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			: str							   =		logging.getLogger(__name__)







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42







@dataclass
class __snake_case				:
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							42
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case				(						_UpperCamelCase		):
   lowerCAmelCase_														=							'train'
   lowerCAmelCase_														=							'dev'
   lowerCAmelCase_														=							'test'







class __snake_case				:




   @staticmethod
   def 	__a						(    _lowercase			:				List[str]       ,	_lowercase			:				Union[Split, str]      ):



     """simple docstring"""






     raise NotImplementedError




   @staticmethod
   def 	__a						(    _lowercase			:				str      ):



     """simple docstring"""






     raise NotImplementedError




   @staticmethod
   def 	__a						(    _lowercase			:				List[InputExample]       ,	_lowercase			:				List[str]       ,	_lowercase			:				int       ,	_lowercase			:				PreTrainedTokenizer       ,	_lowercase			:				Dict=False       ,	_lowercase			:				int="[CLS]"       ,	_lowercase			:				List[Any]=1       ,	_lowercase			:				Optional[int]="[SEP]"       ,	_lowercase			:				Any=False       ,	_lowercase			:				int=False       ,	_lowercase			:				Tuple=0       ,	_lowercase			:				Any=0       ,	_lowercase			:				int=-1_00       ,	_lowercase			:				List[Any]=0       ,	_lowercase			:				str=True       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      {label: i for i, label in enumerate(_UpperCAmelCase      )}

     SCREAMING_SNAKE_CASE__										=      []
     for ex_index, example in enumerate(_UpperCAmelCase      ):
       if ex_index % 1_00_00 == 0:
         logger.info("""Writing example %d of %d"""       ,	_UpperCAmelCase       ,	len(_UpperCAmelCase      )      )

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

         # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
         if len(_UpperCAmelCase      ) > 0:
           tokens.extend(_UpperCAmelCase      )
           # 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(_UpperCAmelCase      ) - 1)      )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
       SCREAMING_SNAKE_CASE__										=      tokenizer.num_special_tokens_to_add()
       if len(_UpperCAmelCase      ) > 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(_UpperCAmelCase      )

       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(_UpperCAmelCase      )

       # 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(_UpperCAmelCase      )

       # Zero-pad up to the sequence length.
       SCREAMING_SNAKE_CASE__										=      max_seq_length - len(_UpperCAmelCase      )
       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(_UpperCAmelCase      ) == max_seq_length
       assert len(_UpperCAmelCase      ) == max_seq_length
       assert len(_UpperCAmelCase      ) == max_seq_length
       assert len(_UpperCAmelCase      ) == max_seq_length

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

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

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


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







   class __snake_case				(						_UpperCamelCase		):
      lowerCAmelCase_														=							42
      lowerCAmelCase_														=							nn.CrossEntropyLoss().ignore_index




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



        """simple docstring"""






        SCREAMING_SNAKE_CASE__										=      os.path.join(
            _UpperCAmelCase       ,	"""cached_{}_{}_{}""".format(mode.value       ,	tokenizer.__class__.__name__       ,	str(_UpperCAmelCase      )      )       ,	)

        # 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(_UpperCAmelCase      ):
          if os.path.exists(_UpperCAmelCase      ) and not overwrite_cache:
            logger.info(f"""Loading features from cached file {cached_features_file}"""      )
            SCREAMING_SNAKE_CASE__										=      torch.load(_UpperCAmelCase      )
          else:
            logger.info(f"""Creating features from dataset file at {data_dir}"""      )
            SCREAMING_SNAKE_CASE__										=      token_classification_task.read_examples_from_file(_UpperCAmelCase       ,	_UpperCAmelCase      )
            # TODO clean up all this to leverage built-in features of tokenizers
            SCREAMING_SNAKE_CASE__										=      token_classification_task.convert_examples_to_features(
                _UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase       ,	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=_UpperCAmelCase       ,	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       ,	_UpperCAmelCase      )




      def __len__(    self			:				Any      ):



        """simple docstring"""






        return len(self.features      )




      def __getitem__(    self			:				Tuple       ,	_lowercase			:				Dict      ):



        """simple docstring"""






        return self.features[i]


if is_tf_available():
   import tensorflow as tf







   class __snake_case				:
      lowerCAmelCase_														=							42
      lowerCAmelCase_														=							-1_00




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



        """simple docstring"""






        SCREAMING_SNAKE_CASE__										=      token_classification_task.read_examples_from_file(_UpperCAmelCase       ,	_UpperCAmelCase      )
        # TODO clean up all this to leverage built-in features of tokenizers
        SCREAMING_SNAKE_CASE__										=      token_classification_task.convert_examples_to_features(
            _UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase       ,	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=_UpperCAmelCase       ,	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(
              _UpperCAmelCase       ,	({"""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(
              _UpperCAmelCase       ,	({"""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 	__a						(    self			:				str      ):



        """simple docstring"""






        SCREAMING_SNAKE_CASE__										=      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features      )      )      )

        return self.dataset




      def __len__(    self			:				Optional[Any]      ):



        """simple docstring"""






        return len(self.features      )




      def __getitem__(    self			:				str       ,	_lowercase			:				int      ):



        """simple docstring"""






        return self.features[i]





'''simple docstring'''

import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					LayoutLMTokenizer
							lowerCAmelCase       :    Tuple   				=					LayoutLMTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True
							lowerCAmelCase       :    int   				=					True







							def 							__lowercase			(							self					:       Dict		):
									super().setUp()

									_a     :      int        =							[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									_a     :      List[str]        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									with open(self.vocab_file	,'w'	,encoding='utf-8'		) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       List[str]		):
									return LayoutLMTokenizer.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Tuple		):
									_a     :      Optional[int]        =							'UNwant\u00E9d,running'
									_a     :      List[Any]        =							'unwanted, running'
									return input_text, output_text







							def 							__lowercase			(							self					:       Optional[int]		):
									_a     :      Optional[Any]        =							self.tokenizer_class(self.vocab_file		)

									_a     :      Optional[Any]        =							tokenizer.tokenize('UNwant\u00E9d,running'		)
									self.assertListEqual(_UpperCAmelCase	,['un', '##want', '##ed', ',', 'runn', '##ing']		)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)	,[7, 4, 5, 10, 8, 9]		)







							def 							__lowercase			(							self					:       Optional[int]		):
									pass




