



"""simple docstring"""


import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    )							->     Union[str, Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     PIL.Image.Image:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Union[List[PIL.Image.Image], PIL.Image.Image]   )							->     np.ndarray:
												if not isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[images]
												UpperCAmelCase			 =					[np.array(lowercase_   ).astype(np.floataa   ) / 255.0 for image in images]
												UpperCAmelCase			 =					np.stack(lowercase_  ,    axis=0   )

												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:np.ndarray   )							->     torch.FloatTensor:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[..., None]

												UpperCAmelCase			 =					torch.from_numpy(images.transpose(0  ,    3  ,    1  ,    2   )   )
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:torch.FloatTensor   )							->     np.ndarray:
												UpperCAmelCase			 =					images.cpu().permute(0  ,    2  ,    3  ,    1   ).float().numpy()
												return images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:List[Any]   )							->     Optional[int]:
												return 2.0 * images - 1.0






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:str   )							->     str:
												return (images / 2 + 0.5).clamp(0  ,    1   )






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:PIL.Image.Image   )							->     PIL.Image.Image:
												UpperCAmelCase			 =					image.convert('RGB'   )
												return image






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:PIL.Image.Image  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     PIL.Image.Image:
												if height is None:
																		UpperCAmelCase			 =					image.height
												if width is None:
																		UpperCAmelCase			 =					image.width

												UpperCAmelCase ,       UpperCAmelCase			 =					(
												    x - x % self.config.vae_scale_factor for x in (width, height)
												)  # resize to integer multiple of vae_scale_factor
												UpperCAmelCase			 =					image.resize((width, height)  ,    resample=PIL_INTERPOLATION[self.config.resample]   )
												return image






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    )							->     torch.Tensor:
												UpperCAmelCase			 =					(PIL.Image.Image, np.ndarray, torch.Tensor)
												if isinstance(lowercase_  ,    lowercase_   ):
																		UpperCAmelCase			 =					[image]
												elif not (isinstance(lowercase_  ,    lowercase_   ) and all(isinstance(lowercase_  ,    lowercase_   ) for i in image   )):
																		raise ValueError(
																		    f"""Input is in incorrect format: {[type(lowercase_   ) for i in image]}. Currently, we only support {', '.join(lowercase_   )}"""   )

												if isinstance(image[0]  ,    PIL.Image.Image   ):
																		if self.config.do_convert_rgb:
																								UpperCAmelCase			 =					[self.convert_to_rgb(lowercase_   ) for i in image]
																		if self.config.do_resize:
																								UpperCAmelCase			 =					[self.resize(lowercase_  ,    lowercase_  ,    lowercase_   ) for i in image]
																		UpperCAmelCase			 =					self.pil_to_numpy(lowercase_   )  # to np
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )  # to pt

												elif isinstance(image[0]  ,    np.ndarray   ):
																		UpperCAmelCase			 =					np.concatenate(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else np.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase			 =					self.numpy_to_pt(lowercase_   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape
																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

												elif isinstance(image[0]  ,    torch.Tensor   ):
																		UpperCAmelCase			 =					torch.cat(lowercase_  ,    axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_  ,    axis=0   )
																		UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					image.shape

																		# don't need any preprocess if the image is latents
																		if channel == 4:
																								return image

																		if self.config.do_resize and (
																		    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
																		):
																								raise ValueError(
																								    f"""Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"""
																								    f"""currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"""   )

        # expected range [0,1], normalize to [-1,1]
												UpperCAmelCase			 =					self.config.do_normalize
												if image.min() < 0:
																		warnings.warn(
																		    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
																		    f"""when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"""  ,    lowercase_  ,    )
																		UpperCAmelCase			 =					False

												if do_normalize:
																		UpperCAmelCase			 =					self.normalize(lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     List[str]:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if output_type == "latent":
																		return image

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												if output_type == "pt":
																		return image

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		return image

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   )







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				CONFIG_NAME






						@register_to_config
						def __init__(   self					:Union[str, Any]  ,    lowercase_					:bool = True  ,    lowercase_					:int = 8  ,    lowercase_					:str = "lanczos"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												super().__init__()






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Optional[int]   )							->     Optional[Any]:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					(images * 2_55).round().astype('uint8'   )
												if images.shape[-1] == 1:
																		# special case for grayscale (single channel) images
																		UpperCAmelCase			 =					[Image.fromarray(image.squeeze()  ,    mode='L'   ) for image in images]
												else:
																		UpperCAmelCase			 =					[Image.fromarray(image[:, :, :3]   ) for image in images]

												return pil_images






						@staticmethod
						def       UpperCAmelCase__	(   lowercase_					:Any   )							->     Union[str, Any]:
												return image[:, :, 1] * 2**8 + image[:, :, 2]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:int   )							->     Tuple:
												if images.ndim == 3:
																		UpperCAmelCase			 =					images[None, ...]
												UpperCAmelCase			 =					images[:, :, :, 3:]
												if images.shape[-1] == 6:
																		UpperCAmelCase			 =					(images_depth * 2_55).round().astype('uint8'   )
																		UpperCAmelCase			 =					[
																		    Image.fromarray(self.rgblike_to_depthmap(lowercase_   )  ,    mode='I;16'   ) for image_depth in images_depth
																		]
												elif images.shape[-1] == 4:
																		UpperCAmelCase			 =					(images_depth * 6_5535.0).astype(np.uintaa   )
																		UpperCAmelCase			 =					[Image.fromarray(lowercase_  ,    mode='I;16'   ) for image_depth in images_depth]
												else:
																		raise Exception('Not supported'   )

												return pil_images






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:torch.FloatTensor  ,    lowercase_					:str = "pil"  ,    lowercase_					:Optional[List[bool]] = None  ,    )							->     Dict:
												if not isinstance(lowercase_  ,    torch.Tensor   ):
																		raise ValueError(
																		    f"""Input for postprocessing is in incorrect format: {type(lowercase_   )}. We only support pytorch tensor"""   )
												if output_type not in ["latent", "pt", "np", "pil"]:
																		UpperCAmelCase			 =					(
																		    f"""the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: """
																		    '`pil`, `np`, `pt`, `latent`'
																		)
																		deprecate('Unsupported output_type'  ,    '1.0.0'  ,    lowercase_  ,    standard_warn=lowercase_   )
																		UpperCAmelCase			 =					'np'

												if do_denormalize is None:
																		UpperCAmelCase			 =					[self.config.do_normalize] * image.shape[0]

												UpperCAmelCase			 =					torch.stack(
												    [self.denormalize(image[i]   ) if do_denormalize[i] else image[i] for i in range(image.shape[0]   )]   )

												UpperCAmelCase			 =					self.pt_to_numpy(lowercase_   )

												if output_type == "np":
																		if image.shape[-1] == 6:
																								UpperCAmelCase			 =					np.stack([self.rgblike_to_depthmap(im[:, :, 3:]   ) for im in image]  ,    axis=0   )
																		else:
																								UpperCAmelCase			 =					image[:, :, :, 3:]
																		return image[:, :, :, :3], image_depth

												if output_type == "pil":
																		return self.numpy_to_pil(lowercase_   ), self.numpy_to_depth(lowercase_   )
												else:
																		raise Exception(f"""This type {output_type} is not supported"""   )







import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

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


__magic_name__  :  List[Any]     			=   logging.get_logger(__name__)

__magic_name__  :  Any     			=   {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__magic_name__  :  Dict     			=   {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__magic_name__  :  Any     			=   {'stas/tiny-wmt19-en-de': 1_0_2_4}
__magic_name__  :  int     			=   {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 1_0_2_4,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}





def        lowerCAmelCase       (				snake_case__   :      Union[str, Any] )->			Dict:
    A_								=					set()
    A_								=					word[0]
    for char in word[1:]:
        pairs.add((prev_char, char) )
        A_								=					char
    return pairs





def        lowerCAmelCase       (				snake_case__   :      Optional[int] )->			List[Any]:
    A_								=					text.replace("，"				,					"," )
    A_								=					re.sub(r"。\s*"				,					". "				,					snake_case__ )
    A_								=					text.replace("、"				,					"," )
    A_								=					text.replace("”"				,					"\"" )
    A_								=					text.replace("“"				,					"\"" )
    A_								=					text.replace("∶"				,					":" )
    A_								=					text.replace("："				,					":" )
    A_								=					text.replace("？"				,					"?" )
    A_								=					text.replace("《"				,					"\"" )
    A_								=					text.replace("》"				,					"\"" )
    A_								=					text.replace("）"				,					")" )
    A_								=					text.replace("！"				,					"!" )
    A_								=					text.replace("（"				,					"(" )
    A_								=					text.replace("；"				,					";" )
    A_								=					text.replace("１"				,					"1" )
    A_								=					text.replace("」"				,					"\"" )
    A_								=					text.replace("「"				,					"\"" )
    A_								=					text.replace("０"				,					"0" )
    A_								=					text.replace("３"				,					"3" )
    A_								=					text.replace("２"				,					"2" )
    A_								=					text.replace("５"				,					"5" )
    A_								=					text.replace("６"				,					"6" )
    A_								=					text.replace("９"				,					"9" )
    A_								=					text.replace("７"				,					"7" )
    A_								=					text.replace("８"				,					"8" )
    A_								=					text.replace("４"				,					"4" )
    A_								=					re.sub(r"．\s*"				,					". "				,					snake_case__ )
    A_								=					text.replace("～"				,					"~" )
    A_								=					text.replace("’"				,					"'" )
    A_								=					text.replace("…"				,					"..." )
    A_								=					text.replace("━"				,					"-" )
    A_								=					text.replace("〈"				,					"<" )
    A_								=					text.replace("〉"				,					">" )
    A_								=					text.replace("【"				,					"[" )
    A_								=					text.replace("】"				,					"]" )
    A_								=					text.replace("％"				,					"%" )
    return text





def        lowerCAmelCase       (				snake_case__   :      List[Any] )->			List[Any]:
    A_								=					[]
    for char in text:
        A_								=					unicodedata.category(snake_case__ )
        if cat.startswith("C" ):
            continue
        output.append(snake_case__ )
    return "".join(snake_case__ )



class 							lowerCamelCase       (		__snake_case					):



  """simple docstring"""





  lowerCAmelCase_								=		VOCAB_FILES_NAMES
  lowerCAmelCase_								=		PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase_								=		PRETRAINED_INIT_CONFIGURATION
  lowerCAmelCase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase_								=		["""input_ids""", """attention_mask"""]

  def __init__(					self						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=None						,    __UpperCamelCase=False						,    __UpperCamelCase="<unk>"						,    __UpperCamelCase="<s>"						,    __UpperCamelCase="</s>"						,    __UpperCamelCase="<pad>"						,    **__UpperCamelCase						,    ):
      super().__init__(
          langs=__UpperCamelCase						,    src_vocab_file=__UpperCamelCase						,    tgt_vocab_file=__UpperCamelCase						,    merges_file=__UpperCamelCase						,    do_lower_case=__UpperCamelCase						,    unk_token=__UpperCamelCase						,    bos_token=__UpperCamelCase						,    sep_token=__UpperCamelCase						,    pad_token=__UpperCamelCase						,    **__UpperCamelCase						,    )

      try:
          import sacremoses
      except ImportError:
          raise ImportError(
              "You need to install sacremoses to use XLMTokenizer. "
              "See https://pypi.org/project/sacremoses/ for installation."					)

      A_								=					sacremoses

      A_								=					src_vocab_file
      A_								=					tgt_vocab_file
      A_								=					merges_file
      A_								=					do_lower_case

      # cache of sm.MosesPunctNormalizer instance
      A_								=					{}
      # cache of sm.MosesTokenizer instance
      A_								=					{}
      A_								=					{}

      if langs and len(__UpperCamelCase					) == 2:
          A_	,	A_								=					langs
      else:
          raise ValueError(
              f'arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '
              "Usually that means that tokenizer can't find a mapping for the given model path "
              "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."					)

      with open(__UpperCamelCase						,    encoding="utf-8"					) as src_vocab_handle:
          A_								=					json.load(__UpperCamelCase					)
      with open(__UpperCamelCase						,    encoding="utf-8"					) as tgt_vocab_handle:
          A_								=					json.load(__UpperCamelCase					)
          A_								=					{v: k for k, v in tgt_vocab.items()}
      with open(__UpperCamelCase						,    encoding="utf-8"					) as merges_handle:
          A_								=					merges_handle.read().split("\n"					)[:-1]
      A_								=					[tuple(merge.split()[:2]					) for merge in merges]
      A_								=					dict(zip(__UpperCamelCase						,    range(len(__UpperCamelCase					)					)					)					)
      A_								=					{}

  def 					lowercase_						(					self					):
      return self.get_src_vocab()

  @property
  def 					lowercase_						(					self					):
      return self.src_vocab_size

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      if lang not in self.cache_moses_punct_normalizer:
          A_								=					self.sm.MosesPunctNormalizer(lang=__UpperCamelCase					)
          A_								=					punct_normalizer
      return self.cache_moses_punct_normalizer[lang].normalize(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      if lang not in self.cache_moses_tokenizer:
          A_								=					self.sm.MosesTokenizer(lang=__UpperCamelCase					)
          A_								=					moses_tokenizer
      return self.cache_moses_tokenizer[lang].tokenize(
          __UpperCamelCase						,    aggressive_dash_splits=__UpperCamelCase						,    return_str=__UpperCamelCase						,    escape=__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      if lang not in self.cache_moses_detokenizer:
          A_								=					self.sm.MosesDetokenizer(lang=__UpperCamelCase					)
          A_								=					moses_detokenizer
      return self.cache_moses_detokenizer[lang].detokenize(__UpperCamelCase					)

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase					):
      A_								=					replace_unicode_punct(__UpperCamelCase					)
      A_								=					self.moses_punct_norm(__UpperCamelCase						,    __UpperCamelCase					)
      A_								=					remove_non_printing_char(__UpperCamelCase					)
      return text

  @property
  def 					lowercase_						(					self					):
      return len(self.encoder					)

  @property
  def 					lowercase_						(					self					):
      return len(self.decoder					)

  def 					lowercase_						(					self					):
      return dict(self.encoder						,    **self.added_tokens_encoder					)

  def 					lowercase_						(					self					):
      return dict(self.decoder						,    **self.added_tokens_decoder					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					tuple(token[:-1]					) + (token[-1] + "</w>",)
      if token in self.cache:
          return self.cache[token]
      A_								=					get_pairs(__UpperCamelCase					)

      if not pairs:
          return token + "</w>"

      while True:
          A_								=					min(__UpperCamelCase						,    key=lambda __UpperCamelCase					: self.bpe_ranks.get(__UpperCamelCase						,    float("inf"					)					)					)
          if bigram not in self.bpe_ranks:
              break
          A_	,	A_								=					bigram
          A_								=					[]
          A_								=					0
          while i < len(__UpperCamelCase					):
              try:
                  A_								=					word.index(__UpperCamelCase						,    __UpperCamelCase					)
              except ValueError:
                  new_word.extend(word[i:]					)
                  break
              else:
                  new_word.extend(word[i:j]					)
                  A_								=					j

              if word[i] == first and i < len(__UpperCamelCase					) - 1 and word[i + 1] == second:
                  new_word.append(first + second					)
                  i += 2
              else:
                  new_word.append(word[i]					)
                  i += 1
          A_								=					tuple(__UpperCamelCase					)
          A_								=					new_word
          if len(__UpperCamelCase					) == 1:
              break
          else:
              A_								=					get_pairs(__UpperCamelCase					)
      A_								=					" ".join(__UpperCamelCase					)
      if word == "\n  </w>":
          A_								=					"\n</w>"
      A_								=					word
      return word

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase="en"						,    __UpperCamelCase=False					):
      A_								=					self.src_lang

      if self.do_lower_case:
          A_								=					text.lower()

      if bypass_tokenizer:
          A_								=					text.split()
      else:
          A_								=					self.moses_pipeline(__UpperCamelCase						,    lang=__UpperCamelCase					)
          A_								=					self.moses_tokenize(__UpperCamelCase						,    lang=__UpperCamelCase					)

      A_								=					[]
      for token in text:
          if token:
              split_tokens.extend(list(self.bpe(__UpperCamelCase					).split(" "					)					)					)

      return split_tokens

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      return self.encoder.get(__UpperCamelCase						,    self.encoder.get(self.unk_token					)					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      return self.decoder.get(__UpperCamelCase						,    self.unk_token					)

  def 					lowercase_						(					self						,    __UpperCamelCase					):
      A_								=					[t.replace(" "						,    ""					).replace("</w>"						,    " "					) for t in tokens]
      A_								=					"".join(__UpperCamelCase					).split()
      # detokenize
      A_								=					self.moses_detokenize(__UpperCamelCase						,    self.tgt_lang					)
      return text

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      A_								=					[self.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
          return token_ids_a + sep
      return token_ids_a + sep + token_ids_a + sep

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None						,    __UpperCamelCase = False					):

      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=__UpperCamelCase						,    token_ids_a=__UpperCamelCase						,    already_has_special_tokens=__UpperCamelCase					)
      # no bos used in fairseq
      if token_ids_a is not None:
          return ([0] * len(__UpperCamelCase					)) + [1] + ([0] * len(__UpperCamelCase					)) + [1]
      return ([0] * len(__UpperCamelCase					)) + [1]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      A_								=					[self.sep_token_id]

      # no bos used in fairseq
      if token_ids_a is None:
          return len(token_ids_a + sep					) * [0]
      return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]

  def 					lowercase_						(					self						,    __UpperCamelCase						,    __UpperCamelCase = None					):
      if not os.path.isdir(__UpperCamelCase					):
          logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
          return

      A_								=					os.path.join(
          __UpperCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]					)
      A_								=					os.path.join(
          __UpperCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]					)
      A_								=					os.path.join(
          __UpperCamelCase						,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]					)

      with open(__UpperCamelCase						,    "w"						,    encoding="utf-8"					) as f:
          f.write(json.dumps(self.encoder						,    indent=2						,    sort_keys=__UpperCamelCase						,    ensure_ascii=__UpperCamelCase					) + "\n"					)

      with open(__UpperCamelCase						,    "w"						,    encoding="utf-8"					) as f:
          A_								=					{v: k for k, v in self.decoder.items()}
          f.write(json.dumps(__UpperCamelCase						,    indent=2						,    sort_keys=__UpperCamelCase						,    ensure_ascii=__UpperCamelCase					) + "\n"					)

      A_								=					0
      with open(__UpperCamelCase						,    "w"						,    encoding="utf-8"					) as writer:
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,    key=lambda __UpperCamelCase					: kv[1]					):
              if index != token_index:
                  logger.warning(
                      f'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
                      " Please check that the tokenizer is not corrupted!"					)
                  A_								=					token_index
              writer.write(" ".join(__UpperCamelCase					) + "\n"					)
              index += 1

      return src_vocab_file, tgt_vocab_file, merges_file

  def __getstate__(					self					):
      A_								=					self.__dict__.copy()
      A_								=					None
      return state

  def __setstate__(					self						,    __UpperCamelCase					):
      A_								=					d

      try:
          import sacremoses
      except ImportError:
          raise ImportError(
              "You need to install sacremoses to use XLMTokenizer. "
              "See https://pypi.org/project/sacremoses/ for installation."					)

      A_								=					sacremoses






