




'''simple docstring'''



import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
 import jieba

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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{'vocab_file': 'vocab.txt'}

lowerCamelCase_			=						{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

lowerCamelCase_			=						{
    'openbmb/cpm-ant-10b': 10_24,
}
def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[str]  )			->			Optional[int]:
   _SCREAMING_SNAKE_CASE	=				collections.OrderedDict()
   with open(__A	,					"r"	,					encoding="utf-8"  ) as reader:
      _SCREAMING_SNAKE_CASE	=				reader.readlines()
   for index, token in enumerate(__A  ):
      _SCREAMING_SNAKE_CASE	=				token.rstrip("\n"  )
      _SCREAMING_SNAKE_CASE	=				index
   return vocab







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Dict  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Any="<unk>"  ,				__lowerCamelCase :     str=2_0_0   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				vocab
      _SCREAMING_SNAKE_CASE	=				unk_token
      _SCREAMING_SNAKE_CASE	=				max_input_chars_per_word



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				list(__lowerCamelCase   )
      if len(__lowerCamelCase   ) > self.max_input_chars_per_word:
         return [self.unk_token]

      _SCREAMING_SNAKE_CASE	=				0
      _SCREAMING_SNAKE_CASE	=				[]
      while start < len(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				None
         while start < end:
            _SCREAMING_SNAKE_CASE	=				"".join(chars[start:end]   )
            if substr in self.vocab:
               _SCREAMING_SNAKE_CASE	=				substr
               break
            end -= 1
         if cur_substr is None:
            sub_tokens.append(self.unk_token   )
            start += 1
         else:
            sub_tokens.append(__lowerCamelCase   )
            _SCREAMING_SNAKE_CASE	=				end

      return sub_tokens







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						VOCAB_FILES_NAMES
   lowerCamelCase_      =						PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_      =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_      =						['''input_ids''', '''attention_mask''']
   lowerCamelCase_      =						False



   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Tuple="<d>"  ,				__lowerCamelCase :     Any="</d>"  ,				__lowerCamelCase :     Dict="<s>"  ,				__lowerCamelCase :     List[str]="</s>"  ,				__lowerCamelCase :     Any="<pad>"  ,				__lowerCamelCase :     Dict="<unk>"  ,				__lowerCamelCase :     List[Any]="</n>"  ,				__lowerCamelCase :     Union[str, Any]="</_>"  ,				__lowerCamelCase :     Any="left"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      requires_backends(self  ,				["jieba"]   )
      super().__init__(
          bod_token=__lowerCamelCase  ,				eod_token=__lowerCamelCase  ,				bos_token=__lowerCamelCase  ,				eos_token=__lowerCamelCase  ,				pad_token=__lowerCamelCase  ,				unk_token=__lowerCamelCase  ,				line_token=__lowerCamelCase  ,				space_token=__lowerCamelCase  ,				padding_side=__lowerCamelCase  ,				**__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				bod_token
      _SCREAMING_SNAKE_CASE	=				eod_token
      _SCREAMING_SNAKE_CASE	=				load_vocab(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				self.encoder[space_token]
      _SCREAMING_SNAKE_CASE	=				self.encoder[line_token]

      del self.encoder[space_token]
      del self.encoder[line_token]

      _SCREAMING_SNAKE_CASE	=				collections.OrderedDict(sorted(self.encoder.items()  ,				key=lambda __lowerCamelCase   : x[1]   )   )
      _SCREAMING_SNAKE_CASE	=				{v: k for k, v in self.encoder.items()}

      _SCREAMING_SNAKE_CASE	=				WordpieceTokenizer(vocab=self.encoder  ,				unk_token=self.unk_token   )



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self.encoder[self.bod_token]



   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      return self.encoder[self.eod_token]



   @property
   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      return self.encoder["\n"]



   @property
   def 							lowerCAmelCase_      (   self :     int   ):

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      return dict(self.encoder  ,				**self.added_tokens_encoder   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      for x in jieba.cut(__lowerCamelCase  ,				cut_all=__lowerCamelCase   ):
         output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCamelCase   )   )
      return output_tokens



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[i for i in token_ids if i >= 0]
      _SCREAMING_SNAKE_CASE	=				[
          x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
      ]
      return super()._decode(__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return token in self.encoder



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      return "".join(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      return self.encoder.get(__lowerCamelCase  ,				self.encoder.get(self.unk_token   )   )



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

      """simple docstring"""
      return self.decoder.get(__lowerCamelCase  ,				self.unk_token   )



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[str] = None   ):

      """simple docstring"""
      if os.path.isdir(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				os.path.join(
             __lowerCamelCase  ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )
      else:
         _SCREAMING_SNAKE_CASE	=				(filename_prefix + "-" if filename_prefix else "") + save_directory
      _SCREAMING_SNAKE_CASE	=				0
      if " " in self.encoder:
         _SCREAMING_SNAKE_CASE	=				self.encoder[" "]
         del self.encoder[" "]
      if "\n" in self.encoder:
         _SCREAMING_SNAKE_CASE	=				self.encoder["\n"]
         del self.encoder["\n"]
      _SCREAMING_SNAKE_CASE	=				collections.OrderedDict(sorted(self.encoder.items()  ,				key=lambda __lowerCamelCase   : x[1]   )   )
      with open(__lowerCamelCase  ,				"w"  ,				encoding="utf-8"   ) as writer:
         for token, token_index in self.encoder.items():
            if index != token_index:
               logger.warning(
                   F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                   " Please check that the vocabulary is not corrupted!"   )
               _SCREAMING_SNAKE_CASE	=				token_index
            writer.write(token + "\n"   )
            index += 1
      return (vocab_file,)



   def 							lowerCAmelCase_      (   self :     str  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     List[int] = None   ):

      """simple docstring"""
      if token_ids_a is None:
         return [self.bos_token_id] + token_ids_a
      return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     List[int]  ,				__lowerCamelCase :     Optional[List[int]] = None  ,				__lowerCamelCase :     bool = False   ):

      """simple docstring"""

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

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







'''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 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return None







class 		lowercase_   :


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return None







class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

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



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      from transformers import BertModel

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

      with TemporaryDirectory() as bert_save_dir:
         _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig(vocab_size=len(__lowerCamelCase   )   )   )
         model.save_pretrained(__lowerCamelCase   )
         self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				__lowerCamelCase   )



   @require_tf
   @slow
   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"tf"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(Path(__lowerCamelCase   )   )

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



   @require_torch
   @slow
   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
         _SCREAMING_SNAKE_CASE	=				self._test_export(__lowerCamelCase  ,				"pt"  ,				1_2  ,				**__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				quantize(__lowerCamelCase   )

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Optional[int]=None  ,				**__lowerCamelCase :     int   ):

      """simple docstring"""
      try:
         # Compute path
         with TemporaryDirectory() as tempdir:
            _SCREAMING_SNAKE_CASE	=				Path(__lowerCamelCase   ).joinpath("model.onnx"   )

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

         # Export
         convert(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				**__lowerCamelCase   )

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



   @require_torch
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      from transformers import BertModel

      _SCREAMING_SNAKE_CASE	=				BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"pt"   )



   @require_tf
   @require_tokenizers
   @slow
   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      from transformers import TFBertModel

      _SCREAMING_SNAKE_CASE	=				TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"   )   )
      _SCREAMING_SNAKE_CASE	=				BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"   )
      self._test_infer_dynamic_axis(__lowerCamelCase  ,				__lowerCamelCase  ,				"tf"   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				FeatureExtractionPipeline(__lowerCamelCase  ,				__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				infer_shapes(__lowerCamelCase  ,				__lowerCamelCase   )

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

      # 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 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				["input_ids", "attention_mask", "token_type_ids"]
      _SCREAMING_SNAKE_CASE	=				{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

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

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

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

      # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				ensure_valid_input(FuncNonContiguousArgs()  ,				__lowerCamelCase  ,				__lowerCamelCase   )

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

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



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				generate_identified_filename(Path("/home/something/my_fake_model.onnx"   )  ,				"-test"   )
      self.assertEqual("/home/something/my_fake_model-test.onnx"  ,				generated.as_posix()   )

