




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
							from tokenizers import Tokenizer
							from tokenizers.models import WordPiece


class 							__SCREAMING_SNAKE_CASE				(						unittest.TestCase    ):

					def       lowercase_				(							self					,			__lowercase   ):







						'''simple docstring'''




						lowerCamelCase_				:						Any				  =				list(tokenizer_class.max_model_input_sizes.keys()   )
						for model_name in sa_models[:1]:
							lowerCamelCase_				:						Union[str, Any]				  =				tokenizer_class.from_pretrained(__lowercase   )
							self.assertIsNotNone(__lowercase   )
							self.assertIsInstance(__lowercase					,			__lowercase   )
							self.assertIsInstance(__lowercase					,			__lowercase   )

							for special_tok in tokenizer.all_special_tokens:
								self.assertIsInstance(__lowercase					,			__lowercase   )
								lowerCamelCase_				:						List[str]				  =				tokenizer.convert_tokens_to_ids(__lowercase   )
								self.assertIsInstance(__lowercase					,			__lowercase   )

					def       lowercase_				(							self					,			__lowercase					,			__lowercase = None   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				pickle.dumps(__lowercase   )
						self.assertIsNotNone(__lowercase   )

						lowerCamelCase_				:						int				  =				pickle.loads(__lowercase   )

						# Ensure is_fast is correctly restored
						self.assertEqual(be_restored.is_fast					,			be_original.is_fast   )

						# Ensure encodings are potentially correctly restored
						if be_original.is_fast:
							self.assertIsNotNone(be_restored.encodings   )
						else:
							self.assertIsNone(be_restored.encodings   )

						# Ensure the keys are the same
						for original_v, restored_v in zip(be_original.values()					,			be_restored.values()   ):
							if equal_op:
								self.assertTrue(equal_op(__lowercase					,			__lowercase   )   )
							else:
								self.assertEqual(__lowercase					,			__lowercase   )

					@slow
					def       lowercase_				(							self   ):







						'''simple docstring'''




						self.check_tokenizer_from_pretrained(__lowercase   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						self.assertEqual(TensorType('tf'   )					,			TensorType.TENSORFLOW   )
						self.assertEqual(TensorType('pt'   )					,			TensorType.PYTORCH   )
						self.assertEqual(TensorType('np'   )					,			TensorType.NUMPY   )

					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						import numpy as np

						lowerCamelCase_				:						Tuple				  =				BertTokenizer.from_pretrained('bert-base-cased'   )
						lowerCamelCase_				:						int				  =				BertTokenizerFast.from_pretrained('bert-base-cased'   )

						# Python no tensor
						with self.subTest('BatchEncoding (Python, return_tensors=None)'   ):
							self.assert_dump_and_restore(tokenizer_p('Small example to encode'   )   )

						with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'   ):
							self.assert_dump_and_restore(
							    tokenizer_p('Small example to encode'					,			return_tensors=TensorType.NUMPY   )					,			np.array_equal   )

						with self.subTest('BatchEncoding (Rust, return_tensors=None)'   ):
							self.assert_dump_and_restore(tokenizer_r('Small example to encode'   )   )

						with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'   ):
							self.assert_dump_and_restore(
							    tokenizer_r('Small example to encode'					,			return_tensors=TensorType.NUMPY   )					,			np.array_equal   )

					@require_tf
					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						import tensorflow as tf

						def tf_array_equals(__lowercase					,			__lowercase   ):
							return tf.reduce_all(tf.equal(__lowercase					,			__lowercase   )   )

						lowerCamelCase_				:						List[Any]				  =				BertTokenizer.from_pretrained('bert-base-cased'   )
						lowerCamelCase_				:						Dict				  =				BertTokenizerFast.from_pretrained('bert-base-cased'   )

						with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'   ):
							self.assert_dump_and_restore(
							    tokenizer_p('Small example to encode'					,			return_tensors=TensorType.TENSORFLOW   )					,			__lowercase   )

						with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'   ):
							self.assert_dump_and_restore(
							    tokenizer_r('Small example to encode'					,			return_tensors=TensorType.TENSORFLOW   )					,			__lowercase   )

					@require_torch
					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						import torch

						lowerCamelCase_				:						Optional[Any]				  =				BertTokenizer.from_pretrained('bert-base-cased'   )
						lowerCamelCase_				:						Tuple				  =				BertTokenizerFast.from_pretrained('bert-base-cased'   )

						with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'   ):
							self.assert_dump_and_restore(
							    tokenizer_p('Small example to encode'					,			return_tensors=TensorType.PYTORCH   )					,			torch.equal   )

						with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'   ):
							self.assert_dump_and_restore(
							    tokenizer_r('Small example to encode'					,			return_tensors=TensorType.PYTORCH   )					,			torch.equal   )

					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				BertTokenizer.from_pretrained('bert-base-cased'   )
						lowerCamelCase_				:						Dict				  =				BertTokenizerFast.from_pretrained('bert-base-cased'   )

						with self.subTest('Python Tokenizer'   ):
							self.assertFalse(tokenizer_p('Small example to_encode'   ).is_fast   )

						with self.subTest('Rust Tokenizer'   ):
							self.assertTrue(tokenizer_r('Small example to_encode'   ).is_fast   )

					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Union[str, Any]				  =				BertTokenizerFast.from_pretrained('bert-base-cased'   )
						lowerCamelCase_				:						Tuple				  =				tokenizer_r(['Test', '\xad', 'test']					,			is_split_into_words=__lowercase   )

						self.assertEqual(encoded.word_to_tokens(0   )					,			TokenSpan(start=1					,			end=2   )   )
						self.assertEqual(encoded.word_to_tokens(1   )					,			__lowercase   )
						self.assertEqual(encoded.word_to_tokens(2   )					,			TokenSpan(start=2					,			end=3   )   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}   )
						lowerCamelCase_				:						int				  =				batch.convert_to_tensors(tensor_type='np'   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(2, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(2,)   )
						# test converting the converted
						with CaptureStderr() as cs:
							lowerCamelCase_				:						Union[str, Any]				  =				batch.convert_to_tensors(tensor_type='np'   )
						self.assertFalse(len(cs.err   )					,			msg=f"should have no warning, but got {cs.err}"   )

						lowerCamelCase_				:						Tuple				  =				BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}   )
						lowerCamelCase_				:						List[str]				  =				batch.convert_to_tensors(tensor_type='np'					,			prepend_batch_axis=__lowercase   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(1, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(1,)   )

					@require_torch
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						int				  =				BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}   )
						lowerCamelCase_				:						List[str]				  =				batch.convert_to_tensors(tensor_type='pt'   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(2, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(2,)   )
						# test converting the converted
						with CaptureStderr() as cs:
							lowerCamelCase_				:						List[Any]				  =				batch.convert_to_tensors(tensor_type='pt'   )
						self.assertFalse(len(cs.err   )					,			msg=f"should have no warning, but got {cs.err}"   )

						lowerCamelCase_				:						Optional[int]				  =				BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}   )
						lowerCamelCase_				:						List[str]				  =				batch.convert_to_tensors(tensor_type='pt'					,			prepend_batch_axis=__lowercase   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(1, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(1,)   )

					@require_tf
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}   )
						lowerCamelCase_				:						int				  =				batch.convert_to_tensors(tensor_type='tf'   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(2, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(2,)   )
						# test converting the converted
						with CaptureStderr() as cs:
							lowerCamelCase_				:						Any				  =				batch.convert_to_tensors(tensor_type='tf'   )
						self.assertFalse(len(cs.err   )					,			msg=f"should have no warning, but got {cs.err}"   )

						lowerCamelCase_				:						int				  =				BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}   )
						lowerCamelCase_				:						List[Any]				  =				batch.convert_to_tensors(tensor_type='tf'					,			prepend_batch_axis=__lowercase   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(1, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(1,)   )

					@require_flax
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Dict				  =				BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}   )
						lowerCamelCase_				:						Dict				  =				batch.convert_to_tensors(tensor_type='jax'   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(2, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(2,)   )
						# test converting the converted
						with CaptureStderr() as cs:
							lowerCamelCase_				:						Dict				  =				batch.convert_to_tensors(tensor_type='jax'   )
						self.assertFalse(len(cs.err   )					,			msg=f"should have no warning, but got {cs.err}"   )

						lowerCamelCase_				:						Dict				  =				BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}   )
						lowerCamelCase_				:						Dict				  =				batch.convert_to_tensors(tensor_type='jax'					,			prepend_batch_axis=__lowercase   )
						self.assertEqual(tensor_batch['inputs'].shape					,			(1, 3)   )
						self.assertEqual(tensor_batch['labels'].shape					,			(1,)   )

					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				[{'input_ids': np.array([0, 1, 2]   )}, {'input_ids': np.array([0, 1, 2, 3]   )}]
						lowerCamelCase_				:						List[str]				  =				BertTokenizer.from_pretrained('bert-base-cased'   )

						lowerCamelCase_				:						Any				  =				tokenizer.pad(__lowercase					,			padding=__lowercase   )
						self.assertTrue(isinstance(batch['input_ids']					,			np.ndarray   )   )
						self.assertEqual(batch['input_ids'].tolist()					,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
						lowerCamelCase_				:						Optional[int]				  =				tokenizer.pad(__lowercase					,			padding=__lowercase					,			return_tensors='np'   )
						self.assertTrue(isinstance(batch['input_ids']					,			np.ndarray   )   )
						self.assertEqual(batch['input_ids'].tolist()					,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )

					@require_torch
					def       lowercase_				(							self   ):







						'''simple docstring'''




						import torch

						lowerCamelCase_				:						Dict				  =				[{'input_ids': torch.tensor([0, 1, 2]   )}, {'input_ids': torch.tensor([0, 1, 2, 3]   )}]
						lowerCamelCase_				:						Optional[Any]				  =				BertTokenizer.from_pretrained('bert-base-cased'   )

						lowerCamelCase_				:						Dict				  =				tokenizer.pad(__lowercase					,			padding=__lowercase   )
						self.assertTrue(isinstance(batch['input_ids']					,			torch.Tensor   )   )
						self.assertEqual(batch['input_ids'].tolist()					,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
						lowerCamelCase_				:						List[str]				  =				tokenizer.pad(__lowercase					,			padding=__lowercase					,			return_tensors='pt'   )
						self.assertTrue(isinstance(batch['input_ids']					,			torch.Tensor   )   )
						self.assertEqual(batch['input_ids'].tolist()					,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )

					@require_tf
					def       lowercase_				(							self   ):







						'''simple docstring'''




						import tensorflow as tf

						lowerCamelCase_				:						Union[str, Any]				  =				[{'input_ids': tf.constant([0, 1, 2]   )}, {'input_ids': tf.constant([0, 1, 2, 3]   )}]
						lowerCamelCase_				:						Any				  =				BertTokenizer.from_pretrained('bert-base-cased'   )

						lowerCamelCase_				:						Any				  =				tokenizer.pad(__lowercase					,			padding=__lowercase   )
						self.assertTrue(isinstance(batch['input_ids']					,			tf.Tensor   )   )
						self.assertEqual(batch['input_ids'].numpy().tolist()					,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
						lowerCamelCase_				:						Any				  =				tokenizer.pad(__lowercase					,			padding=__lowercase					,			return_tensors='tf'   )
						self.assertTrue(isinstance(batch['input_ids']					,			tf.Tensor   )   )
						self.assertEqual(batch['input_ids'].numpy().tolist()					,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )

					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Tuple				  =				Tokenizer(WordPiece(unk_token='[UNK]'   )   )
						PreTrainedTokenizerFast(tokenizer_object=__lowercase   )





					@require_tokenizers
					def       lowercase_				(							self   ):







						'''simple docstring'''




						lowerCamelCase_				:						Optional[int]				  =				Tokenizer(WordPiece(unk_token='[UNK]'   )   )
						with tempfile.TemporaryDirectory() as tmpdirname:
							bert_tokenizer.save(os.path.join(__lowercase					,			'tokenizer.json'   )   )
							PreTrainedTokenizerFast(tokenizer_file=os.path.join(__lowercase					,			'tokenizer.json'   )   )


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






