




"""simple docstring"""

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


a					=			logging.get_logger(__name__)

a					=			"▁"

a					=			{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

a					=			{
    "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",
    },
}

a					=			{
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
a					=			{
    "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 				lowercase_       (     _lowerCamelCase							):



 '''simple docstring'''
 UpperCAmelCase    :     str        =	VOCAB_FILES_NAMES
 UpperCAmelCase    :     Dict        =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase    :     Dict        =	PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase    :     List[str]        =	['''input_ids''', '''attention_mask''']

 UpperCAmelCase    :     Optional[Any]        =	[]
 UpperCAmelCase    :     Optional[Any]        =	[]

 def __init__( self       :       Tuple    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Optional[Any]    ,							_UpperCAmelCase       :       Any=None    ,							_UpperCAmelCase       :       str=None    ,							_UpperCAmelCase       :       Tuple="<s>"    ,							_UpperCAmelCase       :       Union[str, Any]="</s>"    ,							_UpperCAmelCase       :       Union[str, Any]="</s>"    ,							_UpperCAmelCase       :       Optional[int]="<pad>"    ,							_UpperCAmelCase       :       List[Any]="<unk>"    ,							_UpperCAmelCase       :       List[str]="m2m100"    ,							_UpperCAmelCase       :       List[Any] = None    ,							_UpperCAmelCase       :       List[Any]=8    ,							**_UpperCAmelCase       :       str    ,							):
       _A					   =   {} if sp_model_kwargs is None else sp_model_kwargs

       _A					   =   language_codes
       _A					   =   FAIRSEQ_LANGUAGE_CODES[language_codes]
       _A					   =   {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

       _A					   =   kwargs.get('additional_special_tokens'    ,							[]		)
       kwargs["additional_special_tokens"] += [
           self.get_lang_token(lowerCAmelCase_		)
           for lang_code in fairseq_language_code
           if self.get_lang_token(lowerCAmelCase_		) not in kwargs["additional_special_tokens"]
       ]

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

       _A					   =   vocab_file
       _A					   =   load_json(lowerCAmelCase_		)
       _A					   =   {v: k for k, v in self.encoder.items()}
       _A					   =   spm_file
       _A					   =   load_spm(lowerCAmelCase_    ,							self.sp_model_kwargs		)

       _A					   =   len(self.encoder		)

       _A					   =   {
           self.get_lang_token(lowerCAmelCase_		): self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase_		)
       }
       _A					   =   {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase_		)}
       _A					   =   {v: k for k, v in self.lang_token_to_id.items()}

       _A					   =   src_lang if src_lang is not None else 'en'
       _A					   =   tgt_lang
       _A					   =   self.get_lang_id(self._src_lang		)
       self.set_src_lang_special_tokens(self._src_lang		)

       _A					   =   num_madeup_words

 @property
 def 			lowerCAmelCase_							( self       :       int		):
       return len(self.encoder		) + len(self.lang_token_to_id		)

 @property
 def 			lowerCAmelCase_							( self       :       Any		):
       return self._src_lang

 @src_lang.setter
 def 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       Dict		):
       _A					   =   new_src_lang
       self.set_src_lang_special_tokens(self._src_lang		)

 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Dict		):
       return self.sp_model.encode(lowerCAmelCase_    ,							out_type=lowerCAmelCase_		)

 def 			lowerCAmelCase_							( self       :       int    ,							_UpperCAmelCase       :       Optional[Any]		):






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

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       str		):






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

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       List[str]		):
       _A					   =   []
       _A					   =   ''
       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(lowerCAmelCase_		) + token
                   _A					   =   []
             else:
                   current_sub_tokens.append(lowerCAmelCase_		)
       out_string += self.sp_model.decode(lowerCAmelCase_		)
       return out_string.strip()

 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       Optional[int]    ,							_UpperCAmelCase       :       Dict = None    ,							_UpperCAmelCase       :       Union[str, Any] = False		):






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

       _A					   =   [1] * len(self.prefix_tokens		)
       _A					   =   [1] * len(self.suffix_tokens		)
       if token_ids_a is None:
             return prefix_ones + ([0] * len(lowerCAmelCase_		)) + suffix_ones
       return prefix_ones + ([0] * len(lowerCAmelCase_		)) + ([0] * len(lowerCAmelCase_		)) + suffix_ones

 def 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Tuple = None		):






       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 			lowerCAmelCase_							( self       :       Union[str, Any]		):
       _A					   =   {self.convert_ids_to_tokens(lowerCAmelCase_		): i for i in range(self.vocab_size		)}
       vocab.update(self.added_tokens_encoder		)
       return vocab

 def __getstate__( self       :       Union[str, Any]		):
       _A					   =   self.__dict__.copy()
       _A					   =   None
       return state

 def __setstate__( self       :       Any    ,							_UpperCAmelCase       :       Optional[int]		):
       _A					   =   d

       # for backward compatibility
       if not hasattr(self    ,							'sp_model_kwargs'		):
             _A					   =   {}

       _A					   =   load_spm(self.spm_file    ,							self.sp_model_kwargs		)

 def 			lowerCAmelCase_							( self       :       Tuple    ,							_UpperCAmelCase       :       Any    ,							_UpperCAmelCase       :       List[str] = None		):
       _A					   =   Path(lowerCAmelCase_		)
       if not save_dir.is_dir():
             raise OSError(F'''{save_directory} should be a directory'''		)
       _A					   =   save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
       )
       _A					   =   save_dir / (
           (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
       )

       save_json(self.encoder    ,							lowerCAmelCase_		)

       if os.path.abspath(self.spm_file		) != os.path.abspath(lowerCAmelCase_		) and os.path.isfile(self.spm_file		):
             copyfile(self.spm_file    ,							lowerCAmelCase_		)
       elif not os.path.isfile(self.spm_file		):
             with open(lowerCAmelCase_    ,							'wb'		) as fi:
                   _A					   =   self.sp_model.serialized_model_proto()
                   fi.write(lowerCAmelCase_		)

       return (str(lowerCAmelCase_		), str(lowerCAmelCase_		))

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       str = "en"    ,							_UpperCAmelCase       :       Any = None    ,							_UpperCAmelCase       :       List[str] = "ro"    ,							**_UpperCAmelCase       :       Tuple    ,							):
       _A					   =   src_lang
       _A					   =   tgt_lang
       self.set_src_lang_special_tokens(self.src_lang		)
       return super().prepare_seqaseq_batch(lowerCAmelCase_    ,							lowerCAmelCase_    ,							**lowerCAmelCase_		)

 def 			lowerCAmelCase_							( self       :       Union[str, Any]    ,							_UpperCAmelCase       :       List[str]    ,							_UpperCAmelCase       :       List[Any]    ,							_UpperCAmelCase       :       Any    ,							**_UpperCAmelCase       :       int		):






       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					   =   src_lang
       _A					   =   self(lowerCAmelCase_    ,							add_special_tokens=lowerCAmelCase_    ,							**lowerCAmelCase_		)
       _A					   =   self.get_lang_id(lowerCAmelCase_		)
       _A					   =   tgt_lang_id
       return inputs

 def 			lowerCAmelCase_							( self       :       Optional[Any]		):
       self.set_src_lang_special_tokens(self.src_lang		)

 def 			lowerCAmelCase_							( self       :       str		):
       self.set_tgt_lang_special_tokens(self.tgt_lang		)

 def 			lowerCAmelCase_							( self       :       Optional[Any]    ,							_UpperCAmelCase       :       Any		):
       _A					   =   self.get_lang_token(lowerCAmelCase_		)
       _A					   =   self.lang_token_to_id[lang_token]
       _A					   =   [self.cur_lang_id]
       _A					   =   [self.eos_token_id]

 def 			lowerCAmelCase_							( self       :       str    ,							_UpperCAmelCase       :       List[str]		):
       _A					   =   self.get_lang_token(lowerCAmelCase_		)
       _A					   =   self.lang_token_to_id[lang_token]
       _A					   =   [self.cur_lang_id]
       _A					   =   [self.eos_token_id]

 def 			lowerCAmelCase_							( self       :       Dict    ,							_UpperCAmelCase       :       int		):
       return self.lang_code_to_token[lang]



 def 			lowerCAmelCase_							( self       :       List[Any]    ,							_UpperCAmelCase       :       Optional[Any]		):
       _A					   =   self.get_lang_token(lowerCAmelCase_		)
       return self.lang_token_to_id[lang_token]






def 						_snake_case     (					_snake_case							:					Any				,  _snake_case							:					Optional[int]							)    ->					sentencepiece.SentencePieceProcessor:






      '''simple docstring'''


      _A					   =   sentencepiece.SentencePieceProcessor(**__A							)
      spm.Load(str(__A							)							)
      return spm






def 						_snake_case     (					_snake_case							:					Tuple							)    ->					Union[Dict, List]:






      '''simple docstring'''


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







def 						_snake_case     (					_snake_case							:					Tuple				,  _snake_case							:					Any							)    ->					None:






      '''simple docstring'''


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



'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   dtype=tf.floataa    ,   )
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,   expected_slice.numpy()    ,   atol=1E-4  )  )

