




import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
     import torch

if is_vision_available():
     import PIL


UpperCamelCase								=	logging.get_logger(__name__)



def 				_A  (		lowerCAmelCase_	:   np.ndarray  ,							lowerCAmelCase_	:   Union[int, Iterable[int]]  ,							lowerCAmelCase_	:   bool  ,							lowerCAmelCase_	:   int     ):
     """simple docstring"""

     def constraint_to_multiple_of(lowerCAmelCase_	:   List[str]  ,							lowerCAmelCase_	:   Optional[Any]  ,							lowerCAmelCase_	:   Union[str, Any]=0  ,							lowerCAmelCase_	:   List[Any]=None     ):
          lowerCAmelCase__            =					round(val / multiple     ) * multiple

          if max_val is not None and x > max_val:
               lowerCAmelCase__            =					math.floor(val / multiple     ) * multiple

          if x < min_val:
               lowerCAmelCase__            =					math.ceil(val / multiple     ) * multiple

          return x

     lowerCAmelCase__            =					(output_size, output_size) if isinstance(lowerCAmelCase_  ,							lowerCAmelCase_     ) else output_size

     lowerCAmelCase__      ,						lowerCAmelCase__            =					get_image_size(lowerCAmelCase_     )
     lowerCAmelCase__      ,						lowerCAmelCase__            =					output_size

     # determine new height and width
     lowerCAmelCase__            =					output_height / input_height
     lowerCAmelCase__            =					output_width / input_width

     if keep_aspect_ratio:
          # scale as little as possible
          if abs(1 - scale_width     ) < abs(1 - scale_height     ):
               # fit width
               lowerCAmelCase__            =					scale_width
          else:
               # fit height
               lowerCAmelCase__            =					scale_height

     lowerCAmelCase__            =					constraint_to_multiple_of(scale_height * input_height  ,							multiple=lowerCAmelCase_     )
     lowerCAmelCase__            =					constraint_to_multiple_of(scale_width * input_width  ,							multiple=lowerCAmelCase_     )

     return (new_height, new_width)






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["pixel_values"]


    def __init__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int] = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BILINEAR  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	int = 1  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float] = 1 / 255  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      ) ->			None:
         super().__init__(**SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					size if size is not None else {"height": 384, "width": 384}
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					do_resize
         lowerCAmelCase__            =					size
         lowerCAmelCase__            =					keep_aspect_ratio
         lowerCAmelCase__            =					ensure_multiple_of
         lowerCAmelCase__            =					resample
         lowerCAmelCase__            =					do_rescale
         lowerCAmelCase__            =					rescale_factor
         lowerCAmelCase__            =					do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__            =					image_std if image_std is not None else IMAGENET_STANDARD_STD


    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Dict[str, int]  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	int = 1  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = PILImageResampling.BICUBIC  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ,      ) ->			np.ndarray:
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'  )
         lowerCAmelCase__            =					get_resize_output_image_size(
             SCREAMING_SNAKE_CASE__  ,      output_size=(size["height"], size["width"])  ,      keep_aspect_ratio=SCREAMING_SNAKE_CASE__  ,      multiple=SCREAMING_SNAKE_CASE__  ,      )
         return resize(SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[int, float]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			Dict:
         return rescale(SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	np.ndarray  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Union[float, List[float]]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, ChannelDimension]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ,      ) ->			np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  ,      data_format=SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	ImageInput  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	int = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	int = None  ,      SCREAMING_SNAKE_CASE__	:	PILImageResampling = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	float = None  ,      SCREAMING_SNAKE_CASE__	:	bool = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[float, List[float]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      SCREAMING_SNAKE_CASE__	:	ChannelDimension = ChannelDimension.FIRST  ,      **SCREAMING_SNAKE_CASE__	:	int  ,      ) ->			PIL.Image.Image:
         lowerCAmelCase__            =					do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__            =					size if size is not None else self.size
         lowerCAmelCase__            =					get_size_dict(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
         lowerCAmelCase__            =					ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
         lowerCAmelCase__            =					resample if resample is not None else self.resample
         lowerCAmelCase__            =					do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__            =					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__            =					do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__            =					image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__            =					image_std if image_std is not None else self.image_std

         lowerCAmelCase__            =					make_list_of_images(SCREAMING_SNAKE_CASE__  )

         if not valid_images(SCREAMING_SNAKE_CASE__  ):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."  )

         if do_resize and size is None or resample is None:
              raise ValueError("Size and resample must be specified if do_resize is True."  )

         if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."  )

         if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."  )

         # All transformations expect numpy arrays.
         lowerCAmelCase__            =					[to_numpy_array(SCREAMING_SNAKE_CASE__  ) for image in images]

         if do_resize:
              lowerCAmelCase__            =					[self.resize(image=SCREAMING_SNAKE_CASE__  ,      size=SCREAMING_SNAKE_CASE__  ,      resample=SCREAMING_SNAKE_CASE__  ) for image in images]

         if do_rescale:
              lowerCAmelCase__            =					[self.rescale(image=SCREAMING_SNAKE_CASE__  ,      scale=SCREAMING_SNAKE_CASE__  ) for image in images]

         if do_normalize:
              lowerCAmelCase__            =					[self.normalize(image=SCREAMING_SNAKE_CASE__  ,      mean=SCREAMING_SNAKE_CASE__  ,      std=SCREAMING_SNAKE_CASE__  ) for image in images]

         lowerCAmelCase__            =					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ) for image in images]

         lowerCAmelCase__            =					{"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__  ,      tensor_type=SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ,      SCREAMING_SNAKE_CASE__	:	List[Tuple] = None  ) ->			Dict:
         lowerCAmelCase__            =					outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
              if len(SCREAMING_SNAKE_CASE__  ) != len(SCREAMING_SNAKE_CASE__  ):
                   raise ValueError(
                       "Make sure that you pass in as many target sizes as the batch dimension of the logits"  )

              if is_torch_tensor(SCREAMING_SNAKE_CASE__  ):
                   lowerCAmelCase__            =					target_sizes.numpy()

              lowerCAmelCase__            =					[]

              for idx in range(len(SCREAMING_SNAKE_CASE__  )  ):
                   lowerCAmelCase__            =					torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0  )  ,      size=target_sizes[idx]  ,      mode="bilinear"  ,      align_corners=SCREAMING_SNAKE_CASE__  )
                   lowerCAmelCase__            =					resized_logits[0].argmax(dim=0  )
                   semantic_segmentation.append(SCREAMING_SNAKE_CASE__  )
         else:
              lowerCAmelCase__            =					logits.argmax(dim=1  )
              lowerCAmelCase__            =					[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

         return semantic_segmentation





'''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				)


