
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        __UpperCamelCase(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    PreTrainedTokenizer  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Optional[int] = None  ,       ):
 __a			:  Optional[int]		    =  {}

 if train_file is not None:
  __a			:  Dict		    =  [train_file]
 if eval_file is not None:
  __a			:  int		    =  [eval_file]
 if test_file is not None:
  __a			:  Any		    =  [test_file]

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

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

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

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

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

 __a			:  List[Any]		    =  (
     tf.data.Dataset.from_generator(
         lowerCAmelCase__  ,       ({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:
  __a			:  Tuple		    =  train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

 __a			:  Tuple		    =  (
     tf.data.Dataset.from_generator(
         lowerCAmelCase__  ,       ({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:
  __a			:  Tuple		    =  val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

 __a			:  int		    =  (
     tf.data.Dataset.from_generator(
         lowerCAmelCase__  ,       ({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:
  __a			:  Union[str, Any]		    =  test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]  )  )  )

 return train_ds, val_ds, test_ds, labelaid


lowercase__          =logging.getLogger(__name__)



@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							int     	=				field(metadata={"help": "Which column contains the label"}   )
   _SCREAMING_SNAKE_CASE				:							str     	=				field(default=__lowercase   ,metadata={"help": "The path of the training file"}   )
   _SCREAMING_SNAKE_CASE				:							Optional[str]     	=				field(default=__lowercase   ,metadata={"help": "The path of the development file"}   )
   _SCREAMING_SNAKE_CASE				:							Optional[str]     	=				field(default=__lowercase   ,metadata={"help": "The path of the test file"}   )
   _SCREAMING_SNAKE_CASE				:							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."
           )
       }   ,)
   _SCREAMING_SNAKE_CASE				:							bool     	=				field(
       default=__lowercase   ,metadata={"help": "Overwrite the cached training and evaluation sets"}   )





@dataclass
class 		UpperCamelCase__  :
   _SCREAMING_SNAKE_CASE				:							str     	=				field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
   _SCREAMING_SNAKE_CASE				:							Optional[str]     	=				field(
       default=__lowercase   ,metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
   _SCREAMING_SNAKE_CASE				:							Optional[str]     	=				field(
       default=__lowercase   ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
   _SCREAMING_SNAKE_CASE				:							bool     	=				field(default=__lowercase   ,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.
   _SCREAMING_SNAKE_CASE				:							Optional[str]     	=				field(
       default=__lowercase   ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,)





def        __UpperCamelCase(    ):
 # 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.
 __a			:  Dict		    =  HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
 __a			:  Dict		    =  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.

 __a			:  List[str]		    =  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  ,       )

 __a			:  List[str]		    =  get_tfds(
     train_file=data_args.train_file  ,       eval_file=data_args.dev_file  ,       test_file=data_args.test_file  ,       tokenizer=lowerCAmelCase__  ,       label_column_id=data_args.label_column_id  ,       max_seq_length=data_args.max_seq_length  ,       )

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

 with training_args.strategy.scope():
  __a			:  Union[str, Any]		    =  TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path  ,       from_pt=bool('''.bin''' in model_args.model_name_or_path  )  ,       config=lowerCAmelCase__  ,       cache_dir=model_args.cache_dir  ,       )

 def compute_metrics(lowerCAmelCase__							:    EvalPrediction  ) -> Dict:
  __a			:  int		    =  np.argmax(p.predictions  ,       axis=1  )

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

 # Initialize our Trainer
 __a			:  Optional[Any]		    =  TFTrainer(
     model=lowerCAmelCase__  ,       args=lowerCAmelCase__  ,       train_dataset=lowerCAmelCase__  ,       eval_dataset=lowerCAmelCase__  ,       compute_metrics=lowerCAmelCase__  ,       )

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

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

  with open(lowerCAmelCase__  ,       '''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(lowerCAmelCase__  )

 return results


if __name__ == "__main__":
      main()


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowercase__          ={
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2TokenizerFast']

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['LayoutLMv2FeatureExtractor']
      lowercase__          =['LayoutLMv2ImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'LayoutLMv2ForQuestionAnswering',
          'LayoutLMv2ForSequenceClassification',
          'LayoutLMv2ForTokenClassification',
          'LayoutLMv2Layer',
          'LayoutLMv2Model',
          'LayoutLMv2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
