


'''simple docstring'''

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_  :				int						= logging.get_logger(__name__)

a_  :				List[str]						= {"""vocab_file""": """sentencepiece.model"""}

a_  :				Union[str, Any]						= {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

a_  :				str						= {
    """google/rembert""": 256,
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  VOCAB_FILES_NAMES
  _lowerCamelCase		    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase		    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=False			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase="[CLS]"			,  UpperCamelCase="[SEP]"			,  UpperCamelCase="[UNK]"			,  UpperCamelCase="[SEP]"			,  UpperCamelCase="[PAD]"			,  UpperCamelCase="[CLS]"			,  UpperCamelCase="[MASK]"			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(
       do_lower_case=UpperCamelCase			,  remove_space=UpperCamelCase			,  keep_accents=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  do_lower_case
   lowerCamelCase_      =  remove_space
   lowerCamelCase_      =  keep_accents
   lowerCamelCase_      =  vocab_file

   lowerCamelCase_      =  spm.SentencePieceProcessor()
   self.sp_model.Load(UpperCamelCase     )






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(UpperCamelCase     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d
   lowerCamelCase_      =  spm.SentencePieceProcessor()
   self.sp_model.Load(self.vocab_file     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase=False     ):







   """simple docstring"""
   lowerCamelCase_      =  self.sp_model.EncodeAsPieces(UpperCamelCase     )
   return pieces






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.PieceToId(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.IdToPiece(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.sp_model.decode_pieces(UpperCamelCase     )
   return out_string






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None			,  UpperCamelCase = False     ):







   """simple docstring"""

   if already_has_special_tokens:
    if token_ids_a is not None:
     raise ValueError(
         "You should not supply a second sequence if the provided sequence of "
         "ids is already formatted with special tokens for the model."     )
    return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

   if token_ids_a is not None:
    return [1] + ([0] * len(UpperCamelCase     )) + [1] + ([0] * len(UpperCamelCase     )) + [1]
   return [1] + ([0] * len(UpperCamelCase     )) + [1]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [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     ) * [0] + len(token_ids_a + sep     ) * [1]







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase     ):
    logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase     )     )
    return
   lowerCamelCase_      =  os.path.join(
       UpperCamelCase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

   if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase     ):
    copyfile(self.vocab_file			,  UpperCamelCase     )

   return (out_vocab_file,)








'''simple docstring'''





import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow






class        lowerCAmelCase_:
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Optional[Any]:
      return None







class        lowerCAmelCase_:
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      return None







class        lowerCAmelCase_(				unittest.TestCase     ):
  '''simple docstring'''




  __lowercase							:      Dict				=      [
      # (model_name, model_kwargs)
      ('''bert-base-cased''', {}),
      ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
  ]




  @require_tf
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		int:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(__UpperCAmelCase  ,"""tf"""  ,12  ,**__UpperCAmelCase      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,**__UpperCAmelCase      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      from transformers import BertModel

      lowerCAmelCase__ :  Optional[int]							      =					["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
      with NamedTemporaryFile(mode="""w+t"""      ) as vocab_file:
          vocab_file.write("""\n""".join(__UpperCAmelCase      )      )
          vocab_file.flush()
          lowerCAmelCase__ :  Dict							      =					BertTokenizerFast(vocab_file.name      )

      with TemporaryDirectory() as bert_save_dir:
          lowerCAmelCase__ :  Tuple							      =					BertModel(BertConfig(vocab_size=len(__UpperCAmelCase      )      )      )
          model.save_pretrained(__UpperCAmelCase      )
          self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,__UpperCAmelCase      )




  @require_tf
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		List[str]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowerCAmelCase__ :  Dict							      =					self._test_export(__UpperCAmelCase  ,"""tf"""  ,12  ,**__UpperCAmelCase      )
          lowerCAmelCase__ :  List[str]							      =					quantize(Path(__UpperCAmelCase      )      )

          # Ensure the actual quantized model is not bigger than the original one
          if quantized_path.stat().st_size >= Path(__UpperCAmelCase      ).stat().st_size:
              self.fail("""Quantized model is bigger than initial ONNX model"""      )




  @require_torch
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
          lowerCAmelCase__ :  Any							      =					self._test_export(__UpperCAmelCase  ,"""pt"""  ,12  ,**__UpperCAmelCase      )
          lowerCAmelCase__ :  Dict							      =					quantize(__UpperCAmelCase      )

          # Ensure the actual quantized model is not bigger than the original one
          if quantized_path.stat().st_size >= Path(__UpperCAmelCase      ).stat().st_size:
              self.fail("""Quantized model is bigger than initial ONNX model"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Optional[Any]:
      try:
          # Compute path
          with TemporaryDirectory() as tempdir:
              lowerCAmelCase__ :  Optional[int]							      =					Path(__UpperCAmelCase      ).joinpath("""model.onnx"""      )

          # Remove folder if exists
          if path.parent.exists():
              path.parent.rmdir()

          # Export
          convert(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,**__UpperCAmelCase      )

          return path
      except Exception as e:
          self.fail(__UpperCAmelCase      )




  @require_torch
  @require_tokenizers
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      from transformers import BertModel

      lowerCAmelCase__ :  List[Any]							      =					BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""      )      )
      lowerCAmelCase__ :  Union[str, Any]							      =					BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""      )
      self._test_infer_dynamic_axis(__UpperCAmelCase  ,__UpperCAmelCase  ,"""pt"""      )




  @require_tf
  @require_tokenizers
  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      from transformers import TFBertModel

      lowerCAmelCase__ :  int							      =					TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""      )      )
      lowerCAmelCase__ :  Optional[int]							      =					BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""      )
      self._test_infer_dynamic_axis(__UpperCAmelCase  ,__UpperCAmelCase  ,"""tf"""      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Tuple:
      lowerCAmelCase__ :  Any							      =					FeatureExtractionPipeline(__UpperCAmelCase  ,__UpperCAmelCase      )

      lowerCAmelCase__ :  List[str]							      =					["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					infer_shapes(__UpperCAmelCase  ,__UpperCAmelCase      )

      # Assert all variables are present
      self.assertEqual(len(__UpperCAmelCase      )  ,len(__UpperCAmelCase      )      )
      self.assertTrue(all(var_name in shapes for var_name in variable_names      )      )
      self.assertSequenceEqual(variable_names[:3]  ,__UpperCAmelCase      )
      self.assertSequenceEqual(variable_names[3:]  ,__UpperCAmelCase      )

      # Assert inputs are {0: batch, 1: sequence}
      for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
          self.assertDictEqual(shapes[var_name]  ,{0: """batch""", 1: """sequence"""}      )

      # Assert outputs are {0: batch, 1: sequence} and {0: batch}
      self.assertDictEqual(shapes["""output_0"""]  ,{0: """batch""", 1: """sequence"""}      )
      self.assertDictEqual(shapes["""output_1"""]  ,{0: """batch"""}      )




  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      lowerCAmelCase__ :  List[str]							      =					["""input_ids""", """attention_mask""", """token_type_ids"""]
      lowerCAmelCase__ :  Union[str, Any]							      =					{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
      lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					ensure_valid_input(FuncContiguousArgs()  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      # Should have exactly the same number of args (all are valid)
      self.assertEqual(len(__UpperCAmelCase      )  ,3      )

      # Should have exactly the same input names
      self.assertEqual(set(__UpperCAmelCase      )  ,set(__UpperCAmelCase      )      )

      # Parameter should be reordered according to their respective place in the function:
      # (input_ids, token_type_ids, attention_mask)
      self.assertEqual(__UpperCAmelCase  ,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])      )

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      lowerCAmelCase__      ,   lowerCAmelCase__ :  int							      =					ensure_valid_input(FuncNonContiguousArgs()  ,__UpperCAmelCase  ,__UpperCAmelCase      )

      # Should have exactly the one arg (all before the one not provided "some_other_args")
      self.assertEqual(len(__UpperCAmelCase      )  ,1      )
      self.assertEqual(len(__UpperCAmelCase      )  ,1      )

      # Should have only "input_ids"
      self.assertEqual(inputs_args[0]  ,tokens["""input_ids"""]      )
      self.assertEqual(ordered_input_names[0]  ,"""input_ids"""      )






  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      lowerCAmelCase__ :  Dict							      =					generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""      )  ,"""-test"""      )
      self.assertEqual("""/home/something/my_fake_model-test.onnx"""  ,generated.as_posix()      )


