

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


snake_case_			  = logging.getLogger(__name__)


@dataclass
class       SCREAMING_SNAKE_CASE__					:
    __lowerCamelCase						:       str
    __lowerCamelCase						:       List[str]
    __lowerCamelCase						:       Optional[List[str]]



@dataclass
class       SCREAMING_SNAKE_CASE__					:
    __lowerCamelCase						:       List[int]
    __lowerCamelCase						:       List[int]
    __lowerCamelCase						:       Optional[List[int]]														=     None
    __lowerCamelCase						:       Optional[List[int]]														=     None



class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
    __lowerCamelCase						:       str														=     """train"""
    __lowerCamelCase						:       Tuple														=     """dev"""
    __lowerCamelCase						:       Union[str, Any]														=     """test"""



class       SCREAMING_SNAKE_CASE__					:





    @staticmethod
    def       snake_case_				(      a						,   a):
         raise NotImplementedError





    @staticmethod
    def       snake_case_				(      a):
         raise NotImplementedError





    @staticmethod
    def       snake_case_				(      a						,   a						,   a						,   a						,   a=False						,   a="[CLS]"						,   a=1						,   a="[SEP]"						,   a=False						,   a=False						,   a=0						,   a=0						,   a=-100						,   a=0						,   a=True						,   ):
         lowercase__    :   Any      =       {label: i for i, label in enumerate(a)}

         lowercase__    :   Tuple      =       []
         for ex_index, example in enumerate(a):
              if ex_index % 1_0000 == 0:
                   logger.info('Writing example %d of %d'						,   a						,   len(a))

              lowercase__    :   List[str]      =       []
              lowercase__    :   Optional[int]      =       []
              for word, label in zip(example.words						,   example.labels):
                   lowercase__    :   Any      =       tokenizer.tokenize(a)

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              lowercase__    :   Dict      =       tokenizer.num_special_tokens_to_add()
              if len(a) > max_seq_length - special_tokens_count:
                   lowercase__    :   List[Any]      =       tokens[: (max_seq_length - special_tokens_count)]
                   lowercase__    :   Any      =       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]
              lowercase__    :   Tuple      =       [sequence_a_segment_id] * len(a)

              if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
              else:
                   lowercase__    :   Optional[int]      =       [cls_token] + tokens
                   lowercase__    :   Optional[Any]      =       [pad_token_label_id] + label_ids
                   lowercase__    :   Tuple      =       [cls_token_segment_id] + segment_ids

              lowercase__    :   List[Any]      =       tokenizer.convert_tokens_to_ids(a)

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              lowercase__    :   Union[str, Any]      =       [1 if mask_padding_with_zero else 0] * len(a)

              # Zero-pad up to the sequence length.
              lowercase__    :   Dict      =       max_seq_length - len(a)
              if pad_on_left:
                   lowercase__    :   Optional[Any]      =       ([pad_token] * padding_length) + input_ids
                   lowercase__    :   Union[str, Any]      =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   lowercase__    :   str      =       ([pad_token_segment_id] * padding_length) + segment_ids
                   lowercase__    :   List[Any]      =       ([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(a) == max_seq_length
              assert len(a) == max_seq_length
              assert len(a) == max_seq_length
              assert len(a) == max_seq_length

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

              if "token_type_ids" not in tokenizer.model_input_names:
                   lowercase__    :   Any      =       None

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


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


       class       SCREAMING_SNAKE_CASE__					(__snake_case     ):
           __lowerCamelCase						:       List[InputFeatures]
           __lowerCamelCase						:       int														=     nn.CrossEntropyLoss().ignore_index





           def __init__(      self						,   a						,   a						,   a						,   a						,   a						,   a = None						,   a=False						,   a = Split.train						,   ):
                # Load data features from cache or dataset file
                lowercase__    :   Union[str, Any]      =       os.path.join(
                    a						,   'cached_{}_{}_{}'.format(mode.value						,   tokenizer.__class__.__name__						,   str(a))						,   )

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                lowercase__    :   int      =       cached_features_file + '.lock'
                with FileLock(a):
                     if os.path.exists(a) and not overwrite_cache:
                          logger.info(f"""Loading features from cached file {cached_features_file}""")
                          lowercase__    :   Union[str, Any]      =       torch.load(a)
                     else:
                          logger.info(f"""Creating features from dataset file at {data_dir}""")
                          lowercase__    :   List[Any]      =       token_classification_task.read_examples_from_file(a						,   a)
                          # TODO clean up all this to leverage built-in features of tokenizers
                          lowercase__    :   str      =       token_classification_task.convert_examples_to_features(
                              a						,   a						,   a						,   a						,   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=a						,   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						,   a)





           def __len__(      self):
                return len(self.features)





           def __getitem__(      self						,   a):
                return self.features[i]


if is_tf_available():
       import tensorflow as tf


       class       SCREAMING_SNAKE_CASE__					:
           __lowerCamelCase						:       List[InputFeatures]
           __lowerCamelCase						:       int														=     -100





           def __init__(      self						,   a						,   a						,   a						,   a						,   a						,   a = None						,   a=False						,   a = Split.train						,   ):
                lowercase__    :   Optional[Any]      =       token_classification_task.read_examples_from_file(a						,   a)
                # TODO clean up all this to leverage built-in features of tokenizers
                lowercase__    :   str      =       token_classification_task.convert_examples_to_features(
                    a						,   a						,   a						,   a						,   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=a						,   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:
                     lowercase__    :   int      =       tf.data.Dataset.from_generator(
                         a						,   ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)						,   (
                             {'input_ids': tf.TensorShape([None]), 'attention_mask': tf.TensorShape([None])},
                             tf.TensorShape([None]),
                         )						,   )
                else:
                     lowercase__    :   Any      =       tf.data.Dataset.from_generator(
                         a						,   ({'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       snake_case_				(      self):
                lowercase__    :   int      =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

                return self.dataset





           def __len__(      self):
                return len(self.features)





           def __getitem__(      self						,   a):
                return self.features[i]




import unittest

from transformers import DonutProcessor


snake_case_			  = '''naver-clova-ix/donut-base'''


class       SCREAMING_SNAKE_CASE__					(unittest.TestCase     ):





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       DonutProcessor.from_pretrained(a)





    def       snake_case_				(      self):
         lowercase__    :   Tuple      =       {
             'name': 'John Doe',
             'age': '99',
             'city': 'Atlanta',
             'state': 'GA',
             'zip': '30301',
             'phone': '123-4567',
             'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
         }

         lowercase__    :   Tuple      =       (
             '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
             '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
             '<s_nicknames><s_nickname>Johnny</s_nickname>'
             '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
         )
         lowercase__    :   str      =       self.processor.tokenajson(a)

         self.assertDictEqual(a						,   a)

