'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   KandinskyInpaintPipeline
      UpperCAmelCase_      :      Optional[Any]      							=   ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
      UpperCAmelCase_      :      Union[str, Any]      							=   [
          '''prompt''',
          '''negative_prompt''',
          '''image_embeds''',
          '''negative_image_embeds''',
          '''image''',
          '''mask_image''',
      ]
      UpperCAmelCase_      :      str      							=   [
          '''generator''',
          '''height''',
          '''width''',
          '''latents''',
          '''guidance_scale''',
          '''negative_prompt''',
          '''num_inference_steps''',
          '''return_dict''',
          '''guidance_scale''',
          '''num_images_per_prompt''',
          '''output_type''',
          '''return_dict''',
      ]
      UpperCAmelCase_      :      Dict      							=   False




      @property
      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         return 32




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



         '''simple docstring'''



         return 32




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



         '''simple docstring'''



         return self.time_input_dim




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



         '''simple docstring'''



         return self.time_input_dim * 4




      @property
      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         return 100




      @property
      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''							)
         return tokenizer




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				Dict   				=  MCLIPConfig(
             numDims=self.cross_attention_dim					,transformerDimensions=self.text_embedder_hidden_size					,hidden_size=self.text_embedder_hidden_size					,intermediate_size=37					,num_attention_heads=4					,num_hidden_layers=5					,vocab_size=1005					,)

         UpperCAmelCase__							:				Optional[Any]   				=  MultilingualCLIP(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  text_encoder.eval()

         return text_encoder




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         torch.manual_seed(0							)

         UpperCAmelCase__							:				Tuple   				=  {
             '''in_channels''': 9,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 8,
             '''addition_embed_type''': '''text_image''',
             '''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''': '''text_image_proj''',
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': None,
         }

         UpperCAmelCase__							:				int   				=  UNetaDConditionModel(**lowerCamelCase_							)
         return model




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return {
             "block_out_channels": [32, 64],
             "down_block_types": ["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",
             ],
             "vq_embed_dim": 4,
         }




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



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				int   				=  VQModel(**self.dummy_movq_kwargs							)
         return model




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.dummy_text_encoder
         UpperCAmelCase__							:				Optional[int]   				=  self.dummy_tokenizer
         UpperCAmelCase__							:				List[Any]   				=  self.dummy_unet
         UpperCAmelCase__							:				Tuple   				=  self.dummy_movq

         UpperCAmelCase__							:				Union[str, Any]   				=  DDIMScheduler(
             num_train_timesteps=1000					,beta_schedule='''linear'''					,beta_start=0.0_0085					,beta_end=0.012					,clip_sample=lowerCamelCase_					,set_alpha_to_one=lowerCamelCase_					,steps_offset=1					,prediction_type='''epsilon'''					,thresholding=lowerCamelCase_					,)

         UpperCAmelCase__							:				Optional[int]   				=  {
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''movq''': movq,
         }

         return components




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  floats_tensor((1, self.cross_attention_dim)					,rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  floats_tensor((1, self.cross_attention_dim)					,rng=random.Random(seed + 1							)							).to(lowerCamelCase_							)
         # create init_image
         UpperCAmelCase__							:				Optional[int]   				=  floats_tensor((1, 3, 64, 64)					,rng=random.Random(lowerCamelCase_							)							).to(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  image.cpu().permute(0					,2					,3					,1							)[0]
         UpperCAmelCase__							:				Optional[Any]   				=  Image.fromarray(np.uinta(lowerCamelCase_							)							).convert('''RGB'''							).resize((256, 256)							)
         # create mask
         UpperCAmelCase__							:				str   				=  np.ones((64, 64)					,dtype=np.floataa							)
         UpperCAmelCase__							:				Optional[Any]   				=  0

         if str(lowerCamelCase_							).startswith('''mps'''							):
            UpperCAmelCase__							:				int   				=  torch.manual_seed(lowerCamelCase_							)
         else:
            UpperCAmelCase__							:				Optional[int]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  {
             '''prompt''': '''horse''',
             '''image''': init_image,
             '''mask_image''': mask,
             '''image_embeds''': image_embeds,
             '''negative_image_embeds''': negative_image_embeds,
             '''generator''': generator,
             '''height''': 64,
             '''width''': 64,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 4.0,
             '''output_type''': '''np''',
         }
         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  '''cpu'''

         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_dummy_components()

         UpperCAmelCase__							:				Tuple   				=  self.pipeline_class(**lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  pipe.to(lowerCamelCase_							)

         pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				List[str]   				=  pipe(**self.get_dummy_inputs(lowerCamelCase_							)							)
         UpperCAmelCase__							:				List[Any]   				=  output.images

         UpperCAmelCase__							:				str   				=  pipe(
             **self.get_dummy_inputs(lowerCamelCase_							)					,return_dict=lowerCamelCase_					,)[0]

         UpperCAmelCase__							:				Union[str, Any]   				=  image[0, -3:, -3:, -1]
         UpperCAmelCase__							:				Optional[Any]   				=  image_from_tuple[0, -3:, -3:, -1]

         print(f'''image.shape {image.shape}'''							)

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

         UpperCAmelCase__							:				List[str]   				=  np.array(
             [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]							)

         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()}'''




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



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




@slow
@require_torch_gpu
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


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



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.ones((768, 768)					,dtype=np.floataa							)
         UpperCAmelCase__							:				Optional[Any]   				=  0

         UpperCAmelCase__							:				List[Any]   				=  '''a hat'''

         UpperCAmelCase__							:				List[str]   				=  KandinskyPriorPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-prior'''					,torch_dtype=torch.floataa							)
         pipe_prior.to(lowerCamelCase_							)

         UpperCAmelCase__							:				Dict   				=  KandinskyInpaintPipeline.from_pretrained(
             '''kandinsky-community/kandinsky-2-1-inpaint'''					,torch_dtype=torch.floataa							)
         UpperCAmelCase__							:				List[Any]   				=  pipeline.to(lowerCamelCase_							)
         pipeline.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  torch.Generator(device='''cpu'''							).manual_seed(0							)
         UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  pipe_prior(
             lowerCamelCase_					,generator=lowerCamelCase_					,num_inference_steps=5					,negative_prompt=''''''					,).to_tuple()

         UpperCAmelCase__							:				Union[str, Any]   				=  pipeline(
             lowerCamelCase_					,image=lowerCamelCase_					,mask_image=lowerCamelCase_					,image_embeds=lowerCamelCase_					,negative_image_embeds=lowerCamelCase_					,generator=lowerCamelCase_					,num_inference_steps=100					,height=768					,width=768					,output_type='''np'''					,)

         UpperCAmelCase__							:				Tuple   				=  output.images[0]

         assert image.shape == (768, 768, 3)

         assert_mean_pixel_difference(lowerCamelCase_					,lowerCamelCase_							)





'''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


UpperCamelCase__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			'▁'

UpperCamelCase__     :	Any				=			{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCamelCase__     :	Union[str, Any]				=			{
    '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',
    },
}

UpperCamelCase__     :	Dict				=			{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
UpperCamelCase__     :	Optional[int]				=			{
    '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	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Optional[int]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      Optional[int]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Optional[int]      							=   ['''input_ids''', '''attention_mask''']

      UpperCAmelCase_      :      List[int]      							=   []
      UpperCAmelCase_      :      List[int]      							=   []




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="m2m100"					,lowerCamelCase_ = None					,lowerCamelCase_=8					,**lowerCamelCase_					,)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase__							:				Dict   				=  language_codes
         UpperCAmelCase__							:				Union[str, Any]   				=  FAIRSEQ_LANGUAGE_CODES[language_codes]
         UpperCAmelCase__							:				Union[str, Any]   				=  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

         UpperCAmelCase__							:				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__							:				Optional[int]   				=  vocab_file
         UpperCAmelCase__							:				Optional[Any]   				=  load_json(lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  {v: k for k, v in self.encoder.items()}
         UpperCAmelCase__							:				List[Any]   				=  spm_file
         UpperCAmelCase__							:				Any   				=  load_spm(lowerCamelCase_					,self.sp_model_kwargs							)

         UpperCAmelCase__							:				int   				=  len(self.encoder							)

         UpperCAmelCase__							:				Optional[int]   				=  {
             self.get_lang_token(lowerCamelCase_							): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_							)
         }
         UpperCAmelCase__							:				List[Any]   				=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_							)}
         UpperCAmelCase__							:				List[str]   				=  {v: k for k, v in self.lang_token_to_id.items()}

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

         UpperCAmelCase__							:				Optional[int]   				=  num_madeup_words




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return len(self.encoder							) + len(self.lang_token_to_id							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         return self._src_lang




      @src_lang.setter
      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



         return self.sp_model.encode(lowerCamelCase_					,out_type=lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''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 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''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 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  []
         UpperCAmelCase__							:				str   				=  ''''''
         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__							:				str   				=  []
            else:
               current_sub_tokens.append(lowerCamelCase_							)
         out_string += self.sp_model.decode(lowerCamelCase_							)
         return out_string.strip()




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''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__							:				Dict   				=  [1] * len(self.prefix_tokens							)
         UpperCAmelCase__							:				Optional[Any]   				=  [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 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



         '''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 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  {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							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				str   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  d

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

         UpperCAmelCase__							:				int   				=  load_spm(self.spm_file					,self.sp_model_kwargs							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  Path(lowerCamelCase_							)
         if not save_dir.is_dir():
            raise OSError(f'''{save_directory} should be a directory'''							)
         UpperCAmelCase__							:				Optional[int]   				=  save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
         )
         UpperCAmelCase__							:				str   				=  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__							:				str   				=  self.sp_model.serialized_model_proto()
               fi.write(lowerCamelCase_							)

         return (str(lowerCamelCase_							), str(lowerCamelCase_							))




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = "en"					,lowerCamelCase_ = None					,lowerCamelCase_ = "ro"					,**lowerCamelCase_					,)			->			BatchEncoding:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  src_lang
         UpperCAmelCase__							:				Tuple   				=  tgt_lang
         self.set_src_lang_special_tokens(self.src_lang							)
         return super().prepare_seqaseq_batch(lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**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__							:				List[str]   				=  src_lang
         UpperCAmelCase__							:				List[str]   				=  self(lowerCamelCase_					,add_special_tokens=lowerCamelCase_					,**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_lang_id(lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  tgt_lang_id
         return inputs




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



         '''simple docstring'''



         self.set_src_lang_special_tokens(self.src_lang							)




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



         '''simple docstring'''



         self.set_tgt_lang_special_tokens(self.tgt_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.get_lang_token(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  self.lang_token_to_id[lang_token]
         UpperCAmelCase__							:				Union[str, Any]   				=  [self.cur_lang_id]
         UpperCAmelCase__							:				Dict   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.get_lang_token(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  self.lang_token_to_id[lang_token]
         UpperCAmelCase__							:				Tuple   				=  [self.cur_lang_id]
         UpperCAmelCase__							:				str   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.lang_code_to_token[lang]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_lang_token(lowerCamelCase_							)
         return self.lang_token_to_id[lang_token]







def __UpperCamelCase(   _A					:				str  ,			_A					:				Dict[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  sentencepiece.SentencePieceProcessor(**_A    )
   spm.Load(str(_A    )    )
   return spm







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''r'''    ) as f:
      return json.load(_A    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''w'''    ) as f:
      json.dump(_A  ,			_A  ,			indent=2    )




