


"""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 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Dict        =   "layoutlmv3"






       def __init__(   self  :    Tuple  ,     snake_case__  :    Tuple=5_0_2_6_5  ,     snake_case__  :    List[Any]=7_6_8  ,     snake_case__  :    Optional[int]=1_2  ,     snake_case__  :    Any=1_2  ,     snake_case__  :    int=3_0_7_2  ,     snake_case__  :    Dict="gelu"  ,     snake_case__  :    int=0.1  ,     snake_case__  :    Any=0.1  ,     snake_case__  :    Tuple=5_1_2  ,     snake_case__  :    List[Any]=2  ,     snake_case__  :    int=0.02  ,     snake_case__  :    Optional[int]=1e-5  ,     snake_case__  :    Tuple=1  ,     snake_case__  :    Dict=0  ,     snake_case__  :    Union[str, Any]=2  ,     snake_case__  :    Dict=1_0_2_4  ,     snake_case__  :    Optional[Any]=1_2_8  ,     snake_case__  :    Dict=1_2_8  ,     snake_case__  :    List[str]=True  ,     snake_case__  :    Dict=3_2  ,     snake_case__  :    Union[str, Any]=1_2_8  ,     snake_case__  :    Optional[Any]=6_4  ,     snake_case__  :    int=2_5_6  ,     snake_case__  :    Union[str, Any]=True  ,     snake_case__  :    List[str]=True  ,     snake_case__  :    str=True  ,     snake_case__  :    Any=2_2_4  ,     snake_case__  :    Tuple=3  ,     snake_case__  :    str=1_6  ,     snake_case__  :    Dict=None  ,     **snake_case__  :    int  ,     ):

              '''simple docstring'''






              super().__init__(
                  vocab_size=snake_case__  ,     hidden_size=snake_case__  ,     num_hidden_layers=snake_case__  ,     num_attention_heads=snake_case__  ,     intermediate_size=snake_case__  ,     hidden_act=snake_case__  ,     hidden_dropout_prob=snake_case__  ,     attention_probs_dropout_prob=snake_case__  ,     max_position_embeddings=snake_case__  ,     type_vocab_size=snake_case__  ,     initializer_range=snake_case__  ,     layer_norm_eps=snake_case__  ,     pad_token_id=snake_case__  ,     bos_token_id=snake_case__  ,     eos_token_id=snake_case__  ,     **snake_case__  ,     )
              lowercase		:int       							=				max_ad_position_embeddings
              lowercase		:int       							=				coordinate_size
              lowercase		:Union[str, Any]       							=				shape_size
              lowercase		:Dict       							=				has_relative_attention_bias
              lowercase		:int       							=				rel_pos_bins
              lowercase		:int       							=				max_rel_pos
              lowercase		:str       							=				has_spatial_attention_bias
              lowercase		:str       							=				rel_ad_pos_bins
              lowercase		:Union[str, Any]       							=				max_rel_ad_pos
              lowercase		:int       							=				text_embed
              lowercase		:List[Any]       							=				visual_embed
              lowercase		:Dict       							=				input_size
              lowercase		:List[Any]       							=				num_channels
              lowercase		:Dict       							=				patch_size
              lowercase		:Any       							=				classifier_dropout







class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      List[str]        =   version.parse("1.12"     )






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''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  :    Optional[int]				):

              '''simple docstring'''






              return 1e-5






       @property
       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              return 1_2







       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    "ProcessorMixin"  ,     snake_case__  :    int = -1  ,     snake_case__  :    int = -1  ,     snake_case__  :    bool = False  ,     snake_case__  :    Optional["TensorType"] = None  ,     snake_case__  :    int = 3  ,     snake_case__  :    int = 4_0  ,     snake_case__  :    int = 4_0  ,     ):

              '''simple docstring'''






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

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

              # Generate dummy bounding boxes
              lowercase		:Optional[int]       							=				[[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
              lowercase		:Tuple       							=				self._generate_dummy_images(snake_case__  ,     snake_case__  ,     snake_case__  ,     snake_case__				)

              lowercase		:List[str]       							=				dict(
                  processor(
                      snake_case__  ,     text=snake_case__  ,     boxes=snake_case__  ,     return_tensors=snake_case__  ,     )				)

              return inputs





"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()






def       lowerCamelCase					(a_						:str				,				a_						:str				,				a_						:str				,				a_						:PreTrainedTokenizer				,				a_						:int				,				a_						:Optional[int] = None				,				)     ->							int:
       lowercase		:Dict       							=				{}

       if train_file is not None:
              lowercase		:Union[str, Any]       							=				[train_file]
       if eval_file is not None:
              lowercase		:str       							=				[eval_file]
       if test_file is not None:
              lowercase		:Dict       							=				[test_file]

       lowercase		:Optional[Any]       							=				datasets.load_dataset('''csv'''				,				data_files=a_)
       lowercase		:int       							=				list(ds[list(files.keys())[0]].features.keys())
       lowercase		:Dict       							=				features_name.pop(a_)
       lowercase		:str       							=				list(set(ds[list(files.keys())[0]][label_name]))
       lowercase		:List[str]       							=				{label: i for i, label in enumerate(a_)}
       lowercase		:Any       							=				tokenizer.model_input_names
       lowercase		:Optional[Any]       							=				{}

       if len(a_) == 1:
              for k in files.keys():
                     lowercase		:Dict       							=				ds[k].map(
                         lambda a_: tokenizer.batch_encode_plus(
                             example[features_name[0]]				,				truncation=a_				,				max_length=a_				,				padding='''max_length''')				,				batched=a_				,				)
       elif len(a_) == 2:
              for k in files.keys():
                     lowercase		:Optional[Any]       							=				ds[k].map(
                         lambda a_: tokenizer.batch_encode_plus(
                             (example[features_name[0]], example[features_name[1]])				,				truncation=a_				,				max_length=a_				,				padding='''max_length'''				,				)				,				batched=a_				,				)

       def gen_train():
              for ex in transformed_ds[datasets.Split.TRAIN]:
                     lowercase		:Any       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:List[str]       							=				labelaid[ex[label_name]]
                     yield (d, label)

       def gen_val():
              for ex in transformed_ds[datasets.Split.VALIDATION]:
                     lowercase		:Optional[Any]       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:Tuple       							=				labelaid[ex[label_name]]
                     yield (d, label)

       def gen_test():
              for ex in transformed_ds[datasets.Split.TEST]:
                     lowercase		:Dict       							=				{k: v for k, v in ex.items() if k in input_names}
                     lowercase		:Optional[int]       							=				labelaid[ex[label_name]]
                     yield (d, label)

       lowercase		:List[str]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
           if datasets.Split.TRAIN in transformed_ds
           else None
       )

       if train_ds is not None:
              lowercase		:Union[str, Any]       							=				train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN])))

       lowercase		:Optional[int]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
           if datasets.Split.VALIDATION in transformed_ds
           else None
       )

       if val_ds is not None:
              lowercase		:List[Any]       							=				val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION])))

       lowercase		:Optional[Any]       							=				(
           tf.data.Dataset.from_generator(
               a_				,				({k: tf.intaa for k in input_names}, tf.intaa)				,				({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([]))				,				)
           if datasets.Split.TEST in transformed_ds
           else None
       )

       if test_ds is not None:
              lowercase		:int       							=				test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST])))

       return train_ds, val_ds, test_ds, labelaid


UpperCAmelCase      		=			logging.getLogger(__name__)
@dataclass
class 					__magic_name__		:
       __A			:      int        =   field(metadata={"help": "Which column contains the label"}     )
       __A			:      str        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the training file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the development file"}     )
       __A			:      Optional[str]        =   field(default=__UpperCAmelCase			,						metadata={"help": "The path of the test file"}     )
       __A			:      int        =   field(
           default=1_28			,						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=__UpperCAmelCase			,						metadata={"help": "Overwrite the cached training and evaluation sets"}     )







@dataclass
class 					__magic_name__		:
       __A			:      str        =   field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}     )
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained config name or path if not the same as model_name"}     )
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}     )
       __A			:      bool        =   field(default=__UpperCAmelCase			,						metadata={"help": "Set this flag to use fast tokenization."}     )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       __A			:      Optional[str]        =   field(
           default=__UpperCAmelCase			,						metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,						)







def       lowerCamelCase					()     ->							Dict:
       # See all possible arguments in src/transformers/training_args.py
       # or by passing the --help flag to this script.
       # We now keep distinct sets of args, for a cleaner separation of concerns.
       lowercase		:Dict       							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
       lowercase							,  lowercase							,  lowercase		:Union[str, Any]       							=				parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir)
           and os.listdir(training_args.output_dir)
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
                  ''' --overwrite_output_dir to overcome.''')

       # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''				,				datefmt='''%m/%d/%Y %H:%M:%S'''				,				level=logging.INFO				,				)
       logger.info(
           F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, """
           F"""16-bits training: {training_args.fpaa}""")
       logger.info(F"""Training/evaluation parameters {training_args}""")

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       lowercase		:Optional[int]       							=				AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,				cache_dir=model_args.cache_dir				,				)

       lowercase							,  lowercase							,  lowercase							,  lowercase		:int       							=				get_tfds(
           train_file=data_args.train_file				,				eval_file=data_args.dev_file				,				test_file=data_args.test_file				,				tokenizer=a_				,				label_column_id=data_args.label_column_id				,				max_seq_length=data_args.max_seq_length				,				)

       lowercase		:Optional[int]       							=				AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,				num_labels=len(a_)				,				labelaid=a_				,				idalabel={id: label for label, id in labelaid.items()}				,				finetuning_task='''text-classification'''				,				cache_dir=model_args.cache_dir				,				)

       with training_args.strategy.scope():
              lowercase		:int       							=				TFAutoModelForSequenceClassification.from_pretrained(
                  model_args.model_name_or_path				,				from_pt=bool('''.bin''' in model_args.model_name_or_path)				,				config=a_				,				cache_dir=model_args.cache_dir				,				)

       def compute_metrics(a_						:EvalPrediction) -> Dict:
              lowercase		:Tuple       							=				np.argmax(p.predictions				,				axis=1)

              return {"acc": (preds == p.label_ids).mean()}

       # Initialize our Trainer
       lowercase		:Any       							=				TFTrainer(
           model=a_				,				args=a_				,				train_dataset=a_				,				eval_dataset=a_				,				compute_metrics=a_				,				)

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir)

       # Evaluation
       lowercase		:Union[str, Any]       							=				{}
       if training_args.do_eval:
              logger.info('''*** Evaluate ***''')
              lowercase		:str       							=				trainer.evaluate()
              lowercase		:int       							=				os.path.join(training_args.output_dir				,				'''eval_results.txt''')

              with open(a_				,				'''w''') as writer:
                     logger.info('''***** Eval results *****''')

                     for key, value in result.items():
                            logger.info(F"""  {key} = {value}""")
                            writer.write(F"""{key} = {value}\n""")

                     results.update(a_)

       return results


if __name__ == "__main__":
 main()

