

'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCAmelCase__      		=			logging.get_logger(__name__)

lowerCAmelCase__      		=			'''▁'''

lowerCAmelCase__      		=			{'''vocab_file''': '''sentencepiece.bpe.model'''}

lowerCAmelCase__      		=			{
    '''vocab_file''': {
        '''facebook/mbart-large-en-ro''': (
            '''https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'''
        ),
        '''facebook/mbart-large-cc25''': (
            '''https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

lowerCAmelCase__      		=			{
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
lowerCAmelCase__      		=			['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''']






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




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

  SCREAMING_SNAKE_CASE					:						List[int]           =    []
  SCREAMING_SNAKE_CASE					:						List[int]           =    []






  def __init__(   self     :  List[str]      ,lowercase__     :  int      ,lowercase__     :  Optional[Any]="<s>"      ,lowercase__     :  Optional[int]="</s>"      ,lowercase__     :  List[str]="</s>"      ,lowercase__     :  Union[str, Any]="<s>"      ,lowercase__     :  Dict="<unk>"      ,lowercase__     :  List[str]="<pad>"      ,lowercase__     :  str="<mask>"      ,lowercase__     :  Tuple=None      ,lowercase__     :  Optional[Any]=None      ,lowercase__     :  int=None      ,lowercase__     :  Optional[Dict[str, Any]] = None      ,lowercase__     :  str=None      ,**lowercase__     :  List[Any]      ,):
   # Mask token behave like a normal word, i.e. include the space before it
   __lowercase													=				AddedToken(lowercase__      ,lstrip=lowercase__      ,rstrip=lowercase__    ) if isinstance(lowercase__      ,lowercase__    ) else mask_token

   __lowercase													=				{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=lowercase__      ,eos_token=lowercase__      ,unk_token=lowercase__      ,sep_token=lowercase__      ,cls_token=lowercase__      ,pad_token=lowercase__      ,mask_token=lowercase__      ,tokenizer_file=lowercase__      ,src_lang=lowercase__      ,tgt_lang=lowercase__      ,additional_special_tokens=lowercase__      ,sp_model_kwargs=self.sp_model_kwargs      ,**lowercase__      ,)

   __lowercase													=				spm.SentencePieceProcessor(**self.sp_model_kwargs    )
   self.sp_model.Load(str(lowercase__    )    )
   __lowercase													=				vocab_file

   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
   # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
   # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

   # Mimic fairseq token-to-id alignment for the first 4 token
   __lowercase													=				{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

   # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
   __lowercase													=				1

   __lowercase													=				len(self.sp_model    )
   __lowercase													=				{
       code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__    )
   }
   __lowercase													=				{v: k for k, v in self.lang_code_to_id.items()}
   __lowercase													=				len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset

   self.fairseq_tokens_to_ids.update(self.lang_code_to_id    )
   __lowercase													=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
   __lowercase													=				list(self.lang_code_to_id.keys()    )

   if additional_special_tokens is not None:
    # Only add those special tokens if they are not already there.
    self._additional_special_tokens.extend(
        [t for t in additional_special_tokens if t not in self._additional_special_tokens]    )

   __lowercase													=				src_lang if src_lang is not None else '''en_XX'''
   __lowercase													=				self.lang_code_to_id[self._src_lang]
   __lowercase													=				tgt_lang
   self.set_src_lang_special_tokens(self._src_lang    )






  def __getstate__(   self     :  Tuple    ):
   __lowercase													=				self.__dict__.copy()
   __lowercase													=				None
   __lowercase													=				self.sp_model.serialized_model_proto()
   return state






  def __setstate__(   self     :  List[str]      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				d

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

   __lowercase													=				spm.SentencePieceProcessor(**self.sp_model_kwargs    )
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return len(self.sp_model    ) + len(self.lang_code_to_id    ) + self.fairseq_offset + 1  # Plus 1 for the mask token






  @property
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return self._src_lang






  @src_lang.setter
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  str    ):
   __lowercase													=				new_src_lang
   self.set_src_lang_special_tokens(self._src_lang    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None      ,lowercase__     :  bool = False    ):

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

   __lowercase													=				[1] * len(self.prefix_tokens    )
   __lowercase													=				[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       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = 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       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  List[int]      ,lowercase__     :  Optional[List[int]] = None    ):
   __lowercase													=				[self.sep_token_id]
   __lowercase													=				[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep    ) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  List[Any]      ,lowercase__     :  str      ,lowercase__     :  Optional[str]      ,lowercase__     :  Optional[str]      ,**lowercase__     :  Any    ):
   if src_lang is None or tgt_lang is None:
    raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''    )
   __lowercase													=				src_lang
   __lowercase													=				self(lowercase__      ,add_special_tokens=lowercase__      ,return_tensors=lowercase__      ,**lowercase__    )
   __lowercase													=				self.convert_tokens_to_ids(lowercase__    )
   __lowercase													=				tgt_lang_id
   return inputs






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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  str    ):
   return self.sp_model.encode(lowercase__      ,out_type=lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any    ):
   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   __lowercase													=				self.sp_model.PieceToId(lowercase__    )

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]      ,lowercase__     :  Dict    ):
   if index in self.fairseq_ids_to_tokens:
    return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  int    ):
   __lowercase													=				''''''.join(lowercase__    ).replace(lowercase__      ,''' '''    ).strip()
   return out_string






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  str      ,lowercase__     :  Optional[str] = None    ):
   if not os.path.isdir(lowercase__    ):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"    )
    return
   __lowercase													=				os.path.join(
       lowercase__      ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

   return (out_vocab_file,)






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  List[str]      ,lowercase__     :  str = "en_XX"      ,lowercase__     :  Optional[List[str]] = None      ,lowercase__     :  str = "ro_RO"      ,**lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				src_lang
   __lowercase													=				tgt_lang
   return super().prepare_seqaseq_batch(lowercase__      ,lowercase__      ,**lowercase__    )






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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   return self.set_tgt_lang_special_tokens(self.tgt_lang    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  List[Any]    ):
   __lowercase													=				self.lang_code_to_id[src_lang]
   __lowercase													=				[]
   __lowercase													=				[self.eos_token_id, self.cur_lang_code]






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  str    ):
   __lowercase													=				self.lang_code_to_id[lang]
   __lowercase													=				[]
   __lowercase													=				[self.eos_token_id, self.cur_lang_code]




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

