




'''simple docstring'''



import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCamelCase_			=						logging.getLogger(__name__)







@dataclass
class 		lowercase_   :


   """simple docstring"""

   lowerCamelCase_      =						42
   lowerCamelCase_      =						42
   lowerCamelCase_      =						42







@dataclass
class 		lowercase_   :


   """simple docstring"""

   lowerCamelCase_      =						42
   lowerCamelCase_      =						42
   lowerCamelCase_      =						None
   lowerCamelCase_      =						None







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''train'''
   lowerCamelCase_      =						'''dev'''
   lowerCamelCase_      =						'''test'''







class 		lowercase_   :


   """simple docstring"""

   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     int  ,				__lowerCamelCase :     Union[Split, str]   ):

      """simple docstring"""
      raise NotImplementedError



   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     str   ):

      """simple docstring"""
      raise NotImplementedError



   @staticmethod
   def 							lowerCAmelCase_      (   __lowerCamelCase :     List[InputExample]  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     PreTrainedTokenizer  ,				__lowerCamelCase :     Tuple=False  ,				__lowerCamelCase :     List[str]="[CLS]"  ,				__lowerCamelCase :     Dict=1  ,				__lowerCamelCase :     int="[SEP]"  ,				__lowerCamelCase :     Tuple=False  ,				__lowerCamelCase :     Tuple=False  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     Union[str, Any]=-1_0_0  ,				__lowerCamelCase :     int=0  ,				__lowerCamelCase :     List[str]=True  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{label: i for i, label in enumerate(__lowerCamelCase   )}

      _SCREAMING_SNAKE_CASE	=				[]
      for ex_index, example in enumerate(__lowerCamelCase   ):
         if ex_index % 1_0_0_0_0 == 0:
            logger.info("Writing example %d of %d"  ,				__lowerCamelCase  ,				len(__lowerCamelCase   )   )

         _SCREAMING_SNAKE_CASE	=				[]
         _SCREAMING_SNAKE_CASE	=				[]
         for word, label in zip(example.words  ,				example.labels   ):
            _SCREAMING_SNAKE_CASE	=				tokenizer.tokenize(__lowerCamelCase   )

            # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
            if len(__lowerCamelCase   ) > 0:
               tokens.extend(__lowerCamelCase   )
               # Use the real label id for the first token of the word, and padding ids for the remaining tokens
               label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__lowerCamelCase   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
         _SCREAMING_SNAKE_CASE	=				tokenizer.num_special_tokens_to_add()
         if len(__lowerCamelCase   ) > max_seq_length - special_tokens_count:
            _SCREAMING_SNAKE_CASE	=				tokens[: (max_seq_length - special_tokens_count)]
            _SCREAMING_SNAKE_CASE	=				label_ids[: (max_seq_length - special_tokens_count)]

         # The convention in BERT is:
         # (a) For sequence pairs:
         #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
         #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
         # (b) For single sequences:
         #  tokens:   [CLS] the dog is hairy . [SEP]
         #  type_ids:   0   0   0   0  0     0   0
         #
         # Where "type_ids" are used to indicate whether this is the first
         # sequence or the second sequence. The embedding vectors for `type=0` and
         # `type=1` were learned during pre-training and are added to the wordpiece
         # embedding vector (and position vector). This is not *strictly* necessary
         # since the [SEP] token unambiguously separates the sequences, but it makes
         # it easier for the model to learn the concept of sequences.
         #
         # For classification tasks, the first vector (corresponding to [CLS]) is
         # used as the "sentence vector". Note that this only makes sense because
         # the entire model is fine-tuned.
         tokens += [sep_token]
         label_ids += [pad_token_label_id]
         if sep_token_extra:
            # roberta uses an extra separator b/w pairs of sentences
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
         _SCREAMING_SNAKE_CASE	=				[sequence_a_segment_id] * len(__lowerCamelCase   )

         if cls_token_at_end:
            tokens += [cls_token]
            label_ids += [pad_token_label_id]
            segment_ids += [cls_token_segment_id]
         else:
            _SCREAMING_SNAKE_CASE	=				[cls_token] + tokens
            _SCREAMING_SNAKE_CASE	=				[pad_token_label_id] + label_ids
            _SCREAMING_SNAKE_CASE	=				[cls_token_segment_id] + segment_ids

         _SCREAMING_SNAKE_CASE	=				tokenizer.convert_tokens_to_ids(__lowerCamelCase   )

         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
         _SCREAMING_SNAKE_CASE	=				[1 if mask_padding_with_zero else 0] * len(__lowerCamelCase   )

         # Zero-pad up to the sequence length.
         _SCREAMING_SNAKE_CASE	=				max_seq_length - len(__lowerCamelCase   )
         if pad_on_left:
            _SCREAMING_SNAKE_CASE	=				([pad_token] * padding_length) + input_ids
            _SCREAMING_SNAKE_CASE	=				([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            _SCREAMING_SNAKE_CASE	=				([pad_token_segment_id] * padding_length) + segment_ids
            _SCREAMING_SNAKE_CASE	=				([pad_token_label_id] * padding_length) + label_ids
         else:
            input_ids += [pad_token] * padding_length
            input_mask += [0 if mask_padding_with_zero else 1] * padding_length
            segment_ids += [pad_token_segment_id] * padding_length
            label_ids += [pad_token_label_id] * padding_length

         assert len(__lowerCamelCase   ) == max_seq_length
         assert len(__lowerCamelCase   ) == max_seq_length
         assert len(__lowerCamelCase   ) == max_seq_length
         assert len(__lowerCamelCase   ) == max_seq_length

         if ex_index < 5:
            logger.info("*** Example ***"   )
            logger.info("guid: %s"  ,				example.guid   )
            logger.info("tokens: %s"  ,				" ".join([str(__lowerCamelCase   ) for x in tokens]   )   )
            logger.info("input_ids: %s"  ,				" ".join([str(__lowerCamelCase   ) for x in input_ids]   )   )
            logger.info("input_mask: %s"  ,				" ".join([str(__lowerCamelCase   ) for x in input_mask]   )   )
            logger.info("segment_ids: %s"  ,				" ".join([str(__lowerCamelCase   ) for x in segment_ids]   )   )
            logger.info("label_ids: %s"  ,				" ".join([str(__lowerCamelCase   ) for x in label_ids]   )   )

         if "token_type_ids" not in tokenizer.model_input_names:
            _SCREAMING_SNAKE_CASE	=				None

         features.append(
             InputFeatures(
                 input_ids=__lowerCamelCase  ,				attention_mask=__lowerCamelCase  ,				token_type_ids=__lowerCamelCase  ,				label_ids=__lowerCamelCase   )   )
      return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset







 class 		lowercase_   (			A       ):


    """simple docstring"""

    lowerCamelCase_      =						42
    lowerCamelCase_      =						nn.CrossEntropyLoss().ignore_index



    def __init__(   self :     Any  ,				__lowerCamelCase :     TokenClassificationTask  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     PreTrainedTokenizer  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     Union[str, Any]=False  ,				__lowerCamelCase :     Split = Split.train  ,				):

       """simple docstring"""
       _SCREAMING_SNAKE_CASE	=				os.path.join(
           __lowerCamelCase  ,				"cached_{}_{}_{}".format(mode.value  ,				tokenizer.__class__.__name__  ,				str(__lowerCamelCase   )   )  ,				)

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       _SCREAMING_SNAKE_CASE	=				cached_features_file + ".lock"
       with FileLock(__lowerCamelCase   ):
          if os.path.exists(__lowerCamelCase   ) and not overwrite_cache:
             logger.info(F"""Loading features from cached file {cached_features_file}"""   )
             _SCREAMING_SNAKE_CASE	=				torch.load(__lowerCamelCase   )
          else:
             logger.info(F"""Creating features from dataset file at {data_dir}"""   )
             _SCREAMING_SNAKE_CASE	=				token_classification_task.read_examples_from_file(__lowerCamelCase  ,				__lowerCamelCase   )
             # TODO clean up all this to leverage built-in features of tokenizers
             _SCREAMING_SNAKE_CASE	=				token_classification_task.convert_examples_to_features(
                 __lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				cls_token_at_end=bool(model_type in ["xlnet"]   )  ,				cls_token=tokenizer.cls_token  ,				cls_token_segment_id=2 if model_type in ["xlnet"] else 0  ,				sep_token=tokenizer.sep_token  ,				sep_token_extra=__lowerCamelCase  ,				pad_on_left=bool(tokenizer.padding_side == "left"   )  ,				pad_token=tokenizer.pad_token_id  ,				pad_token_segment_id=tokenizer.pad_token_type_id  ,				pad_token_label_id=self.pad_token_label_id  ,				)
             logger.info(F"""Saving features into cached file {cached_features_file}"""   )
             torch.save(self.features  ,				__lowerCamelCase   )



    def __len__(   self :     Tuple   ):

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



    def __getitem__(   self :     Optional[Any]  ,				__lowerCamelCase :     str   ):

       """simple docstring"""
       return self.features[i]


if is_tf_available():
 import tensorflow as tf







 class 		lowercase_   :


    """simple docstring"""

    lowerCamelCase_      =						42
    lowerCamelCase_      =						-100



    def __init__(   self :     List[str]  ,				__lowerCamelCase :     TokenClassificationTask  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     PreTrainedTokenizer  ,				__lowerCamelCase :     List[str]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     Optional[int]=False  ,				__lowerCamelCase :     Split = Split.train  ,				):

       """simple docstring"""
       _SCREAMING_SNAKE_CASE	=				token_classification_task.read_examples_from_file(__lowerCamelCase  ,				__lowerCamelCase   )
       # TODO clean up all this to leverage built-in features of tokenizers
       _SCREAMING_SNAKE_CASE	=				token_classification_task.convert_examples_to_features(
           __lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase  ,				cls_token_at_end=bool(model_type in ["xlnet"]   )  ,				cls_token=tokenizer.cls_token  ,				cls_token_segment_id=2 if model_type in ["xlnet"] else 0  ,				sep_token=tokenizer.sep_token  ,				sep_token_extra=__lowerCamelCase  ,				pad_on_left=bool(tokenizer.padding_side == "left"   )  ,				pad_token=tokenizer.pad_token_id  ,				pad_token_segment_id=tokenizer.pad_token_type_id  ,				pad_token_label_id=self.pad_token_label_id  ,				)

       def gen():
          for ex in self.features:
             if ex.token_type_ids is None:
                yield (
                    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                    ex.label_ids,
                )
             else:
                yield (
                    {
                        "input_ids": ex.input_ids,
                        "attention_mask": ex.attention_mask,
                        "token_type_ids": ex.token_type_ids,
                    },
                    ex.label_ids,
                )

       if "token_type_ids" not in tokenizer.model_input_names:
          _SCREAMING_SNAKE_CASE	=				tf.data.Dataset.from_generator(
              __lowerCamelCase  ,				({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)  ,				(
                  {"input_ids": tf.TensorShape([None]   ), "attention_mask": tf.TensorShape([None]   )},
                  tf.TensorShape([None]   ),
              )  ,				)
       else:
          _SCREAMING_SNAKE_CASE	=				tf.data.Dataset.from_generator(
              __lowerCamelCase  ,				({"input_ids": tf.intaa, "attention_mask": tf.intaa, "token_type_ids": tf.intaa}, tf.intaa)  ,				(
                  {
                      "input_ids": tf.TensorShape([None]   ),
                      "attention_mask": tf.TensorShape([None]   ),
                      "token_type_ids": tf.TensorShape([None]   ),
                  },
                  tf.TensorShape([None]   ),
              )  ,				)



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

       """simple docstring"""
       _SCREAMING_SNAKE_CASE	=				self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

       return self.dataset



    def __len__(   self :     List[str]   ):

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



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

       """simple docstring"""
       return self.features[i]









'''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()   )

