





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase				:	List[str]      						=  logging.get_logger(__name__)

lowercase				:	str      						=  "▁"

lowercase				:	Union[str, Any]      						=  {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase				:	Any      						=  {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase				:	List[Any]      						=  {
    "facebook/m2m100_418M": 1_0_2_4,
}

# fmt: off
lowercase				:	Dict      						=  {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}





class 			A__   (							_SCREAMING_SNAKE_CASE     ):




   """simple docstring"""



   __A       :    Union[str, Any]	  =  VOCAB_FILES_NAMES
   __A       :    List[Any]	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Dict	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    Optional[Any]	  =  ['''input_ids''', '''attention_mask''']

   __A       :    Optional[int]	  =  []
   __A       :    Any	  =  []

   def __init__( self ,  lowercase ,  lowercase ,  lowercase=None ,  lowercase=None ,  lowercase="<s>" ,  lowercase="</s>" ,  lowercase="</s>" ,  lowercase="<pad>" ,  lowercase="<unk>" ,  lowercase="m2m100" ,  lowercase = None ,  lowercase=8 ,  **lowercase ,  )      ->						None:







          '''simple docstring'''

          a__						:     Dict			=    {} if sp_model_kwargs is None else sp_model_kwargs

          a__						:     Dict			=    language_codes
          a__						:     int			=    FAIRSEQ_LANGUAGE_CODES[language_codes]
          a__						:     int			=    {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

          a__						:     int			=    kwargs.get('additional_special_tokens' ,  [])
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(lowercase)
              for lang_code in fairseq_language_code
              if self.get_lang_token(lowercase) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=lowercase ,  tgt_lang=lowercase ,  bos_token=lowercase ,  eos_token=lowercase ,  sep_token=lowercase ,  unk_token=lowercase ,  pad_token=lowercase ,  language_codes=lowercase ,  sp_model_kwargs=self.sp_model_kwargs ,  num_madeup_words=lowercase ,  **lowercase ,  )

          a__						:     List[str]			=    vocab_file
          a__						:     Any			=    load_json(lowercase)
          a__						:     int			=    {v: k for k, v in self.encoder.items()}
          a__						:     Optional[int]			=    spm_file
          a__						:     Union[str, Any]			=    load_spm(lowercase ,  self.sp_model_kwargs)

          a__						:     str			=    len(self.encoder)

          a__						:     str			=    {
              self.get_lang_token(lowercase): self.encoder_size + i for i, lang_code in enumerate(lowercase)
          }
          a__						:     Optional[Any]			=    {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase)}
          a__						:     Any			=    {v: k for k, v in self.lang_token_to_id.items()}

          a__						:     Union[str, Any]			=    src_lang if src_lang is not None else """en"""
          a__						:     int			=    tgt_lang
          a__						:     Dict			=    self.get_lang_id(self._src_lang)
          self.set_src_lang_special_tokens(self._src_lang)

          a__						:     str			=    num_madeup_words

   @property
   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          return len(self.encoder) + len(self.lang_token_to_id)

   @property
   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          return self._src_lang

   @src_lang.setter
   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     Any			=    new_src_lang
          self.set_src_lang_special_tokens(self._src_lang)

   def 							__lowercase		( self ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          return self.sp_model.encode(lowercase ,  out_type=lowercase)

   def 							__lowercase		( self ,  lowercase)      ->						Dict:







          '''simple docstring'''

          if token in self.lang_token_to_id:
                 return self.lang_token_to_id[token]
          return self.encoder.get(lowercase ,  self.encoder[self.unk_token])

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          if index in self.id_to_lang_token:
                 return self.id_to_lang_token[index]
          return self.decoder.get(lowercase ,  self.unk_token)

   def 							__lowercase		( self ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     List[Any]			=    []
          a__						:     Optional[Any]			=    """"""
          for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                        out_string += self.sp_model.decode(lowercase) + token
                        a__						:     str			=    []
                 else:
                        current_sub_tokens.append(lowercase)
          out_string += self.sp_model.decode(lowercase)
          return out_string.strip()

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = False)      ->						List[int]:







          '''simple docstring'''

          if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=lowercase ,  token_ids_a=lowercase ,  already_has_special_tokens=lowercase)

          a__						:     Optional[Any]			=    [1] * len(self.prefix_tokens)
          a__						:     Union[str, Any]			=    [1] * len(self.suffix_tokens)
          if token_ids_a is None:
                 return prefix_ones + ([0] * len(lowercase)) + suffix_ones
          return prefix_ones + ([0] * len(lowercase)) + ([0] * len(lowercase)) + suffix_ones

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          if token_ids_a is None:
                 return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

   def 							__lowercase		( self)      ->						Dict:







          '''simple docstring'''

          a__						:     List[str]			=    {self.convert_ids_to_tokens(lowercase): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab

   def __getstate__( self)      ->						Dict:







          '''simple docstring'''

          a__						:     str			=    self.__dict__.copy()
          a__						:     Optional[Any]			=    None
          return state

   def __setstate__( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     Tuple			=    d

          # for backward compatibility
          if not hasattr(self ,  'sp_model_kwargs'):
                 a__						:     List[Any]			=    {}

          a__						:     Dict			=    load_spm(self.spm_file ,  self.sp_model_kwargs)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						Tuple[str]:







          '''simple docstring'''

          a__						:     List[Any]			=    Path(lowercase)
          if not save_dir.is_dir():
                 raise OSError(F'{save_directory} should be a directory')
          a__						:     str			=    save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
          )
          a__						:     Optional[int]			=    save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
          )

          save_json(self.encoder ,  lowercase)

          if os.path.abspath(self.spm_file) != os.path.abspath(lowercase) and os.path.isfile(self.spm_file):
                 copyfile(self.spm_file ,  lowercase)
          elif not os.path.isfile(self.spm_file):
                 with open(lowercase ,  'wb') as fi:
                        a__						:     List[str]			=    self.sp_model.serialized_model_proto()
                        fi.write(lowercase)

          return (str(lowercase), str(lowercase))

   def 							__lowercase		( self ,  lowercase ,  lowercase = "en" ,  lowercase = None ,  lowercase = "ro" ,  **lowercase ,  )      ->						BatchEncoding:







          '''simple docstring'''

          a__						:     Tuple			=    src_lang
          a__						:     List[Any]			=    tgt_lang
          self.set_src_lang_special_tokens(self.src_lang)
          return super().prepare_seqaseq_batch(lowercase ,  lowercase ,  **lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  **lowercase)      ->						Dict:







          '''simple docstring'''

          if src_lang is None or tgt_lang is None:
                 raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
          a__						:     int			=    src_lang
          a__						:     Optional[Any]			=    self(lowercase ,  add_special_tokens=lowercase ,  **lowercase)
          a__						:     Optional[int]			=    self.get_lang_id(lowercase)
          a__						:     int			=    tgt_lang_id
          return inputs

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          self.set_src_lang_special_tokens(self.src_lang)

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          self.set_tgt_lang_special_tokens(self.tgt_lang)

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     Tuple			=    self.get_lang_token(lowercase)
          a__						:     Optional[Any]			=    self.lang_token_to_id[lang_token]
          a__						:     Optional[int]			=    [self.cur_lang_id]
          a__						:     Union[str, Any]			=    [self.eos_token_id]

   def 							__lowercase		( self ,  lowercase)      ->						None:







          '''simple docstring'''

          a__						:     Optional[Any]			=    self.get_lang_token(lowercase)
          a__						:     Dict			=    self.lang_token_to_id[lang_token]
          a__						:     Dict			=    [self.cur_lang_id]
          a__						:     int			=    [self.eos_token_id]

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          return self.lang_code_to_token[lang]

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    self.get_lang_token(lowercase)
          return self.lang_token_to_id[lang_token]



def A_     (		A__		,				A__    )  ->		int:
       a__						:     int			=    sentencepiece.SentencePieceProcessor(**A__    )
       spm.Load(str(A__    )    )
       return spm



def A_     (		A__    )  ->		List[Any]:





       with open(A__		,				'r'    ) as f:
              return json.load(A__    )



def A_     (		A__		,				A__    )  ->		str:





       with open(A__		,				'w'    ) as f:
              json.dump(A__		,				A__		,				indent=2    )









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer





@require_tf
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          self.assertEqual(len(lowercase) ,  len(lowercase))
          for a, b in zip(lowercase ,  lowercase):
                 self.assertAlmostEqual(lowercase ,  lowercase ,  delta=lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    GradientAccumulator()
          accumulator([tf.constant([1.0, 2.0])])
          accumulator([tf.constant([-2.0, 1.0])])
          accumulator([tf.constant([-1.0, 2.0])])
          with self.assertRaises(lowercase):
                 accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
          self.assertEqual(accumulator.step ,  3)
          self.assertEqual(len(accumulator.gradients) ,  1)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [-2.0, 5.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [0.0, 0.0] ,  tol=1e-2)

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    None
          ops.enable_eager_execution_internal()
          a__						:     Optional[int]			=    tf.config.list_physical_devices('CPU')
          if len(lowercase) == 1:
                 tf.config.set_logical_device_configuration(
                     physical_devices[0] ,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
          a__						:     int			=    tf.config.list_logical_devices(device_type='CPU')
          a__						:     Union[str, Any]			=    tf.distribute.MirroredStrategy(devices=devices[:2])

          with strategy.scope():
                 a__						:     str			=    GradientAccumulator()
                 a__						:     Tuple			=    tf.Variable([4.0, 3.0])
                 a__			,							a__						:     Tuple			=    create_optimizer(5e-5 ,  10 ,  5)
                 a__						:     Tuple			=    tf.Variable([0.0, 0.0] ,  trainable=lowercase)

          def accumulate_on_replica(lowercase):
                 accumulator([gradient])

          def apply_on_replica():
                 optimizer.apply_gradients(list(zip(accumulator.gradients ,  [variable])))

          @tf.function
          def accumulate(lowercase ,  lowercase):
                 with strategy.scope():
                        a__						:     Union[str, Any]			=    strategy.experimental_local_results(lowercase)
                        local_variables[0].assign(lowercase)
                        local_variables[1].assign(lowercase)
                        strategy.run(lowercase ,  args=(gradient_placeholder,))

          @tf.function
          def apply_grad():
                 with strategy.scope():
                        strategy.run(lowercase)

          def _check_local_values(lowercase ,  lowercase):
                 a__						:     Union[str, Any]			=    strategy.experimental_local_results(accumulator._gradients[0])
                 self.assertListAlmostEqual(values[0].value() ,  lowercase ,  tol=1e-2)
                 self.assertListAlmostEqual(values[1].value() ,  lowercase ,  tol=1e-2)

          accumulate([1.0, 2.0] ,  [-1.0, 1.0])
          accumulate([3.0, -1.0] ,  [-1.0, -1.0])
          accumulate([-2.0, 2.0] ,  [3.0, -2.0])
          self.assertEqual(accumulator.step ,  3)
          _check_local_values([2.0, 3.0] ,  [1.0, -2.0])
          apply_grad()
          self.assertListAlmostEqual(variable.value() ,  [4.0, 3.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          _check_local_values([0.0, 0.0] ,  [0.0, 0.0])


