



"""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


UpperCamelCase     =							logging.get_logger(__name__)






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					str							  =     field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()      )}      )
			A__	:					str							  =     field(
			    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}      )
			A__	:					int							  =     field(
			    default=1_2_8       ,		metadata={
			        "help": (
			            "The maximum total input sequence length after tokenization. Sequences longer "
			            "than this will be truncated, sequences shorter will be padded."
			        )
			    }       ,		)
			A__	:					bool							  =     field(
			    default=_lowerCAmelCase       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}      )


			def   snake_case__	(      self		)      ->	Tuple:
				A__											=					self.task_name.lower()






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					str							  =     "train"
			A__	:					Union[str, Any]							  =     "dev"
			A__	:					Union[str, Any]							  =     "test"






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					GlueDataTrainingArguments
			A__	:					str
			A__	:					List[InputFeatures]


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = Split.train						,	SCREAMING_SNAKE_CASE__ = 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"						,	SCREAMING_SNAKE_CASE__						,	)
				A__											=					args
				A__											=					glue_processors[args.task_name]()
				A__											=					glue_output_modes[args.task_name]
				if isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					try:
						A__											=					Split[mode]
					except KeyError:
						raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
				A__											=					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}"""						,	)
				A__											=					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)
					A__							,							A__											=					label_list[2], label_list[1]
				A__											=					label_list

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


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







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				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


UpperCamelCase     =							logging.get_logger(__name__)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


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


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







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

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

				# Generate dummy bounding boxes
				A__											=					[[[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)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




