




"""simple docstring"""

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)
class      A__   (   __snake_case  ):




	def __init__( self	:						List[Any]   ,     lowercase	:						List[Any]   ,     lowercase	:						Tuple   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						int   ,     lowercase	:						Tuple   ,     lowercase	:						int = 0.93   ,     lowercase	:						Any = True   ,     )       ->			Union[str, Any]:
						__snake_case        =						mixing_coeff
						__snake_case        =						leapfrog_steps

						super().__init__()
						self.register_modules(
						    vae=__UpperCamelCase   ,     text_encoder=__UpperCamelCase   ,     tokenizer=__UpperCamelCase   ,     unet=__UpperCamelCase   ,     scheduler=__UpperCamelCase   ,     )

						__snake_case        =						2 ** (len(self.vae.config.block_out_channels						) - 1)
						__snake_case        =						VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)




	def 				a_		( self	:						str   ,     lowercase	:						str   ,     lowercase	:						Any = None   ,     lowercase	:						int = False						)       ->			Optional[Any]:
						__snake_case        =						self.tokenizer(
						    __UpperCamelCase   ,     padding="""max_length"""   ,     max_length=self.tokenizer.model_max_length   ,     truncation=__UpperCamelCase   ,     return_tensors="""pt"""   ,     )

						__snake_case        =						self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state

						__snake_case        =						prompt_embeds.to(dtype=self.text_encoder.dtype   ,     device=self.device						)

						if do_classifier_free_guidance:
											__snake_case        =						"""""" if negative_prompt is None else negative_prompt

											__snake_case        =						self.tokenizer(
											    __UpperCamelCase   ,     padding="""max_length"""   ,     max_length=self.tokenizer.model_max_length   ,     truncation=__UpperCamelCase   ,     return_tensors="""pt"""   ,     )

											__snake_case        =						self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state

											__snake_case        =						torch.cat([negative_prompt_embeds, prompt_embeds]						)

						return prompt_embeds




	def 				a_		( self	:						str   ,     lowercase	:						int   ,     lowercase	:						Any						)       ->			Any:
						__snake_case        =						self.mixing_coeff * x + (1 - self.mixing_coeff) * y
						__snake_case        =						self.mixing_coeff * y + (1 - self.mixing_coeff) * x

						return [x, y]




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						Any   ,     lowercase	:						List[Any]						)       ->			Optional[Any]:
						__snake_case        =						(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
						__snake_case        =						(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

						return [x, y]




	def 				a_		( self	:						Any   ,     lowercase	:						Union[str, Any]						)       ->			Union[str, Any]:
						# as self.alphas_cumprod is always in cpu
						__snake_case        =						int(__UpperCamelCase						)

						__snake_case        =						self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

						return alpha_prod, 1 - alpha_prod




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						Dict   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						Optional[Any]   ,     )       ->			Optional[int]:
						__snake_case        =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

						__snake_case   ,   __snake_case        =						self._get_alpha_and_beta(__UpperCamelCase						)
						__snake_case   ,   __snake_case        =						self._get_alpha_and_beta(__UpperCamelCase						)

						__snake_case        =						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
						__snake_case        =						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

						__snake_case        =						(base - b_t * model_output) / a_t

						return model_input, next_model_input.to(base.dtype						)




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						Tuple   ,     lowercase	:						Optional[Any]   ,     lowercase	:						List[str]   ,     lowercase	:						Tuple   ,     )       ->			str:
						__snake_case        =						timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

						__snake_case   ,   __snake_case        =						self._get_alpha_and_beta(__UpperCamelCase						)
						__snake_case   ,   __snake_case        =						self._get_alpha_and_beta(__UpperCamelCase						)

						__snake_case        =						(alpha_prod_t_prev / alpha_prod_t) ** 0.5
						__snake_case        =						-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
						__snake_case        =						a_t * base + b_t * model_output

						return model_input, next_model_input.to(base.dtype						)




	@torch.no_grad()
	def 				a_		( self	:						str   ,     lowercase	:						int						)       ->			List[Any]:
						__snake_case        =						1 / self.vae.config.scaling_factor * latents
						__snake_case        =						self.vae.decode(__UpperCamelCase						).sample
						__snake_case        =						(image / 2 + 0.5).clamp(0   ,     1						)
						return image




	@torch.no_grad()
	def 				a_		( self	:						List[Any]   ,     lowercase	:						int   ,     lowercase	:						Tuple   ,     lowercase	:						Tuple   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Optional[int] = None   ,     )       ->			int:
						__snake_case        =						guidance_scale > 1.0

						__snake_case        =						image.to(device=self.device   ,     dtype=text_embeds.dtype						)
						__snake_case        =						self.vae.encode(__UpperCamelCase						).latent_dist.sample(__UpperCamelCase						)

						__snake_case        =						self.vae.config.scaling_factor * latent

						__snake_case        =						[latent.clone(), latent.clone()]

						for i, t in tqdm(enumerate(__UpperCamelCase						)   ,     total=len(__UpperCamelCase						)						):
											__snake_case        =						self.noise_mixing_layer(x=coupled_latents[0]   ,     y=coupled_latents[1]						)

											# j - model_input index, k - base index
											for j in range(2						):
																__snake_case        =						j ^ 1

																if self.leapfrog_steps:
																					if i % 2 == 0:
																										__snake_case   ,   __snake_case        =						j, k

																__snake_case        =						coupled_latents[j]
																__snake_case        =						coupled_latents[k]

																__snake_case        =						torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

																__snake_case        =						self.unet(__UpperCamelCase   ,     __UpperCamelCase   ,     encoder_hidden_states=__UpperCamelCase						).sample

																if do_classifier_free_guidance:
																					__snake_case   ,   __snake_case        =						noise_pred.chunk(2						)
																					__snake_case        =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																__snake_case   ,   __snake_case        =						self.noise_step(
																    base=__UpperCamelCase   ,     model_input=__UpperCamelCase   ,     model_output=__UpperCamelCase   ,     timestep=__UpperCamelCase   ,     )

																__snake_case        =						model_input

						return coupled_latents




	@torch.no_grad()
	def __call__( self	:						Optional[Any]   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						Any   ,     lowercase	:						Tuple   ,     lowercase	:						List[Any] = 3.0   ,     lowercase	:						Optional[int] = 50   ,     lowercase	:						Optional[int] = 0.8   ,     lowercase	:						Dict = None   ,     lowercase	:						int = None   ,     lowercase	:						List[Any] = "pil"   ,     )       ->			Any:
						__snake_case        =						guidance_scale > 1.0

						__snake_case        =						self.image_processor.preprocess(__UpperCamelCase						)

						__snake_case        =						self._encode_prompt(__UpperCamelCase   ,     __UpperCamelCase   ,     __UpperCamelCase						)
						__snake_case        =						self._encode_prompt(__UpperCamelCase   ,     __UpperCamelCase   ,     __UpperCamelCase						)

						self.scheduler.set_timesteps(__UpperCamelCase   ,     self.device						)

						__snake_case        =						num_inference_steps - int(num_inference_steps * strength						)
						__snake_case        =						self.scheduler.timesteps[t_limit:]
						__snake_case        =						fwd_timesteps.flip(0						)

						__snake_case        =						self.prepare_latents(__UpperCamelCase   ,     __UpperCamelCase   ,     __UpperCamelCase   ,     __UpperCamelCase   ,     __UpperCamelCase						)

						for i, t in tqdm(enumerate(__UpperCamelCase						)   ,     total=len(__UpperCamelCase						)						):
											# j - model_input index, k - base index
											for k in range(2						):
																__snake_case        =						k ^ 1

																if self.leapfrog_steps:
																					if i % 2 == 1:
																										__snake_case   ,   __snake_case        =						j, k

																__snake_case        =						coupled_latents[j]
																__snake_case        =						coupled_latents[k]

																__snake_case        =						torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input

																__snake_case        =						self.unet(__UpperCamelCase   ,     __UpperCamelCase   ,     encoder_hidden_states=__UpperCamelCase						).sample

																if do_classifier_free_guidance:
																					__snake_case   ,   __snake_case        =						noise_pred.chunk(2						)
																					__snake_case        =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																__snake_case   ,   __snake_case        =						self.denoise_step(
																    base=__UpperCamelCase   ,     model_input=__UpperCamelCase   ,     model_output=__UpperCamelCase   ,     timestep=__UpperCamelCase   ,     )

																__snake_case        =						model_input

											__snake_case        =						self.denoise_mixing_layer(x=coupled_latents[0]   ,     y=coupled_latents[1]						)

						# either one is fine
						__snake_case        =						coupled_latents[0]

						if output_type not in ["latent", "pt", "np", "pil"]:
											__snake_case        =						(
											    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
											    """`pil`, `np`, `pt`, `latent`"""
											)
											deprecate("""Unsupported output_type"""   ,     """1.0.0"""   ,     __UpperCamelCase   ,     standard_warn=__UpperCamelCase						)
											__snake_case        =						"""np"""

						if output_type == "latent":
											__snake_case        =						final_latent
						else:
											__snake_case        =						self.decode_latents(__UpperCamelCase						)
											__snake_case        =						self.image_processor.postprocess(__UpperCamelCase   ,     output_type=__UpperCamelCase						)

						return image




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






