperCamelCase   :						int												=  self.dummy_vae
							_UpperCamelCase   :						str												=  self.dummy_text_encoder
							_UpperCamelCase   :						Optional[Any]												=  XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'					)
							_UpperCamelCase   :						str												=  77

							_UpperCamelCase   :						str												=  self.dummy_image.to(lowerCamelCase__					)

							# put models in fp16
							_UpperCamelCase   :						Union[str, Any]												=  unet.half()
							_UpperCamelCase   :						str												=  vae.half()
							_UpperCamelCase   :						Dict												=  bert.half()

							# make sure here that pndm scheduler skips prk
							_UpperCamelCase   :						Optional[Any]												=  AltDiffusionImgaImgPipeline(
							    unet=lowerCamelCase__					,scheduler=lowerCamelCase__					,vae=lowerCamelCase__					,text_encoder=lowerCamelCase__					,tokenizer=lowerCamelCase__					,safety_checker=lowerCamelCase__					,feature_extractor=self.dummy_extractor					,)
							_UpperCamelCase   :						List[Any]												=  VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor					,do_normalize=lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  alt_pipe.to(lowerCamelCase__					)
							alt_pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						List[str]												=  'A painting of a squirrel eating a burger'
							_UpperCamelCase   :						Optional[int]												=  torch.manual_seed(0					)
							_UpperCamelCase   :						Dict												=  alt_pipe(
							    [prompt]					,generator=lowerCamelCase__					,num_inference_steps=2					,output_type='np'					,image=lowerCamelCase__					,).images

							assert image.shape == (1, 32, 32, 3)





	@unittest.skipIf(torch_device != 'cuda'					,'This test requires a GPU'					)
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'					)
							# resize to resolution that is divisible by 8 but not 16 or 32
							_UpperCamelCase   :						Tuple												=  init_image.resize((760, 504)					)

							_UpperCamelCase   :						Dict												=  'BAAI/AltDiffusion'
							_UpperCamelCase   :						str												=  AltDiffusionImgaImgPipeline.from_pretrained(
							    lowerCamelCase__					,safety_checker=lowerCamelCase__					,)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)
							pipe.enable_attention_slicing()

							_UpperCamelCase   :						Any												=  'A fantasy landscape, trending on artstation'

							_UpperCamelCase   :						Optional[Any]												=  torch.manual_seed(0					)
							_UpperCamelCase   :						int												=  pipe(
							    prompt=lowerCamelCase__					,image=lowerCamelCase__					,strength=0.7_5					,guidance_scale=7.5					,generator=lowerCamelCase__					,output_type='np'					,)
							_UpperCamelCase   :						Tuple												=  output.images[0]

							_UpperCamelCase   :						str												=  image[255:258, 383:386, -1]

							assert image.shape == (504, 760, 3)
							_UpperCamelCase   :						List[Any]												=  np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]					)

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






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





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/img2img/sketch-mountains-input.jpg'					)
							_UpperCamelCase   :						Optional[Any]												=  init_image.resize((768, 512)					)
							_UpperCamelCase   :						Tuple												=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'					)

							_UpperCamelCase   :						List[str]												=  'BAAI/AltDiffusion'
							_UpperCamelCase   :						Optional[int]												=  AltDiffusionImgaImgPipeline.from_pretrained(
							    lowerCamelCase__					,safety_checker=lowerCamelCase__					,)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)
							pipe.enable_attention_slicing()

							_UpperCamelCase   :						Union[str, Any]												=  'A fantasy landscape, trending on artstation'

							_UpperCamelCase   :						Union[str, Any]												=  torch.manual_seed(0					)
							_UpperCamelCase   :						int												=  pipe(
							    prompt=lowerCamelCase__					,image=lowerCamelCase__					,strength=0.7_5					,guidance_scale=7.5					,generator=lowerCamelCase__					,output_type='np'					,)
							_UpperCamelCase   :						Dict												=  output.images[0]

							assert image.shape == (512, 768, 3)
							# img2img is flaky across GPUs even in fp32, so using MAE here
							assert np.abs(expected_image - image					).max() < 1E-2







'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


snake_case_					:      Union[str, Any]    			=    logging.get_logger(__name__)

snake_case_					:      Tuple    			=    '▁'

snake_case_					:      List[str]    			=    {
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

snake_case_					:      Tuple    			=    {
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

snake_case_					:      Union[str, Any]    			=    {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
snake_case_					:      List[Any]    			=    {
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}






class 				lowercase__      (				lowercase  ):
	lowercase__    							=     VOCAB_FILES_NAMES
	lowercase__    							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowercase__    							=     PRETRAINED_VOCAB_FILES_MAP
	lowercase__    							=     ["""input_ids""", """attention_mask"""]

	lowercase__    							=     []
	lowercase__    							=     []





	def __init__(					self   :							Union[str, Any]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Dict=None					,lowerCamelCase__   :							Optional[int]=None					,lowerCamelCase__   :							Optional[int]="<s>"					,lowerCamelCase__   :							Optional[int]="</s>"					,lowerCamelCase__   :							str="</s>"					,lowerCamelCase__   :							Union[str, Any]="<pad>"					,lowerCamelCase__   :							Optional[int]="<unk>"					,lowerCamelCase__   :							str="m2m100"					,lowerCamelCase__   :							Optional[Dict[str, Any]] = None					,lowerCamelCase__   :							Optional[Any]=8					,**lowerCamelCase__   :							str					,):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  {} if sp_model_kwargs is None else sp_model_kwargs

							_UpperCamelCase   :						Optional[int]												=  language_codes
							_UpperCamelCase   :						Union[str, Any]												=  FAIRSEQ_LANGUAGE_CODES[language_codes]
							_UpperCamelCase   :						str												=  {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

							_UpperCamelCase   :						Optional[Any]												=  kwargs.get('additional_special_tokens'					,[]					)
							kwargs["additional_special_tokens"] += [
							    self.get_lang_token(lowerCamelCase__					)
							    for lang_code in fairseq_language_code
							    if self.get_lang_token(lowerCamelCase__					) not in kwargs["additional_special_tokens"]
							]

							super().__init__(
							    src_lang=lowerCamelCase__					,tgt_lang=lowerCamelCase__					,bos_token=lowerCamelCase__					,eos_token=lowerCamelCase__					,sep_token=lowerCamelCase__					,unk_token=lowerCamelCase__					,pad_token=lowerCamelCase__					,language_codes=lowerCamelCase__					,sp_model_kwargs=self.sp_model_kwargs					,num_madeup_words=lowerCamelCase__					,**lowerCamelCase__					,)

							_UpperCamelCase   :						Any												=  vocab_file
							_UpperCamelCase   :						int												=  load_json(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  {v: k for k, v in self.encoder.items()}
							_UpperCamelCase   :						Any												=  spm_file
							_UpperCamelCase   :						Optional[Any]												=  load_spm(lowerCamelCase__					,self.sp_model_kwargs					)

							_UpperCamelCase   :						Union[str, Any]												=  len(self.encoder					)

							_UpperCamelCase   :						Dict												=  {
							    self.get_lang_token(lowerCamelCase__					): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__					)
							}
							_UpperCamelCase   :						Any												=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase__					)}
							_UpperCamelCase   :						Dict												=  {v: k for k, v in self.lang_token_to_id.items()}

							_UpperCamelCase   :						Any												=  src_lang if src_lang is not None else 'en'
							_UpperCamelCase   :						int												=  tgt_lang
							_UpperCamelCase   :						Optional[int]												=  self.get_lang_id(self._src_lang					)
							self.set_src_lang_special_tokens(self._src_lang					)

							_UpperCamelCase   :						List[Any]												=  num_madeup_words





	@property
	def       UpperCamelCase_    (					self   :							int					):







							'''simple docstring'''
							return len(self.encoder					) + len(self.lang_token_to_id					)





	@property
	def       UpperCamelCase_    (					self   :							Optional[int]					):







							'''simple docstring'''
							return self._src_lang





	@src_lang.setter
	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Tuple												=  new_src_lang
							self.set_src_lang_special_tokens(self._src_lang					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							return self.sp_model.encode(lowerCamelCase__					,out_type=lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							if token in self.lang_token_to_id:
													return self.lang_token_to_id[token]
							return self.encoder.get(lowerCamelCase__					,self.encoder[self.unk_token]					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							int					):







							'''simple docstring'''
							if index in self.id_to_lang_token:
													return self.id_to_lang_token[index]
							return self.decoder.get(lowerCamelCase__					,self.unk_token					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  []
							_UpperCamelCase   :						Union[str, Any]												=  ''
							for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																			out_string += self.sp_model.decode(lowerCamelCase__					) + token
																			_UpperCamelCase   :						Optional[int]												=  []
													else:
																			current_sub_tokens.append(lowerCamelCase__					)
							out_string += self.sp_model.decode(lowerCamelCase__					)
							return out_string.strip()





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







							'''simple docstring'''

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

							_UpperCamelCase   :						Tuple												=  [1] * len(self.prefix_tokens					)
							_UpperCamelCase   :						str												=  [1] * len(self.suffix_tokens					)
							if token_ids_a is None:
													return prefix_ones + ([0] * len(lowerCamelCase__					)) + suffix_ones
							return prefix_ones + ([0] * len(lowerCamelCase__					)) + ([0] * len(lowerCamelCase__					)) + suffix_ones





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							List[int]					,lowerCamelCase__   :							Optional[List[int]] = None					):







							'''simple docstring'''
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						List[Any]												=  {self.convert_ids_to_tokens(lowerCamelCase__					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





	def __getstate__(					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  self.__dict__.copy()
							_UpperCamelCase   :						int												=  None
							return state





	def __setstate__(					self   :							Optional[int]					,lowerCamelCase__   :							Dict					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  d

							# for backward compatibility
							if not hasattr(self					,'sp_model_kwargs'					):
													_UpperCamelCase   :						int												=  {}

							_UpperCamelCase   :						Dict												=  load_spm(self.spm_file					,self.sp_model_kwargs					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Optional[str] = None					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  Path(lowerCamelCase__					)
							if not save_dir.is_dir():
													raise OSError(F'{save_directory} should be a directory'					)
							_UpperCamelCase   :						List[str]												=  save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
							)
							_UpperCamelCase   :						Optional[Any]												=  save_dir / (
							    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
							)

							save_json(self.encoder					,lowerCamelCase__					)

							if os.path.abspath(self.spm_file					) != os.path.abspath(lowerCamelCase__					) and os.path.isfile(self.spm_file					):
													copyfile(self.spm_file					,lowerCamelCase__					)
							elif not os.path.isfile(self.spm_file					):
													with open(lowerCamelCase__					,'wb'					) as fi:
																			_UpperCamelCase   :						Union[str, Any]												=  self.sp_model.serialized_model_proto()
																			fi.write(lowerCamelCase__					)

							return (str(lowerCamelCase__					), str(lowerCamelCase__					))





	def       UpperCamelCase_    (					self   :							Tuple					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							str = "en"					,lowerCamelCase__   :							Optional[List[str]] = None					,lowerCamelCase__   :							str = "ro"					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  src_lang
							_UpperCamelCase   :						Any												=  tgt_lang
							self.set_src_lang_special_tokens(self.src_lang					)
							return super().prepare_seqaseq_batch(lowerCamelCase__					,lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							List[str]					,lowerCamelCase__   :							Optional[str]					,lowerCamelCase__   :							Optional[str]					,**lowerCamelCase__   :							Optional[int]					):







							'''simple docstring'''
							if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'					)
							_UpperCamelCase   :						int												=  src_lang
							_UpperCamelCase   :						Any												=  self(lowerCamelCase__					,add_special_tokens=lowerCamelCase__					,**lowerCamelCase__					)
							_UpperCamelCase   :						List[Any]												=  self.get_lang_id(lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  tgt_lang_id
							return inputs





	def       UpperCamelCase_    (					self   :							Dict					):







							'''simple docstring'''
							self.set_src_lang_special_tokens(self.src_lang					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							self.set_tgt_lang_special_tokens(self.tgt_lang					)





	def       UpperCamelCase_    (					self   :							str					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  self.get_lang_token(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  self.lang_token_to_id[lang_token]
							_UpperCamelCase   :						Any												=  [self.cur_lang_id]
							_UpperCamelCase   :						Union[str, Any]												=  [self.eos_token_id]





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  self.get_lang_token(lowerCamelCase__					)
							_UpperCamelCase   :						Union[str, Any]												=  self.lang_token_to_id[lang_token]
							_UpperCamelCase   :						Union[str, Any]												=  [self.cur_lang_id]
							_UpperCamelCase   :						List[str]												=  [self.eos_token_id]





	def       UpperCamelCase_    (					self   :							Any					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							return self.lang_code_to_token[lang]





	def       UpperCamelCase_    (					self   :							List[Any]					,lowerCamelCase__   :							str					):







							'''simple docstring'''
							_UpperCamelCase   :						List[str]												=  self.get_lang_token(lowerCamelCase__					)
							return self.lang_token_to_id[lang_token]






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Union[str, Any]												=  sentencepiece.SentencePieceProcessor(**UpperCAmelCase_				)
						spm.Load(str(UpperCAmelCase_				)				)
						return spm






def A__       (	UpperCAmelCase_				):
						with open(UpperCAmelCase_    ,    'r'				) as f:
												return json.load(UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						with open(UpperCAmelCase_    ,    'w'				) as f:
												json.dump(UpperCAmelCase_    ,    UpperCAmelCase_    ,    indent=2				)


