"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()					)}					)
				_lowerCAmelCase     :    str			  =						field(
				    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}					)
				_lowerCAmelCase     :    int			  =						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."""
				        )
				    }     ,    )
				_lowerCAmelCase     :    bool			  =						field(
				    default=lowerCAmelCase     ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.task_name.lower()





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""train"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""dev"""
				_lowerCAmelCase     :    List[Any]			  =						"""test"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    GlueDataTrainingArguments
				_lowerCAmelCase     :    str
				_lowerCAmelCase     :    List[InputFeatures]

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = Split.train		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										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'		,    lowerCAmelCase		,    )
										snake_case								=			args
										snake_case								=			glue_processors[args.task_name]()
										snake_case								=			glue_output_modes[args.task_name]
										if isinstance(lowerCAmelCase		,    lowerCAmelCase    ):
																try:
																						snake_case								=			Split[mode]
																except KeyError:
																						raise KeyError('mode is not a valid split name'    )
        # Load data features from cache or dataset file
										snake_case								=			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}"""		,    )
										snake_case								=			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)
																snake_case					,snake_case								=			label_list[2], label_list[1]
										snake_case								=			label_list

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										snake_case								=			cached_features_file + '.lock'
										with FileLock(lowerCAmelCase    ):
																if os.path.exists(lowerCAmelCase    ) and not args.overwrite_cache:
																						snake_case								=			time.time()
																						snake_case								=			torch.load(lowerCAmelCase    )
																						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:
																												snake_case								=			self.processor.get_dev_examples(args.data_dir    )
																						elif mode == Split.test:
																												snake_case								=			self.processor.get_test_examples(args.data_dir    )
																						else:
																												snake_case								=			self.processor.get_train_examples(args.data_dir    )
																						if limit_length is not None:
																												snake_case								=			examples[:limit_length]
																						snake_case								=			glue_convert_examples_to_features(
																						    lowerCAmelCase		,    lowerCAmelCase		,    max_length=args.max_seq_length		,    label_list=lowerCAmelCase		,    output_mode=self.output_mode		,    )
																						snake_case								=			time.time()
																						torch.save(self.features		,    lowerCAmelCase    )
																						# ^ 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    ):






										"""simple docstring"""

										return len(self.features    )

				def __getitem__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.features[i]

				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.label_list



"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    vocab_size=lowerCAmelCase		,    hidden_size=lowerCAmelCase		,    num_hidden_layers=lowerCAmelCase		,    num_attention_heads=lowerCAmelCase		,    intermediate_size=lowerCAmelCase		,    hidden_act=lowerCAmelCase		,    hidden_dropout_prob=lowerCAmelCase		,    attention_probs_dropout_prob=lowerCAmelCase		,    max_position_embeddings=lowerCAmelCase		,    type_vocab_size=lowerCAmelCase		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										setattr(processor.image_processor		,    'apply_ocr'		,    lowerCAmelCase    )

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

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * 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)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


