






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__      :	Any       	=      logging.get_logger(__name__)


@dataclass
class 						_a       :






  """simple docstring"""





  A_						=   field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()    )}    )
  A_						=   field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}    )
  A_						=   field(
      default=128						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.task_name.lower()




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """train"""
  A_						=   """dev"""
  A_						=   """test"""




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = Split.train		,		_UpperCAmelCase = None		,		)			-> Tuple:
     warnings.warn(
         'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
         'library. You can have a look at this example script for pointers: '
         'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'		,		_UpperCAmelCase		,		)
     UpperCamelCase_									=     args
     UpperCamelCase_									=     glue_processors[args.task_name]()
     UpperCamelCase_									=     glue_output_modes[args.task_name]
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        try:
           UpperCamelCase_									=     Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
     UpperCamelCase_									=     os.path.join(
         cache_dir if cache_dir is not None else args.data_dir		,		f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""		,		)
     UpperCamelCase_									=     self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
     UpperCamelCase_									=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     UpperCamelCase_									=     cached_features_file + '.lock'
     with FileLock(_UpperCAmelCase ):
        if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
           UpperCamelCase_									=     time.time()
           UpperCamelCase_									=     torch.load(_UpperCAmelCase )
           logger.info(
               f"""Loading features from cached file {cached_features_file} [took %.3f s]"""		,		time.time() - start )
        else:
           logger.info(f"""Creating features from dataset file at {args.data_dir}""" )

           if mode == Split.dev:
              UpperCamelCase_									=     self.processor.get_dev_examples(args.data_dir )
           elif mode == Split.test:
              UpperCamelCase_									=     self.processor.get_test_examples(args.data_dir )
           else:
              UpperCamelCase_									=     self.processor.get_train_examples(args.data_dir )
           if limit_length is not None:
              UpperCamelCase_									=     examples[:limit_length]
           UpperCamelCase_									=     glue_convert_examples_to_features(
               _UpperCAmelCase		,		_UpperCAmelCase		,		max_length=args.max_seq_length		,		label_list=_UpperCAmelCase		,		output_mode=self.output_mode		,		)
           UpperCamelCase_									=     time.time()
           torch.save(self.features		,		_UpperCAmelCase )
           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
           logger.info(
               f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )




  def __len__(     self )			-> List[str]:
     return len(self.features )




  def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
     return self.features[i]




  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.label_list













from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
 from ...processing_utils import ProcessorMixin
 from ...utils import TensorType


snake_case__      :	Tuple       	=      logging.get_logger(__name__)

snake_case__      :	str       	=      {
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """layoutlmv3"""




  def __init__(     self		,		_UpperCAmelCase=50265		,		_UpperCAmelCase=768		,		_UpperCAmelCase=12		,		_UpperCAmelCase=12		,		_UpperCAmelCase=3072		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=2		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=1e-5		,		_UpperCAmelCase=1		,		_UpperCAmelCase=0		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1024		,		_UpperCAmelCase=128		,		_UpperCAmelCase=128		,		_UpperCAmelCase=True		,		_UpperCAmelCase=32		,		_UpperCAmelCase=128		,		_UpperCAmelCase=64		,		_UpperCAmelCase=256		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=224		,		_UpperCAmelCase=3		,		_UpperCAmelCase=16		,		_UpperCAmelCase=None		,		**_UpperCAmelCase		,		)			-> Tuple:
     super().__init__(
         vocab_size=_UpperCAmelCase		,		hidden_size=_UpperCAmelCase		,		num_hidden_layers=_UpperCAmelCase		,		num_attention_heads=_UpperCAmelCase		,		intermediate_size=_UpperCAmelCase		,		hidden_act=_UpperCAmelCase		,		hidden_dropout_prob=_UpperCAmelCase		,		attention_probs_dropout_prob=_UpperCAmelCase		,		max_position_embeddings=_UpperCAmelCase		,		type_vocab_size=_UpperCAmelCase		,		initializer_range=_UpperCAmelCase		,		layer_norm_eps=_UpperCAmelCase		,		pad_token_id=_UpperCAmelCase		,		bos_token_id=_UpperCAmelCase		,		eos_token_id=_UpperCAmelCase		,		**_UpperCAmelCase		,		)
     UpperCamelCase_									=     max_ad_position_embeddings
     UpperCamelCase_									=     coordinate_size
     UpperCamelCase_									=     shape_size
     UpperCamelCase_									=     has_relative_attention_bias
     UpperCamelCase_									=     rel_pos_bins
     UpperCamelCase_									=     max_rel_pos
     UpperCamelCase_									=     has_spatial_attention_bias
     UpperCamelCase_									=     rel_ad_pos_bins
     UpperCamelCase_									=     max_rel_ad_pos
     UpperCamelCase_									=     text_embed
     UpperCamelCase_									=     visual_embed
     UpperCamelCase_									=     input_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     classifier_dropout




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   version.parse("""1.12"""    )




  @property
  def    _UpperCAmelCase     (     self )			-> Mapping[str, Mapping[int, str]]:
     # The order of inputs is different for question answering and sequence classification
     if self.task in ["question-answering", "sequence-classification"]:
        return OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'sequence'}),
                ('attention_mask', {0: 'batch', 1: 'sequence'}),
                ('bbox', {0: 'batch', 1: 'sequence'}),
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ] )
     else:
        return OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'sequence'}),
                ('bbox', {0: 'batch', 1: 'sequence'}),
                ('attention_mask', {0: 'batch', 1: 'sequence'}),
                ('pixel_values', {0: 'batch', 1: 'num_channels'}),
            ] )




  @property
  def    _UpperCAmelCase     (     self )			-> float:
     return 1e-5




  @property
  def    _UpperCAmelCase     (     self )			-> int:
     return 12




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = -1		,		_UpperCAmelCase = False		,		_UpperCAmelCase = None		,		_UpperCAmelCase = 3		,		_UpperCAmelCase = 40		,		_UpperCAmelCase = 40		,		)			-> Mapping[str, Any]:
     setattr(processor.image_processor		,		'apply_ocr'		,		_UpperCAmelCase )

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     UpperCamelCase_									=     compute_effective_axis_dimension(
         _UpperCAmelCase		,		fixed_dimension=OnnxConfig.default_fixed_batch		,		num_token_to_add=0 )
     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     UpperCamelCase_									=     processor.tokenizer.num_special_tokens_to_add(_UpperCAmelCase )
     UpperCamelCase_									=     compute_effective_axis_dimension(
         _UpperCAmelCase		,		fixed_dimension=OnnxConfig.default_fixed_sequence		,		num_token_to_add=_UpperCAmelCase )
     # Generate dummy inputs according to compute batch and sequence
     UpperCamelCase_									=     [[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size

     # Generate dummy bounding boxes
     UpperCamelCase_									=     [[[48, 84, 73, 128]]] * batch_size

     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
     UpperCamelCase_									=     self._generate_dummy_images(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

     UpperCamelCase_									=     dict(
         processor(
             _UpperCAmelCase		,		text=_UpperCAmelCase		,		boxes=_UpperCAmelCase		,		return_tensors=_UpperCAmelCase		,		) )

     return inputs





