
'''simple docstring'''





import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a__		: Optional[int]  							=							logging.get_logger(__name__)





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      tf.image.resize(UpperCAmelCase   ,	(3_4_6, 3_4_6)						)
      __A													=      tf.image.crop_to_bounding_box(UpperCAmelCase   ,	(3_4_6 - 2_8_9) // 2   ,	(3_4_6 - 2_8_9) // 2   ,	2_8_9   ,	2_8_9						)
      return image





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetConfig.from_pretrained('google/efficientnet-b7'						)
      __A													=      2_8_9
      __A													=      6_4_0
      __A													=      {'0': 'LABEL_0', '1': 'LABEL_1'}
      __A													=      {'LABEL_0': 0, 'LABEL_1': 1}
      __A													=      []

      __A													=      BertConfig()
      __A													=      AlignConfig.from_text_vision_configs(
          text_config=UpperCAmelCase   ,	vision_config=UpperCAmelCase   ,	projection_dim=6_4_0						)
      return config





def 							snake_case				(		)->      Any:







      """simple docstring"""
      __A													=      'http://images.cocodataset.org/val2017/000000039769.jpg'
      __A													=      Image.open(requests.get(UpperCAmelCase   ,	stream=UpperCAmelCase						).raw						)
      return im





def 							snake_case				(		)->      List[Any]:







      """simple docstring"""
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	rescale_factor=1 / 127.5   ,	rescale_offset=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	resample=Image.BILINEAR   ,	)
      __A													=      BertTokenizer.from_pretrained('bert-base-uncased'						)
      __A													=      6_4
      __A													=      AlignProcessor(image_processor=UpperCAmelCase   ,	tokenizer=UpperCAmelCase						)
      return processor





def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      # EfficientNet image encoder
      __A													=      [v.split('_'						)[0].split('block'						)[1] for v in original_param_names if v.startswith('block'						)]
      __A													=      list(set(UpperCAmelCase						)						)
      __A													=      sorted(UpperCAmelCase						)
      __A													=      len(UpperCAmelCase						)
      __A													=      {b: str(UpperCAmelCase						) for b, i in zip(UpperCAmelCase   ,	range(UpperCAmelCase						)						)}

      __A													=      []
      rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')						)
      rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')						)
      rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')						)
      rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')						)
      rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')						)

      for b in block_names:
            __A													=      block_name_mapping[b]
            rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight')						)
            rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight')						)
            rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias')						)
            rename_keys.append(
                (f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')						)
            rename_keys.append(
                (f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')						)
            rename_keys.append(
                (f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')						)
            rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')						)
            rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')						)
            rename_keys.append(
                (f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')						)
            rename_keys.append(
                (f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')						)

            rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')						)
            rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')						)
            rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')						)
            rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')						)
            rename_keys.append(
                (f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight')						)
            rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight')						)
            rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias')						)
            rename_keys.append(
                (f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean')						)
            rename_keys.append(
                (f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var')						)

      __A													=      {}
      for item in rename_keys:
            if item[0] in original_param_names:
                  __A													=      'vision_model.' + item[1]

    # BERT text encoder
      __A													=      []
      __A													=      'tf_bert_model/bert'
      __A													=      'text_model'
      for i in range(1_2						):
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/query/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.self.query.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/query/bias:0',
                    f'{new}.encoder.layer.{i}.attention.self.query.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/key/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.self.key.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/key/bias:0',
                    f'{new}.encoder.layer.{i}.attention.self.key.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/value/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.self.value.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/self/value/bias:0',
                    f'{new}.encoder.layer.{i}.attention.self.value.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/dense/kernel:0',
                    f'{new}.encoder.layer.{i}.attention.output.dense.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/dense/bias:0',
                    f'{new}.encoder.layer.{i}.attention.output.dense.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0',
                    f'{new}.encoder.layer.{i}.attention.output.LayerNorm.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0',
                    f'{new}.encoder.layer.{i}.attention.output.LayerNorm.bias',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/intermediate/dense/kernel:0',
                    f'{new}.encoder.layer.{i}.intermediate.dense.weight',
                )						)
            rename_keys.append(
                (
                    f'{old}/encoder/layer_._{i}/intermediate/dense/bias:0',
                    f'{new}.encoder.layer.{i}.intermediate.dense.bias',
                )						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/dense/kernel:0', f'{new}.encoder.layer.{i}.output.dense.weight')						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/dense/bias:0', f'{new}.encoder.layer.{i}.output.dense.bias')						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0', f'{new}.encoder.layer.{i}.output.LayerNorm.weight')						)
            rename_keys.append(
                (f'{old}/encoder/layer_._{i}/output/LayerNorm/beta:0', f'{new}.encoder.layer.{i}.output.LayerNorm.bias')						)

      rename_keys.append((f'{old}/embeddings/word_embeddings/weight:0', f'{new}.embeddings.word_embeddings.weight')						)
      rename_keys.append(
          (f'{old}/embeddings/position_embeddings/embeddings:0', f'{new}.embeddings.position_embeddings.weight')						)
      rename_keys.append(
          (f'{old}/embeddings/token_type_embeddings/embeddings:0', f'{new}.embeddings.token_type_embeddings.weight')						)
      rename_keys.append((f'{old}/embeddings/LayerNorm/gamma:0', f'{new}.embeddings.LayerNorm.weight')						)
      rename_keys.append((f'{old}/embeddings/LayerNorm/beta:0', f'{new}.embeddings.LayerNorm.bias')						)

      rename_keys.append((f'{old}/pooler/dense/kernel:0', f'{new}.pooler.dense.weight')						)
      rename_keys.append((f'{old}/pooler/dense/bias:0', f'{new}.pooler.dense.bias')						)
      rename_keys.append(('dense/kernel:0', 'text_projection.weight')						)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')						)
      rename_keys.append(('dense/bias:0', 'text_projection.bias')						)
      rename_keys.append(('temperature:0', 'temperature')						)

      for item in rename_keys:
            if item[0] in original_param_names:
                  __A													=      item[1]
      return key_mapping





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      list(hf_params.keys()						)

      for key, value in tf_params.items():
            if key not in key_mapping:
                  continue

            __A													=      key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						).permute(3   ,	2   ,	0   ,	1						)
            elif "embeddings" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						)
            elif "depthwise_kernel" in key:
                  __A													=      torch.from_numpy(UpperCAmelCase						).permute(2   ,	3   ,	0   ,	1						)
            elif "kernel" in key:
                  __A													=      torch.from_numpy(np.transpose(UpperCAmelCase						)						)
            elif "temperature" in key:
                  __A													=      value
            elif "bn/gamma" or "bn/beta" in key:
                  __A													=      torch.from_numpy(np.transpose(UpperCAmelCase						)						).squeeze()
            else:
                  __A													=      torch.from_numpy(UpperCAmelCase						)

            # Replace HF parameters with original TF model parameters
            hf_params[hf_key].copy_(UpperCAmelCase						)





@torch.no_grad()
def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







      """simple docstring"""
      __A													=      6_4
      __A													=      Tokenizer(UpperCAmelCase						)
      __A													=      align.Align('efficientnet-b7'   ,	'bert-base'   ,	6_4_0   ,	UpperCAmelCase   ,	tok.get_vocab_size()						)
      original_model.compile()
      original_model.load_weights(UpperCAmelCase						)

      __A													=      original_model.trainable_variables
      __A													=      original_model.non_trainable_variables
      __A													=      {param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            __A													=      param.numpy()
      __A													=      list(tf_params.keys()						)

      # Load HuggingFace model
      __A													=      get_align_config()
      __A													=      AlignModel(UpperCAmelCase						).eval()
      __A													=      hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print('Converting parameters...'						)
      __A													=      rename_keys(UpperCAmelCase						)
      replace_params(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

      # Initialize processor
      __A													=      get_processor()
      __A													=      processor(
          images=prepare_img()   ,	text='A picture of a cat'   ,	padding='max_length'   ,	max_length=6_4   ,	return_tensors='pt'						)

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            __A													=      hf_model(**UpperCAmelCase						)

      __A													=      outputs.image_embeds.detach().numpy()
      __A													=      outputs.text_embeds.detach().numpy()

      # Original model inference
      __A													=      False
      __A													=      EfficientNetImageProcessor(
          do_center_crop=UpperCAmelCase   ,	do_rescale=UpperCAmelCase   ,	do_normalize=UpperCAmelCase   ,	include_top=UpperCAmelCase   ,	resample=Image.BILINEAR   ,	)
      __A													=      tf_image_processor(images=prepare_img()   ,	return_tensors='tf'   ,	data_format='channels_last'						)['pixel_values']
      __A													=      tok(tf.constant(['A picture of a cat']						)						)

      __A													=      original_model.image_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)
      __A													=      original_model.text_encoder(UpperCAmelCase   ,	training=UpperCAmelCase						)

      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)
      __A													=      tf.nn.la_normalize(UpperCAmelCase   ,	axis=-1						)

      # Check whether original and HF model outputs match  -> np.allclose
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						):
            raise ValueError('The predicted image features are not the same.'						)
      if not np.allclose(UpperCAmelCase   ,	UpperCAmelCase   ,	atol=1e-3						):
            raise ValueError('The predicted text features are not the same.'						)
      print('Model outputs match!'						)

      if save_model:
            # Create folder to save model
            if not os.path.isdir(UpperCAmelCase						):
                  os.mkdir(UpperCAmelCase						)
            # Save converted model and image processor
            hf_model.save_pretrained(UpperCAmelCase						)
            processor.save_pretrained(UpperCAmelCase						)

      if push_to_hub:
            # Push model and image processor to hub
            print('Pushing converted ALIGN to the hub...'						)
            processor.push_to_hub('align-base'						)
            hf_model.push_to_hub('align-base'						)


if __name__ == "__main__":
 a__		: str  							=							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_path",
     default="./weights/model-weights",
     type=str,
     help="Path to the pretrained TF ALIGN checkpoint.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="hf_model",
     type=str,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--save_model", action="store_true", help="Save model to local")
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

 a__		: List[Any]  							=							parser.parse_args()
 convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








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






